High spatial resolution decade-time scale land cover change at multiple locations in the Beringian Arctic (1948–2000s)

Abstract

Analysis of time series imagery from satellite and aircraft platforms is useful for detecting land cover change at plot to regional scales. In this study, we created multi-temporal high spatial resolution land cover maps for seven locations in the Beringian Arctic and assessed the change in land cover over time. Land cover classifications were site specific and mostly aligned with a soil moisture gradient. Time series varied between 60 and 21 years. Four of the five landscapes studied in Alaska underwent an expansion of drier land cover classes while the two landscapes studies in Chukotka, Russia showed an expansion of wetter land cover types. While a range of land cover types was present across the landscapes studied, the extent of shrubs (in Chukotka) and open water (in Alaska) increased in all landscapes where these land cover types were present. The results support trends documented for regional change in NDVI (a measure of vegetation greenness and productivity) as well as a host of other long term, experimental and modeling studies. Using historic change trends for each land cover type at each landscape, we use a simple probabilistic vegetation model to establish hypotheses of future change trajectories for different land cover types at each of the landscapes investigated. This study is a contribution to the International Polar Year Back to the Future project (IPY-BTF).

Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

“…At the 32-year scale, such changes are most likely due to the proliferation of relatively fast-growing shrubs in tundra and open forests. This finding is indirectly supported by numerous studies conducted throughout the circumpolar Arctic (e.g., [7,11,17,[54][55][56][57]). Specifically, within this study area a comparative analysis was done between high-resolution Gambit imagery from the 1960s and contemporary GeoEye-1 imagery over a 58 km 2 area in the vicinity of Dudinka (Figure 1).…”

“…Numerous observational studies indicate that such changes related to the structure, composition, and functioning of tundra and boreal forest biomes are ongoing. For example, increased photosynthetic productivity under warming climatic conditions, derived from remotely sensed normalized difference vegetation index (NDVI) data, frequently referred to as "arctic greening," has been reported for several Eurasian arctic regions (e.g., [4][5][6][7][8]). One of the major drivers of the observed greening trend is the increased abundance of shrub species in tundra ecosystems (e.g., [9][10][11][12][13]).…”

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

“…The criteria for grouping was based on previous research Remote Sens. 2017, 9, 1227 6 of 21 for these regions [40,61]. K-means was applied in ENVI (Exelis Visual Information Solutions, Boulder, CO, USA).…”

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

“…Multispectral data were often used, applying pixel-based, object-oriented, and other special classification methods [Král, 2009;Atkinson and Treitz, 2012;Moody et al, 2014;Reese et al, 2014;Virtanen and Ek, 2014]. Time series of imagery from satellite and aircraft platforms was employed by Lin et al [2012]. The potential of data fusioncombination of radar data (PolSAR, TerraSAR-X and Radarsat-2) with multispectral Landsat 8 data was tested by Ullman et al [2014].…”