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Forest Changes by Precipitation Zones in Northern China after the Three-North Shelterbelt Forest Program in China

Authors :
Han Li
Fu Xu
Zhichao Li
Nanshan You
Hui Zhou
Yan Zhou
Bangqian Chen
Yuanwei Qin
Xiangming Xiao
Jinwei Dong
Source :
Remote Sensing, Vol 13, Iss 4, p 543 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

China launched the Three-North Shelterbelt Forest Program (TNSFP) in 1978 in northern China to combat desertification and dust storms, but it is still controversial in ecologically fragile arid and semi-arid areas, which is partly due to the uncertainties of monitoring of the spatial-temporal changes of forest distribution. In this study, we aim to provide an overall retrospect of the forest changes (i.e., forest gain and forest loss) in northern China during 2007–2017, and to analyze the forest changes in different precipitation zones. We first generated annual forest maps at 30 m spatial resolution during 2007–2017 in northern China through integrating Landsat and PALSAR/PALSAR-2 data. We found the PALSAR/Landsat-based forest maps outperform other four existing products (i.e., JAXA F/NF, FROM-GLC, GlobeLand30, and NLCD-China) from either PALSAR or Landsat data, with a higher overall accuracy 96% ± 1%. The spatial-temporal analyses of forests showed a substantial forest expansion from 316,898 ± 34,537 km2 in 2007 to 384,568 ± 35,855 km2 in 2017 in the central and eastern areas. We found a higher forest loss rate (i.e., 35%) in the precipitation zones with the annual mean precipitation less than 400 mm (i.e., the arid and semi-arid areas) comparing to that (i.e., 25%) in the zones with more than 400 mm (i.e., the humid areas), which suggests the lower surviving rate in the drylands. This study provides satellite-based evidence for the forest changes in different precipitation zones, and suggests that the likely impacts of precipitation on afforestation effectiveness should be considered in future implementation of ecological restoration projects like TNSFP.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.25a92c81599c4ed0af3212b47f21ac46
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13040543