Reference 1
Reference: Ansari, Amir and Mohammad H. Golabi. 2018. Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands – A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research. 7: 67-70. Doi. https://doi.org/10.1016/j.iswcr.2018.10.001.
The Meighan wetland is an unprotected wetland habitat in Iran that is used by thousands of birds annually. It is an important wetland that has been degraded over time and this research project focused on predicting land use changes to better protect wetland habitat. The authors studied land sat images from 2000, 2007, and 2015 and used the land change model (LCM) module in Idrisi GIS software to analyze and predict land use changes in the area around the wetland. The authors used land sat images, created maps in ArcGIS for topographic maps and road networks and conducted a field survey to help classify the satellite images. The study found that from 2000 to 2015 the wetland area became more degraded due to human land use changes and predicts the wetland to become even more degraded by 2030. The paper concluded by saying the land sat image analysis can be used to predict changes and help manage and protect wetland habitat.
Value to my Interest: This paper discussed how to assess different land uses and use GIS software to better predict future land use changes to wetland areas.
Reference 2
Reference: Brophy, Laura S., Correigh M. Greene, Van C. Hare, Brett Holycross, Andy Lanier, Walter N. Heady, Kevin O’Connor, Hiroo Imaki, Tanya Haddad, Randy Dana. 2019. Insights into estuary habitat loss in the western United States using a new method for mapping maximum extent of tidal wetlands. PLoS ONE. 14(8): e0218558. Doi. https://doi.org/10.1371/journal.pone.0218558.
The authors created an elevation-based estuary extent model that combined LiDAR digital elevation models and water level models to map the historical extent of estuarine wetland. The study was located on the west coast and included the coastlines of California, Oregon and Washington. An indirect way to map wetland losses was identified with the created elevation model. The study was based on a definition of estuarine wetlands that indicates they are formed with repeated tidal inundation. The study then looked at potential wetland areas using land surface elevations and tidal inundation frequency. To find the upper landward side of the boundary, the study looked at the highest annual tide data from NOAA and took the 50 percent exceedance levels; for areas with no data from NOAA, they interpolated the 50 percent exceedance levels. To find the lower water-ward side of the boundary, the authors used NOAA’s Continually Updated Shoreline Product (CUSP) and used bathymetric data to identify areas 4 meters below mean low lower water (MLLW). The data was then ground truthed at 14 locations in all three states. Combining and analyzing this data produced the historical extent of estuarine wetlands. The authors then included the National Wetlands Inventory (NWI) mapping into the system to identify current locations of estuarine wetlands. By comparing the NWI, current wetland extent, with the projected/historical wetland areas, they identified areas where wetland area was lost or is degraded. The authors would like the results to be used to identify restoration opportunities and inform land use groups and managers to make land use decisions and policy changes.
Interest Value: The authors found a great way to map wetlands using data that was available. This was a great example of utilizing different data sets and finding the answers you need through analysis. This seems like it would be appropriate way of mapping large areas of wetlands.
Reference 3
Reference: Chignell, Stephen M., Matthew W. Luizza, Sky Skach, Nicholas E. Young and Paul H. Evangelista. 2018. An integrative modeling approach to mapping wetlands and riparian areas in a heterogeneous Rocky Mountain watershed. Remote Sensing in Ecology and Conservation. 4: 150-165. Doi: 10.1002/rse2.63.
The study mapped the distribution of wetland and riparian areas in the Cache la Poudre River watershed in Colorado, United States. The study used machine learning software, Landsat 8 imagery and geomorphic indices. A presence-background approach was used to compare predictions from three algorithms: boosted regression trees, MaxEnt and random forests. Hydrology is important to be able to effectively map wetland habitat. Many difficulties with mapping in mountain conditions can be addressed through inclusion of other environmental data such as soil, bedrock and topography information. However, this data is not available in all places. The study acquired a spatial layer of the watershed boundary, National Wetlands Inventory (NWI) data for the area, four L1T terrain corrected Landsat 8 images, temperature data and 1 arc-second SRTM DEM data for the analysis. Images for the study were from 2014 and within a single scene. ArcGIS Spatial Analyst toolbox was used to create a slope map, and the Geomorphometry and Gradients Metrics toolbox was used to derive a suite of geomorphometric indices. The analysis also included the moisture index. NWI digital polygons were used for training in the model. The models for the study were developed using the Software for Assisted Habitat Modeling. The authors developed a boosted regression trees, MasEnt and random forests model for each elevation zone within the study area. The results showed the models developed correctly classified the habitats approximately 95 percent of the time. Model performance varied by elevation type and the algorithms performed consistently when compared to each other. Greenness and wetness indices were important in the predictions of the models.
