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

Fangfang Yang,Jixing Chen,Kui Li,Yanxu Liu,Panpan Hu,Linlin Xu

doi:10.3390/rs12060999

Abstract

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”.

Highlights

As an important indicator of global warming, the long-term change in glaciers can provide strong evidence for the understanding of the recent environmental changes [1,2]
(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 backward path is mainly used for updating the network weights and biases that are learned to approximate the complex relationship between the input features and the output characteristics [17]

Summary

Introduction

As an important indicator of global warming, the long-term change in glaciers can provide strong evidence for the understanding of the recent environmental changes [1,2]. With the warming climate and the increasing intensity of human activities, most glaciers around the world have been receding [3]. Since 1960, global glaciers have experienced a strong contraction in the past 50 years, with a contraction rate of 11.3%; the annual percentage change was 0.35% [4]. The fast and continuous shrinkage of glaciers has some benefits on the environment and human activities, including vegetation growth, economic development and increasing the water and energy supply [5,6]. The accurate monitoring of environmental changes in glacier regions is necessary for climate change research, natural disaster prediction, and preserving human life

Methods

Results

Discussion

Conclusion