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Tracking Reforestation in the Loess Plateau, China after the "Grain for Green" Project through Integrating PALSAR and Landsat Imagery
Zhou, Hui1,2; Xu, Fu1; Dong, Jinwei2; Yang, Zhiqi2; Zhao, Guosong2; Zhai, Jun3; Qin, Yuanwei4; Xiao, Xiangming4
2019-11-02
Source PublicationREMOTE SENSING
Volume11Issue:22Pages:22
Corresponding AuthorXu, Fu(xufu@bjfu.edu.cn)
AbstractAn unprecedented reforestation process happened in the Loess Plateau, China due to the ecological restoration project 'Grain for Green Project', which has affected regional carbon and water cycles as well as brought climate feedbacks. Accurately mapping the area and spatial distribution of emerged forests in the Loess Plateau over time is essential for forest management but a very challenging task. Here we investigated the changes of forests in the Loess Plateau after the forest reconstruction project. First, we used a pixel and rule-based algorithm to identify and map the annual forests from 2007 to 2017 in the Loess Plateau by integrating 30 m Landsat data and 25 m resolution PALSAR data in this study. Then, we carried out the accuracy assessment and comparison with several existing forest products. The overall accuracy (OA) and Kappa coefficient of the resultant map, were about 91% and 0.77 in 2010, higher than those of the other forest products (FROM-GLC, GlobeLand30, GLCF-VCF, JAXA, and OU-FDL) with OA ranging from 83.57% to 87.96% and Kappa coefficients from 0.52 to 0.68. Based on the annual forest maps, we found forest area in the Loess Plateau has increased by around 15,000 km(2) from 2007 to 2017. This study clearly demonstrates the advantages of data fusion between PALSAR and Landsat images for monitoring forest cover dynamics in the Loess Plateau, and the resultant forest maps with lower uncertainty would contribute to the regional forest management.
Keywordforest change PALSAR Landsat Loess Plateau Grain for Green Project (GGP) spatiotemporal pattern
DOI10.3390/rs11222685
WOS KeywordCONTERMINOUS UNITED-STATES ; ALOS PALSAR ; FOREST BIOMASS ; COVER DATABASE ; SNOW DETECTION ; CLOUD SHADOW ; MAPS ; ACCURACY ; AFFORESTATION ; DATASETS
Indexed BySCI
Language英语
Funding ProjectChinese Academy of Sciences (CAS)[XDA19040301] ; Chinese Academy of Sciences (CAS)[QYZDB-SSW-DQC005] ; National Natural Science Foundation of China[61772078] ; Key R&D Program of Beijing[D171100001817003]
Funding OrganizationChinese Academy of Sciences (CAS) ; National Natural Science Foundation of China ; Key R&D Program of Beijing
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000502284300090
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/131020
Collection中国科学院地理科学与资源研究所
Corresponding AuthorXu, Fu
Affiliation1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
3.Minist Ecol & Environm, Satellite Environm Ctr, Beijing 100094, Peoples R China
4.Univ Oklahoma, Ctr Spatial Anal, Dept Microbiol & Plant Biol, Norman, OK 73019 USA
Recommended Citation
GB/T 7714
Zhou, Hui,Xu, Fu,Dong, Jinwei,et al. Tracking Reforestation in the Loess Plateau, China after the "Grain for Green" Project through Integrating PALSAR and Landsat Imagery[J]. REMOTE SENSING,2019,11(22):22.
APA Zhou, Hui.,Xu, Fu.,Dong, Jinwei.,Yang, Zhiqi.,Zhao, Guosong.,...&Xiao, Xiangming.(2019).Tracking Reforestation in the Loess Plateau, China after the "Grain for Green" Project through Integrating PALSAR and Landsat Imagery.REMOTE SENSING,11(22),22.
MLA Zhou, Hui,et al."Tracking Reforestation in the Loess Plateau, China after the "Grain for Green" Project through Integrating PALSAR and Landsat Imagery".REMOTE SENSING 11.22(2019):22.
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