Interest Value: The study discussed remote mapping of wetland habitat. It was interesting the understand the indices and processes used to map the wetlands.
Reference 4
Reference: Dahl, T.E., J. Dick, J. Swords, and B.O. Wilen. 2015. Data Collection Requirements and Procedures for Mapping Wetland, Deepwater and Related Habitats of the United States. Division of Habitat and Resource Conservation (Version 2), National Standards and Support Team, Madison, WI. 92p.
The United States Fish and Wildlife Service (USFWS) established the National Wetlands Inventory (NWI) to help inform on the location, extent and types of wetland and deepwater habitats. The goal of the NWI is to produce “medium resolution information on the location, type and size of these habitats” (Dahl et al 2015). The document was produced to describe technical procedures to provide quality assurance to create accurate wetland mapping products. The USFWS has mapped 100 percent of the United States wetland areas using remote sensing data and has relied on photo interpretation. This document gives guidance for updating maps and indicates that the maps should avoid going into detail and wetlands should not be updated unless it is extremely clear it is no longer there. ArcGIS is used to map the wetlands and make changes to the maps. Topographic maps, hydrographic maps, soil surveys, NOAA navigational charts and local mapped data are used to help depict the extent of wetland habitat. The document also indicates that field visits can be used to help map the system or verify your results and they have their own data sheet specifically for site visits.
Interest Value: I use the NWI maps all the time for the work that I do. This was very interesting to me to see how they collect the data and what they are looking for and how the agency validates data. It seems like they rely on photo interpretation and use other sources of data to identify boundaries they depict on maps. I was also surprised to read that field visits are encouraged to verify data because I thought they just used things like mapped wetland soils and photo interpretation.
Reference 5
Reference: Guo, M., Li, J., Sheng, C., Xu, J., & Wu, L. 2017. A Review of Wetland Remote Sensing. Sensors. 17(4), 777. Doi:10.3390/s17040777.
The paper reviews seven types of sensors that are used for remote sensing wetland data. The sensors reviewed include: aerial photos of coarse resolution, medium resolution, high resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. The study also looked into the applicability of these sensors to wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest and sea level rise. The study reviewed nearly 6,000 research papers that dealt with wetland habitats and remote sensing. Aerial photographs were the most widespread to aid in wetland research. Coarse resolution images and data was used a lot with large areas and generally in combination with other indices for things such as moisture or vegetation. Medium resolution images are were used for research, mapping and flooding information. High resolution data and images were used the least and are expensive to use; however, they provide more detailed data and would aid mapping and classification efforts. The Hyperspectral images can be used for wetland classification, species identification and leaf chlorophyll levels. The radar data seemed to have been used the most for flood extent mapping but was also used for estimating biomass and wildlife habitat locations. LiDAR was used the most by combing the data with aerial photography; LiDAR can be used with mapping and classification of wetlands.
Interest Value: I was interested in learning about the different types of remote sensing methods. It was helpful to learn that remote sensing includes different resolutions of aerial photography, radar, LiDAR and hyperspectral images. Also, it was good to learn what types of research and information can be obtained from these types of data.
Reference 6
Reference: Halls, Joanne and Kaitlyn Costin. 2016. Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery. Remote Sensing. 8, 718. Doi: 10.3390/rs8090718.
