Category Archives: My Spatial Problem

Monitoring Postfire Salvage Logging Effects on Woodpecker Population Dynamics

Research Question

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

Dataset

This project will use datasets targeting the 2015 Canyon Creek fire complex on the Malheur National Forest in eastern Oregon. A salvage logging operation occurred in the burn area in July 2016. My research is in cooperation with a Rocky Mountain Research Station study examining salvage logging effects on three woodpecker species in the Canyon Creek complex. In 2016 and 2017 I led crews on this project collecting extensive postfire woodpecker occupancy and nest productivity datasets for black-backed, white-headed, and Lewis’s woodpecker populations. This resulted in a 148-nest dataset for 2016 and 2017, representing woodpecker populations before and after salvage logging. A polygon shapefile outlining ten RMRS woodpecker point count survey units serves as the area of interest (6 treatment, 4 control). Within the 6 treatment units, another polygon shapefile outlining 34 salvage harvest units indicates treatment areas. Three silvicultural prescriptions replicating optimal habitat types for each woodpecker species designate salvage variables like post-harvest stand density and diameter. Each salvage unit adheres to one of these three harvest prescriptions. 2016 pre-salvage and 2017 post-salvage lidar datasets are in processing for eventual correlation between 3D forest structure variables and woodpecker nest site selection before and after harvest. Supplementary geospatial data includes a 2015 WorldView-3 1 m raster and ArcGIS basemaps.

Image result for canyon creek fire oregon   

Above: The 2015 Canyon Creek Fire burning near John Day, OR.

Above: The Canyon Creek fire complex as a false color WorldView-3 1 m raster. The area of interest includes 10 study units in blue, labeled with yellow text (6 treatment, 4 control). This visual orients the survey units to an area in eastern Oregon southeast of John Day and Canyon City. The false color image displays healthy vegetation as red, with the darkest areas displaying high burn severity. The survey units are found within some of the highest burn severity areas in the fire complex.

Above: A close-up of the 34 salvage treatment polygons outlined in red and labeled with white text. Control units lack red salvage polygons. This image does not include Overholt Creek.

Above: A subset of the 2016 and 2017 nest points featuring survey and salvage unit polygons.

Hypotheses

I expect to see dispersed nests in 2016 with possible trends indicating species habitat preferences. Previous research indicates species-specific preferences for certain forest habitat variables. Black-backed woodpeckers prefer dense, small-diameter stands for foraging and nest excavation. White-headed woodpeckers prefer a mosaic of live and dead variable density and diameter stands for pine cone foraging. Lewis’s woodpeckers prefer tall medium to large-diameter stands for aerial foraging maneuvers. I expect to see nest sites in both years clustered in areas with these forest structures. In 2017 I also expect to see nest sites clustered near salvage treatments implemented for each species. Overall I expect the control units to exhibit nest dispersal and high woodpecker activity.

Image result for black-backed woodpecker              Image result for white headed woodpecker             Image result for lewis's woodpecker

Black-backed woodpecker (Picodies arcticus)     White-headed woodpecker (Picoides albolarvatus)                       Lewis’s woodpecker (Melanerpes lewis)

 

A graphic depicting 3 salvage harvest treatment types and a control designed to benefit each of the target woodpecker species (Dave Halemeier 2016).

Approaches

Analyses will consider pre- and post-salvage variables to determine changes in forest structure alongside woodpecker population dynamics. I would like to learn about spatial autocorrelation analyses such as Moran’s I. It is likely I will find patterns of dependent observations based on localized conditions. Woodpecker species presence and nest locations may be affected by burn severity, since highly weakened trees will host their primary food source, bark beetle larvae. In 2017 woodpecker species presence may be grouped according to salvage treatments targeting each species, or control areas. Regression analyses showing the relationship strength between nest distance from salvage units and salvage treatment types could indicate certain forest variables affecting postfire woodpecker colonization.

Expected Outcome

Regression coefficients describing the relationship between woodpecker presence and salvage treatment location and type will help develop inferences towards postfire management effects. I will create interpretive maps of nest locations showing survey unit and salvage unit polygons. These maps could include statistical and geospatial relationships represented with different colors and symbols. Eventually, I will geovisualize the lidar data with these maps and statistical relationships for a comprehensive and communicative representation of woodpecker population and forest structure change dynamics.

Significance

I am processing two lidar datasets of the study area from 2016 and 2017. These datasets were acquired before and after the salvage logging treatments occurred. I will produce forest metrics such as stand density, diameter class, and height in salvage and survey units. I will then correlate geospatial and statistical properties of the nest dataset to quantified forest variables affecting woodpecker nest site selection. I will examine trends between 2016 and 2017 nest selection to understand the effects of harvest treatments on woodpecker populations. At least two more years of woodpecker data will exist for 2018 and 2019, so future research will add these datasets to the analyses. I would like to see a machine learning algorithm developed from this study that could predict areas of optimal habitat suitability for snag-dependent wildlife species. Postfire wildlife habitat prediction will be crucial to resource managers as wildfires increase in the coming decades alongside accelerated landscape restoration.

This spatial problem is important to science and resource managers as climate change amplifies wildfire effects. Using 3D remote sensing datasets for resource management is the future trend across all disciplines. Increased wildfire activity around the world necessitates cutting-edge methods for active fire and postfire ecosystem quantification. In the Blue Mountains ecoregion in eastern Oregon, Rocky Mountain Research Station, Malheur National Forest, and Blue Mountains Forest Partners rely on this project’s lidar quantification for their research and management decisions. Determining acceptable levels of salvage harvest for wildlife habitat affects whether government agencies and rural economies in this region will allow small businesses to profit from sustainable harvest operations. Science overall will benefit from the continued investigation of wildlife response to anthropogenic disturbance, specifically postfire forest management decisions and the controversial practice of salvage logging.

Above: A salvage treatment in the Crazy Creek woodpecker survey unit.

Preparation

I took an ArcInfo class (ARC Macro Language) during my undergraduate program. I am currently taking a Python class for geospatial programming. I have experience in image processing through an undergraduate degree in land use and GIS, multiple years of professional experience in remote sensing and GIS, and my current MS program in Forest Geomatics. I have academic and professional experience with R, C++, ArcGIS, and multiple types of remote sensing software for 2D and 3D data analysis.

The Biogeography of Coastal Bottlenose Dolphins off of California, USA between 1981-2016

Background/Description:

Common bottlenose dolphins (Tursiops truncatus), hereafter referred to as bottlenose dolphins, are long-lived, marine mammals that inhabit the coastal and offshore waters of the California Current Ecosystem. Because of their geographical diversity, bottlenose dolphins are divided into many different species and subspecies (Hoelzel, Potter, and Best 1998). Bottlenose dolphins exist in two distinct ecotypes off the west coast of the United States: a coastal (inshore) ecotype and an offshore (island) ecotype. The coastal ecotype inhabits nearshore waters, generally less than 1 km from shore, between Ensenada, Baja California, Mexico and San Francisco, California, USA (Bearzi 2005; Defran and Weller 1999). Less is known about the range of the offshore ecotype , which is broadly defined as more than 2 km offshore off the entire west coast of the USA (Carretta et al. 2016). Current population abundance estimates are 453 coastal individuals and 1,924 offshore individuals (Carretta et al. 2017). The offshore and coastal bottlenose dolphins off of California are genetically distinct (Wells and Scott 1990).

Both ecotypes breed in summer and calve the following summer, which may be thermoregulatory adaptation (Hanson and Defran 1993). These dolphins are crepuscular feeders that predominantly hunt prey in the early morning and late afternoon (Hanson and Defran 1993), which correlates to the movement patterns of their fish prey. Out of 25 prey fish species, surf perches and croakers make up nearly 25% of coastal T. truncatus diet (Hanson and Defran 1993). These fish, unlike T. truncatus, are not federally protected, and neither are their habitats. Therefore, major threats to dolphins and their prey species include habitat degradation, overfishing, and harmful algal blooms (McCabe et al. 2010).

This project aims to better understand that distribution of coastal bottlenose dolphins in the waters off of California, specifically in relation to distance from shore, and how that distance has changed over time.

Data:

This part of the overarching project focuses on understanding the biogeography of coastal bottlenose dolphins. Later stages in the project will require the addition of offshore bottlenose sightings to compare population habitats.

Beginning in 1981, georeferenced sighting data of coastal bottlenose dolphin off the California, USA coast were collected by R.H. Defran and team. The data were provided in the datum, NAD 1983. Small boats less than 10 meters in length were used to collect the majority of the field data, including GPS points, photographs, and biopsy samples. These surveys followed similar tracklines with a specific start and end location, which will be used to calculate the sighting per unit effort. Over the next four decades, varying amounts of data were collected in six different regions (Fig. 1). Coastal T. truncatus sightings from 1981-2015 parallel much of the California land mass, concentrating in specific areas (Fig. 2). Many of the sightings are clustered nearby larger cities due to logistics of port locations. The greater number of coastal dolphin sightings is due to the bias in effort toward proximity to shore and longer study period. All samples were collected under a NOAA-NMFS permit.Additional data required will likely be sourced from publicly-available, long-term data collections, such as ERDDAP or MODIS.

Distance from shore will be calculated in a program such as ArcGIS or R package. These data will be used later in the project to compare to additional static, dynamic, and long-term environmental drivers. These factors will be tested as possible layers to add in mapping and finally estimating population distribution patterns of the dolphins.

Figure 1. Breakdown of coastal bottlenose dolphin sightings by decade. Image source: Alexa Kownacki.

 

 

 

 

 

 

 

 

 

 

 

Hypotheses:

I predict that the coastal bottlenose dolphins will be associated with different bathymetry patterns and appear clustered based on a depth profile via mechanisms such as prey distribution and abundance, nutrient plumes, and predator avoidance.

Approaches:

My objective is to first find a bathymetric layer that covers the coast of the entirety of California, USA to import into ArcMap 10.6. Then I need to interpolate the data to create a smooth surface. Then, I can add my dolphin sighting points and create a way to associate each point with a depth. These depth and point data would be exported to R for further analysis. Once I have extracted these data, I can run a KS-test to compare the shape of distribution based on two different factors, such as points from El Niño years versus La Niña years to see if there is a difference in average sighting depth or more common sighting depths based on the climatic patterns. I am also interested in using the spatial statistic analysis tool, Moran’s I, to see if the sightings are clustered. If so, I would run a cluster analysis to see if the sightings are clustered by depth. If not, then maybe there are other drivers that I can test, such as distance from shore, upwelling index values, or sea surface temperature. Additionally, these patterns would be analyzed over different time scales, such as monthly, seasonally, or decadally.

Expected Outcome:

Ideally, I would produce multiple maps from ArcGIS representing different spatial scales at defined increments, such as by month (all Januaries, all Februaries, etc.), by year or binned time increment (i.e. 1981-1989, 1990-1999), and also potentially grouping based on El Niño or La Niña year. Different symbologies would represent coastal dolphin sightings distances from shore. The maps would visually display seafloor depths in a color spectrum by 10 meter difference. Because the coastlines of California vary in terms of depth profiles, I would expect there to be clusters of sightings at different distances from shore, but similar depth profiles if my hypothesis is true. Also, data with the quantified values of seafloor depth would be associated with each data point (dolphin sighting) for further analysis in R.

Significance:

This project draws upon decades of rich spatiotemporal and biological information of two neighboring long-lived cetacean populations that inhabit contrasting coastal and offshore waters of the California Bight. The coastal ecotype has a strong, positive relationship with distance to shore, in that it is usually sighted within five kilometers, and therefore is in frequent contact with human-related activities. However, patterns of distances to shore over decades, related to habitat type and possibly linked to prey species distribution, or long-term environmental drivers, is largely unknown. By better understanding the distribution and biogeography of these marine mammals, managers can better mitigate the potential effects of humans on the dolphins and see where and when animals may be at higher risk of disturbance.

Preparation:

I have a moderate amount of experience in ArcMap from past coursework (GEOG 560 and 561), as well as practical applications and map-making. I have very little experience in Modelbuilder and Python-based GIS programming. I am becoming more familiar with the R program after two statistics courses and analyzing some of my own preliminary data. I am experienced in image processing in ACDSee, PhotoShop, ImageJ, and other analyses mainly from marine vertebrate data through NOAA Fisheries.

Literature Cited:

Bearzi, Maddalena. 2005. “Aspects of the Ecology and Behaviour of Bottlenose Dolphins (Tursiops Truncatus) in Santa Monica Bay, California.” Journal of Cetacean Research Managemente 7 (1): 75–83. https://doi.org/10.1118/1.4820976.

Carretta, James V., Kerri Danil, Susan J. Chivers, David W. Weller, David S. Janiger, Michelle Berman-Kowalewski, Keith M. Hernandez, et al. 2016. “Recovery Rates of Bottlenose Dolphin (Tursiops Truncatus) Carcasses Estimated from Stranding and Survival Rate Data.” Marine Mammal Science 32 (1): 349–62. https://doi.org/10.1111/mms.12264.

Carretta, James V, Karin A Forney, Erin M Oleson, David W Weller, Aimee R Lang, Jason Baker, Marcia M Muto, et al. 2017. “U.S. Pacific Marine Mammal Stock Assessments: 2016.” NOAA Technical Memorandum NMFS, no. June. https://doi.org/10.7289/V5/TM-SWFSC-5.

Defran, R. H., and David W Weller. 1999. “Occurrence , Distribution , Site Fidelity , and School Size of Bottlenose Dolphins ( Tursiops T R U N C a T U S ) Off San Diego , California.” Marine Mammal Science 15 (April): 366–80.

