Remote Sensing Applications

By Leila Lemos, PhD candidate

Fisheries and Wildlife Department, OSU


I am finally starting my 3rd and last year of my PhD. Just a year left and yet so many things to do. As per department requirements, I still need to take some class credits, but what classes could I take? In this short amount of time it is important to focus on my research project and on what could help me better understand the many branches of the project and what could improve my analyses. Thinking of that, both my advisor (Dr. Leigh G. Torres) and I agreed that it would be useful for me to take a class on remote sensing. So, I could learn more about this field, as well as try to include some remote sensing analyses in my project, such as sea surface temperature (SST) and chlorophyll (i.e., as a productivity indicator) conditions over the years we have collected data on gray whales off the Oregon coast.


Our photogrammetry data indicates that whales gradually increased their body condition over the feeding seasons of 2016 and 2018, while 2017 is different. Whales were still looking skinny in the middle of the season, and we were not collecting many fecal samples up to that point (indicating not much feeding). These findings made us wonder if this was related to delayed seasonal upwelling events and consequently low prey availability. These questions are what motivated me the most to join this class so that we might be able to link environmental correlates with our observations of gray whale body condition.

Figure 01: Skinny body condition state of the gray whale “Pancake” in August 2017.
Source: Leila S. Lemos


If we stop to think about what remote sensing is, we have already been implementing this method in our project since the beginning, as my favorite definition for remote sensing is “the art of collecting information of objects or phenomenon without touching it”. So, yes, the drone is a type of sensor that remotely collects information of objects (in this case, whales).

Figure 02: Drone remotely collecting information of a whale in September 2018. Drone in detail. Collected under NOAA/NMFS permit #16111.
Source: Leila Lemos


However, satellites, all the way up in the space, are also remotely sensing the Earth and its objects and phenomena. Even from thousands of km above Earth, these sensors are capable of generating a great amount of detailed data that is easily and freely accessible (i.e., NASA, NOAA), and can be used for multiple applications in different fields of study. Satellites are also able to collect data from remote areas like the Antarctica and the Arctic, as well as other areas that are not easily reached by humans. One important application of the use of satellite imagery is wildlife monitoring.

For example, satellite data was used to detect variation in the abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica (LaRue et al., 2011). Because this is a well-studied seal population, the object of this study was to test if satellite imagery could produce reliable abundance estimates. The authors used high-resolution (0.6 m) satellite imagery (from satellites Quick-Bird-2 and WorldView-1) to compare counts from the ground with counts from satellite images in the same locations at the same time. This study demonstrated a reliable methodology for further studies to replicate.

Figure 03: WorldView-1 image (0.6 m resolution) of Weddell seals hauled out east of Inaccessible Island, Erebus Bay, Antarctica.
Source: LaRue et al. (2011).


Satellite imagery was also applied to estimate colony sizes of Adélie penguins in Antarctica (LaRue et al., 2014). High-resolution (0.6 m) satellite imagery combined with spectral analysiswas used to estimate the sizes of the penguin breeding colonies. Ground counts were also used in order to check the reliability of the applied method. The authors then created a model to predict the abundance of breeding pairs as a function of the habitat, which was identified terrain slope as an important component of nesting density.

The identification of whales using satellite imagery is also possible. Fretwell et al. (2014)pioneered this method by successfully identifing Southern Right Whales (Eubalaena australis) in the Golfo Nuevo, Península Valdés, in Argentina in satellite images. By using very high-resolution satellite imagery (50 cm resolution) and a water penetrating coastal band that was able to see deeper into the water column, the researchers were able to successfully identify and count the whales (Fig. 04). The importance of this study was very significant, since this species was extensively hunted from the 17ththrough to the 20thcentury. Since then, the species has shown a strong recovery, but population estimates are still at <15% of historical estimates. Thus, being able to use new tools to identify, count and monitor individuals in this recovering population is a great development, especially in remote and hard to reach areas.

Figure 04: Identification of Southern Right Whales by using imagery from the WorldView2 satellite in the Golfo Nuevo Bay, Península Valdés, Argentina.
Source: Fretwell et al. (2014).


Polar bears (Ursus maritimus) have also been studied in the Foxe Basin, in Nunavut and Quebec, Canada (LaRue et al., 2015). Researchers used high-resolution satellite imagery in an attempt to identify and count the bears, but spectral signature differences between bears and other objects were insufficient to yield useful results. Therefore, researchers developed an automated image differencing, also known as change detection, that identifies differences between remotely sensed images collected at different times and “subtract of one image from another”. This method correctly identified nearly 90% of the bears. The technique also generated false positives, but this problem can be corrected by a manual review.

Figure 05 shows the difference in resolution of two types of satellite imagery, the panchromatic (0.6 m resolution) and the multispectral (2.4 m resolution). LaRue et al. (2015)decided not to use the multispectral imagery due to resolution constraints.