The authors of this paper were testing methods for using high spatial resolution mapping of benthic and emergent habitats using WorldView-2 (WV-2) imagery and bathymetry data. One of the main objectives of the project was to assess how well WV-2 satellite imagery and LiDAR could adequately map tidal creek wetland habitats. The study was conducted in five steps that include: gathering and processing WV-2 imagery, LiDAR data and other data sets; conducting a field investigation to collect ground reference points for habitat and bathymetry measurements; mapping tidal creek wetlands using imagery, data and image processing techniques; conducting an accuracy assessment; and deriving water depth measurements from the WV-2 imagery. The study was conducted at Pages Creek, which is located in North Carolina, United States. WV-2 imagery contains new spectral bands that consist of coastal blue, yellow, red-edge and NIR-2; the new bands provided more information that is not available in other satellite data sources. The classification resulted in a total of 10 identified classes: High Density cordgrass; Low Density cordgrass; Black needlerush; Submerged Oysters; Emergent oysters; Deep water; Shallow water; Scrub/shrub; Docks/ Rubble; and Shadows. The field effort was conducted to test and assess the results. The authors found that using LiDAR data increased the accuracy of the mapping efforts. The overall project results were that the supervised habitat classification produced the highest accuracy of mapping and had a 95 percent accuracy rate. In addition, WV-2 imagery was capable of determining water depths. The authors hoped that the data could be used by agencies to inform management and policy decisions.
Interest Value: The article discussed the use of different satellite imagery to better map wetland habitats. It was interesting that they found the new spectral band widths could map shallow bathymetry.
Reference 7
Reference: Ludwig, Christina, Andreas Walli, Christian Schleicher, Jurgen Weichselbaum, and Michael Riffler. 2019. A highly automated algorithm for wetland detection using multi-temporal optical satellite data. 224(2019), 333-351. Doi: https://do.org/10.1016/j.rse.2019.01.017.
The purpose of the study was to evaluate methodology for mapping large wetland areas with the least amount of effort and training data. The authors used Sentinel-2 MultiSpectral Instrument (MSI) imagery and used an algorithm for water and wetness detection. The water and wetness detection is based on optical imagery and topographic data. To demonstrate the methodology, a total of three case studies were examined. The Sentinel-2 data used was from December 2015 to July 2017. A digital elevation model was generated from the NASA Shuttle Radar Topography Mission in 2000; this data was used to create a Topographic Wetness Index. To identify hydrology, the project used a combination of the modified normalized difference water index (mNDWI) and the normalized difference water index (NDWI). The mapping was found to be 92 percent accurate, although there are some issues with identifying areas that are not ponded or considered to have a water surface. The authors concluded that because the success rate was so high, the method is applicable on large scale areas.
Interest Value: This paper was interesting because hydrology seems to be the most difficult parameter to identify when trying to map wetland habitats. This paper provides a way to identify hydrology.
Reference 8
Reference: Mayer, Audrey L., and Azad Henareh Khalyani. 2011. Grass Trumps Trees with Fire. Science. 334(188): 188-189. Doi: 10.1126/science.1213908.
The paper titled “Grass Trumps Trees with Fire,” is about feedback mechanisms involving rainfall, fire and vegetation that governs the transitions between forests, savannas and grasslands (Mayer et. al. 2011). Mayer et. al (2011) summarized two papers that used satellite data and global data for tree cover measurements, seasonal fire frequency and precipitation patterns. According to the Mayer et. al. paper, it has long been assumed that forest, savanna and grassland vegetation communities change gradually over space and time, with tree cover responding to gradients in precipitation, aridity, fire disturbance and grazing pressure (2011). There is evidence that now suggests that these vegetation communities are self-reinforcing and transitions between them are governed by feedback mechanisms at local and regional scales (Mayer et. al. 2011). The two papers Mayer et. al. examined, by Staver et. al. and by Hirota et. al found evidence for the feedback interactions and transitions at a global scale utilizing and analyzing satellite and other global available data (Mayer et. al. 2011). The results suggest that climate change will be substantially influenced by non-linear behaviors and feedbacks between biophysical and human systems (Meyer et. al. 2011).
Interest Value: I was intrigued how the scientists took tree cover measurements derived from satellite data and global data for annual and seasonal fire frequency and precipitation patterns to analyze vegetation patterns around the globe. For my work with wetlands, I typically need to know vegetation growth patterns and precipitation data.
Reference 9
Reference: Mondal, Biswajit, Gour Dolui, Malay Pramanik, Santu Maity, Sumantra Sarathi Biswas, Raghunath pal. 2017. Urban expansion and wetland shrinkage estimation using a GIS-based model in the East Kolkata Wetland, India. Ecological Indicators. 83(2017): 62-73. Doi. http://dx.doi.org/10.1016/j.ecolind.2017.07.037.