Hanson, Mark T, and R.H. Defran. 1993. “The Behavior and Feeding Ecology of the Pacific Coast Bottlenose Dolphin, Tursiops Truncatus.” Aquatic Mammals 19 (3): 127–42.

Hoelzel, A. R., C. W. Potter, and P. B. Best. 1998. “Genetic Differentiation between Parapatric ‘nearshore’ and ‘Offshore’ Populations of the Bottlenose Dolphin.” Proceedings of the Royal Society B: Biological Sciences 265 (1402): 1177–83. https://doi.org/10.1098/rspb.1998.0416.

McCabe, Elizabeth J.Berens, Damon P. Gannon, Nélio B. Barros, and Randall S. Wells. 2010. “Prey Selection by Resident Common Bottlenose Dolphins (Tursiops Truncatus) in Sarasota Bay, Florida.” Marine Biology 157 (5): 931–42. https://doi.org/10.1007/s00227-009-1371-2.

Wells, Randall S., and Michael D. Scott. 1990. “Estimating Bottlenose Dolphin Population Parameters From Individual Identification and Capture-Release Techniques.” Report International Whaling Commission, no. 12.

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Contact information: this post was written by Alexa Kownacki, Wildlife Science Ph.D. Student at Oregon State University. Twitter: @lexaKownacki


 

My Spatial Problem: Streamflow variability and associated processes in rain-dominated, coastal basins

My research explores the variability in streamflow patterns in rain-dominated systems of the Pacific Northwest. Rain-dominated climate regimes occur primarily in the coastal portion of the PNW. Because precipitation only occurs as rain and does not occur in significant quantities during the summer season, underground storage is a crucial component of both the water cycle and streamflow stability in these systems. The objectives of my research are: first) to describe variations in stream hydrograph stability across multiple catchments and multiple catchment scales, and second) to use estimations of catchment storage processes to help explain potential variations in streamflow patterns.

I will be analyzing multiple datasets in order to meet my objectives. Those include: geophysical data, land-use/management data, and streamflow data. All landscape data will be analyzed at the finest spatial resolution available for the dataset. Hourly streamflow data will be analyzed for the available period of record, which varies by site. Second-fourth order streams in the Siletz and Smith river basins have hourly discharge data for the recent 5-8 years. USGS hydrological stations have similar data for 10-30 years.

I expect to see that streams in different hydrogeologic setting demonstrate different streamflow patterns over time. Streams located in more permeable, thicker lithosphere, may demonstrate more stable stream flows. In the winter, that may appear as muted storm peaks, while in the summer, that may appear as more sustained baseflows through the non-rainy season. Land cover may play an important role in streamflow regimes as well. The amount of water taken up and stored by vegetation may depend on the density, age, structure, and species composition of the forest. Watersheds with a large areas managed under industrial timber production may confound streamflow behavior.

I envision approaching my objectives by first developing some descriptive statistics for the hydrographs at each site. Such descriptors may include: recession analysis, dynamic storage, 7q10, arc peak, and various other streamflow analysis metrics with which I am not yet familiar. All sites are of varying contributing areas, so that will have to be taken into consideration in the analysis. Once descriptive statistics are developed across each site, I would like to integrate an analysis of the landscape attributes as potential explanatory variables for any variations observed in hydrograph statistics across space to see how much explanatory power they have, and how much variation there is.

The product of this project is expected to include both maps and statistical relationships. For maps, I would like to produce: 1) a depiction of the hydrogeologic settings across the study area, and 2) a depiction of expected streamflow patterns given the hydrogeologic settings. I would like to understand the statistical relationship between various streamflow metrics and the hydrogeologic setting of the given stream.

The results of this analysis may be important to resource managers and the scientific community as it will contribute information about hydrological processes, with a focus on the critical zone, in the PNW coastal landscapes. The persistence of streams and rivers in this region is crucial to several species of native salmonids, as well as to the economic well-being of the local communities. With potential variations in the climate, understanding underlying hydrological processes and the drivers of such processes may contribute to more proactive management approaches. Furthermore, an analysis that is relevant to the fine-scale processes that exist in the study area may contribute more accurate classifications of hydrologic regimes and analyses of streamflow vulnerability to long-term changes in the hydrological cycle.

As far as preparation for this project: I am well-versed in ArcGIS software. I have used Model Builder and Python some, though could use more practice. I am most comfortable with spatial and statistical analyses through R software. I have only used imagery analyses in a class, and I’d like to use it for my analysis so I am looking forward to learning more through this class.

A UAS and LiDAR based approach to maximizing forest aesthetics in a timber harvest

Bryan Begay

Research Question: 

Can LiDAR derived from an Unmanned Aerial System (UAS) create a point cloud driven visualization model for maximizing forest aesthetics in a highly visible timber harvest?

Context

A variable retention thinning is planned to be implemented in a harvest unit on the McDonald-Dunn Forest in a visible area near Corvallis. UAS systems offer an efficient way to collect data over large areas to create high quality data sets from LiDAR that can capture the structure of a forest stand. There is a need  for a model/methodology that utilizes UAS LiDAR point clouds to generate a visualization model to create a  timber harvest in an areas with high visibility that maximize forest aesthetics. Inputs for the model include DTMs, Google Earth Pro view shed tool, and point clouds. The point clouds can be manipulated to visualize an optimal silvicultural prescription that maximizes forest landscape aesthetics. Ancillary data of view shed and terrain from DTMs are inputs expected to help create a visualization model.

A description of the data set you will be analyzing, including the spatial and temporal resolution and extent: 

The data set I will be using will include high resolution LiDAR point clouds of a stand, Digital Terrain Models (DTM) from LiDAR point clouds flown by the USFS previously, and additional ancillary data from Google Earth Pro. The Google Earth Pro data will use the view shed tool for assessing the visual impact of regions in the harvesting unit. The spatial resolution will be using high resolution LiDAR point clouds on an area that is a few square kilometers. The temporal resolution will span data acquisition before the harvest, and then an assessment of the computer based prescription after harvest. The temporal resolution of the point cloud collected from the UAV will be collected in a discrete time frame of one day. The DTM data set and google earth pro data sets will be variable, but I anticipate them to be newer high resolution Google Earth imagery and high resolution LiDAR data sets.

Hypotheses:

I hypothesizes that LiDAR point clouds can be used in a visualization model to create a silvicultural prescription in a timber harvest that maximizes forest aesthetics in a logged area . Google Earth Pro view shed tool, high quality LiDAR point clouds, and a large body of literature on forest aesthetics provide a data set that is very rich in inputs to create a visualization model for timber harvests that maximizes forest aesthetics.

Approaches:

I would like to do some sort of analysis looking at the spatial relationship between forest aesthetics and timber harvests. A part of this analysis would look at the relationship of the spatial pattern of residual structure left from the thinning and the landscape aesthetics.

Expected outcome:

I would like an expected outcome to be a visualization model of the harvest unit that utilizes view shed and point clouds that maximizes forest aesthetics in a high viewership area.

Significance:

This spatial problem is important to the profession of forestry as well as other land managers, since it helps maintain the social license for foresters to practice forestry in areas that are highly visible. Public acceptance of harvesting practices is increased when forest aesthetics is taken into account, so creating a methodology and model to assist in creating silvicultural prescriptions that increase forest aesthetics is critical for public acceptance of forestry.

Level of preparation:

A. I have experience in ArcGIS.

B. No experience in modelbuilder and Python programming in GIS.

C. Some experience in R.

D. Experience in Digital Image Processing.

E. I’ve used Google Earth Engine and very little experience with MATLAB.

How winter wetland habitat change over time affects songbird communities

A description of the research question that you are exploring.

For my research, I am exploring the relationship between the spatial pattern of the differences between present (2019) and past (1995) wintering songbird community composition metrics (abundance, richness, evenness, and weighted rarity) and the spatial pattern of landscape-level land cover variable changes (listed here: Landscape Variables) in the same timeframe via the mechanisms of change over time and landscape-level variable importance to habitat suitability. I will be looking at data for 20 wetland wintering habitat sites in the Corvallis area.

I am interested in comparing the community composition metrics (abundance, richness, evenness, weighted rarity) of songbirds from 1995 to those found in 2019. I will then look at how the above-mentioned landscape level variables at these sites (within a 100m buffer, 500m buffer, and 1km buffer from the wetland) have changed from 1995 to 2019 at those same sites to determine if and what variable changes influence songbird community composition.

Other spatial factors likely influence songbird community composition metrics but I am only concerned with those that were included in Adamus’s 2002 dissertation study.

A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

I have species richness data in the form of a spreadsheet from 1995 including songbird abundance data for my 20 sites recorded via point count surveys from January 4th to March 20th. I have the same information collected by the same methods at those same sites from January 4th 2019 to March 20th 2019. I will use this data to calculate the species metrics (listed above).

I am still in the process of gathering my spatial datasets for this project. As of now, I have open source areal imagery from Google Earth Engine that my advisor used to analyze the sites in 1995 and that same areal imagery from 2019. I hope to locate LIDAR data, NVI data, and more sophisticated ground cover data for my sites in this class. One of the reasons I enrolled in this class was to get ideas and aid with obtaining this data.

Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I predict that the greater the change in the landscape level variables at a site from 1995 to 2019 the greater the difference in community composition measurements between those years via the landscape level variables influence on habitat suitability for wintering songbirds. Additionally, I think the changes in the variables that my advisor determined to be most influential to wintering songbird community composition metrics at these sites in 1995 will have the greatest effect on the change in species measurements from 1995 to 2019. For example, he found that wetlands with a higher percentage of open canopy forest cover in the surrounding area had a positive correlation with high abundance (Adamus, 2002) at a site so I hypothesis that those sites that have lost the most open canopy forest will also have the greatest decrease in abundance.

Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I would like to learn how to use LIDAR and/or NVI data to determine ground cover in regards to my advisors’ categories (attached). I would also like to explore methods for comparing the amount of change from 1995 to 2019 at my sites that is suitable to my data and my purposes.
Expected outcome: what do you want to produce — maps? statistical relationships? other?
I expect to have a spreadsheet with quantified variable change as well as species richness measurements in order to reevaluate variable importance as well as the statistical relationship between landscape level variable differences and species richness differences. As an intermediate step, I will also produce a map(s) portraying the change at my sites from 1995 to 2019.

Significance. How is your spatial problem important to science? to resource managers?

The quality of wintering habitat is correlated with overall survivability and reproductive success for songbirds the following year (Norris et al. 2004). It is important to know how these habitats have changed as well as the consequences of those changes in regards to songbird community metrics. Therefore, it is extremely important for both science and resource managers. If we want to assure that our environment remains healthy and balanced with stable songbird communities it is this work and work like it is necessary. It is also important to those who wish to manage songbird populations so they know where to allocate resources when it comes to habitat variables to preserve.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

a. I am in between proficient and having a working knowledge of the basics of Arc-Info from taking various courses and teaching the basics of Arc-Pro.b. I have a working knowledge with modelbuilder and GIS programming in Python because I took courses on both subjects and now help teach a programming for ArcGIS class.
c. I have a working knowledge of R because I took an R course related to species distributions and I have used R in two statistics courses.
d. I am a novice in image processing but did take a digital terrain modeling course last term.
e. I am a novice in but would like to learn about software that helps me analyze NVI and LIDAR data

References

Adamus P,. 2002. Multiscale Relationships of Wintering Birds with Riparian and Wetland Habitat in the Willamette Valley, Oregon. Oregon State University.

Norris R., Marra P., Kyser K., Sherrt W., & Ratcliffe L. 2004. Tropical Winter Habitat Limits Reproductive Success on the Temperate Breeding Grounds in a Migratory Bird. Biological Sciences (271).

Assessing western juniper sapling re-establishment in a semiarid watershed

1. My spatial problem

  • A description of the research question that you are exploring.

Research question: How is the spatial pattern of juniper density (A) related to the spatial pattern of slope, aspect, and a combination of the two (B) via soil moisture and solar radiation?

The expansion of western juniper has become a concern in many rangeland areas, and is associated with a number of ecological and hydrological impacts [1,2]. The study site is located in a semiarid watershed in central OR, and was established to assess ecohydrological characteristics associated with juniper expansion and removal. The majority of western juniper was removed from this watershed in 2005 -2006 and juniper saplings have become re-established in this area. The objectives of this project are 1) to determine the relationship between the density of western juniper sapling re-establishment and slope and aspect in this watershed and 2) assess density changes in vegetation cover at this watershed since juniper removal.

The intent of this project is also to establish a methodology that will be expanded to include patterns of soil moisture, soil surface temperature, and soil type (but additional data needs to be collected). While juniper density and vegetation cover for the purposes of this project are the dependent variables, in the future I am exploring how certain ecohydrological characteristics vary with juniper density.

  • A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

A combination of National Agriculture Imagery Program (NAIP), unmanned aerial vehicle (UAV) imagery, and ground-based data will be used. Forty-one belt transects (3m by 30m) representing areas of varying aspect and slope were conducted in summer of 2018 to assess juniper density across the entire watershed.  UAV-based multispectral imagery (red, green, blue, red-edge, and near-infrared bands) was collected at a study plot in the watershed in October 2018, but needs to be expanded to represent topography of the watershed. Resolution of the UAV imagery is approximately 2.5cm/pixel. NAIP imagery (1 m resolution) will be used to assess density over time. A 10 m digital elevation model (DEM) will be used to model the topography of the watershed. Alternatively, a higher-resolution DEM created from UAV imagery may be used if it is available. Prior to analysis, support vector machine (SVM) supervised classification will be used to identify juniper in the UAV and NAIP imagery and to assess overall vegetation cover in NAIP imagery. It should be noted that while I have used SVM classification successfully with multispectral UAV imagery to assess juniper density (shown in the figure below), the accuracy of this process will need to be assessed with the NAIP data as there is a large difference in spatial resolution (2.5 cm compared to 1 m). If I am unable to identify juniper successfully in NAIP imagery, then only UAV and ground-based data will be used to assess juniper density.

