scholarly journals Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery

2021 ◽  
pp. 1-14
Author(s):  
Rebecca L. Dell ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold ◽  
Anna Ruth W. Halberstadt ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Clustering Algorithm ◽  
Google Earth ◽  
Expert Elicitation ◽  
Future Research ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Air Content

Abstract Surface meltwater is becoming increasingly widespread on Antarctic ice shelves. It is stored within surface ponds and streams, or within firn pore spaces, which may saturate to form slush. Slush can reduce firn air content, increasing an ice-shelf's vulnerability to break-up. To date, no study has mapped the changing extent of slush across ice shelves. Here, we use Google Earth Engine and Landsat 8 images from six ice shelves to generate training classes using a k-means clustering algorithm, which are used to train a random forest classifier to identify both slush and ponded water. Validation using expert elicitation gives accuracies of 84% and 82% for the ponded water and slush classes, respectively. Errors result from subjectivity in identifying the ponded water/slush boundary, and from inclusion of cloud and shadows. We apply our classifier to the Roi Baudouin Ice Shelf for the entire 2013–20 Landsat 8 record. On average, 64% of all surface meltwater is classified as slush and 36% as ponded water. Total meltwater areal extent is greatest between late January and mid-February. This highlights the importance of mapping slush when studying surface meltwater on ice shelves. Future research will apply the classifier across all Antarctic ice shelves.

A record of slush and water extent on Antarctic ice shelves from 2013 to present day

2021 ◽  
Author(s):  
Rebecca Dell ◽  
Alison Banwell ◽  
Neil Arnold ◽  
Ian Willis ◽  
Anna Ruth W. Halberstadt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Google Earth ◽  
Expert Elicitation ◽  
Water Bodies ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Air Temperatures ◽  
Run Off ◽  
Shelf Dynamics

<p>Supraglacial melt is observed across the majority of Antarctic ice shelves and is expected to increase in line with rising air temperatures. Surface meltwater may run off the ice shelf edge and into the ocean, or be stored within firn pore spaces (slush) and supraglacial water bodies (ponds, lakes or streams). When stored either as slush or supraglacial water bodies, the water can indirectly impact ice shelf dynamics, and potentially facilitate ice shelf collapse. Numerous studies have quantified ice shelf meltwater in supraglacial water bodies, however, despite its importance, no studies exist that quantify the extent of slush on a pan-Antarctic scale.</p><p>Here, we develop a supervised classifier in Google Earth Engine capable of identifying both slush and ponded water on a pan-Antarctic scale using Landsat 8 imagery. We train and test our classifier on six ice shelves: (1) Nivlisen, (2) Roi Baudouin, (3) Amery, (4) Shackleton, (5) Nansen, (6) George VI. A k-means clustering algorithm is applied to selected Landsat 8 training scenes, and the output clusters are manually interpreted to form training classes (i.e. slush, water, and other surface types (e.g. blue ice, dirty ice)). These training classes are then used to train a Random Forest Classifier, and the accuracy of the outputs are assessed using expert elicitation. Overall, the classifier accuracy for water and slush is 78 % and 70 % respectively. The validated classifier is then applied to numerous ice shelves across Antarctica, in order to produce estimates of slush and water extent from 2013 to the present day.</p>

scholarly journals The 32-year record-high surface melt in 2019/2020 on the northern George VI Ice Shelf, Antarctic Peninsula

2021 ◽  
Vol 15 (2) ◽  
pp. 909-925
Author(s):  
Alison F. Banwell ◽  
Rajashree Tri Datta ◽  
Rebecca L. Dell ◽  
Mahsa Moussavi ◽  
Ludovic Brucker ◽  
...  
Keyword(s):  
High Surface ◽  
Microwave Radiometer ◽  
Austral Summer ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Near Surface ◽  
Ice Shelves ◽  
Air Content ◽  
Air Temperatures ◽  
Surface Melt

