Abstract
Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land cover in areas that are otherwise difficult to access. With the continuous development of new satellites, it is important to optimize the existing maps for further monitoring of Arctic ecosystems. This study presents a scalable classification framework, producing novel 10 m resolution land cover maps for Kobbefjord, Disko, and Zackenberg in Greenland. Based on Sentinel-2, a digital elevation model, and Google Earth Engine (GEE), this framework classifies the areas into nine classes. A vegetation land cover classification for 2019 is achieved through a multi-temporal analysis based on 41 layers comprising phenology, spectral indices, and topographical features. Reference data (1164 field observations) were used to train a random forest classifier, achieving a cross-validation accuracy of 91.8%. The red-edge bands of Sentinel-2 data proved to be particularly well suited for mapping the fen vegetation class. The study presents land cover mapping in the three study areas with an unprecedented spatial resolution and can be extended via GEE for further ecological monitoring in Greenland.
Highlights
Accepted: 2 September 2021Arctic studies are increasingly relevant in the scope of climate change as the region’s seasonal variables provide important information on the global climate system [1]
The classification framework developed in Google Earth Engine was able to produce high-resolution land cover maps (10 m resolution) covering different climatic and phenological conditions in Greenland
Based on 41 extracted features derived from Sentinel-2 data during 2019 and a DEM, the Random Forest (RF) classifier was able to reach a high level of accuracy (OA of 91.8%) when compared against a total of 1164 ground reference data (GRD) points
Summary
Accepted: 2 September 2021Arctic studies are increasingly relevant in the scope of climate change as the region’s seasonal variables provide important information on the global climate system [1]. Mapping of the biophysical cover of Arctic surfaces is fundamental for monitoring purposes and form an important basis for studies of various ecosystem processes and states such as greenhouse gas exchange [2,3,4], surface energy balance [5,6], and permafrost [7,8] These interactions are often measured on a local scale, but to interpret their influence in a regional context, it is essential to identify the physical extent of the land cover. GEE is a cloud-based platform consisting of a large data catalog from different satellite sources and spatial analysis tools. It is operated through a web-based application programming interface
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