 

  • Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I expect that juniper density will be positively related to north aspects and negatively related to slope angle as these characteristics promote higher levels of soil moisture. However, these patterns may vary with both soil type and amount of non-juniper vegetation cover.  Similar to past studies [2], we may anticipate that overall vegetation cover may change at this watershed in response to the removal of juniper. Changes in vegetation cover associated with juniper density may be related to juniper transpiration, soil moisture, and canopy interception.

  • Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

Initially, I will assess the spatial pattern of slope, aspect, and a combination of the two in the watershed. Further, the relationship between observed juniper density and the slope angle and aspect characteristics associated with highest soil moisture will be assessed. I would like to integrate the NAIP imagery, DEM, transect data, and UAV imagery for analysis of juniper density. During this term, this model will focus on slope and aspect but I would like to establish a model that I can expand to use to assess other characteristics as well (such as soil moisture, soil temperature, and vegetation cover).  Using the NAIP imagery, I would like to see assess temporal changes in vegetation cover. In particular, I am interested in understanding how to best use data with different resolutions and how to expand this methodology for more complicated analysis in future research.

  • Expected outcome: what do you want to produce — maps? statistical relationships?

I would like to create maps characterizing juniper density within the watershed and determine if there is a statistical relationship between juniper density and slope and aspect (that can be expanded as more variables are addressed). Additionally, I would like to assess changes in total vegetation cover over time in this watershed.

  • How is your spatial problem important to science? to resource managers?

The expansion of western juniper is associated with a number of ecohydrological changes, to include reduced undercanopy soil moisture [3], reduced productivity[4] and increased erosion[5]. The removal of western juniper is labor intensive and expensive, and can be difficult to implement. An improved understanding of the spatial patterns associated with juniper re-establishment after removal can be used to target treatment when appropriate but also to improve our understanding of the ecohydrologic impacts of juniper expansion within these communities. This analysis can also provide information about which areas are at greatest risk based on topography. Additionally, understanding changing in vegetation cover helps to determine the timing and impacts of removal. While beyond the scope of this project, this information may be used to determine how the rate of juniper expansion relates to other ecohydrological characteristics over time.

2. Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

  • I have used ArcMap/Pro a moderate amount in a classroom setting (GIS 1&2, GIS in water resources), for independent research, and in the workplace. Outside of a classroom setting, I have largely used ArcInfo for map creation and geospatial analysis (primarily unsupervised and supervised classification).
  • I have used Modelbuilder a limited amount, and it has largely been limited to class assignments in Geog 560/561. I do not have experience with GIS programming in Python.
  • I have some experience using R, primarily for basic statistical analysis. I have no experience using R for spatial statistics.
  • I have limited experience using ENVI, but I would need to refresh the basic procedures. I do not have experience with MATLAB.
  • I have some experience with Google Earth Engine, although I would need to review the basics to be effective.

References

  1. Coultrap, D.E.; Fulgham, K.O.; Lancaster, D.L.; Gustafson, J.; Lile, D.F.; George, M.R. Relationships between western juniper (Juniperus occidentalis) and understory vegetation. Invasive Plant Sci. Manag. 2008, 1, 3–11.
  2. Dittel, J.W.; Sanchez, D.; Ellsworth, L.M.; Morozumi, C.N.; Mata-Gonzalez, R. Vegetation response to juniper reduction and grazing exclusion in sagebrush-steppe habitat in eastern Oregon. Rangel. Ecol. Manag. 2018, 71, 213–219.
  3. Lebron, I.; Madsen, M.D.; Chandler, D.G.; Robinson, D.A.; Wendroth, O.; Belnap, J. Ecohydrological controls on soil moisture and hydraulic conductivity within a pinyon-juniper woodland. Water Resour. Res. 2007, 43, W08422.
  4. Miller, R.F.; Svejcar, T.J.; Rose, J.A. Impacts of western juniper on plant community composition and structure. J. Range Manag. 2000, 53, 574–585.
  5. Reid, K.; Wilcox, B.; Breshears, D.; MacDonald, L. Runoff and erosion in a pinon-juniper woodland: Influence of vegetation patches. Soil Sci. Soc. Am. J. 1999, 63, 1869–1879.

Cross-sectional Change in the Andrews Forest

Background and Research Questions

This project explores stream bed mobility in the HJ Andrews experimental forest in relationship to peak discharge events and channel geometry. The HJ Andrews Experimental Forest is a Long Term Ecological Research (LTER) Site located east of Eugene on the western slope of the Cascade Range. The site has been managed since the 1950s for ecological and forestry research, and the largest stream in the forest has been gauged since 1950. From 1978 to 2011, researchers conducted repeated cross sectional surveys on five reaches in three different creeks in the forest. An analysis of these cross-sectional profiles will help researchers and managers gain a better understanding of how, when, and where stream beds responds to extreme hydrologic events.

Water flowing through streams exerts stresses on the bed material. Whether or not these stresses have the capacity to mobilize sediment and change the shape of a stream bed depends on the amount of water moving through the stream along with other factors. As with many geomorphological processes, bed sediment transport is dominated by movement during extreme events. Although cross sectional changes overall appear to be strongly related to the magnitude of greatest flow between measurements, I am interested in investigating confounding factors including the extent of temporal and spatial autocorrelation in the data set.

I would like to explore (a) if and (b) in what manner aggradation or erosion in one cross section might be related to changes in adjacent cross sections. I would also like to know if aggradation or erosion in one cross section in one year are related to changes in the same cross section in an adjacent year.

Data

I am analyzing cross sectional measurements of five reaches within the Andrews Forest, which range from 12 m to 55 m in horizontal extent and 1.2 to 7.3 meters in vertical extent. The cross sections are variously spaced along an along-stream dimension, and they are were surveyed every one to five years over a period of 30 years between 1978 and 2011. The vertical precision of the data set is roughly 1 cm (though the data are unlikely to be accurate to 1 cm) and the horizontal precision varies from 1 cm to several decimeters.

The cross sectional change between two adjacent pairs of years at one cross section is shown below. Areas of erosion are shown in red and areas of aggradation are shown in green.

Hypothesis and Approach

I predict that there will be a relationship between changes at one cross section during one year and the same cross section at another year. Portions of the stream bed that have been recently scoured or contain newly emplaced sediment may be less armored than undisturbed portions of the stream bed. These less armored portions of the stream bed may be more susceptible to future disturbance. Alternately, changes at a cross section may represent longer-term processes related to channel geometry: e.g. a series of cross sections could show continued incision of a cut bank over multiple years.

I do not expect to see a detectable relationship between changes at adjacent cross sections because I expect that the most important geographic controls on channel change are either smaller (e.g. pools) or much larger (e.g. along-stream variation in discharge) than the distance between cross sections.

I want to use this class as an opportunity to explore statistical relationships, but I don’t know yet what kinds of analyses are best suited to this problem. I’d like to learn more in general about how to handle spatial autocorrelation, and when it is and isn’t a statistical issue.

Justification
This project could be scientifically useful for improving our understanding of sediment transport in Pacific Northwest mountain streams. Resource managers may also have an interest in sediment transport because it relates to stream channel mobility (“How much can we depend on this creek staying in the same place?”) and ecology (“How vulnerable is stream life to disruption via bed transport?”). From a resource management perspective, it is becoming increasingly useful to study peak flow events because downscaled climate models for our region predict increased frequencies of large storms.

Preparation

I feel good about my experience with spatial technology, and I’m most interested in learning about how to use that technology to answer statistical questions. I am highly proficient with Arc software. I have TAed one undergraduate class and independently taught another short undergraduate class in ArcGIS. I have a working knowledge of ArcPy, but I still need to use references regularly to write code. I conducted some undergraduate remote sensing research using ArcPy and used ArcPy for research at a government science agency. I work extensively in R. I’ve done image processing using Arc software, Python, ENVI, ImageJ, and raster tools in R, but it’s a very broad field, and I definitely think I could learn more.

 

Aerial remote sensing detection of Leptosphaeria spp. on Turnip

Introduction

Of the many pathogens disrupting healthy growth of brassica species in the valley is the pathogen commonly referred to as Blackleg. This fungal pathogen has been reported to nearly wipe out canola production in Europe, Australia, Canada and in more recent years has devastated the United States (West et al., 2001). In 2013 the pathogen was reported for the first time in the pacific northwest since the 1970’s (Agostini et al., 2013) and has since been reported in the Willamette Valley (Claassen, 2016). There are two known species of Blackleg, Leptosphaeria maculuns and Leptosphaeria biglobosa. These are not to be mistaken with the potato bacterial pathogen Pectobacterium atrosepticum, which is also referred to as Blackleg. While much of the crop failure in canola has been associated with Leptosphaeria maculuns, both species are found in the valley and seem to be of similar consequence to turnip.

Classification of cercospera leaf spot for instance has been accomplished applying a support vector machine model but utilized a hyperspectral camera with high spectral and spatial resolution (Rumpf et al., 2010). Because plant diseases can oftentimes be difficult to see even with the naked eye, researchers have struggled to successfully detect specific plant diseases as spatial resolution decreased. While this analysis focuses solely on detection at 1.5 meters, it is possible for detection of blackleg despite lowered spatial resolution as result of increased flying elevations.

Here we consider how spatial patterns from diseased leaves is related to ground truth disease ratings of turnip leaves based on spectral signatures. With the application of a support vector machine model, classification of diseased versus non-diseased tissue is expected to generate a predictive model. This model will be used to determine if single turnip leaves which are diseased and non-diseased are accurately categorized based on the ground truth.

 

Data

This project will be using a data set derived from roughly 500 turnip leaves, harvested here in the valley during the months of February to April of 2019. Roughly 200 of these leaves will be used in training the data model. The remaining leaves will be used for the data analysis in determining accuracy of the model versus ground truth. The spatial resolution is less than 1 mm and images are in raster format of single turnip leaves with five bands (blue, green, red, far-red, NIR). I do not anticipate using every band for the analysis and will likely apply some VI as a variable. The camera being used is a mica sense Red-edge which is 12-bit radiometric resolution.

 

Hypothesis

I hypothesize that spatial patterns of pixels based on image classification are related to manually classified spatial patterns of observed disease on turnip leaves because disease has a characteristic spectral signature of infection on the leaves.

 

Approaches

In order to accomplish this analysis, I will be using ArcGIS Pro where I have quite a bit of experience, but not particularly on this subject or type of analysis. The workflow for analysis will begin with image processing where I have little experience but don’t require expertise in this area. I hope to conduct the image processing in Pix4D where I will begin with image calibration based on the reflectance panel in each image. Followed by cropping down to simply the leaf under assessment. From here there may be some smoothing and enhancing the contrast of the image but is still undetermined.

Images will then be brought into ArcGIS Pro for conducting a spatial analysis. I intend to use spatial pattern analysis of manually classified disease versus unsupervised segmentation of the leaves for exercise 1. Next I plan on then using this information in spatial regression to improve image-based classification for exercise 2. For exercise 3 I intend to use the support vector model wizard, which will be used for training a model. This involves highlighting regions of diseased tissue and regions of non-diseased tissue to obtain a trained model when a sufficient number of pixels to create support vectors is reached. The x and y-axis for the model are yet to be determined but will likely be NIR and red-edge digital number values. Some alternatives are using different VI’s such as NDVI as explanatory variable. Turnip images which were never used for training the model will be used for analysis of the support vector machine’s ability to classify diseased or non-diseased regions and then leaves entirely. I anticipate every leaf to have at least a few pixels which will classify as diseased and will therefore set a threshold for a maximum number of diseased pixels in the image, while yet classifying it as non-diseased. I also might require a certain number of pixels to be bordering one another qualify as diseased region. The methodology may require some troubleshooting, but the expectations are clear and the methods to reach that outcome are mostly drawn out.

 

Expected Outcome

I expect the model to have very high accuracy after the model is fine tuned for accuracy based on contrast in spectral signatures I expect to see between diseased versus non-diseased leaves. Below I have outlined the three outcomes I would like to ultimately achieve. Due to time restrictions, the scope of my research is limited to outcomes 1 & 2 below.

  1. Train a support vector machine model for classification of pixels in turnip leaves as either diseased or non-diseased.
  2. Accurately apply the SVM model on turnip leaves from many geographical locations in the valley with different levels of diseases severity and different times in the year.
  3. Scale up from 1.5 meters and test the ability of the model to maintain accurate classification of blackleg on turnip.

 

Significance

I intend to publish and further the collective knowledge in aerial remote sensing. This more specifically applies to those in the area of agronomy or plant pathology. This is very applied science and is a resource for those in the industry of agriculture.

Traditionally detection for this pathogen has depended on a reliable field scout who may need to cover fifty acres or more looking for signs or symptoms of this disease. Nowadays, precision agriculture has introduced the use of drones to perform unbiased field scouting for the grower. This saves time and can be very reliable if done properly. An important aspect of disease control relies on early detection. If early detection can be accomplished, growers have time to respond accordingly. This may allow for early sprays with lighter applications rates or less controlled substances, cultural control of nearby fields, etc. in order to stop the spread of disease.

 

Works cited

Agostini, A., Johnson, D. A., Hulbert, S., Demoz, B., Fernando, W. G. D., & Paulitz, T. (2013). First report of blackleg caused by Leptosphaeria maculans on canola in Idaho. Plant disease, 97(6), 842-842.