Abstract. In the 2019/2020 austral summer, the surface melt duration and extent on the northern George VI Ice Shelf (GVIIS) was exceptional compared to the 31 previous summers of distinctly lower melt. This finding is based on analysis of near-continuous 41-year satellite microwave radiometer and scatterometer data, which are sensitive to meltwater on the ice shelf surface and in the near-surface snow. Using optical satellite imagery from Landsat 8 (2013 to 2020) and Sentinel-2 (2017 to 2020), record volumes of surface meltwater ponding were also observed on the northern GVIIS in 2019/2020, with 23 % of the surface area covered by 0.62 km3 of ponded meltwater on 19 January. These exceptional melt and surface ponding conditions in 2019/2020 were driven by sustained air temperatures ≥0 ∘C for anomalously long periods (55 to 90 h) from late November onwards, which limited meltwater refreezing. The sustained warm periods were likely driven by warm, low-speed (≤7.5 m s−1) northwesterly and northeasterly winds and not by foehn wind conditions, which were only present for 9 h total in the 2019/2020 melt season. Increased surface ponding on ice shelves may threaten their stability through increased potential for hydrofracture initiation; a risk that may increase due to firn air content depletion in response to near-surface melting.

scholarly journals CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

Irriga ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 160-169
Author(s):  
Cesar De Oliveira Ferreira Silva
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Supervised Classification ◽  
Source Code ◽  
Regression Trees ◽  
Google Earth ◽  
Landsat 8 ◽  
Classification And Regression ◽  
Google Earth Engine

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE   CÉSAR DE OLIVEIRA FERREIRA SILVA1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: [email protected].     1 RESUMO   Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.   Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.     SILVA, C. O .F SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE     2 ABSTRACT   Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.   Keywords: cloud computing, remote sensing, hydrology, modeling.

scholarly journals 32-year record-high surface melt in 2019/2020 on north George VI Ice Shelf, Antarctic Peninsula

2020 ◽  
Author(s):  
Alison F. Banwell ◽  
Rajashree Tri Datta ◽  
Rebecca L. Dell ◽  
Mahsa Moussavi ◽  
Ludovic Brucker ◽  
...  
Keyword(s):  
High Surface ◽  
Microwave Radiometer ◽  
Austral Summer ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Near Surface ◽  
Ice Shelves ◽  
Air Content ◽  
Air Temperatures ◽  
Surface Melt

Abstract. In the 2019/2020 austral summer, the surface melt duration and extent on the northern George VI Ice Shelf (GVIIS) was exceptional compared to the 31 previous summers of dramatically lower melt. This finding is based on analysis of near-continuous 41-year satellite microwave radiometer (and scatterometer) data, which are sensitive to meltwater on the ice-shelf surface and in the near-surface snow. Using optical satellite imagery from Landsat 8 (since 2013) and Sentinel-2 (since 2017), record volumes of surface meltwater ponding are also observed on north GVIIS in 2019/2020, with 23 % of the surface area covered by 0.62 km3 of meltwater on January 19. These exceptional melt and surface ponding conditions in 2019/2020 were driven by sustained air temperatures ≥ 0 °C for anomalously long periods (55–90 hours) from late November onwards, likely driven by warmer northwesterly and northeasterly low-speed winds. Increased surface ponding on ice shelves may threaten their stability through increased potential for hydrofracture initiation; a risk that may increase due to firn air content depletion in response to near-surface melting.

scholarly journals Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize

2019 ◽  
Vol 11 (7) ◽  
pp. 823 ◽  
Author(s):  
Carly Voight ◽  
Karla Hernandez-Aguilar ◽  
Christina Garcia ◽  
Said Gutierrez
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Agricultural Practices ◽  
Google Earth ◽  
Training Data ◽  
Forest Loss ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Change Analysis

Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management.

Inter-annual variability in supraglacial lakes around East Antarctica

2021 ◽  
Author(s):  
Jennifer Arthur ◽  
Chris Stokes ◽  
Stewart Jamieson ◽  
Rachel Carr ◽  
Amber Leeson
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Flow Acceleration ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Global Sea Level

