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Mapping the forests and their spatiotemporal changes in the Yellow River Basin (Gansu section) in China from 2008 to 2018

文献类型：期刊论文

作者Niu, Quanfu3,4,5; Liu, Mingzhi5; Liu, Bo5; Wang, Gang5; Wang, Zhenyu5; Liu, Xiujie5; Cheng, Weiming2; Li, Kegong1
刊名EUROPEAN JOURNAL OF REMOTE SENSING
出版日期2025-12-31
卷号58期号:1页码:16
关键词The Yellow River Basin (Gansu section) forest/non-forest PALSAR Landsat reliability
DOI10.1080/22797254.2025.2451046
通讯作者Niu, Quanfu(Niuqf@lut.edu.cn)
英文摘要Efficient and precise extraction of forest information is crucial for assessing the effectiveness of ecological projects and informing ecological policy adjustments in the Gansu section of the Yellow River Basin, China. Utilizing PALSAR (Phased Array L-band Synthetic Aperture Radar) and Landsat data, this study gathered forest/non-forest samples and formulated classification rules using the backscatter coefficients from PALSAR's HH, HV, HH-HV, and HH/HV polarizations to generate monthly 30-meter forest data. These results were further refined using Landsat's NDVImax and B7max indices. Our findings are as follows: The forest/non-forest data extracted from PALSAR and Landsat aligns more closely with publicly available forest data, achieving an overall accuracy of 95.4% and a Kappa coefficient of 0.87, which demonstrates the reliability and accuracy of our extraction method. Driven by ecological engineering projects and climate factors, the forest area has expanded significantly, from 13,200 square kilometers in 2008 to 19,800 square kilometers in 2018, with an average annual growth rate of 5%. Approximately 94.09% of the forest area is located at elevations up to 3,500 meters, with stable and increasing forest types predominantly found on the northern and western slopes, spanning elevations between 3,000 and 3,500 meters and slopes ranging from 20 degrees to 35 degrees. Our study provides valuable insights into regions characterized by rugged terrain and low forest density.
WOS关键词REMOTE-SENSING DATA ; ALOS PALSAR ; VEGETATION COVERAGE ; LAND ; ACCURACY ; UNCERTAINTY ; PROGRAM ; REGION ; MODIS ; MAPS
资助项目National Natural Science Foundation of China[42261069] ; Leading Talent Training Project of Gansu Department of Natural Resources of China[202211]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001394818200001
出版者TAYLOR & FRANCIS LTD
资助机构National Natural Science Foundation of China ; Leading Talent Training Project of Gansu Department of Natural Resources of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/212608]  
专题中国科学院地理科学与资源研究所
通讯作者Niu, Quanfu
作者单位1.Gansu Acad Surveying & Mapping Engn, Lanzhou, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.Academician Expert Workstat Gansu Dayu Jiuzhou Spa, Lanzhou, Peoples R China
4.Emergency Mapping Engn Res Ctr Gansu Prov, Lanzhou, Peoples R China
5.Lanzhou Univ Technol, Sch Civil Engn, 287 Langongping Rd, Lanzhou 730050, Gansu Province, Peoples R China
推荐引用方式
GB/T 7714		 Niu, Quanfu,Liu, Mingzhi,Liu, Bo,et al. Mapping the forests and their spatiotemporal changes in the Yellow River Basin (Gansu section) in China from 2008 to 2018[J]. EUROPEAN JOURNAL OF REMOTE SENSING,2025,58(1):16.
APA Niu, Quanfu.,Liu, Mingzhi.,Liu, Bo.,Wang, Gang.,Wang, Zhenyu.,...&Li, Kegong.(2025).Mapping the forests and their spatiotemporal changes in the Yellow River Basin (Gansu section) in China from 2008 to 2018.EUROPEAN JOURNAL OF REMOTE SENSING,58(1),16.
MLA Niu, Quanfu,et al."Mapping the forests and their spatiotemporal changes in the Yellow River Basin (Gansu section) in China from 2008 to 2018".EUROPEAN JOURNAL OF REMOTE SENSING 58.1(2025):16.

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来源：地理科学与资源研究所

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