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Vegetation-Ice-Bare Land Cover Conversion in the Oceanic Glacial Region of Tibet Based on Multiple Machine Learning Classifications

1
School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3
College of Resource, Environmental and Tourism, Capital Normal University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 999; https://doi.org/10.3390/rs12060999
Received: 9 February 2020 / Revised: 11 March 2020 / Accepted: 17 March 2020 / Published: 20 March 2020
Oceanic glaciers are one of the most sensitive indicators of climate change. However, remotely sensed evidence of land cover change in the oceanic glacial region is still limited due to the cloudy weather during the growing season. In addition, the performance of common machine learning classification algorithms is also worth testing in this cloudy, frigid and mountainous region. In this study, three algorithms, namely, the random forest, back-propagation neural network (BPNN) and convolutional neural network algorithms, were compared in their interpretation of the land cover change in south-eastern Tibet and resulted in three findings. (1) The BPNN achieves the highest overall accuracy and Kappa coefficient compared with the other two algorithms. The overall accuracy was 97.82%, 98.07%, 98.92%, and 94.63% in 1990, 2000, 2007, and 2016, and the Kappa coefficient was 0.958, 0.959, 0.980, and 0.918 in these four years, respectively. (2) From 1990 to 2000, the dominant land cover was ice at the landscape level. The landscape fragmentation decreased and the landscape aggregation increased. From 2000 to 2016, the dominant land cover transformed from ice to vegetation. The vegetation aggregation increased, while the ice aggregation decreased. (3) When the elevation was less than 4 km, the vegetation was usually transformed into bare land; otherwise, the probability of direct transformation between vegetation and ice increased. The findings on the land cover transformation in the oceanic glacial region by multiple classification algorithms can provide both long-term evidence and methodological indications to understand the recent environmental change in the “third pole”. View Full-Text
Keywords: machine learning; landscape pattern; oceanic glaciers; land cover change; elevation machine learning; landscape pattern; oceanic glaciers; land cover change; elevation
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MDPI and ACS Style

Yang, F.; Liu, Y.; Xu, L.; Li, K.; Hu, P.; Chen, J. Vegetation-Ice-Bare Land Cover Conversion in the Oceanic Glacial Region of Tibet Based on Multiple Machine Learning Classifications. Remote Sens. 2020, 12, 999.

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