Figure 05: Polar Bears on panchromatic (0.6 m resolution) and multispectral (2.4 m resolution) imagery.
Source: LaRue et al. (2015).


A more recent study is being conducted by my fellow OSU Fisheries and Wildlife graduate student, Jane Dolliveron breeding colonies of three species of North Pacific albatrosses (Phoebastria immutabilis, Phoebastria nigripes, and Phoebastria albatrus)(Dolliver et al., 2017). Jane is using high-resolution multispectral satellite imagery (DigitalGlobe WorldView-2 and -3) and image processing techniques to enumerate the albatrosses. They are also using albatross species at multiple reference colonies in Hawaii and Japan (Fig. 06) to determine species identification accuracy and required correction factor(s). This will allow scientists to accurately count unknown populations on the Senkakus, which are uninhabited islands controlled by Japan in the East China Sea.

Figure 06: Satellite image of a colony of short-tailed albatrosses (Phoebastria albatrus) in Torishima, Japan, 2016.
Source: Satellite image provided by Jane Dolliver.


Using satellite imagery to count seals, penguins, whales, bears and albatrosses is just the start of this rapidly advancing technology. Techniques and resolutions are continuously improving. Methods can also be applied to many other endangered species, especially in remote areas, providing data on presence, abundance, annual productivity, population estimates and trends, changes in distribution, and breeding ground usage.

Other than directly monitoring wildlife, satellite images can also provide information on the environmental variables that can be related to wildlife presence, abundance, productivity and distribution.

Gentemann et al. (2017), for example, used satellite data from NASA to analyze SST variations along the west coast of the United States from 2002 to 2016. The NASA Jet Propulsion Laboratory produces global, daily, 1 km, multiscale ultra-high resolution, motion-compensated analysis of SST, and incorporates SSTs from eight different satellites. Researchers were able to identify warmer than usual SSTs (also called anomalies) along the Washington, Oregon, and California coasts from January 2014 to August 2016 (Fig.07) relative to previous years. This marine heat wave started in the Gulf of Alaska and ended in Southern California, where SST reached a maximum temperature anomaly of 6.2°C, causing major disturbances and substantial economic impacts.

Figure 07: Monthly SST anomalies in the West Coast of United States, from January 2014 to August 2016.
Source: Gentemann et al. (2017).


Changes in SST and winds may alter events such as the coastal upwelling that supplies nutrients to sustain a whole food chain. A marine heat-wave event as described by Gentemann et al. (2017)could have significant impacts on the health of the marine ecosystem in the subsequent season (Gentemann et al., 2017).

These findings may even relate to our questions regarding the poor gray whale body condition we noticed in 2017: this marine heat wave that lasted until August 2016 along the US west coast could have impacted the ecosystem in the subsequent season. However, I must conduct a more detailed study to determine if this heat wave was related or if another oceanographic process was involved.

So, whether remotely sensed data is generated by satellites, drones, thermal imagery, robots (as I previously wrote about), or another type of technology, it can have important  and informative applications to monitor wildlife or environmental variables associated with their ecology and biology. We can take advantage of remotely sensed technology to aid wildlife conservation efforts.



Dolliver, J., et al., Multispectral processing of high resolution satellite imagery to determine the abundance of nesting albatross. Ecological Society of America, Portland, OR, United States., 2017.

Fretwell, P. T., et al., 2014. Whales from Space: Counting Southern Right Whales by Satellite. Plos One. 9,e88655.

Gentemann, C. L., et al., 2017. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophysical Research Letters. 44,312-319.

LaRue, M. A., et al., 2014. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. Polar Biology. 37,507-517.

LaRue, M. A., et al., 2011. Satellite imagery can be used to detect variation in abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica. Polar Biology. 34,1727–1737.

LaRue, M. A., et al., 2015. Testing Methods for Using High-Resolution Satellite Imagery to Monitor Polar Bear Abundance and Distribution. Wildlife Society Bulletin. 39,772-779.






Scratching the Surface

By Dr. Leigh Torres, Assistant Professor, Oregon State University, Geospatial Ecology of Marine Megafauna Lab

I have been reminded of a lesson I learned long ago: Never turn your back on the sea – it’s always changing.

The blue whales weren’t where they were last time. I wrongly assumed oceanographic patterns would be similar to our last time out in 2014 and that the whales would be in the same area. But the ocean is dynamic – ever changing. I knew this. And I know it better now.

Below (Fig. 1) are two satellite images of sea surface temperature (SST) within the South Taranaki Bight and west coast region of New Zealand that we surveyed in Jan-Feb 2014 and again recently during Jan-Feb 2016. The plot on the left describes ocean surface conditions in 2014 and illustrates how SST primarily ranged between 15 and 18 ⁰C. By comparison, the panel on the right depicts the sea surface conditions we just encountered during the 2016 field season, and a huge difference is apparent: this year SST ranged between 18 and 23 ⁰C, barely overlapping with the 2014 field season conditions.

