Mapping lichen distribution on the Antarctic Peninsula using remote sensing, lichen spectra and photographic documentation by citizen scientists

On the Antarctic Peninsula, lichens are the most diverse botanical component of the terrestrial ecosystem. However, detailed information on the distribution of lichens on the Antarctic Peninsula region is scarce, and the data available exhibit significant heterogeneity in sampling frequency and effort. Satellite remote sensing, in particular the use of the Normalized Difference Vegetation Index (NDVI), has facilitated determination of vegetation richness and cover distribution in some remote and otherwise inaccessible environments. However, it is known that using NDVI for the detection of vegetation can overlook the presence of lichens even if their land cover is extensive. We tested the use of known spectra of lichens in a matched filtering technique for the detection and mapping of lichen-covered land from remote sensing imagery on the Antarctic Peninsula, using data on lichen presence collected by citizen scientists and other non-specialists as ground truthing. Our results confirm that the use of this approach allows for the detection of lichen flora on the Antarctic Peninsula, showing an improvement over the use of NDVI alone for the mapping of flora in this are

Using Deep Learning to Count Albatrosses from Space

In this paper we test the use of a deep learning approach to automatically count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery. We use a dataset of manually labelled imagery provided by the British Antarctic Survey to train and develop our methods. We employ a U-Net architecture, designed for image segmentation, to simultaneously classify and localise potential albatrosses. We aid training with the use of the Focal Loss criterion, to deal with extreme class imbalance in the dataset. Initial results achieve peak precision and recall values of approximately 80%. Finally we assess the model’s performance in relation to interobserver variation, by comparing errors against an image labelled by multiple observers. We conclude model accuracy falls within the range of human counters. We hope that the methods will streamline the analysis of VHR satellite images, enabling more frequent monitoring of a species which is of high conservation concern

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

An automated methodology for differentiating rock from snow, clouds and sea in Antarctica from Landsat 8 imagery: A new rock outcrop map and area estimation for the entire Antarctic continent

As the accuracy and sensitivity of remote-sensing satellites improve, there is an increasing demand for more accurate and updated base datasets for surveying and monitoring. However, differentiating rock outcrop from snow and ice is a particular problem in Antarctica, where extensive cloud cover and widespread shaded regions lead to classification errors. The existing rock outcrop dataset has significant georeferencing issues as well as overestimation and generalisation of rock exposure areas. The most commonly used method for automated rock and snow differentiation, the normalised difference snow index (NDSI), has difficulty differentiating rock and snow in Antarctica due to misclassification of shaded pixels and is not able to differentiate illuminated rock from clouds. This study presents a new method for identifying rock exposures using Landsat 8 data. This is the first automated methodology for snow and rock differentiation that excludes areas of snow (both illuminated and shaded), clouds and liquid water whilst identifying both sunlit and shaded rock, achieving higher and more consistent accuracies than alternative data and methods such as the NDSI. The new methodology has been applied to the whole Antarctic continent (north of 82°40′ S) using Landsat 8 data to produce a new rock outcrop dataset for Antarctica. The new data (merged with existing data where Landsat 8 tiles are unavailable; most extensively south of 82°40′ S) reveal that exposed rock forms 0.18 % (21 745 km2) of the total land area of Antarctica: half of previous estimates

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

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

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

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

Experimental determination of reflectance spectra of Antarctic krill (Euphausia superba) in the Scotia Sea

Antarctic krill are the dominant metazoan in the Southern Ocean in terms of biomass; however, their wide and patchy distribution means that estimates of their biomass are still uncertain. Most currently employed methods do not sample the upper surface layers, yet historical records indicate that large surface swarms can change the water colour. Ocean colour satellites are able to measure the surface ocean synoptically and should theoretically provide a means for detecting and measuring surface krill swarms. Before we can assess the feasibility of remote detection, more must be known about the reflectance spectra of krill. Here, we measure the reflectance spectral signature of Antarctic krill collected in situ from the Scotia Sea and compare it to that of in situ water. Using a spectroradiometer, we measure a strong absorption feature between 500 and 550 nm, which corresponds to the pigment astaxanthin, and high reflectance in the 600–700 nm range due to the krill's red colouration. We find that the spectra of seawater containing krill is significantly different from seawater only. We conclude that it is tractable to detect high-density swarms of krill remotely using platforms such as optical satellites and unmanned aerial vehicles, and further steps to carry out ground-truthing campaigns are now warranted

Semiautomated detection and mapping of vegetation distribution in the Antarctic environment using spatial-spectral characteristics of WorldView-2 imagery.

Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change