Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images
Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset....
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MDPI AG
2024-12-01
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| author | Bo Wei Yili Zhang Linshan Liu Binghua Zhang Dianqing Gong Changjun Gu Lanhui Li Basanta Paudel |
| author_facet | Bo Wei Yili Zhang Linshan Liu Binghua Zhang Dianqing Gong Changjun Gu Lanhui Li Basanta Paudel |
| author_sort | Bo Wei |
| collection | DOAJ |
| description | Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R<sup>2</sup> was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas. |
| format | Article |
| id | doaj-art-0b8c0bd6d5b9457cb37a21cb1e0e013e |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0b8c0bd6d5b9457cb37a21cb1e0e013e2025-01-10T13:20:09ZengMDPI AGRemote Sensing2072-42922024-12-011717810.3390/rs17010078Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat ImagesBo Wei0Yili Zhang1Linshan Liu2Binghua Zhang3Dianqing Gong4Changjun Gu5Lanhui Li6Basanta Paudel7Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaNational Disaster Reduction Center, Ministry of Emergency Management, Beijing 100124, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaClimate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R<sup>2</sup> was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas.https://www.mdpi.com/2072-4292/17/1/78Himalayasvegetation lineedge detectionelevation difference |
| spellingShingle | Bo Wei Yili Zhang Linshan Liu Binghua Zhang Dianqing Gong Changjun Gu Lanhui Li Basanta Paudel Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images Remote Sensing Himalayas vegetation line edge detection elevation difference |
| title | Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images |
| title_full | Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images |
| title_fullStr | Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images |
| title_full_unstemmed | Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images |
| title_short | Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images |
| title_sort | upper elevational limit of vegetation in the himalayas identified from landsat images |
| topic | Himalayas vegetation line edge detection elevation difference |
| url | https://www.mdpi.com/2072-4292/17/1/78 |
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