The goal of the paper is to find patterns of urban wetland shrinkage and factors responsible for the shrinkage through a geospatial modeling approach. To do this the authors created the Wetland Shrinkage Monitoring (WSM) model. The model included six steps that involved quantification of land use changes, selection of explanatory factors, simulation of wetland transition, calibration of the Markov transition matrix, multi-objective land allocation techniques and the model was judged using the Receiver Operating Characteristics Curve. Land use and cover classifications used in the study included: built up area, plantation and green space, wetland, cropland and open land. The study found that wetlands which are closer to built-up areas are more likely to shrink and be degraded.
Interest Value: I was interested in this article because being able to observe impacts would be helpful. Trying to predict where wetland impacts would occur could be helpful when trying to get an idea of overall planning and where it would be best to create or restore wetland habitat.
Reference 10
Reference: NASA ( National Aeronautics and Space Administration). 2000. Measuring Vegetation (NDVI & EVI). Available at: https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php.
Satellites monitor vegetation through being able to detect solar radiation reflections on multiple wavelength bands. Since vegetation absorbs red and blue wavelengths and reflect near infrared wavelengths, methods for analyzing vegetation have been developed. An example of this is the Normalized Difference Vegetation Index (NDVI) which can be used to measure and map the density of green vegetation on Earth. Almost 40 years ago, scientists began using remote sensors to measure and map green vegetation on the Earth to try and understand fluctuations in vegetation.
Interest Value: Vegetation is another component of what makes a wetland a wetland. I have used NDVI in the past but never really understood how it works. This article allowed me to gain insight into how the data is gathered and how it helps me identify wetlands.
Reference 11
Reference: NASA Jet Propulsion Laboratory. 2019. Human Activities are Drying out the Amazon: NASA Study. Available at: https://www.jpl.nasa.gov/news/news.php?feature=7533.
Scientists from NASA analyzed satellite and ground data in the Amazon rainforest “to track both how much moisture was in the atmosphere and how much moisture was needed to maintain” the Amazon rainforest (NASA Jet Propulsion Laboratory 2019). The study lasted over the past 20 years and compared the trend and data to climate change from thousands of years ago. The study found that there has been an increase in the dryness of the atmosphere and in the demand for water over the rainforest. According to the scientists, it is being caused by elevated greenhouse gas levels and human activities such as burning forests. This is important because the trees in the rainforest absorbs greenhouse gasses and helps control the weather patterns in the area. If the trees are stressed or die because of warmer conditions, it could cause a significant climate change impact worldwide.
Interest Value: Climate change will affect wetland habitat and areas. In addition, moisture levels in the air can affect vegetation in wetlands. This article allowed me to know that we can use satellite data to look at moisture in the atmosphere.
Reference 12
Reference: NOAA (National Oceanic and Atmospheric Administration). 2013. Satellites. Available at: https://oceanexplorer.noaa.gov/technology/tools/satellites/satellites.html.
This website gives a brief overview of satellites history and what they are used for in regard to monitoring. The website starts off by explaining how Sputnik I, which launched in 1957, was the first artificial satellite. Satellites that monitor characteristics and features of the Earth’s atmosphere, lands and oceans are called environmental satellites. These types of satellites either have a geosynchronous or sun-synchronous orbit; geosynchronous orbits match the earth’s rotation and sun-synchronous satellites pass over a point on the Earth at the same time each day. Geosynchronous environmental satellites are used for weather forecasting; at the time of this article, NOAA had GOES-8 and GOES-10 in orbit monitoring the east and west areas of the United States. The sun-synchronous environmental satellites orbit the earth in a north and south direction, and they view anywhere from a file miles wide swath to a 1500-mile-wide swath. Currently, there are many satellites that are included as sun synchronous: NOAA POES satellites, Landsat, SeaWiFS, IKONOS and more. Functions the satellites are now providing include sea surface temperature and color and mapping features such as coral reefs. Knowing the temperature of the ocean helps identify ocean health, water circulation and even fish behavior. Some of the satellites that provide imagery can be used with other data such as animal transmitters to identify animal and ocean characteristics.
Interest Value: When we are discussing using satellite data for use in environmental monitoring, it is important to understand the different types of satellites that are collecting data and why kinds of data that is being collected.