<p>Surface meltwater ponding can weaken and trigger the rapid disintegration of Antarctic ice shelves which buttress the ice sheet, causing ice flow acceleration and global sea-level rise. While supraglacial lakes (SGLs) are relatively well documented during some years and selected ice shelves in Antarctica, we have little understanding of how Antarctic-wide SGL coverage varies between melt seasons. Here, we present a record of SGL evolution around the peak of the melt season on the East Antarctic Ice Sheet (EAIS) over seven consecutive years. Our findings are based on a threshold-based algorithm applied to 2175 Landsat 8 images during the month of January from 2014 to 2020. We find that EAIS-wide SGL volume fluctuates inter-annually by up to ~80%. Moreover, patterns within regions and on neighbouring ice shelves are not necessarily synchronous. Over the whole EAIS, total SGL volume was greatest in January 2017, dominated by the Amery and Roi Baudouin ice shelves, and lowest in January 2016. Excluding these two ice shelves, SGL volume peaked in January 2020. Preliminary results suggest EAIS-wide total SGL volume and extent are weakly correlated with firn model simulations of firn air content, surface melt and minimum ice lens depth predicted by the regional climate model MAR. On certain ice shelves, years with peak SGL volume correspond with minimum firn air content. This work provides important constraints for numerical ice-shelf and ice-sheet model predictions of future Antarctic surface meltwater distributions and the potential impact on ice-sheet stability and flow.  </p>

scholarly journals Formation of sea ice ponds from ice-shelf runoff, adjacent to the McMurdo Ice Shelf, Antarctica

2020 ◽  
Vol 61 (82) ◽  
pp. 73-77 ◽  
Author(s):  
Grant J. Macdonald ◽  
Predrag Popović ◽  
David P. Mayer
Keyword(s):  
Sea Ice ◽  
Added Mass ◽  
Surface Melting ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Surface Hydrology ◽  
Ice Shelves ◽  
Arctic Ponds ◽  
Sentinel 2

AbstractPonds that form on sea ice can cause it to thin or break-up, which can promote calving from an adjacent ice shelf. Studies of sea ice ponds have predominantly focused on Arctic ponds formed by in situ melting/ponding. Our study documents another mechanism for the formation of sea ice ponds. Using Landsat 8 and Sentinel-2 images from the 2015–16 to 2018–19 austral summers, we analyze the evolution of sea ice ponds that form adjacent to the McMurdo Ice Shelf, Antarctica. We find that each summer, meltwater flows from the ice shelf onto the sea ice and forms large (up to 9 km2) ponds. These ponds decrease the sea ice's albedo, thinning it. We suggest the added mass of runoff causes the ice to flex, potentially promoting sea-ice instability by the ice-shelf front. As surface melting on ice shelves increases, we suggest that ice-shelf surface hydrology will have a greater effect on sea-ice stability.

scholarly journals Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification

2020 ◽  
Vol 12 (8) ◽  
pp. 1327 ◽  
Author(s):  
Anna Ruth W. Halberstadt ◽  
Colin J. Gleason ◽  
Mahsa S. Moussavi ◽  
Allen Pope ◽  
Luke D. Trusel ◽  
...  
Keyword(s):  
Large Scale ◽  
Seasonal Pattern ◽  
Ice Sheet ◽  
Training Data ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Continental Scale ◽  
Lake Evolution ◽  
Supervised Classifiers

Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product.

Recent glacial advance in the western Weddell Sea Sector driven by anomalous sea ice circulation

2020 ◽  
Author(s):  
Frazer Christie ◽  
Toby Benham ◽  
Julian Dowdeswell
Keyword(s):  
Sea Ice ◽  
Antarctic Peninsula ◽  
Ice Sheet ◽  
Weddell Sea ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Total Contribution ◽  
Antarctic Ice Sheet ◽  
Ice Shelves ◽  
The Antarctic

