The Forest Change Footprint of the Upper Indus Valley, from 1990 to 2020
文献类型：期刊论文
| 作者 | Yan, Xinrong1,4; Wang, Juanle1,2,3,4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 14期号:3页码:19 |
| 关键词 | forest disturbance forest recovery footprint information LandTrendr spectral-temporal segmentation algorithm upper Indus Valley |
| DOI | 10.3390/rs14030744 |
| 通讯作者 | Wang, Juanle(wangjl@igsnrr.ac.cn) |
| 英文摘要 | The upper Indus Valley is the most important and vulnerable water tower in the South Asian subcontinent, which provides a vital water supply for 230 million people in the basin. Forests play an important role in water conservation in this region, and the security of upstream forests forms the foundation downstream water and food security. However, a big challenge is to effectively monitor the dynamics of the forest in this region. Thus, we used the LandTrendr spectral-temporal segmentation algorithm combined with 8203 scenes of multi-source remote sensing data to study the forest change footprint in the upper Indus Valley. The overall accuracy of LandTrendr extraction for forest disturbance and recovery was 86.01%, and the Kappa coefficient was 0.73. The results showed the following: (1) From 1990 to 2020, the area of forest recovery was 1.01% more than that of disturbance, 70% of disturbance occurred between 1990 and 2001, and 60% of recovery occurred between 1999 and 2012. (2) Although the overall trend of forest disturbance and recovery was balanced, there were significant differences in forest management status among the different regions. Nepal has the highest forest stability, India has the largest area of forest disturbance, and Pakistan and China have the largest areas of forest recovery. (3) India's Himachal Pradesh and Jammu and Kashmir are the two provinces with the largest disturbed areas, primarily due to grazing, fires, and commercial tree planting. Pakistan's North-West Frontier, Azad Kashmir, and China's Tibet Ali region were major contributors to the recovery, which was driven by afforestation policies in both countries. This study provides an important data base and monitoring method for planning land and forest use in Indus Valley countries, protecting fragile environments, and promoting policies for the Sustainable Development Goals. |
| WOS关键词 | DISTURBANCE ; TRANSFORMATION ; VULNERABILITY ; LANDTRENDR ; DERIVATION ; LANDSAT-7 ; ENSEMBLE ; CLIMATE ; BIOMASS |
| 资助项目 | China-Pakistan Joint Research Center on Earth Sciences ; Construction Project of the China Knowledge Center for Engineering Sciences and Technology[CKCEST-2020-2-4] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000759435600001 |
| 出版者 | MDPI |
| 资助机构 | China-Pakistan Joint Research Center on Earth Sciences ; Construction Project of the China Knowledge Center for Engineering Sciences and Technology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171775]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.China Pakistan Earth Sci Res Ctr, Islamabad 45320, Pakistan 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Xinrong,Wang, Juanle. The Forest Change Footprint of the Upper Indus Valley, from 1990 to 2020[J]. REMOTE SENSING,2022,14(3):19. |
| APA | Yan, Xinrong,&Wang, Juanle.(2022).The Forest Change Footprint of the Upper Indus Valley, from 1990 to 2020.REMOTE SENSING,14(3),19. |
| MLA | Yan, Xinrong,et al."The Forest Change Footprint of the Upper Indus Valley, from 1990 to 2020".REMOTE SENSING 14.3(2022):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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