Reference 13
Reference: NOAA. 2018. Monitoring Oceans and Coasts. Available at: https://oceanservice.noaa.gov/observations/monitoring/.
NOAA has several mapping databases and warning systems that are available to the public and are constantly being updated as new monitoring data is collected. Available mapping information includes environmental sensitivity index maps, physical oceanographic real-time system, sea level rise and coastal flooding impacts viewer, maps of different benthic habitats and provides advance warnings of harmful algal blooms. NOAA has also created an integrated information system called U.S. integrated Ocean Observing System (IOOS) that brings people and technology together. IOOS is building a network to fill gaps in scientific knowledge about ocean health and also establishes standards for data collection. The goal of IOOS is to collect more data to better understand the Earth biosphere.
Interest Value: This website was interesting because it provides a lot of information and explanations of what data NOAA captures. It also provided me with types of information that I can download and use for my wetland projects. For example, there are biodiversity, ocean currents and weather data to be downloaded.
Reference 14
Reference: Petropoulos, George P., Dionissios P. Kalivas, Hywel M. Griffiths, Paraskevi P. Dimou. 2014. Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers, Greece. International Journal of Applied Earth Observation and Geoinformation. 35(2015): 217-228. Doi. Http://dx.doi.org/10.1016/j.jag.2014.08.004.
The authors of the paper used direct observations of satellite imagery and a semi-automatic image classification method to identify changes over time in two wetland systems in the Mediterranean river delta. In addition, the authors looked at deposition and erosion in both wetland systems using both methods (direct observation and semi-automatic classification). The goal of the study was for the research to be used for coastal and river delta mapping, future policy implementation and land use management decisions. The authors used Earth observation (EO) data for the study which used Landsat data, and GIS techniques for the time period between 1984 and 2009. There were four Landsat images of the area during this time period. The direct observation of the satellite images involved looking at visible bands of data. The semi-automated method used support vector machines (SVM) which were fed predetermined classifications of the wetland types. They study found that both methods showed changes to the wetlands, but the semi-automatic image classification indicated different sediment erosion and deposition rates. The difference between the methods ranged from 5 to 20 percent.
Interest Value: The paper interested me because it was discussing using remote sensing and GIS analysis to map wetland areas.
Reference 15
Reference: Rapinel, S. E. Fabre, S. Dufour, D. Arvor, C. Mony and L. Hubert-Moy. 2019. Mapping potential, existing and efficient wetlands using free remote sensing data. 247(2019), 829-839. Doi:https://doi.org/10.1016/j.jenvman.2019.06.098.
The authors of the paper used free remote sensing data to map wetland areas in the bottom of valleys in France. The study used Sentinel – 1/2 and moderate resolution imaging spectroradiometer (MODIS) time series data acquired during November 2016 to October 2017. The strategy they developed is called Potential, Existing, Efficient Wetlands. Potential wetlands were mapped from digital terrain models (from LiDAR data), existing wetlands were delineated from land cover maps derived from Sentinel images over a period of time, and efficient wetlands were identified from MODIS annual time-series data. The study addressed whether automatic classifications based on remote sensing data can accurately map wetlands at the watershed scale, identify whether efficient wetlands can be identified using temporal trajectories of functional indices and whether it is possible to map the influence of management on wetland functions. Soil and vegetation were reviewed from field site visits. In addition, other information for vegetation was reviewed such as databases with crop type. LiDAR was also used to identify flow patterns such as ditches and streams. The results showed that 82 percent of the time potential wetlands were delineated, and that 44 percent of existing wetlands had been lost. The authors would like the results of this study to identify restoration sites and understand policy and management decisions.
Interest Value: My interest in this was to try and find out how free remote sensing data aided the authors in mapping wetland locations. This study led me to find out more about the MODIS time series data that can be downloaded from NASA.
Reference 16
Reference: Rawat, J.S. and Manish Kumar. 2015. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science. 18(1): 77-84. Doi. https://doi.org/10.1016/j.ejrs.2015.02.002.
