Using deep learning to count albatrosses from space: Assessing results in light of ground truth uncertainty

Many wildlife species inhabit inaccessible environments, limiting researchers ability to conduct essential population surveys. Recently, very high resolution (sub-metre) satellite imagery has enabled remote monitoring of certain species directly from space; however, manual analysis of the imagery is time-consuming, expensive and subjective. State-of-the-art deep learning approaches can automate this process; however, often image datasets are small, and uncertainty in ground truth labels can affect supervised training schemes and the interpretation of errors. In this paper, we investigate these challenges by conducting both manual and automated counts of nesting Wandering Albatrosses on four separate islands, captured by the 31 cm resolution WorldView-3 sensor. We collect counts from six observers, and train a convolutional neural network (U-Net) using leave-one-island-out cross-validation and different combinations of ground truth labels. We show that (1) interobserver variation in manual counts is significant and differs between the four islands, (2) the small dataset can limit the networks ability to generalise to unseen imagery and (3) the choice of ground truth labels can have a significant impact on our assessment of network performance. Our final results show the network detects albatrosses as accurately as human observers for two of the islands, while in the other two misclassifications are largely caused by the presence of noise, cloud cover and habitat, which was not present in the training dataset. While the results show promise, we stress the importance of considering these factors for any study where data is limited and observer confidence is variable

Emperor penguins breeding on iceshelves

We describe a new breeding behaviour discovered in emperor penguins; utilizing satellite and aerial-survey observations four emperor penguin breeding colonies have been recorded as existing on ice-shelves. Emperors have previously been considered as a sea-ice obligate species, with 44 of the 46 colonies located on sea-ice (the other two small colonies are on land). Of the colonies found on ice-shelves, two are newly discovered, and these have been recorded on shelves every season that they have been observed, the other two have been recorded both on ice-shelves and sea-ice in different breeding seasons. We conduct two analyses; the first using synthetic aperture radar data to assess why the largest of the four colonies, for which we have most data, locates sometimes on the shelf and sometimes on the sea-ice, and find that in years where the sea-ice forms late, the colony relocates onto the ice-shelf. The second analysis uses a number of environmental variables to test the habitat marginality of all emperor penguin breeding sites. We find that three of the four colonies reported in this study are in the most northerly, warmest conditions where sea-ice is often sub-optimal. The emperor penguin’s reliance on sea-ice as a breeding platform coupled with recent concerns over changed sea-ice patterns consequent on regional warming, has led to their designation as “near threatened” in the IUCN red list. Current climate models predict that future loss of sea-ice around the Antarctic coastline will negatively impact emperor numbers; recent estimates suggest a halving of the population by 2052. The discovery of this new breeding behaviour at marginal sites could mitigate some of the consequences of sea-ice loss; potential benefits and whether these are permanent or temporary need to be considered and understood before further attempts are made to predict the population trajectory of this iconic species

Record low 2022 Antarctic sea ice led to catastrophic breeding failure of emperor penguins

The spring season of 2022 saw record low sea ice extent in Antarctica that persisted throughout the year. At the beginning of December, the Antarctic sea ice extent was tracking with the all-time low set in 2021. The greatest regional negative anomaly of this low extent was in the central and eastern Bellingshausen Sea region, west of the Antarctic Peninsula where, during November, some regions experienced a 100% loss in sea ice concentration. We provide evidence of a regional breeding failure of emperor penguin colonies due to sea ice loss using Sentinel2 satellite imagery. Of the five breeding sites in the region all but one experienced total breeding failure after sea ice break-up before the start of the fledging period of the 2022 breeding season. This is the first recorded incident of a widespread breeding failure of emperor penguins that is clearly linked with large-scale contractions in sea ice extent

Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock

Using super-high resolution satellite imagery to census threatened albatrosses

This study is the first to utilize 30-cm resolution imagery from the WorldView-3 (WV-3) satellite to count wildlife directly. We test the accuracy of the satellite method for directly counting individuals at a well-studied colony of Wandering Albatross Diomedea exulans at South Georgia, and then apply it to the closely related Northern Royal Albatross Diomedea sanfordi, which is near-endemic to the Chatham Islands and of unknown recent population status due to the remoteness and limited accessibility of the colonies. At South Georgia, satellite-based counts were comparable to ground-based counts of Wandering Albatross nests, with a slight over-estimation due to the presence of non-breeding birds. In the Chatham Islands, satellite-based counts of Northern Royal Albatross in the 2015/2016 season were similar to ground-based counts undertaken on the Forty-Fours islands in 2009/2010, but much lower than ground-based counts undertaken on The Sisters islands in 2009/2010, which is of major conservation concern for this endangered albatross species. We conclude that the ground-breaking resolution of the newly available WV-3 satellite will provide a step change in our ability to count albatrosses and other large birds directly from space without disturbance, at potentially lower cost and with minimal logistical effort

Quantifying the causes and consequences of variation in satellite-derived population indices: a case study of emperor penguins

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Labrousse, S., Iles, D., Viollat, L., Fretwell, P., Trathan, P. N., Zitterbart, D. P., Jenouvrier, S., & LaRue, M. Quantifying the causes and consequences of variation in satellite-derived population indices: a case study of emperor penguins. Remote Sensing in Ecology and Conservation, (2021), https://doi.org/10.1002/rse2.233.Very high-resolution satellite (VHR) imagery is a promising tool for estimating the abundance of wildlife populations, especially in remote regions where traditional surveys are limited by logistical challenges. Emperor penguins Aptenodytes forsteri were the first species to have a circumpolar population estimate derived via VHR imagery. Here we address an untested assumption from Fretwell et al. (2012) that a single image of an emperor penguin colony is a reasonable representation of the colony for the year the image was taken. We evaluated satellite-related and environmental variables that might influence the calculated area of penguin pixels to reduce uncertainties in satellite-based estimates of emperor penguin populations in the future. We focused our analysis on multiple VHR images from three representative colonies: Atka Bay, Stancomb-Wills (Weddell Sea sector) and Coulman Island (Ross Sea sector) between September and December during 2011. We replicated methods in Fretwell et al. (2012), which included using supervised classification tools in ArcGIS 10.7 software to calculate area occupied by penguins (hereafter referred to as ‘population indices’) in each image. We found that population indices varied from 2 to nearly 6-fold, suggesting that penguin pixel areas calculated from a single image may not provide a complete understanding of colony size for that year. Thus, we further highlight the important roles of: (i) sun azimuth and elevation through image resolution and (ii) penguin patchiness (aggregated vs. distributed) on the calculated areas. We found an effect of wind and temperature on penguin patchiness. Despite intra-seasonal variability in population indices, simulations indicate that reliable, robust population trends are possible by including satellite-related and environmental covariates and aggregating indices across time and space. Our work provides additional parameters that should be included in future models of population size for emperor penguins.Geospatial support for this work was provided by the Polar Geospatial Center under NSF-OPP awards 1043681 and 1559691. NCAR- PPC visitor funds and Ian Nisbet that supported the internship of LV. WWF-UK supported PNT and PTF under grant GB095701. DZ was supported by The Penzance Endowed Fund and The Grayce B. Kerr Fund in Support of Assistant Scientists. To SJ, ML, SL, LV, NSF OPP 1744794

Bathymetry and geological setting of the South Sandwich Islands volcanic arc

The South Sandwich Islands and associated seamounts constitute the volcanic arc of an active subduction system situated in the South Atlantic. We introduce a map of the bathymetry and geological setting of the South Sandwich Islands and the associated East Scotia Ridge back-arc spreading centre that consists of two sides: side 1, a regional overview of the volcanic arc, trench and back-arc, and side 2, detailed maps of the individual islands. Side 1 displays the bathymetry at scale 1:750 000 of the intra-oceanic, largely submarine South Sandwich arc, the back-arc system and other tectonic boundaries of the subduction system. Satellite images of the islands on side 2 are at scales of 1:50 000 and 1:25 000 with contours and main volcanological features indicated. These maps are the first detailed topological and bathymetric maps of the area. The islands are entirely volcanic in origin, and most have been volcanically or fumarolically active in historic times. Many of the islands are ice-covered, and the map forms a baseline for future glaciological changes caused by volcanic activities and climate change. The back-arc spreading centre consists of nine segments, most of which have rift-like morphologie

