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China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC

Authors :
Hui Wang
Xiaojin Wen
Yijia Wang
Liping Cai
Da Peng
Yanxu Liu
Source :
Remote Sensing, Vol 13, Iss 3, p 341 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

New types of remote sensed land cover datasets provide key evidence for understanding global environmental change. However, low data consistency makes understanding the changes unclear. China has become a hot spot of land cover change in the world due to climate change and a series of human measures, such as ecological engineering, land consolidation, and urbanization. However, due to the inconsistencies in interpretation of signs and thresholds, the understanding of yearly-continued land cover changes in China is still unclear. We aim to produce China’s land cover fraction dataset from 2001 to 2015 by weighted consistency analysis. We compare the Moderate-resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC) datasets, and a new land cover fraction dataset named China-LCFMCD-CCI, produced with a 1 km resolution. The obvious increased forest areas only accounted for 4.6% of the total forest areas, and were mainly distributed in northeast China. Approximately 75.8% of the grassland and shrubland areas decreased in size, and these areas were relatively concentrated in northeast and south China. The obvious increased areas of cropland (3.7%) were equal to the obvious decreased areas (3.6%), and the increased cropland areas were in northwest China. The change in bare land was not obvious, as the obvious increased areas only accounted for 0.75% of the bare land areas. The results not only prove that the data fusion of the weighted consistency method is feasible to form a land cover fraction dataset, but also helps to fully reveal the trends in land cover fraction change in China.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.3d3e9701f60e4c079345d449db59f1e7
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13030341