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CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

Authors :
Y. T. Guo
X. M. Zhang
T. F. Long
W. L. Jiao
G. J. He
R. Y. Yin
Y. Y. Dong
Source :
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-3-W10, Pp 855-862 (2020)
Publication Year :
2020
Publisher :
Copernicus Publications, 2020.

Abstract

Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

Details

Language :
English
ISSN :
16821750 and 21949034
Volume :
XLII-3-W10
Database :
Directory of Open Access Journals
Journal :
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.5e3d258c601743f888bd81488d8f9c6f
Document Type :
article
Full Text :
https://doi.org/10.5194/isprs-archives-XLII-3-W10-855-2020