Claassen, B. J. (2016). Investigations of Black Leg and Light Leaf Spot on Brassicaceae Hosts in Oregon.

Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91-99.

West, J. S., Kharbanda, P. D., Barbetti, M. J., & Fitt, B. D. (2001). Epidemiology and management of Leptosphaeria maculans (phoma stem canker) on oilseed rape in Australia, Canada and Europe. Plant pathology, 50(1), 10-27.

Courtney’s Spatial Problem

In the formula “How is the spatial pattern of A related to the spatial pattern of B via mechanism C?”, my research question for this class is made of the following parts:

  • A: ion and isotope concentrations in wells tapping the basalt aquifer
  • B: mapped faults
  • C: groundwater flow as determined by hydraulic conductivity of the geologic setting

This all adds up into:

“How is the spatial pattern of ion and isotope concentrations in wells tapping the basalt aquifer related to the spatial pattern of mapped faults via the mechanism of groundwater flow as determined by hydraulic conductivity of the geologic setting?”

This question might have thrown you, the reader, into the figurative deep end! For context, I’m studying the basalt aquifer in the Oregon portion of the Walla Walla Basin. This is located in north eastern Oregon, about thirty minutes northeast of Pendleton.

map showing the state of Oregon, with an inset map showing the study area

Figure 1: Walla Walla Sub-Basin Location

The wells that I am studying are drilled into the three most extensive formations within the Columbia River Basalts that are present in my study area: the shallow Saddle Mountains layer, the intermediate Wanapum Basalt , and the deepest Grande Ronde Basalt. The wells and faults that I am studying are predominantly on the transition area between the “lowland” areas where the basalts are covered by layers of sediment deposited in the ~15 million years since they were deposited and the upland areas where the basalt is exposed at the surface.

geologic map showing formations, folds, and faults in the entire Walla Walla Basin, highlighting my study area

Figure 2: Geologic map of the study area

Wells in this area are between 300 and 1,200 feet in depth, and primarily serve irrigation and municipal uses. Over the past 50 years there has been a noticeable downward trend in groundwater elevations in many of the wells. My research is part of a larger Oregon Water Resource Department project that seeks to better understand this groundwater system, faults and all. Faults add an element of the unknown to models of groundwater flow unless they are specifically studied, as they can formed either barrier or pathways for groundwater flow depending on their structures and characteristics. Faults with low hydraulic conductivity can block or significantly slow groundwater flow, while faults with higher hydraulic conductivity allow water to flow through them more easily. My research uses ion chemistry and isotope concentrations to characterize the path that groundwater has taken through the subsurface into the well.

Datasets:

A: In my research I have analytical data for 32 wells, whose XY locations were determined by field confirmation of my collaborators’ well log database. As groundwater is a 3D system, I have to consider Z values as well. The well depths and lithology information is also from my collaborators’ database, and was based on information of varying quality recorded at the time that the well was drilled. My analytical data provides a snapshot of water chemistry during the summer of 2018. I have only one temporal data point per well. At all 32 wells, I collected samples to be analyzed for pH, temperature, specific conductivity, oxygen isotopes 16 and 18, and hydrogen isotopes 1 and 2. At a subset of 18 of those wells I collected additional samples for tritium, carbon 14, and major ion analysis.

B: The shapefile of faults mapped at the surface was created by Madin and Geitgey of the USGS in their 2007 publication on the geology of the Umatilla Basin. There is some uncertainty in my analysis as far as extending this surface information into the subsurface. USGS studies have constrained proposed ranges of dip angles for the families of faults that I am studying, but not exact angles for any single mapped fault.

Figure 3: locations of wells that were sampled mapped with mapped fault locations.

Hypotheses:

Where faults act as barriers, I hypothesize that parameter values will differ in groups on either side of a fault. Specifically, a barrier fault might cause older, warmer water to rise into upper aquifer layers, and the downstream well might show a signature of more local recharge.

Where faults act as conduits, I hypothesize that water chemistry and isotopes of samples from wells on either side of the fault would indicate a relatively direct flowpath from the upstream well to a downstream well. Over a short distance, this means that ion and isotope concentrations would not differ significantly in wells across the fault.

Approaches:

I would like to use principal component analysis to identify grouping trends of the samples, and then map the results. Additionally, a bivariate comparison of wells on either side of the fault could be interesting? I would like to find some way to bring in distance from a fault into the model too.

Expected outcome: My output would be a mixture of statistical relationships and maps of those relationships.

Significance.  How is your spatial problem important to science? to resource managers?

The Walla Walla Subbasin’s basalt aquifers have recently been deemed over-allocated by the Oregon Water Resource Department (OWRD), and water managers are looking for methods to better regulate the aquifer when wells run dry. However, the faults are a big unknown when considering how to enforce the prior appropriation doctrine where junior permit holders are regulated off in times of water shortage. If a junior and senior water permit holder have wells that are separated by a fault, is it likely that stopping the junior permittee’s water use would actually result in more water available to the senior permit holder?

My approach is not novel in my scientific field. Several studies have evaluated similar parameters elsewhere in the Columbia River Basalts and also used statistical methods, but have not focused specifically on faults.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

Arc-GIS: Highly proficient in Desktop and Pro

R – novice, can copy/paste/edit code to suit my basic needs

Python – novice, took a class two years ago but have forgotten much of it

Image processing – working knowledge of ENVI from GEOG 580, and of Gravit from GEOG 572

Other spatiotemporal analysis – I haven’t really worked with software besides ArcGIS or QGIS?

 

 

Individual foraging specialisations of gray whales along the Oregon coast through prey preferences

Research Question

The Pacific Coast Feeding Group (PCFG) is a subgroup of the Eastern North Pacific (ENP) population of gray whales (Scordino et al.2018). The ENP, a population of 24,000-26,000 individuals, migrates along the U.S. west coast from breeding grounds in the lagoons of Baja California, Mexico to feeding grounds in the Bering Sea (Omura 1988). The PCFG, currently estimated at 264 individuals (Calambokidis et al.2017), stray from this norm and do not complete the full migration, instead choosing to spend their summer feeding months along the Pacific Northwest coast (Scordino et al.2011). Since gray whales as a species already exhibit specialization (by being the only baleen whale that benthically forages) and since the PCFG display a second tier of specialization by not using the Bering Sea feeding grounds, it seems plausible that individuals within the PCFG might have individual foraging specializations and preferences. Therefore, my research aims to investigate whether individual gray whales in Port Orford exhibit individual foraging specializations. Individual foraging specializations can occur in a number of different ways including habitat type (rocky reef vs sand/soft sediment), distance to kelp, time and distance of foraging bouts, and prey type and density. For this class, my research question is whether prey species drives the amount of time a foraging whale will spend at a specific foraging location.

 

Data

Prey data

The prey data has been obtained through zooplankton tow net samples from a research kayak in the summers of 2016, 2017 and 2018. Kayak sampling effort varies widely between the three years due to weather creating unsafe conditions for the team to collect samples. These samples have been sorted and enumerated to the zooplankton species level so that for each day when a prey sample was collected, we have known absolute abundances of prey species at each sampling station.

Whale data

The whale data is in the form of theodolite tracklines of gray whales that used the Port Orford study area during the summers of 2016, 2017 and 2018. Since whale tracking occurs at the same sites as prey sampling, we are able to map the prey community present at a particular location that whales forage at. The tracklines occur on a very fine spatial resolution as the study area is approximately 2.5 km in diameter, though some of the tracklines extend out to approximately 8 km offshore. Furthermore, as whales forage in the area, photographs are taken of each individual in order to match the trackline with a particular individual. This way, potential individual specializations may be detected if there are repeat tracklines of an individual.

 

Hypotheses

There will be differences in time spent by individual gray whales foraging in an areas with different prey communities. However, these differences will likely not be constant/stable over time. Most likely, foraging will be largely driven by availability of prey and therefore individual whales will be rather flexible in their foraging strategies.

 

Approaches

Individual patterns in time and space use within the Port Orford study area will be assessed through the identification of foraging bouts. Theodolite tracks from 2016-2018 longer than one hour will be included in this analysis. Using ArcGIS, tracklines will be clipped so that only foraging points within the study sites are included in this analysis. Radii will be created around each of the 12 zooplankton sampling locations to identify overlap between whale foraging and known prey communities at sampling stations. Time spent within these radii will be calculated. Statistical analyses (likely GAMs) will be run in order to identify whether individuals spend more and/or less time at a foraging location based on the prey species that are present at that location.

 

Expected Outcomes

This analysis will likely result in plots of time spent at a foraging locations against abundance of different prey species, which will be based on the results of the statistical analyses. This will allow the comparison of whether individual whales have preferences for different kinds of prey species. Maps might also be very nice to visualize the movements of whales however I am unsure how the time element of foraging bouts could be incorporated into this (perhaps with shading of color?).

 

Significance

This spatial problem is important to science since genetic evidence suggests that there are significant differences in mtDNA between the ENP and PCFG (Frasier et al.2011; Lang et al.2014), and therefore it has been recommended that the PCFG should be recognized as being demographically independent. In the face of a proposed resumption of the Makah gray whale hunt as well as increased anthropogenic coastal use, there is a strong need to better understand the distribution and foraging ecology of the PCFG. This subgroup has an important economic value to many coastal PNW towns as many tourists are interested in seeing the gray whales. Therefore, understanding what drives their distribution and foraging habits will allow us to properly manage the areas where they prefer to forage.

 

Proficiencies

I have novice/working knowledge of Arc-Info and Modelbuilder. I have never used Python before. I have working knowledge of R and am proficient in image processing.

 

Literature Cited

Calambokidis, J. C., Laake, J. L. and A. Pérez. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. Draft Document for EIS.

Frasier, T. R., Koroscil, S. M., White, B. N. and J. D. Darling. 2011. Assessment of population substructure in relation to summer feeding ground use in the eastern North Pacific gray whale. Endangered Species Research 14:39-48.

Lang, A. R., Calambokidis, J. C., Scordino, J., Pease, V. L., Klimek, A., Burkanov, V. N., Gearin, P., Litovka, D. I., Robertson, K. M., Mate, B. R., Jacobsen, J. K. and B. L. Taylor. 2014. Assessment of genetic structure among eastern North Pacific gray whales on their feeding grounds. Marine Mammal Science 30(4):1473-1493.

Omura, H. 1988. Distribution and migration of the Western Pacific stock of the gray whale. The Scientific Reports of the Whales Research Institute 39:1-9.

Scordino, J., Bickham, J., Brandon, J. and A. Akmajian. 2011. What is the PCFG? A review of available information. Paper SC/63/AWMP1 submitted to the International Whaling Commission Scientific Committee.

Scordino, J., Weller, D., Reeves, R., Burnham, R., Allyn, L., Goddard-Codding, C., Brandon, J., Willoughby, A., Lui, A., Lang, A., Mate, B., Akmajian, A., Szaniszlo, W. and L. Irvine. 2018. Report of gray whale implementation review coordination call on 5 December 2018.

Grant Z’s Spatial Problem

My spatial problem is about land use/land cover (LULC) change associated with the establishment of artisanal, small-scale gold mines (ASGM) in rural Senegal. Put in the vernacular we’ve learned in the course, I’d phrase my question this way:

How is the spatial pattern of LULC change related to the spatial pattern of ASGM establishment via the mechanism of deforestation?

As part of their establishment, ASGM requires clearing the land where the mines will be, as well as additional timber harvesting to build the homes where the miners will live while working and additionally to bolster the mines shafts themselves. As such, I’m curious as to what the exact change is that accompanies ASGM establishment, as this is a sub-set of my graduate thesis which seeks to understand more broadly how ASGM impacts the environment and the lives of the miners themselves, to understand better if a household diversifying into ASGM is better suited towards adapting to future climate change than if they hadn’t.

The dataset I have is very high resolution (VHR) satellite imagery (panchromatic and multispectral) courtesy of the Digital Globe Foundation. The panchromatic imagery has a resolution of .3m while the MS imagery is around 1.24m — as part of the preprocessing I’ve pansharpened the images so the overall imagery is stills sub-meter, which is necessary to investigate ASGM as its footprint is too small for detection with Landsat or other sensors. The imagery covers about 16 gold mines I’ve identified, and has imagery from 2018 and 2009/2010.

My present hypothesis is that, while obviously there will be a decrease in LULC at the mine in general, the area around the mine will also be indicative of some change — in my literature review, I’ve found some information that the environment up to 20km around a gold mine can be impacted. I think in this case the impact won’t be as drastic, but it’s what I’m expecting.

Currently I’m not sure how to approach the problem. The first step will be to just map out the mining locations first in ArcPro and from there try to see how the forest cover has changed between present and past. Beyond that I’m not sure how to answer the question.

Ultimately I’d like to produce maps, to demonstrate the environmental impact that ASGM has (or not, potentially!). I’ve also considered producing similar maps showing the relative impact subsistence agriculture has had in non-mining villages as a comparison.

I feel somewhat prepared for this task. I’m comfortable working with ArcGIS and have some knowledge of ArcPy (though I’m a bit rusty). I can use ModelBuilder pretty competently and feel that I have a good grasp of what to do in that arena. My major hurdle right now is not knowing what I don’t know — I need some more exposure to the tools available to me for addressing this problem. However, I feel confident that with enough time and work, this problem is not intractable!

One problem is that, given the rural area and part of the world I’m looking at, Digital Globe does not frequently sample the area and so the imagery I have is not necessarily on the anniversary date. Given the drastic climate differences (rainy season and dry season), the vegetation may look drastically different.

As an example, bellow is Kharakhena in November 2008, with the original village highlighted in red.