Relationship between soil fungal diversity and temperature in the maritime Antarctic

Soil fungi have pivotal ecological roles as decomposers, pathogens and symbionts1, 2. Alterations to their diversity arising from climate change could have substantial effects on ecosystems, particularly those undergoing rapid warming that contain few species3, 4. Here, we report a study using pyrosequencing to assess fungal diversity in 29 soils sampled from a 1,650 km climatic gradient through the maritime Antarctic, the most rapidly warming region in the Southern Hemisphere5, 6. Using a ‘space-for-time’ substitution approach, we show that soil fungal diversity is higher in warmer habitats, with increases of 4.7 (observed) and 11.3 (predicted) fungal taxa per degree Celsius rise in surface temperature along the transect. Among 22 predictor variables, air temperature was the strongest and most consistent predictor of diversity. We propose that the current rapid warming in the maritime Antarctic (0.34 °C per decade6) will facilitate the colonization of soil by a wider diversity of fungi than at present, with data from regression models suggesting 20–27% increases in fungal species richness in the southernmost soils by 2100. Such increases in diversity, which provide a sentinel for changes at lower latitudes, are likely to have substantial effects on nutrient cycling and, ultimately, productivity in the species-poor soils of maritime Antarctica

Exploratory mapping of blue ice regions in Antarctica using very high resolution satellite remote sensing data

Mapping spatiotemporal changes in the distribution of blue ice regions (BIRs) in Antarctica requires repeated, precise, and high-resolution baseline maps of the blue ice extent. This study demonstrated the design and application of a newly-developed semi-automatic method to map BIRs in the Antarctic environment using very high-resolution (VHR) WorldView-2 (WV-2) satellite images. We discussed the potential of VHR satellite data for the mapping of BIRs in the Antarctic environment using a customized normalized-difference blue-ice index (NDBI) method devised using yellow, green, and near-infrared spectral bands of WV-2 data. We compared the viability of the newly developed customized NDBI approach against state-of-the-art target detection (TD), spectral processing (SP) and pixel-wise supervised (PSC) feature extraction (FE) approaches. Four semi-automatic FE approaches (three existing plus one newly developed) consisting of 16 standalone FE methods (12 existing + four customized) were evaluated using an extensive quantitative and comparative assessment for mapping BIRs in the vicinity of Schirmacher Oasis, on the continental Antarctic coastline. The results suggested that the customized NDBI approach gave a superior performance and the highest statistical stability when compared with existing FE techniques. The customized NDBI generally rendered the lowest level of misclassification (average RMSE = 654.48 ± 58.26 m2), followed by TD (average RMSE = 987.81 ± 55.05 m2), SP (average RMSE = 1327.09 ± 127.83 m2) and PSC (average RMSE = 2259.43 ± 115.36 m2) for mapping BIRs. Our results indicated that the use of the customized NDBI approach can greatly improve the semi-automatic mapping of BIRs in the Antarctic environment. This study presents the first refined map of distribution of BIRs around the Schirmacher Oasis. The total area of blue ice in the study area was estimated to be 106.875 km2, approximately 61% of the study area. The WV-2 derived BIR map area presented in this study locally refined the existing BIR map derived using Landsat Enhanced Thematic Mapper Plus (ETM+) and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based mosaic of Antarctica (MOA) dataset by ~31% (~33.40 km2). Finally, we discussed the practical challenges and future directions in mapping BIRs across Antarctica