Mapping vegetation types in Antarctic Peninsula and South Shetlands islands using Sentinel-2 images and Google Earth Engine cloud computing

The Antarctic vegetation maps are usually made using very high-resolution images collected by orbital sensors or unmanned aerial vehicles, generating isolated maps with information valid only for the time of image acquisition. In the context of global environmental change, mapping the current Antarctic vegetation distribution on a regular basis is necessary for a better understanding of the changes in this fragile environment. This work aimed to generate validated vegetation maps for the North Antarctic Peninsula and South Shetlands Islands based on Sentinel-2 images using cloud processing. Sentinel-2 imagery level 1C, acquired between 2016 and 2021 (January-April), were used. Land pixels were masked with the minimum value composite image for the "water vapor" band. The NDVI maximum value composite image was sliced, and its classes were associated with the occurrence of algae (0.15 - 0.20), lichens (0.20 - 0.50), and mosses (0.50 - 0.80). The vegetation map was validated by comparing it with those from the literature. The present study showed that Sentinel-2 images allow building a validated vegetation type distribution map for Antarctica Peninsula and South Shetlands Islands.

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Identifying multi-day to sub-daily supraglacial lake change in Greenland from Sentinel 2 and Landsat 8 imagery using Google Earth Engine

10.5194/egusphere-egu21-7965 ◽

2021 ◽

Author(s):

Qiao Li ◽

James Lea ◽

Stephen Brough

Keyword(s):

Google Earth ◽

Landsat 8 ◽

Lake Area ◽

High Latitudes ◽

Large Area ◽

Large Lakes ◽

North East ◽

The North ◽

Google Earth Engine ◽

Sentinel 2

Supraglacial lakes (SGLs) are a major component of Greenland&#8217;s surface hydrology and mass balance. Monitoring their evolution at multi-day to sub-daily timescales has traditionally been performed by relatively low-resolution sensors such as MODIS Terra, though opportunities exist for using higher spatial resolution sensors at high latitudes.In this study, we take advantage of frequent orbital crossovers of Sentinel 2 and Landsat 8 imagery at high latitudes to monitor lakes at multi-day to sub-day temporal resolution, and spatial resolutions up to/over an order of magnitude higher than MODIS Terra (10 m to 30 m, compared to ~250 m for MODIS Terra). Through leveraging the cloud computing resources of Google Earth Engine (GEE), we have developed a workflow to track the evolution of lakes for all available Sentinel 2 and Landsat 8 images over a melt season.Our workflow builds on the approach of Moussavi et al. (2020) that was developed for Antarctica, implementing it within GEE to explore its sensitivity and suitability for application to the catchment of the North East Greenland Ice Stream (NEGIS) for the 2019 melt season. To improve the efficiency of analysis, we analyse 282 large lakes (>0.125 km^2) that were previously identified through analysis of MODIS Terra imagery. All lake outlines are appended with image ID and lake area metadata to facilitate subsequent analysis, and allow each lake outline to be traced back to the original image that it was derived from. Our approach is able to monitor lake growth and drainage at unprecedented spatial and temporal resolutions over a large area, allowing the widespread characterization of seasonal lake evolution.

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The geology of King George Island, South Shetland Islands: uniting local geological maps and stratigraphical columns

10.5194/egusphere-egu21-13145 ◽

2021 ◽

Author(s):

Bastian Lopez ◽

Joaquin Bastias ◽

Daniela Matus ◽

Ricardo Jaña ◽

Marcelo Leppe

Keyword(s):