Here is the village of Kharakhena in March 2017. The village is highlighted in yellow and the mine in blue.

The difference is very dramatic, but I’m not sure how to approach this analytically.

This is my spatial problem! I believe that this is significant as a part of my thesis work exploring livelihood diversification in rural Kedougou. As 70% of the population in the region lives below the poverty line, and most people engage in subsistence farming as their primary livelihood, I am interested in how ASGM operates as a livelihood diversification option. Specifically, I’m interested in assessing whether it is an “erosive” coping strategy: one which households undertake out of lack of other options and one which may diminish the household’s overall capacity to react to future stresses and shocks. I’m assessing this through an evaluation of the “Three Capitals” (sometimes five) which constitute a household’s assets. The three capitals are Economic, Environmental, and Social. I’m assessing the impact of ASGM on miners’ Social and Economic capital through interviews which I’ve already conducted, and I’m assessing the impact of ASGM on miners’ Environmental capital through remote sensing. Together, I hope to paint a holistic picture of ASGM’s impact on miners’ livelihood capitals in the region in order to better understand if it is indeed an “erosive” coping strategy, which, if it is, needs to be known in order to help miners and farmers find different ways to adapt to future climate change.

Spatial pattern of invasion

  1.  A description of the research question that you are exploring.

I am interested in how the spatial pattern of invasion by the recently introduced annual grass, ventenata, is influenced by the spatial pattern of suitable habitat patches (scablands) via the susceptibility of these habitat patches to invasion and ventenata’s invasion potential.  Habitat invisibility is determined by the environmental characteristics, community composition, and spatial arrangement of suitable habitat patches and the invasibility of ventenata is influenced by its dispersal ability and fecundity.

I am also interested in understanding how spatial autocorrelation influences relationships between ventenata, plant community composition, and environmental variables within and across my sample units.

  1. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

I collected spatial data (coordinates and environmental variables) and plant species abundance data (including ventenata) data for 110 plots within and surrounding seven burn perimeters across the Blue Mountain Ecoregion of eastern Oregon (Fig. 1). Target areas were located to capture a range of ventenata cover from 0% ventenata cover to over 90% cover across a range of plant community types and environmental variables including aspect, slope, and canopy cover within and just outside recently burned areas. Once a target area was identified, plot centers were randomly located using a random azimuth and random number of paces between 5 and 100 from the target areas. Sample plots were restricted to public lands within 1600m of the nearest road to aid plot access. Environmental data for sample plots includes canopy cover, soil variables (depth, pH, carbon content, texture, color, and phosphorus content), rock cover, average yearly precipitation, elevation, slope, aspect, litter cover, and percent bare ground cover. I am planning to identify and calculate spatial pattern of habitat patches using Simpson Potential Vegetation Type raster data (Simpson 2013) to 30m resolution in Arc GIS which was developed to identify potential vegetation types across the Blue Mountain Ecoregion.

  1. Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

Ventenata appears to readily invade unforested, rocky scabland patches, but is less prominent in surrounding forested areas. These areas may act as “hotspots” of invasion from which ventenata can spread into surrounding, less ideal habitat types. The “biodviersty-invasion hypothesis” (Elton 1958) posits that more biodiverse areas will be less susceptible to invasion, but propagule pressure hypotheses suggests that areas close to areas that are heavily invaded will be more likely to be invaded (Colautti et al. 2005). If environmental factors such as biodiversity influence invasion success, I would expect diverse habitats to have a higher resistance to the ventenata invasion and be less invaded, but if propagule pressure is a stronger driver of the ventenata invasion, diversity may be trumped by proximity to invaded patches which may increase these patches risk of invasion despite species composition.

  1. Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I would like to develop an overall understanding of the spatial pattern of invasion by performing Moran’s I to test for spatial autocorrelation. Additionally, I would like to identify hotspots of invasion using hotspot analysis. From these hotspots, I am interested in predicting spread using kernel density functions that estimate ventenata distribution. Finally I would like to relate the spatial pattern of vententata to environmental characteristics of habitat patches (including species composition) through the use of cross-correlation and geographically weighted regression.

  1. Expected outcome: what do you want to produce — maps? statistical relationships? other?

I would like to produce figures that represent multivariate statistical relationships between ventenata, patch size, location, and environmental/ community variables. I am also interested in creating a map depicting areas at highest risk of invasion based on spatial and environmental data if appropriate considering the statistical relationships that result from the analysis.

  1. How is your spatial problem important to science? to resource managers?

Ventenata is rapidly invading natural and agricultural areas throughout the inland Northwest where associated ecological and economic losses are readily becoming evident. However, possibly the most concerning aspect of the invasion is ventenata’s potential to increase fire intensity and frequency in invaded scabland patches, that prior to invasion supported light fuel loads and acted as natural fire breaks for the surrounding forest.  Such shifts to the fire regime could dramatically alter landscape-scale biodiversity and cause additional socioeconomic losses. Despite these concerns, little is known of the drivers influencing ventenata’s invasion potential and few management options exist. Understanding how the spatial arrangement and size of scabland patch influences susceptibility to invasion by ventenata could help managers target areas at the highest risk for invasion and mitigate losses.

  1. Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

I have a working knowledge of programming in R and manipulating spatial data/ map making in ArcGIS. I have taken introductory level classes in R and ArcGIS, and used these tools for work and for my research. I have no experience in modelbuilder, GIS programming in python, and image processing. I am eager to learn how to use these tools and apply them to help answer ecological research questions.

References:

Colautti, R. I., Grigorovich, I. A., & MacIsaac, H. J. (2006). Propagule pressure: a null model for biological invasions. Biological Invasions, 8(5), 1023-1037.

Elton, C.S. (1958). The ecology of invasions by animals andplants. T. Methuen and Co., London.

Simpson, M. 2013. Developer of the forest vegetation zone map. Ecologist, Central Oregon Area Ecology and Forest Health Program. USDA Forest Service, Pacific Northwest Region, Bend, Oregon, USA

 

Using Spatial Statistics to Determine the Subsurface Spatial Distribution of Lava Flows in Northern California

Research Question

I am trying to determine potential fluid flow paths based on the spatial distribution of lava flows in the Medicine Lake, Lassen Peak Big Valley Mountain area of Northern California. The final goal of this project is a 3-D subsurface framework of the geology from which we can model fluid and heat flow.

Thus we want to know “How the spatial pattern of the depth of the lava flows attributes of wells is related to the spatial pattern of lava flow depth (B1), which in turn is related to pre-lava-flow topography (B2) and the regional geology (B3), because lava flows follow topography and subside post emplacement at rates that follow basin subsidence rates in the region, and form barriers for/conduits of groundwater (C1).”

Dataset

My data are a series of more than 1500 well logs. Each contain data that pertains to a map coordinate and with information about changes in lithology, or rock type, with respect to depth. Well logs are collected the year the well is made. I have well logs that range from the 1960s to the present. Temporal data is less important for the initial part of my study (Fig 1).

Figure 1: What is a Well Log? A well log is a record of the changes in rock type that occur with changes in depth and a location at the surface of the earth.

I also have data from geologic maps. Geologic maps provide the spatial extent of the surface exposure of a geologic unit, which includes information about its contacts with other rock units in x,y space as well as information about the elevation (Fig 2).

Figure 2: My study area in Northern California, it lies between the Big Valley Mountain, Medicine Lake Volcano and Lassen Peak with the Pit River draining the middle of it. The green dots represent the center of the township and range section that the wells are located in, and the size of the circle indicates the number of wells in that section.

Hypothesis

 

Figure 3: Cross section of a Basalt Column (Lyle 2000). In Volcanic systems, fluid flow is limited to the Vesicular flow tops, the rubbly bases (P.P.C in this diagram) and sedimentary interbeds that lie between vertically stacked flows. These sections of the rock are the only locations with high enough permeability and porosity to allow the movement of water.

This means that if we know where the lava beds lie, and we know their contacts, then we can outline potential zone of flow. Determining the patterns of how lavas flow and emplacement then allows us to determine the lava flow’s spatial extent, and therefore potential flow paths. I expect lavas to follow the laws of physics. They travel as viscous fluids and fill basins, and so I would expect them to be thickest where paleotopography was lowest, they will likely thin out at the edges, and they will be down slope from the volcano or vent that erupted them. Given the regional geology, I would expect the thickest lava flows to lie in the Pit River basin, near the range bounding Big Valley Mountain Fault (Fig 3).

At its simplest, we expect lava flows to follow the geologic principals of original horizontality and of cross cutting relationships.

Approaches

I would like to learn both how to apply variograms and kriging and how they work; plus any new techniques that I am not yet aware of. We can also make the assumption that all our residuals with follow the rules of stationarity. This means that any irregularities in the data represent unacknowledged geologic features.

Expected outcome

My first goal is to create a 3-D subsurface map of the connectivity and contacts of lava flows in the Medicine Lake, Lassen Peak Big Valley Mountain region.  Ideally I would begin the process with Geog 566. I would like to have a few surface I can test in the field by the end of this term, but understanding different methods with which I can make this framework is my first goal.

Relevance

Understanding the distribution of lava flows in the region ties into the regional geology of my study area. As I stated in Question 3, lavas fill basins. Basin morphology, and the amount of space lavas can fill depends on the slip rates and distribution on faults in the Medicine Lake, Lassen Peak Big Valley mountain triangle. By constraining slip rates in the basin, we both build a better picture of the regional geology, and we can make more rigorous checks on the validity of our statistical outputs.

 

Another equally important point is the next step in the project. After we make the 3-D framework, the USGS will use it to model fluid and heat flow in the region. Comprehending potential changes in groundwater flow in the region will allow city manages in the region to better manage water in the future.

 

Preparation

 

Arc-Info Not much, ArcMap, yes
Modelbuilder and/or GIS programming in Python Working knowledge of python outside of GIS programming
R None
Image Processing Working Knowledge
Relevant Software Matlab

 

Sources

Lyle, P. “The Eruption Environment of Multi-Tiered Columnar Basalt Lava Flows.” Journal Of The Geological Society, vol. 157, 2000, pp. 715–722.

Epidemiology of Western Hemlock Dwarf Mistletoe Post – Mixed Severity Fire

Description of the Research Question

My study site is located in the HJ Andrews Experimental Forest, where the two most recent mixed severity fires burned leaving a sizable fire refugia in the middle of the stand. Western hemlock dwarf mistletoe (WHDM) survived the fire in this refugia. WHDM spreads via explosively discharged seed and rarely by animals. This means that for the pathogen to spread, the seed must reach susceptible hosts. WHDM is a obligatory parasite of western hemlock. The regeneration post fire may pose a barrier to the pathogens spread because of the structure and composition. Lastly, the structure of the fire refugia may determine the rate of spread. This is because the structure largely determines where infections exist in the vertical profile of the canopy.

My research question is: How is the spatial pattern of WHDM spread extent and intensification from fire refugia related to the spatial pattern of the structure and composition of the fire refugia and the surrounding regeneration via barriers to viable seed reaching susceptible hosts?

I have three objectives related to the spatial organization or the regenerating forest surrounding the fire refugia and one related to the fire refugia itself. My objectives are to determine how fire refugia affects dwarf mistletoe’s spread and intensification through:

  1. The stand density of regenerating trees and of surviving trees, post fire.
  2. The stand age and structure of regenerating trees and surviving trees in fire refugia.
  3. The tree species composition of the regenerating trees adjacent to fire refugia.

AND…

  1. Whether intensification dynamics of WHDM inside a fire refugia resemble those of WHDM in other infection centers.

Dataset Description

Spatial locations of the extent of spread and intensity (also referred to as severity) of WHDM infection include presence/absence of infection in susceptible hosts and will have a severity rating for each infected tree. The presence/absence data will be two measurements, one from 1992 and one from 2019. The intensity rating will be from 2019 only.

Spatial locations/descriptions of the structure and composition of the fire refugia and regeneration surrounding refugia include X,Y of each tree, a variety of forest inventory attributes such as diameters, heights, and species for each tree, and delineation of the fire refugia boundaries. This data has been measured several times: 1992, 1997, 2013, and 2019. The GPS coordinates were recorded with handheld GPs units most likely under canopy cover so resolution may vary.

These data sets are bounded by a 2.2 ha rectangle that confines the study area.

Hypotheses

The spatial pattern of western hemlock dwarf mistletoe in the study site will be several discrete clusters. This arrangement forms because WHDM spreads from an initial infection point outward. The initial infection serves as an approximate center point and the cluster, or infection center, grows outward from there. Separations between clusters are maintained by forest structure and composition or disturbances. New clusters form from remnant trees surviving disturbances, or random dispersal of seed long distances by animals. In this case, the fire refugia protect the remnant trees from disturbance and these trees are the new focal points for infection centers

The spatial pattern of the forest structure and composition will drive the direction and rate of spread of WHDM because variances in these two attributes will cause varying amounts of barriers to WHDM seed dispersal. Because seeds are shot from an infected tree, that seed needs to reach a new uninfected branch. This means physical barriers to spread can affect seed dispersal and non susceptible species will stop spread.

Approaches

I would like to utilize spatial analyses that allow me to understand how the clustering of WHDM is related to the boundaries of the fire refugia. Also, how the infection centers have changed over time utilizing forest structure and composition metrics of the regeneration surrounding the refugia. Lastly, something that can incorporate a severity rating on a scale instead of simply presence/absence data and describe the distribution of severely infected trees vs lightly infected trees and how that relates to the fire refugia boundary and forest metrics of the regeneration surrounding the refugia.