The paper is about using Landsat satellite imagery of different time periods to quantify land use cover and use in the Hawalbagh block in India. The article looked at a 20-year time period (from 1990 to 2010) and had five classes of land use and cover. The classification types included “vegetation, agriculture, barren, built-up and waterbody” (Rawat and Kumar 2015). The paper found that in the 20-year period, vegetation and built-up increased while agriculture, barren and waterbody decreased. Once conclusion of the paper was that using remotely sensed data completed the work in less time and usually with better accuracy, in association with GIS. The GIS provides a suitable platform for data analysis, update and retrieval. The paper also discussed several similar studies that have been completed for other areas within India and seemed to show similar results, with built up areas increasing and waterbody areas decreasing. The paper concluded by saying, that the satellite imagery and GIS software are vital to being able to map land use and cover.
Interest Value: This paper is interesting to me because a lot of times we are asked to identify wetland habitat in areas we are not allowed to access, or we are trying to get an idea of what has happened to the wetland/area in the past. Being able to use Landsat and GIS to do those analysis for me would be a great help.
Reference 17
Reference: Rebelo, L.-M, C.M. Finlayson, N. Nagabhatla. 2008. Remote Sensing and Gis for Wetland Inventory, Mapping and Change Analysis. Journal of Environmental Management. 90: 2144-2153. Doi. 10.1016/j.jenvman.2007.06.027.
Wetland information is needed globally, regionally and locally to make better informed wetland policy decisions. There are gaps in current wetland inventories because various methods and global data sets are varied, or the results are inconsistent. Using remote sensing and GIS can fill in the gaps in the baseline inventory. The paper also discussed studying wetlands that they know to identify important and rare wetlands and other areas that need to be investigated. The goal is to form partnerships to become more uniform in the global wetland and mapping efforts. Landsat data was obtained and processed and classified to identify wetland habitats. From this data, we can also see potential impacts and changes over time to the wetland.
Interest Value: This article did a great job of explaining some of the issues of wetland area estimates. There is a large range of wetland areas that are given by different researchers and this article helped me to put into perspective why there is that variability.
Reference 18
Reference: Tsou, Ming-Hsiang. 2004. Integrating web-based GIS and Image processing tools for environmental monitoring and natural resource management. Journal of Geographical Systems. 6(2004): 1-20. Doi. 10.1007/s10109-004-0131-6.
The author discusses the project to integrate web -based GIS and remote sensing tools into one location. The project combined three GIS services that included: data archive, information display and spatial analyses. A website was also created that had web-based GIS and analytic tools for people to use. The goal of this project is to help inform land use management decisions and reduce high costs and labor that is normally required to gather and use this information. First, the project developed a web-based warehouse for archiving, accessing and downloading GIS data sets and remotely sensed imagery. The next step in the project was to develop multiple interactive map servers to show land use, vegetation, soils, trails, roads and satellite imagery. The third step was to develop Java-based online tools that would provide functions for land use cover change analysis. The system and website were reviewed twice by park rangers and GIS professionals associated with the Mission Trail Regional Park facility in San Diego, California. The overall feedback was positive, and feedback included being able to display multiple years of imagery to be able to address long term changes in habitat and participants wanted to be able to include Global Positioning System (GPS) data and mobile devices.
Interest Value: This website would be extremely helpful to identify land use cover data and potentially changes in habitat. It is good to know that people are trying to make tools like this accessible to the public and accessible to people who may not have GIS software or other tools.
Reference 19
Reference: Wu, Qiusheng. 2018. GIS and Remote Sensing Applications in Wetland Mapping and Monitoring. ResearchGate. 140-155. Doi. 10.1016/B978-0-12-409548-9.10460-9.
Remotely sensed data and GIS analysis can be used to help identify wetland areas and habitats. Knowing where wetlands are located can help with land use management and conservation efforts. There are have been a lot of wetland estimates based on remotely sensed data; however, there are also many methodologies to identify wetlands, different definitions of wetlands and different classifications of wetlands. As a result of these differences, there are vastly different wetland area estimates. The author of the paper used hydrology, soil types and vegetation to identify potential wetland habitats. Hydrology was estimated with the Normalized Difference Water Index (NDWI) along with Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) technology. The presence of vegetation was made with the Normalized Difference Vegetation Index (NDVI). Wetland soils were identified using digitally available mapped data from the United States Department of Agriculture (USDA) – Natural Resource Conservation Service (NRCS). In addition, the author also looked at Digital Elevation Models (DEMs) to find low areas such as depressions which might be more susceptible to wet conditions. The author conducted a case study by mapping prairie wetlands in North Dakota, United States.