Antarctic Peninsula ◽

South Shetland Islands ◽

King George Island ◽

The South ◽

Moby Dick ◽

Shetland Islands ◽

Ice Cap ◽

The North ◽

South Shetlands ◽

Geological Map

King George Island is the largest one of the South Shetland Islands group distributed parallel to and separated by the Bransfield Strait of the northern tip of Antarctic Peninsula. The archipelago of the South Shetlands is mainly composed of the products of the active margin developed as a result of the subduction of the Phoenix Plate beneath the continental crust of the Antarctic Peninsula (e.g. Barker, 1982; Bastias et al., 2019). The lithologies are largely dominated by Mesozoic and Cenozoic sedimentary and volcanic successions that are cut by a few hypabyssal plutons. While some authors have suggested a southwest to northeast trend along the archipelago from older to younger magmatic activity (e.g. Haase et al., 2012), others have indicated that some of the magmatic events may have been recorded along the entire archipelago (e.g. Valanginian arc rocks; Bastias et al., 2019). Regardless, King George Island hosts an exceptional stratigraphical record of the Cenozoic period. Moreover, this island is mostly covered by an ice cap at the present day, which is commonly terminated with ice cliffs around much of the island. The southern edge of the island host Mesozoic and Paleogene successions, these rocks are dominated by volcanic and volcaniclastic units. The rocks in King George Island are generally young to the east and to the north ends. Cape Melville, the southeast extreme of the island, hosts the youngest sedimentary rocks known on the island: the Moby Dick Group (Birkenmajer, 1985).While several authors have presented local studies in the King George Island over the last three decades, an integrated assessment of the outcropping units in the entire island remains unexplored. A new geological map for King George Island will allow to update the current understanding of the stratigraphy of the South Shetland Islands, which will help to support not only the geological studies but also those focused on the environmental and paleontological record.Barker, 1982. Journal of the Geological Society 19, 787-801. (DOI: 10.1144/gsjgs.139.6.0787)Bastias et al. (2019). International Geology Review 62 (11), 1467-1484. (DOI: 10.1080/00206814.2019.1655669)Birkenmajer (1985). Bulletin Polish Academic Earth Sciences 33:15-23.Haase et al. (2012). Contributions to Mineralogy and Petrology 163, 1103-1119. (DOI: 10.1007/s00410-012-0719-7).

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Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

Remote Sensing ◽

10.3390/rs13152935 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2935

Author(s):

Chunhua Qian ◽

Hequn Qiang ◽

Feng Wang ◽

Mingyang Li

Keyword(s):

Learning Algorithm ◽

Vegetation Type ◽

Research Area ◽

Classification Model ◽

Guizhou Province ◽

Model Accuracy ◽

Spatiotemporal Evolution ◽

Rocky Desertification ◽

The North ◽

Svm Model

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

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Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Remote Sensing ◽

10.3390/rs13122301 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2301

Author(s):

Zander Venter ◽

Markus Sydenham

Keyword(s):

Land Cover ◽

Atmospheric Correction ◽

Google Earth ◽

Tree Planting ◽

Training Data ◽

Classification Model ◽

Night Time ◽

Continental Scale ◽

Land Cover Maps ◽

Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

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Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

Applied Sciences ◽

10.3390/app11094258 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4258

Author(s):

Jordan R. Cissell ◽

Steven W. J. Canty ◽

Michael K. Steinberg ◽

Loraé T. Simpson

Keyword(s):

Google Earth ◽

Mangrove Forests ◽

Random Forest Classification ◽

High Resolution Mapping ◽

Mangrove Ecosystems ◽

Forest Classification ◽

Resolution Mapping ◽

Google Earth Engine ◽

Terrestrial Biodiversity ◽

Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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Climate change in the western Antarctic Peninsula since 1945: observations and possible causes

Annals of Glaciology ◽

10.3189/1998aog27-1-571-575 ◽

1998 ◽

Vol 27 ◽

pp. 571-575 ◽

Cited By ~ 115

Author(s):

J. C. King ◽

S. A. Harangozo

Keyword(s):

Sea Ice ◽

Atmospheric Circulation ◽

Antarctic Peninsula ◽

The West ◽

Sea Ice Extent ◽

The North ◽

Ultimate Cause ◽

Temperature Records ◽

High Level ◽

The Antarctic

Temperature records from slations on the west roast of the Antarctic Peninsula show a very high level of interannual variability and, over the last 50 years, larger warming trends than are seen elsewhere in Antarctica. in this paper we investigate the role of atmospheric circulation variability and sea-ice extent variations in driving these changes. Owing to a lack of independent data, the reliability of Antarctic atmospheric analyses produced in the 1950s and 1960s cannot be readily established, but examination of the available data suggests that there has been an increase in the northerly component of the circulation over the Peninsula since the late 1950s. Few observations of sea-ice extent are available prior to 1973, but the limited data available indicate that the ice edge to the west of the Peninsula lay to the north of recently observed extremes during the very cold conditions prevailing in the late 1950s. The ultimate cause of the atmospheric-circulation changes remains to be determined and may lie outside the Antarctic region.

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The rapid Land Use and Land Cover change analysis using the Sentinel-2 images in Google Earth Engine: A Case Study of Xiong’an New Area from 2017 to 2020

10.1109/agro-geoinformatics50104.2021.9530306 ◽

2021 ◽

Author(s):

Jiansong Luo ◽

Rujin Yuan ◽

Yujia Cao ◽

Min Xie ◽

Yikai Wang

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Google Earth ◽

Change Analysis ◽

Google Earth Engine ◽

Land Cover Change Analysis ◽

Sentinel 2

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ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1193 ◽

2021 ◽

pp. 777

Author(s):

Andi Tenri Waru ◽

Athar Abdurrahman Bayanuddin ◽

Ferman Setia Nugroho ◽

Nita Rukminasari

Keyword(s):

Random Forest ◽

Google Earth ◽

Multi Temporal ◽

Google Earth Engine ◽

Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

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