Expected Outcome

I would like to produce statistical relationships that can determine the significance of forest density, species composition, age, and structure on the ability of WHDM to spread. Also, I would like to produce statistical relationships that can describe whether or not a fire refugia alters the way WHDM spreads and intensifies when compared to commonly observed models. Maps as visuals for describing the change over time would be a useful end product as well.

Significance

Understanding the spread patterns of WHDM is important for resource managers seeking to increase biodiversity and produce forest products. Focus has shifted to creating silvivultural prescriptions that emulate natural disturbances that are still economically viable and that maintain ecosystem functions. Disturbance events can control WHDM but also create opportunities to increase its spread and intensification so managers need to have an understanding of how a particular forest structure will affect WHDM. Also, if we want to maintain biodiversity, understanding how WHDM infection centers created by fire develop is important. Fire frequency and severity may be increasing in the future and the loss of mixed severity fire would mean a significant loss of WHDM. Land managers seeking to emulate burns can use this information to plan burns that preserve patches of WHDM if desired and understand how the pathogen will progress 25 years later. This is not usually the case for forest pathogens.

Level of Preparation

I have worked in ArcMap quite a bit, but I haven’t much experience with the wide range of functionality of ArcInfo. I used ModelBuilder somewhat to keep my queries and basic analyses organized. No experience programming in Python. I have taken two stats classes before this using R and feel I have a working knowledge and have no problem learning new tools in it. However, I have very little work with image processing such as working with rasters and some small exposure to LiDAR processing.

Predicting spatial distribution of Olympia oysters in the Yaquina estuary, OR

My spatial problem

  • A description of the research question that you are exploring.

My research aims to determine the current abundance and spatial distribution of native Olympia oysters in Yaquina Bay, Oregon. This oyster species has experienced massive decline in population due to overharvest during European settlement of the western United States. Yet its value to the ecosystem, its cultural importance, and its tastiness have made the Olympia oyster a current priority for population enhancement. For my research, I will be focusing on a local population of Olympia oysters in the Yaquina estuary. The goal of my project is to gather baseline information about their current abundance and spatial distribution, then develop a repeatable biological monitoring protocol for assessing this population in the future. Using spatial technology, I will first assess whether the distribution of Olympia oysters can be predicted using three habitat parameters: salinity, substrate availability, and elevation. In collaboration with the Oregon Department of Fish and Wildlife (ODFW), I will use the results of this spatial analysis and field surveys to determine ‘index sites’, which are specific locations within the estuary that are indicative of the larger population. These index sites will be revisited in the future by ODFW’s Shellfish and Estuarine Assessment of Coastal Oregon (SEACOR) team to assess changes in population size and spread over time. If predictions of Olympia oyster distribution are accurate based on the habitat parameters I’ve identified, then I’d also like to analyze potential species distribution under future environmental conditions and under different management scenarios, including habitat restoration and population enhancement.

For this course, I will be exploring this main research question:

How is the spatial pattern of Olympia oysters in the Yaquina estuary [A] related to (caused by) the spatial pattern of three habitat parameters (salinity, substrate, elevation) [B]?

 

  • A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

I will be using three spatial datasets, representing each of the habitat parameters, overlaid on one another to rank most to least likely locations for Olympia oyster presence. The salinity dataset is based on historical measurements (1960-2006) and represents a gradient from highest salinity (~32psu) at the mouth of the estuary to fresher water up stream (<16psu). Elevation is represented through a bathymetric dataset from 2001-2002, sourced from the Environmental Protection Agency office in Newport, OR. The substrate data comes from the Oregon ShoreZone mapping effort in 2014, which is managed and updated by the Oregon Coastal Management Program. There’s a couple different ways this data can be used, either as a substrate layer that characterizes substrate type broadly (low resolution) or through vector data with associated data tables that describe the substrate within a tidal zone along the shoreline (higher resolution, but spatial extent is limited).

The images here show the three habitat parameter spatial datasets:

Yaquina Bay bathymetry derived from subtidal soundings in 1953, 1999, 1998, and 2000 by U.S. Army Corps of Engineers.
Data from EPA.

Salinity figure digitized from Lewis et al. (2019) based on Oregon’s wet-season salinity measurements (average salinity November-April).
Lewis, N. S., E. W. Fox, and T. H. DeWitt. 2019. Estimating the distribution of harvested
estuarine bivalves with natural history-based habitat suitability models. Estuarine, Coastal and Shelf Science, 219: 453-472.

Substrate component classes of Yaquina Bay based on data classifications from Coastal and Marine Ecological Classification Standard (CMECS) ‘Estuarine Substrate Component’ layer.
Data from Oregon Coastal Management Program.

  • Predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I am hypothesizing that the distribution/spread of Olympia oysters in Yaquina Bay is influenced by availability of appropriate habitat parameters; where these parameters align within the appropriate range will determine where the oysters can be found. However, I think that I will find that not all of the parameters equally influence oyster distribution. For example, Olympia oysters have been observed to tolerate a broad salinity range, but are absolutely not present without suitable substrate. I am expecting to see that the influence of a particular habitat parameter changes depending on where the oysters are located within the estuary. I’m curious to see, if possible, which parameter will be most important at what life stage and what may drive changes in population per specific site in the estuary.

I do expect that I will be able to make a prediction about where the oysters will be located based on the habitat parameters, though I am uncertain that the resolution of the spatial data is sophisticated enough to capture nuances in distribution. For example, Olympia oysters are known to be opportunistic in finding suitable substrate and will settle on a wide variety of hard surfaces, including derelict boating equipment, discarded shopping carts, and pilings.

 

  • Describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I want to be able to produce a model that can predict where the oysters are located based not just on the three habitat parameters of interest, but under various environmental conditions and different management scenarios. For example, where might the oysters settle in a given year if rainfall is substantially higher, or if adult oysters spawn earlier, or if oyster growers create Olympia oyster beds for harvest, or if a new habitat restoration site is established, etc.

I’m not especially handy at statistical analysis, so I would like to gain a better understanding of statistics through spatial data. I know that I will need to use statistics to determine how successfully the prediction of Olympia oysters aligns with actual observations in the field, but currently unsure how to do that. A recent study in Yaquina estuary was just released using a similar approach for predicting the distribution of five other bivalve species. This study used R to generate a logistic regression model to determine the probability of each species presence within a given area. I would like to do something similar for my analysis, but need some help.

 

  • What do you want to produce — maps? statistical relationships?

The desired products of this research are habitat suitability maps of the current and future (pending the success of the initial effort) distribution of native Olympia oysters for use by ODFW. As a part of this effort, I will create a map of index site locations to be used in future species monitoring. I would also like to generate a predictive model that can determine distribution of oysters based on annual changes in the local environment (El Nino conditions, heavy rainfall, restoration efforts, introduction of invaders, etc.). While salinity, substrate, and elevation seem to be the main factors influencing oyster distribution, there are a number of other factors that can have effects, including temperature, proximity to the mouth of the estuary, and tidal retention.

 

  • How is your spatial problem important to science? To resource managers?

ODFW currently does not have reliable baseline information on the distribution of Olympia oysters in Oregon. As an ecological engineer, the species provides a number of important benefits to the ecosystem, including water filtration and habitat for other marine creatures. It is culturally significant to local tribes, including the Confederated Tribes of Siletz. This species is not currently listed as threatened or endangered, but if it becomes listed one day, then that designation will trigger a number of mitigation and conservation measures that will be difficult and expensive for agencies and private landowners. Additionally, there’s been some exploration that if the population can become robust again, there is potential to grow and harvest this species as a specialty food product. Given the current slow food movement and interest in local products, Olympia oysters could fit well in this niche.

 

  • How much experience do you have with:

(a) Arc-Info – Little experience, used a bit with older versions of ArcMap.

(b) Modelbuilder and/or GIS programming in Python – I am comfortable with ModelBuilder, but have no experience with Python.

(c) R – Some experience; I took Stats 511 where we used R heavily in a series of lab exercises. I have not applied my own data in R.

(d) Image processing – I have used a variety of Adobe products for graphic design, including Photoshop and InDesign.

My Spatial Problem – Elevated Blood Lead Levels in Bangladeshi Children: What is to blame?

Background:

No level of lead exposure is considered safe, and humans have experienced its toxicity to nearly every organ system, especially the central nervous system (1,2).  Exposures to lead, especially for the developing neurological system of children, are known to be hazardous and greatly impact children (2).

Environmental contamination is a widespread public health issue throughout the country of Bangladesh. Within the 1990s, widespread arsenic poisoning was described in populations which used groundwater wells for drinking water and were contaminated with naturally-occurring arsenic (3). In addition, Bangladesh is a developing country with increasing industry and continued burning of gasoline pointing to high exposures of lead through air pollution (4). Children also make up about 13% of the labor force in Bangladesh which include pottery glazing, lead melting, welding, car repair, and ship breaking which are all known exposures of lead (5). Based on historical air pollution and industries within urban areas of Bangladesh, the exposures to lead and known elevated childhood blood levels alarm health authorities and more information is needed as to determine exact exposures within communities.

I will utilize a 16-year developed maternal cohort study (R01ES015533 PI:Christiani/CoI Kile; P42ES16454: Bellinger/CoI: Kile; R01ES023441,PI: Kile) in Pabna and Sirajdikhan Upazilas in Bangladesh to examine the possible spatial relationships of lead exposures. Children born from the participating mothers in the maternal study were followed from early childhood and were tested for blood lead levels at 4 to 5 years old. Many children within this cohort showed elevated blood lead levels above the U.S. recommendation level of 5 ug/dL (Table 1).

Table 1: Summary results of blood lead levels (ug/dL) of children aged 4-5 years in Pabna and Sirajdikhan (n=348)

Location

Minimum Mean Maximum Above 5 ug/dL and percentage
Combined

0.00

3.86 19.6

165, 47%

Pabna (n=183)

0.00

1.32 10.8

51, 28%

Sirajdikahn (n=165) 0.00 6.67 19.6

114, 69%

Research question:

What are the potential spatial relationships of blood lead levels in children aged 4-5 years old within two areas in Bangladesh and association with possible sources of exposure from high traffic or urban areas?

Dataset analyzing:

Blood lead concentrations (ug/dL) of children at ages 4-5 years old only taken at one timepoint with coordinates of their homes (n=348). There is one other categorical variable of if the children are above or below the 5 ug/dL US recommendation for lead exposure. The resolution is fine scale within meters of one another over the span of square kilometers. I will also be employing base maps of the two districts where the children live. This will allow network review for the major traffic and highway areas and distance to urban centers. I have a slight hesitation for the level of information I will have within the country of Bangladesh for understanding the street network. My pilot data from analysis in R made it difficult to gather these data. I am looking to import the data into Arc for more extensive data analysis.

Hypotheses:

I hypothesize that the blood lead levels of the children are spatially correlated and increased blood lead levels in either specific hot spots and/or locations closer to roads and/or urban centers will be due to inhalation and dermal exposure from air pollution.

Approaches:

I would first like to perform spatial autocorrelation to determine if the blood lead levels of children are spatially correlated.  Secondly, I would like to employ the hot-spot analysis tool from Arc to determine if within the sample of children there are specific hotspots of higher blood lead levels. We do know that between the two districts of samples taken, the district closer to urban landscape has higher, more prevalent levels of lead in children (Table 1). To try and pinpoint more specific exposures, I would like to intersect the locations of the children’s homes with a buffer (100 m) from major highway and traffic areas. I would then aggregate the intersected homes and determine if those blood lead levels are higher comparatively to the children that live greater than 100 m. The first attempt would be crude analysis of within or outside the buffer, but if we do find that individuals within the buffer have significantly higher blood lead levels, I would like to see if there is a drop off of between 100-250 m, 250 – 500 m, and 500 m + in distance from high traffic roadways. Lastly, if we do not find trends with distance to roadways and blood lead levels, I will overlay base maps of industry related areas and identify if they overlap with hot spots within our spatial spread of the blood lead levels.

Expected outcomes:

  1. Spatial autocorrelation: I hope to describe the blood lead level data as spatially autocorrelated by producing linear plots as a visual representation of their autocorrelated relationships
  2. Hot spot analysis: From Arc, I will produce maps through the hot-spot analysis tool to hone into different areas of interest that might have the highest levels comparatively. This map would be useful to understand blood lead level relationships within the two different districts, and if there are areas of interest for further analysis.
  3. Distance to roadways: From Arc, I want to produce maps that show a 100 m buffer around major roadway systems and intersection of locations of children’s homes from the cohort. I would then like to graphically produce a simple boxplot of blood lead levels within the buffer and outside of the buffer zone to compare the mean blood lead levels. If I do find differences, I would like to produce another map of the gradient change of going farther away from high traffic roadways and blood lead levels (further buffer/intersection analysis).
  4. Intersection of Hot Spots and Industry: I would like to produce a map similar to the hot-spot analysis which overlays the areas of known industry from a base map with the potential hot-spots of children’s homes.

Significance:

In a 16+ year cohort understanding the consequences of chronic arsenic exposures, my work is significant to help communities better protect children from undue burden of environmental pollution. I hope to bring a better understanding to the communities as to why their children have high blood lead levels compared to US averages. My research will help explain if the areas the children live in are correlated with increased exposures and lead to more help public health actions on how to limit lead exposures.

Level of preparation:

a) Intermediate knowledge of ArcGIS Pro

b) I will concurrently be taking a GIS programming course in Python, and I have taken intro Python coding coursework.

c)I have completed coursework (GEOG 561) programming in R with these data to understand spatial relationships. I also have extensive statistical modeling experience in R.