<p>The Antarctic Peninsula is one of the most rapidly warming regions on Earth. There, the recent destabilization of the Larsen A and B ice shelves has been directly attributed to this warming, in concert with anomalous changes in ocean circulation. Having rapidly accelerated and retreated following the demise of Larsen A and B, the inland glaciers once feeding these ice shelves now form a significant proportion of Antarctica’s total contribution to global sea-level rise, and have become an exemplar for the fate of the wider Antarctic Ice Sheet under a changing climate. Together with other indicators of glaciological instability observable from satellites, abrupt pre-collapse changes in ice shelf terminus position are believed to have presaged the imminent disintegration of Larsen A and B, which necessitates the need for routine, close observation of this sector in order to accurately forecast the future stability of the Antarctic Peninsula Ice Sheet. To date, however, detailed records of ice terminus position along this region of Antarctica only span the observational period c.1950 to 2008, despite several significant changes to the coastline over the last decade, including the calving of giant iceberg A-68a from Larsen C Ice Shelf in 2017.</p><p>Here, we present high-resolution, annual records of ice terminus change along the entire western Weddell Sea Sector, extending southwards from the former Larsen A Ice Shelf on the eastern Antarctic Peninsula to the periphery of Filchner Ice Shelf. Terminus positions were recovered primarily from Sentinel-1a/b, TerraSAR-X and ALOS-PALSAR SAR imagery acquired over the period 2009-2019, and were supplemented with Sentinel-2a/b, Landsat 7 ETM+ and Landsat 8 OLI optical imagery across regions of complex terrain.</p><p>Confounding Antarctic Ice Sheet-wide trends of increased glacial recession and mass loss over the long-term satellite era, we detect glaciological advance along 83% of the ice shelves fringing the eastern Antarctic Peninsula between 2009 and 2019. With the exception of SCAR Inlet, where the advance of its terminus position is attributable to long-lasting ice dynamical processes following the disintegration of Larsen B, this phenomenon lies in close agreement with recent observations of unchanged or arrested rates of ice flow and thinning along the coastline. Global climate reanalysis and satellite passive-microwave records reveal that this spatially homogenous advance can be attributed to an enhanced buttressing effect imparted on the eastern Antarctic Peninsula’s ice shelves, governed primarily by regional-scale increases in the delivery and concentration of sea ice proximal to the coastline.</p>

scholarly journals Seasonal evolution of Antarctic supraglacial lakes in 2015–2021 and links to environmental controls

2021 ◽  
Author(s):  
Mariel Christina Dirscherl ◽  
Andreas J. Dietz ◽  
Claudia Kuenzer
Keyword(s):  
Antarctic Peninsula ◽  
Time Lags ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Shelf Stability ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Environmental Controls ◽  
Lake Formation ◽  
Increased Risk

Abstract. Supraglacial meltwater accumulation on ice shelves may have important implications for future sea-level-rise. Despite recent progress in the understanding of Antarctic surface hydrology, potential influences on ice shelf stability as well as links to environmental drivers remain poorly constrained. In this study, we employ state-of-the-art machine learning on Sentinel-1 Synthetic Aperture Radar (SAR) and optical Sentinel-2 satellite imagery to provide new insight into the inter-annual and intra-annual evolution of surface hydrological features across six major Antarctic Peninsula and East Antarctic ice shelves. For the first time, we produce a record of supraglacial lake extent dynamics for the period 2015–2021 at unprecedented 10 m spatial resolution and bi-weekly temporal scale. Through synergetic use of optical and SAR data, we obtain a more complete mapping record enabling the delineation of also buried lakes. Our results for Antarctic Peninsula ice shelves reveal below average meltwater ponding during most of melting seasons 2015–2018 and above average meltwater ponding throughout summer 2019–2020 and early 2020–2021. Meltwater ponding on investigated East Antarctic ice shelves was far more variable with above average lake extents during most of melting seasons 2016–2019 and below average lake extents during 2020–2021. This study is the first to investigate relationships with climate drivers both, spatially and temporally including time lag analysis. The results indicate that supraglacial lake formation in 2015–2021 is coupled to the complex interplay of varying air temperature, solar radiation, snowmelt, wind and precipitation, each at different time lags and directions and with strong local to regional discrepancies, as revealed through pixel-based correlation analysis. Southern Hemisphere atmospheric modes as well as the local glaciological setting including melt-albedo feedbacks and the firn air content were revealed to strongly influence the spatio-temporal evolution of supraglacial lakes as well as below or above average meltwater ponding despite variations in the strength of forcing. Recent increases of Antarctic Peninsula surface ponding point towards a further reduction of the firn air content implying an increased risk for ponding and hydrofracture. In addition, lateral meltwater transport was observed over both Antarctic regions with similar implications for future ice shelf stability.

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