Figure 1. A comparison of satellite images of sea surface temperature (SST) in the South Taranaki Bight region of New Zealand between late January 2014 and early February 2016. The white circles on each image denote where the majority of blue whales were encountered during each field season.
Figure 1. A comparison of satellite images of sea surface temperature (SST) in the South Taranaki Bight region of New Zealand between late January 2014 and early February 2016. The white circles on each image denote where the majority of blue whales were encountered during each field season.

While whales can live in a wide range of water temperatures, their prey is much pickier. Krill, tiny zooplankton that blue whales seek and devour in large quantities, tend to aggregate in pockets of nutrient-rich, cool water in this region of New Zealand. During the 2014 field season, we encountered most blue whales in an area where SST was about 15 ⁰C (within the white circle in the left panel of Fig. 1). This year, there was no cool water anywhere and we mainly found the whales off the west coast of Kahurangi shoals in about 21 ⁰C water (within the white circle in the right panel of Fig. 1. NB: the cooler water in the Cook Strait in the southeast region of the right panel is a different water mass than preferred by blue whales and does not contain their prey.)

The hot water we found this year across the survey region can likely be attributed, at least in part, to the El Niño conditions that are occurring across the Pacific Ocean currently. El Niño has brought unusually settled conditions to New Zealand this summer, which means relatively few high wind events that normally churn up the ocean and mix the cool, nutrient rich deep water with the hot surface layer water. These are ideal conditions for Kiwi sun-bathers, but the ocean remains highly stratified with a stable layer of hot water on top. However, this stratification does not necessarily mean the ocean is un-productive – it only means that the SST satellite images are virtually useless for helping us to find whales this year.

Although SST data can be informative about ocean conditions, it only reflects what is happening in the thin, top slice of the ocean. Sub-surface conditions can be very different. Ocean conditions during our two survey periods in 2014 and 2016 could be more similar when compared underwater than when viewed from above. This is why sub-surface sensors and data collection is critical to marine studies. Ocean conditions in 2014 and 2016 could both potentially provide good habitat for the whales. In fact, where and when we encountered whales during both 2014 and 2016 we also detected high densities of krill through hydro-acoustics (Fig. 2). However, in 2014 we observed many surface swarms of krill that we rarely saw this recent field season, which could be due to elevated SST. But, we did capture cool drone footage this year of a brief sub-surface foraging event:

An overhead look of a blue whale foraging event as the animal approaches the surface. Note how the distended ventral (throat) grooves of the buccal cavity (mouth) are visible. This is a big gulp of prey (krill) and water. The video was captured using a DJI Phantom 3 drone in the South Taranaki Bight of New Zealand in on February 2, 2016 under a research permit from the New Zealand Department of Conservation (DOC) permit # 45780-MAR issued to Oregon State University.

Figure 2. An echo-sounder image of dense krill patches at 50-80 m depth captured through hydroacoustics in the South Taranaki Bight region of New Zealand.
Figure 2. An echo-sounder image of dense krill patches at 50-80 m depth captured through hydroacoustics in the South Taranaki Bight region of New Zealand.

Below are SST anomaly plots of January 2014 and January 2016 (Fig. 3). These anomaly plots show how different the SST was compared to the long-term average SST across the New Zealand region. As you can see, in 2014 (left panel) SST conditions in our study area were ~1 ⁰C below average, while in 2016 (right panel) SST conditions were ~1 ⁰C above average. So, what are normal conditions? What can we expect next year when we come back to survey again for blue whales across this region? These are challenging questions and illustrate why marine ecology studies like this one must be conducted over many years. One year is just a snap shot in the lifetime of the oceans.

Figure 3. Comparison of sea surface temperature (SST) anomaly plots of the New Zealand region between January 2014 (left) and January 2016 (right). The white box in both plots denotes the general location of our blue whale study region. (Apologies for the different formats of these plots - the underlying data is directly comparable.)
Figure 3. Comparison of sea surface temperature (SST) anomaly plots of the New Zealand region between January 2014 (left) and January 2016 (right). The white box in both plots denotes the general location of our blue whale study region. (Apologies for the different formats of these plots – the underlying data is directly comparable.)

Like all marine megafauna, blue whales move far and fast to adjust their distribution patterns according to ocean conditions. So, I can’t tell you what the ocean will be like in January 2017 or where the whales will be, but as we continue to study this marine ecosystem and its inhabitants our understanding of ocean patterns and whale ecology will improve. With every year of new data we will be able to better predict ocean and blue whale distribution patterns, providing managers with the tools they need to protect our marine environment. For now, we are just beginning to scratch the (sea) surface.