Interest Value: This was extremely interesting to me because some of the areas that I have to visit to identify and delineate wetlands are very remote and terrain is difficult. In addition, regulations in Washington State require me to identify the entire wetland boundary even if the wetland extends offsite and I don’t have owner permission to be on that parcel. These methods could help me estimate the offsite wetland boundaries and get a better understanding of how a wetland is functioning. However, to be classified as a wetland, we are looking for specific plant species that like to grow in wet areas and wetlands are often found in areas that are not mapped as having wetland soils. I’ve used NDVI before and it doesn’t tell us species, although if you overlay NDVI data with the hydrology data you could infer it is a wetland if they hydrology is present.
Reference 20
Reference: Zhang, Xuejun and Qiuhong Tang. 2015. Combining satellite precipitation and long -term ground observations for hydrological monitoring in China. Journal of Geophysical Research: Atmospheres. 120(13). 6426-6443. Doi. https://doi.org/10.1002/2015JD023400.
The paper titled “Combining satellite precipitation and long-term ground observations for hydrological monitoring in China,” is about an experiment that was done to gauge the accuracy of precipitation data before and after integrating the Tropical Rainfall Measuring Mission (TRMM) real time precipitation “into a 62 year gauge-based retrospective product, the Institute of Geographical Sciences and Natural Resources Research dataset” (Zhang and Qiuhong 2015). The problem is that satellite real-time precipitation data was not as available or accurate for remote areas of China and there may have been bias in the real time monitoring efforts. China has experienced floods and droughts that have brought significant impacts to human well-being and economics with destruction of millions of hectares of crop land. A hydrological model is important to monitor real time precipitation and hydrology data to better identify reliable streamflow productions. The results of the study indicate that integrating the two models provides more accurate hydrologic data and therefore satellite real-time precipitation data can be used to model drought and flood conditions.
Interest Value: A wetland has certain hydrology conditions (soils must be saturated 10 percent of the growing season) and because we evaluate hydrology we need to know if precipitation is normal or if we are in drought conditions. This paper was interesting to me because we can use real-time satellite data to identify flood and drought conditions.
Additional References
Bourgeau-Chavez, Laura L., Yu Man Lee, Michael Battaglia, Sarah L. Endres, Zachary M. Lauback and Kirk Scarbrough. 2016. Identification of Woodland Vernal Pools with Seasonal Change PALSAR Data for Habitat Conservation. Remote Sensing. 8(6): 490. Doi: https://doi.org/10.3392/rs8060490.
Bustamante, Javier, David Aragones, Isabel Afan, Carlos J. Luque, Andres Perez-Vazquez, Eloy M. Castellanos, and Ricardo Diaz-Delgado. 2016. Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Donana Wetlands. Remote Sensing. 8(12): 1001. Doi: https://doi.org/10.3390/rs8121001.
Dahl, Thomas E. 2004. Remote Sensing as a Tool for Monitoring Wetland Habitat Change. Monitoring Science and Technology Symposium: Unifying Knowledge for Sustainability in the Western Hemisphere. 990p. Available at: https://www.fws.gov/wetlands/Documents/Remote-Sensing-as-a-Tool-for-Monitoring-Wetland-Habitat-Change.pdf.
Dettmering, Denise, Christian Schwatke, Eva Boergens and Florian Seitz. 2016. Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland. Remote Sensing. 8(7): 596. Doi: https://doi.org/10.3390/rs8070596.
Huang, Chundong, Xinyue Ye, Chengbin Deng, Zili Zhang and Zi Wan. 2016. Mapping Above=Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China. Remote Sensing. 8(8): 647. Doi: https://doi.org/10.3390/rs8080647.
Jia, Mingming, Mingyue Liu, Zongming Wang, Dehua Mao, Chunying Ren and Haishan Cui. 2016. Evaluating the Effectiveness of Conservation on Mangroves: A Remote Sensing Based Comparison for Two Adjacent Protected Areas in Shenzhen and Hong Kong, Ching. Remote Sensing. 8(8): 627. Doi: https://doi.org/10.3390/rs8080627.