Works Cited:

  1. Tong S, Schirnding YE von, Prapamontol T. Environmental lead exposure: a public health problem of global dimensions. Bull World Health Organ. 2000;78:1068–77.
  2. WHO | Lead [Internet]. WHO. [cited 2019 Mar 17]. Available from: http://www.who.int/ipcs/assessment/public_health/lead/en/
  3. Smith AH, Lingas EO, Rahman M. Contamination of drinking-water by arsenic in Bangladesh: a public health emergency. Bull World Health Organ. 2000;78(9):1093–103.
  4. Kaiser R, Henderson A K, Daley W R, Naughton M, Khan M H, Rahman M, et al. Blood lead levels of primary school children in Dhaka, Bangladesh. Environ Health Perspect. 2001 Jun 1;109(6):563–6.
  5. Mitra AK, Haque A, Islam M, Bashar SAMK. Lead Poisoning: An Alarming Public Health Problem in Bangladesh. Int J Environ Res Public Health. 2009 Jan;6(1):84–95.

Faye Andrews, MPH

andrewsf@oregonstate.edu

Deaggregation of infrastructure damages and functionality based on a joint earthquake/tsunami event: an application to Seaside, Oregon.

Research Question and Background

The Pacific Northwest is subject to a rupture of the Cascadia Subduction Zone (CSZ) which will consequently result in both an earthquake and tsunami. While all communities along the coast are vulnerable to the earthquake hazard (e.g. ground shaking), low lying communities are particularly vulnerable to both the earthquake as well as the subsequent tsunami. Completely mitigating all damage resulting from the joint earthquake/tsunami event is impossible, however, understanding the risks associated with each hazard individually can allow community planners and resource managers to isolate particularly vulnerable areas and infrastructure within the city.

The city of Seaside, Oregon is a low-lying community that is subject to both the earthquake and tsunami resulting from a rupture of the CSZ. The infrastructure at Seaside can be divided into four components: (1) buildings, (2) electric power system, (3) transportation system, and (4) water supply system. Similarly, the hazards can be viewed jointly (both earthquake and tsunami), as well as independently (just earthquake or tsunami).

Within this context, I’m particularly interested in looking at how the spatial pattern of infrastructure damage and functionality is related to individual earthquake and tsunami hazards via ground shaking and inundation respectively. Furthermore, I’m interested in looking at how these spatial patterns change as the intensity of the hazard increases.

Description of Dataset

The dataset I will be analyzing consists of two components: (1) spatial maps, and (2) infrastructure damage and functionality codes. Part of this analysis will be merging these two components to spatially view the infrastructure damage and functionality.

The spatial maps consist of:

  1. Building locations (represented as tax lots)
  2. Hazard maps: earthquake ground shaking and tsunami inundation hazard maps

The infrastructure damage and functionality codes implement Monte-Carlo methods to probabilistically define damages, losses, and connectivity. The four infrastructure codes consist of:

  1. Buildings: expected damage and economic losses to buildings.
  2. Electric power system: a connectivity analysis of each building to the electric substation. There is one electric substation within Seaside.
  3. Transportation system: a connectivity analysis of each building to critical infrastructure. Critical infrastructure at Seaside consists of two fire stations and one hospital.
  4. Water supply system: a connectivity analysis of each building to their respective pumping station. There are three water pumping stations within Seaside, and each building is assigned to a single pumping station.

Hypotheses

I hypothesize that the infrastructure damage is not spatially variable for the earthquake hazard, however it will be for the tsunami hazard (e.g. distance from coast). The relative damages due to tsunami will also increase as the intensity of the hazard increases.  That is, for small events, the damages will be dominated by earthquake, whereas for larger events, the damages will be dominated by the tsunami.

Approaches

While color-coordinating tax-lots based on economic losses provides a means to visualize damages throughout a study region, I am interested in learning about kernel density estimation and hot spot analysis to identify vulnerable regions (not just individual buildings). I am also interested in learning about different spatial network analysis methods, as only connectivity analyses within the infrastructure networks (electric, transportation, and water) have been considered so far.

Expected outcome

I’m hoping to produce maps showing how damages and economic losses relate to both joint hazards (earthquake and tsunami), as well as independent hazards (just earthquake or tsunami). I would also like to produce maps showing the connectivity of individual tax-lots to critical infrastructure. Furthermore, I would like to investigate visualizing both the economic losses and connectivity analysis through color-coordinating tax-lots, kernel density estimation and hot-spot analysis.

Significance

The ability to spatially isolate vulnerable areas will allow community planners and resource managers a means to better prepare mitigation plans. Deaggregating the damages and losses by infrastructure and hazard will isolate the relative importance of each, and can assist in mitigation measures. For example, identifying that the earthquake is the dominating force in producing building damages within a specific region, planners and resource managers can support retrofit options for homeowners within that region.

Level of preparation

  1. Arc-info: novice
  2. ModelBuilder and/or GIS programming in Python: Although I haven’t done GIS programming in Python, I am highly proficient in Python and am comfortable working with GIS data. Learning how to merge python and GIS should not be difficult.
  3. R: novice
  4. Image processing: novice
  5. Other relevant software: I’m proficient in QGIS.

Examining the Spatial Relationships between Seascapes and Forage Fishes

Description of Research Question

My objective is to study the spatial relationships between sea-surface conditions and assemblages of forage fish in the California Current System from 1998 to 2015. Forage fish are a class of fishes that are of importance to humans and resource managers, as they serve as the main diet for economically and recreationally valuable large-game fishes. Using a combination of remotely sensed and in-situ data, sea-surface conditions can be classified into distinct classes, known as “seascapes,” that change gradually over time. These seascapes, which are based on a conglomeration of measurable oceanographic conditions, can be used to infer conditions within the water column. My goal is to determine if any relationship exists between forage fish assemblages and certain seascape classes by examining the changes in the spatial patterns related to each over time. Forage fish assemblage may be related to seascapes as certain seascape classes may correspond to physical (temperature) or biological (chlorophyll concentration) conditions, either on the surface or in the water column, which happen to be favorable for a specific species or group of species.

My question can be formatted as: “How is the spatial pattern of forage fish assemblage in the California Current System related to the spatial pattern of seascapes based on the sea-surface conditions used to classify the seascapes (temperature, salinity, and chlorophyll)?

Description of Data

Midwater trawls have been conducted annually by the National Oceanic and Atmospheric Administration’s (NOAA) Southwest Fisheries Science Center (SWFSC) in an attempt to monitor the recruitment of pelagic rockfish (Sebastes spp.) and other epipelagic micronekton at SWFSC stations off California. The trawls have informed a dataset that represents overall abundance of all midwater pelagic species that commonly reside along the majority of the nearshore coast of California from 1998 to 2015. Each trawl contains both fish abundance, recorded in absolute abundance, and location data, recorded in the form of latitude and longitude. The dataset also includes a breakdown of species by taxa, which will be used to determine if a fish is a “forage fish.”

Seascapes have been classified using a combination of in-situ data (from the trawls) and remotely sensed data from NASA’s MODIS program. Seascapes were classified using the methods described in Kavanaugh et al., 2014 and represent the seascape class in the immediate area that each trawl occurred. Seascapes are classified at 1 km and 4 km spatial resolution and at 8-day and monthly temporal resolution. Each seascape has been assigned an ID number which is used to identify similar conditions throughout the dataset.

The map below shows the locations of every trawl over the course of the study.

Figure 1: Map showing all trawl sites contained in the dataset. Trawls occurred at a consistent depth using consistent methods between and including the years of 1998 and 2015

Hypotheses

I hypothesize that any measurable spatial changes in the spatial extend of certain seascape classes will also be identifiable in the spatial variability of forage fish assemblage over time. Preliminary multivariate community structure analysis has shown some statistically significant relationships between certain species and certain seascape classes using this data. If spatial patterns do exist, I expect there to be some relationship between the surface conditions and the fish found at depth of the midwater trawls.

Hypothesis: I expect the spatial distribution of forage fish species to be related to spatial distribution of seascape conditions based on the variables used to classify the seascapes (temperature, salinity, chlorophyll).

Potential Approaches

I hope to utilize the tools within both R and the ArcGIS Suite of products to identify and measure spatial patterns in both seascape classes and forage fish assemblages over the designated time period. I also aim to run analyses to determine if any relationship exists between the variability in spatial extent of each variable. These analyses will be used to supplement the previously completed multivariate community structure analyses done on these data.

For Exercise 1, I will identify and test for the spatial patterns of the forage fish family Gobiidae (Goby) and Seascape Class 10, as initial indicator species analyses indicated that there may be a relationship between the two. In Ex. 2, cross-correlation and/or GWR will examine relationships between these patterns.

Expected Outcome/Ideal Outcome

Ideally, I would like to determine and define the relationship between seascape classes and forage fishes in the California Current System over the designated period of time. Any sort of definitive answer, positive, negative, or none, provides valuable insight into the relationships between this remotely sensed data and these fishes. If that claim could be bolstered by a visual which outlines the relationship between my variables (or lack thereof), that would be icing on the theoretical cake.

Significance of Research

Measuring the predictability of forage fish assemblage has wide-ranging impacts and could be found useful by policymakers, fishermen, conservationists, and even members of the general public. Additionally, this research can be used to underscore the importance of seascape-based management or seascape approaches to ecology or management. This research could also be used as inspiration for future studies about different species, taxa, or geographic locations.

Level of Preparation

I completed a minor in GIS during my undergraduate studies, but have not had to utilize those skills for about 15 months. After some time, I believe that I will be extremely comfortable using the software. I have basic exposure to R software (mostly in the context of statistical analysis) and have used CodeAcademy to further my understanding of Python. I did some image processing during my undergraduate studies as well, but am not particularly comfortable with that set of skills. I have used leaflet to embed my maps and create time series before, so that could be an option for this work.

WORKS CITED

Kavanaugh M. T., Hales B., Saraceno M., Spitz Y.H., White A. E., Letelier R. M. 2014. Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology, Progress in Oceanography, Volume 120, Pages 291-304, ISSN 0079-6611, https://doi.org/10.1016/j.pocean.2013.10.013.

Sakuma, K., Lindley, S. 2017. Rockfish Recruitment and Ecosystem Assessment Cruise Report.  United States Department of Commerce: National Oceanic and Atmospheric Administration, National Marine Fisheries Service.

-Willem Klajbor, 2019

Seth Rothbard My Spatial Problem

A description of the research question that you are exploring

Of the 31 pathogens known to cause foodborne illness, Salmonella is estimated to contribute to the second highest number of illnesses, the most hospitalizations, and the highest number of deaths in the US when compared to other domestically acquired foodborne illnesses1. Salmonellosis is the bacterial illness caused by Salmonella infection. It is estimated there are approximately 1.2 million cases of salmonellosis and around 450 deaths every year in the US due to Salmonella1. Over time there has been marked variability in the number of reported cases per year. Salmonellosis is a mandatory reportable illness in Oregon and available information indicates that incidence rates of this disease have been stable since the new millennium2. The objective of this study is to perform spatial analysis of lab-confirmed Salmonella in Oregon counties for the years 2008-2017 for which county level data are available and determine whether some counties have a higher risk of Salmonella infection compared to others. I also wish to explore the socioeconomic factors associated with high incidence rate counties. My research question that I wish to explore is:How are spatial patterns of Salmonella related to spatial patterns of socioeconomic factors? Certain socioeconomic patterns such as lower levels of education and income may increase rates of Salmonella in these populations as a result of improperly preparing/cooking foods, less strict sanitation practices, and/or higher rates of eating high risk foods.

A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent

The Oregon Health Authority has created a database called the Oregon Public Health Epidemiology User System (ORPHEUS) as a repository for relevant exposure and geospatial data related to disease cases reported to public health departments all across the state. This database has been maintained by the state since 1989 and includes information regarding various diseases. The dataset I will be using is a collection of every single reported non-typhoidal Salmonella case within Oregon from 2008-2017. The distinction between typhoidal Salmonella and non-typhoidal is that the typhoidal variety of Salmonella causes typhoid fever while non-typhoidal Salmonella causes salmonellosis (a common gastrointestinal disease and a type of “food poisoning” as it is usually referred to). The spatial resolution of this data has been obscured to the county level to protect personal privacy and confidentiality. I will also be using data from the American Community Survey and the CDC’s Social Vulnerability Index. These datasets contain social vulnerability related variables for Oregon at the county level. In the case of the American Community Survey, data is available for the years 2009-2017 and the Social Vulnerability Index has data available for 2014 and 2016. Yearly county population estimates will also be used from Portland State University’s Population Research Center. Because of the high amounts of available data I will choose to start my exploratory analysis for Oregon in 2014 as all data is reported for that year.

Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I expect counties with younger populations (higher proportions of infants and newborns) as well as counties with higher proportions of females to have higher adjusted incidences of Salmonella. Prior surveillance suggests that children under the age of 5 are at the highest risk for Salmonella infection likely due to their developing immune system and how they interact with their environment. Specifically, many young children do not/are unable to wash their hands prior to touching their mouths. Females are also known to have a higher risk of Salmonella infection, however the mechanism behind this is relatively unknown with some explanations suggesting that it is due to that females are more likely to have more interactions with young children. I also expect counties with lower Social Vulnerability scores to have higher rates of Salmonella infections. Higher rates of poverty and lower amounts of education are often associated with more negative health outcomes.

Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I would like to calculate age and sex adjusted rates of disease for each county in Oregon. I am also interested in undertaking cluster analysis and calculate spatial autocorrelation among Oregon counties over time. Finally, I would like to perform a regression of county disease incidence rates by the different socio-economic factors found in the American Community Survey and Social Vulnerability Index. I would be interested in learning about spatial Poisson regression to assess which variables are significantly associated with the presence of disease. I would also be interested in learning about hotspot analysis to evaluate if there are areas of Oregon with significantly higher disease rates. Ideally, all of my analyses will be performed in R and ArcGIS.

Expected outcome: what do you want to produce — maps? statistical relationships? other?

I would like to produce choropleth maps of adjusted Salmonella infection rates as well as for hotspot analysis. I want to produce regression models to describe how incidence rates of Salmonella vary across different socioeconomic indicators. I also want to create graphs to describe spatial autocorrelation patterns as well as to show disease rates over time.

Significance. How is your spatial problem important to science? to resource managers?

This analysis will be helpful to identify county populations which are at higher risk for Salmonella infections. The inclusion of social vulnerability variables will be useful for state/local policy makers. Reforms can be proposed or further studied to assess how addressing the needs of particularly vulnerable populations will affect the incidence of Salmonella. This research will be beneficial for further public health research as trends found here may also hold true for other foodborne illness. The aim of this research is to benefit the health of communities in Oregon by highlighting the association between social vulnerability and the risk of foodborne illness.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

I have no experience with Arc-Info, programming in Python, and image processing. I have some limited experience within Modelbuilder. I am very comfortable performing statistical analyses within R and have some experience using the software to create maps using various packages.

References

  1. Estimates of Foodborne Illness in the United States. Centers for Disease Control and Prevention. https://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html#modalIdString_CDCTable_0. Published July 15, 2016. Accessed July 31, 2018.
  2. Oregon Health Authority. Salmonellosis 2016 Report. Oregon Public Health Division. Available at: https://www.oregon.gov/OHA/PH/DISEASESCONDITIONS/COMMUNICABLEDISEASE/DISEASESURVEILLANCEDATA/ANNUALREPORTS/Documents/2016/2016-Salmon.pdf. Accessed July 31, 2018.

Natural Resource Governance Perceptions and Environmental Restoration

Research Question

How is the spatial pattern of individuals perception of natural resource governance related to the spatial pattern of environmental restoration sites via distance and abundance of improved sites?

  

My Datasets

Puget Sound Partnership Environmental Outputs Data

The Puget Sound Partnership—a governmental monitoring entity—keeps records of environmental restoration projects throughout the Sound. There are GPS points for restoration site locations across their governing boundaries. I have downloaded the points, but I am still working on figuring out this dataset. There are over 12,000 entries, and many appear duplicative.

Puget Sound Partnership Social Data

I stratified a random sample (28% response, n= 2323) of the general public from the Puget Sound in Washington from one time period. They data are from a survey of subjective wellbeing related to natural environments. I am specifically examining the first block of seven questions related to perceptions of natural resource governance. These questions have been indexed into one perception score. Around 1770 individuals gave location data (cross street and zip code) which have been converted to GPS points. I also have demographic information for individuals.

 

Hypotheses

Based on current research, there is a significant correlation between environmental metrics and subjective wellbeing such as green space and air pollution (Diener, Oishi, and Tay 2018). I hypothesize that 1) shorter distances between individuals and restoration sites, and greater number of restoration sites near individuals, will correlate positively with governance perceptions, and 2) positive environmental outcomes will correlate positively with governance perceptions.

 

Approaches

I would like to test the statistical significance of distance from individual to restoration sites on governance perceptions, and test whether the number of sites within a radius moderates that relationship. I have previously created a plot of perception versus distance from other individuals, and perceptions are not spatially autocorrelated. To expand on this work, I would like to use spatial relationship modeling approaches, such as geographically weighted regression.

  

Expected outcome

I would like to produce statistical relationships between my dependent and independent variables. My dependent variables are good governance and life satisfaction (collected with demographic information). My independent variables are age, sex, race, area (self-indicated urban, suburban, or rural), years lived in the Puget Sound, political ideology (a proxy from voting precincts), income, education, number of restoration sites, and environmental improvement score.

I expect my relationships to be correlational and produce betas, p-values, and r2 values, which I will display as tables. The large volume of points (n = 1770 individuals & n = 12,000 restoration sites) I do not believe maps would provide visually relevant images. I already have maps of both perception points, and restoration points.

 

Significance

Incorporating aspects of subjective wellbeing and general public perspectives about natural resources into scientific assessment and decision-making processes could help managers improve human wellbeing and environmental outcomes simultaneously. The links between metrics of subjective wellbeing related to natural environments and metrics of ecosystem health have not been studied holistically. There are gaps in knowledge around understanding the connections among these systems. Research suggests that good governance plays an important role in improving wellbeing because governing systems provide goods and services that make people better off (Landman 2003). Current research, around good governance perceptions, has shown links to support for environmental improvement measures, but also shows individuals care less about environmental effectiveness of measures (Bennett et al. 2017). Research lacks knowledge in whether positive perceptions are linked to environmental conditions. To understand the connections between natural systems and subjective wellbeing, further research is needed that includes case studies that can illuminate general trends, as well as analyses that can show connections spatially (Milner‐Gulland et al. 2014).

 

Level of preparation

  • Arc-Info

I have taken one class that used ArcPro; GEOG 560.

  • Modelbuilder and/or GIS programming in Python

In GOEG 560 we completed one exercise that used Modelbuilder.

  • R

I have taken one class on R (FW 599), and have been using it actively for my own analyses for a few months, as well as taken GEOG 561, which primarily used R.

  • image processing

I took three digital photo classes using adobe photoshop and am very proficient in its use. I often use it to amend maps I make in Arc.

  • other relevant software

I do not believe I have expertise in any other relevant software.

 

Literature Cited

Bennett, Nathan J., Robin Roth, Sarah C. Klain, Kai Chan, Patrick Christie, Douglas A. Clark, Georgina Cullman, et al. 2017. “Conservation Social Science: Understanding and Integrating Human Dimensions to Improve Conservation.” Biological Conservation 205 (January): 93–108. https://doi.org/10.1016/j.biocon.2016.10.006.

Diener, Ed, Shigehiro Oishi, and Louis Tay. 2018. “Advances in Subjective Well-Being Research.” Nature Human Behaviour 2 (4): 253. https://doi.org/10.1038/s41562-018-0307-6.

Landman, Todd. 2003. “Map-Making and Analysis of the Main International Initiatives on Developing Indicators on Democracy and Good Governance.” Human Rights Centre University of Essex.

Milner‐Gulland, E. J., J. A. Mcgregor, M. Agarwala, G. Atkinson, P. Bevan, T. Clements, T. Daw, et al. 2014. “Accounting for the Impact of Conservation on Human Well-Being.” Conservation Biology 28 (5): 1160–66. https://doi.org/10.1111/cobi.12277.

 

Exploring spatial variation in drivers of soil CO2 efflux in HJ Andrews Forest

Description of Research Question

My objective is to capture modes of variance that exist between and within a subset of variables that I expect to correlate most strongly with soil CO2 efflux in the HJ Andrews (HJA) forest. By stratifying the forest, I plan to determine future sampling sites that will be used to explore the relationship between soil C inputs, soil C stocks and CO2 outputs. Aboveground and belowground biomass are major sources of soil carbon and drivers of soil respiration, so biomass will be used as a proxy for soil CO2 efflux for the purposes of this analysis.

Research Question: How is the spatial distribution of biomass in the HJA forest related to stand age, slope, aspect, elevation and geomorphon as a result of varying degrees of exposure to solar radiation, wind gusts, precipitation, humidity, etc.? What is the overall variance of the HJA forest along these vectors and is that variance spatially autocorrelated?

Description of Dataset

I will use LiDAR data from 2011 and 2014 at 0.3-0.5 m vertical resolution and at 1 m2 horizontal resolution covering the Lookout Creek Watershed and the Upper Blue River Watershed to the northern extent of HJA. These LiDAR data include a high hit model and a bare earth model. I will also use NAIP imagery to approximate forest stand age, which is 1 m resolution and covers years between 2002 and 2018.

Hypotheses

I expect areas containing more biomass to positively correlate with south-facing slopes due to more exposure to solar radiation resulting in faster rates of vegetation growth. I expect older stands to positively correlate with greater biomass. I expect steeper slopes to correspond to less biomass due to more weathering and thinner soil horizons, supporting less growth. I expect higher elevations to correspond to lower biomass due to greater sustained winds, higher windspeeds and more snow accumulation. As geomopohon describes the geometric structure of the terrain, it is a collection of multiple factors that could positively or negatively correlate with biomass. For example, I expect a ridge to negatively correlate with biomass because of the combination of greater slopes and more exposure to winds, while I expect a valley to positively correlate with biomass due to more wind protection and thicker soil horizons with more organic matter and more water retention.

Approaches

With an end goal of identifying sampling sites, I’ll need to cluster or stratify the HJA forest. I’ll begin with a clustering analysis, then perform supervised and unsupervised classification, followed by sensitivity analysis comparing the results of the clustering analysis and both classifications. I will need to address spatial autocorrelation either as part of the clustering analysis or separately. I’ll need to plan sampling by accessibility, so I’ll examine an HJA roads layer as well.

Expected Outcome

I plan to produce maps (or use/improve on already available ones) at a spatial scale relevant to my study so I can identify potential sampling sites. I plan to map biomass across the HJA forest and produce a stratification of factors most closely related to soil CO2 efflux. Depending on resolution of the data and the results of the stratification, I may need to constrain my analysis. I plan to produce a statistical summary of the strata relating and describing the covariates and how much variance is explained by the stratification.

Significance

Carbon sequestration is a highly relevant research area where many unknowns still exist. Given that soil is an enormous C reservoir, small changes in soil C stocks can have huge impacts on the rest of the C cycle. As CO2 is a potent greenhouse gas, more release of C from soil can cause greater warming of our planet and can lead to a positive feedback loop where the warming cycle is amplified. It is in our best interest to have a good understanding of current soil C stocks and fluxes in forested systems so we can hypothesize how they might change under different or future conditions. By using biomass as a proxy for soil CO2 efflux, I will identify locations that are likely to have greater CO2 efflux and I will be able to make informed predictions about which drivers are most significantly correlated to CO2 efflux. I will be able to test these analyses in the future using field sampling techniques.

My level of preparation/proficiency

I have limited experience with Arc-Info (GEOG 560) and I’ve used Modelbuilder a few times. I have no experience with GIS programming in Python or R, but am proficient with coding in R (3 statistics courses and my own data analysis) and am comfortable seeking answers to questions in the R environment. I have no experience image processing.

What is rural? Creation and comparison of health disparity-inclined rural indices in Texas

A description of the research question that you are exploring.

I am exploring rural classification of counties in the state of Texas by creating two rural indices and comparing them to one another to determine the effects of specific weighted measures on rurality index score. One of the indices will contain basic rural indicator variables, while the other will contain the basic variables plus more complex indicators of rurality. Specifically, I would like to compare the indicator variables to one another to see how much each contributes to an overall rurality score in Texas

A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

Various rural indicator variables will need to be obtained before combining them into indices. Previous geographical research has indicated that a variety of measures can indicate how rural or urban an area is. Some of these measures include population density, ethnic diversity, land use, household income, road density, percent of population with health insurance, and more. For the majority of these indicator variables, the sources will be basic 2010 US census data, 2014 US census TIGER/Line data, and 2011 national land cover database (NLCD) data. Basic US census data exists at the census block and census tract level in polygons, while NLCD data exists at 30m by 30m spatial resolution in raster grid form and TIGER/Line data exists at 1km spatial resolution in raster grid form.

Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I expect there will be significant differences in rurality index score for Texas counties when comparing a basic rural index containing only population density, income, and land use to a more complex index that also measures rural/urban status via diversity, percent uninsured, and road density. Rural areas in comparison to urban areas commonly have lower healthcare access, lower average socioeconomic status, and have a higher percent Caucasian population than urban areas, so these variables could be indicative of what constitutes rural and urban. I also expect specific variables to contribute significantly more to rurality than others. For example, population density is likely to have high contribution to rurality. More concisely, I expect the spatial and statistical pattern of rurality in Texas will become more dispersed and even across the state when including health-related variables because of the increased multidimensionality and contextual factors these variables will provide.

Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I am planning to use various methods to convert the census block/tract and raster grid indicator variables to county data; likely via zonal statistics or other similar methods in ArcGIS. I would also like to use statistical weighting procedures to create both the “basic” and “complex” rural indices. Some weighting procedures I have heard of that could work for this include principle component analysis and factor analysis. A PCA procedure specifically could be used because of its robust ability to produce indices that are weighted via proportion of variance that can be attributed to each variable in the measurement of rurality.

Expected outcome: what do you want to produce — maps? statistical relationships? other?

I would like to create maps of Texas comparing county rural index scores for the two indices for visual comparison. In addition, I would like to statistically compare the two indices and determine which specific indicator variables attributed most to the differences in county rurality scores between the two indices.

Significance. How is your spatial problem important to science? to resource managers?

This spatial problem is significant because rural/urban classification is inconsistent in rural health disparities research and is commonly an after-thought in comparison to the health outcome being studied. Existing measures of rurality were not created for health disparities research and are instead most useful for bureaucratic and economic purposes (Meilleur et al., 2013). This research will improve the classification methods for rurality by introducing a more scientific and health research-inclined method. Further, this research statistically compares specific indicator variables within indices to determine those that are most significant for rural classification.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

I am an intermediate user of ArcGIS but have little experience in modelbuilder and Python GIS programming. I am proficient in statistical programming in R, an intermediate user of ENVI for image processing, and have also used R for spatial analysis.

 

References

Meilleur, A., Subramanian, S. V., Plascak, J. J., Fisher, J. L., Paskett, E. D., & Lamont, E. B. (2013). Rural residence and cancer outcomes in the United States: issues and challenges. Cancer Epidemiology and Prevention Biomarkers, 22(10), 1657–1667.