Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images
The accurate estimation of grassland carrying capacity (GCC) in the alpine grasslands of the Changjiang River source region is crucial for managing livestock loads and ensuring ecological security on the Qinghai-Tibetan Plateau. Previous remote sensing methods have predominantly focused on yield ind...
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MDPI AG
2024-12-01
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| author | Xuejun Cheng Maoxin Liao Shuangyin Zhang Siying Wang Yiyun Chen Teng Fei |
| author_facet | Xuejun Cheng Maoxin Liao Shuangyin Zhang Siying Wang Yiyun Chen Teng Fei |
| author_sort | Xuejun Cheng |
| collection | DOAJ |
| description | The accurate estimation of grassland carrying capacity (GCC) in the alpine grasslands of the Changjiang River source region is crucial for managing livestock loads and ensuring ecological security on the Qinghai-Tibetan Plateau. Previous remote sensing methods have predominantly focused on yield indicators, often neglecting quality indicators, which hampers precise GCC estimation. Here, we collected 25 samples from the Dangqu basin, analyzing various grass parameters including yield, crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Then, we developed models to optimize GCC using quality indicators derived from GF5B images, assessing performance through Pearson correlation coefficient (R<sup>2</sup>), root mean square error (RMSE), and relative root mean square error (rRMSE). Results were found to show an average yield of 61.26 g/m<sup>2</sup>, with CP, ADF, and NDF ranging from 5.81% to 18.75%, 45.47% to 58.80%, and 27.50% to 31.81%, respectively. Spectra in the near-infrared range, such as 1918 nm, and spectral indices improved the accuracy of the hyperspectral inversion of grass parameters. The GCC increased from 0.51 SU·hm<sup>−2</sup> to 0.63 SU·hm<sup>−2</sup> post-optimization, showing an increasing trend from northwest to southeast. This study enhances GCC estimation accuracy, aiding in reasonable livestock management and effective ecological preservation. |
| format | Article |
| id | doaj-art-a88d5915fd2e48e9a47f6f22ae0e1bc2 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-a88d5915fd2e48e9a47f6f22ae0e1bc22025-08-20T02:56:51ZengMDPI AGRemote Sensing2072-42922024-12-011624480710.3390/rs16244807Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral ImagesXuejun Cheng0Maoxin Liao1Shuangyin Zhang2Siying Wang3Yiyun Chen4Teng Fei5Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, ChinaChangjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, ChinaChangjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, ChinaDepartment of Urban Planning and Design, The University of Hong Kong, Hong Kong SAR, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaThe accurate estimation of grassland carrying capacity (GCC) in the alpine grasslands of the Changjiang River source region is crucial for managing livestock loads and ensuring ecological security on the Qinghai-Tibetan Plateau. Previous remote sensing methods have predominantly focused on yield indicators, often neglecting quality indicators, which hampers precise GCC estimation. Here, we collected 25 samples from the Dangqu basin, analyzing various grass parameters including yield, crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Then, we developed models to optimize GCC using quality indicators derived from GF5B images, assessing performance through Pearson correlation coefficient (R<sup>2</sup>), root mean square error (RMSE), and relative root mean square error (rRMSE). Results were found to show an average yield of 61.26 g/m<sup>2</sup>, with CP, ADF, and NDF ranging from 5.81% to 18.75%, 45.47% to 58.80%, and 27.50% to 31.81%, respectively. Spectra in the near-infrared range, such as 1918 nm, and spectral indices improved the accuracy of the hyperspectral inversion of grass parameters. The GCC increased from 0.51 SU·hm<sup>−2</sup> to 0.63 SU·hm<sup>−2</sup> post-optimization, showing an increasing trend from northwest to southeast. This study enhances GCC estimation accuracy, aiding in reasonable livestock management and effective ecological preservation.https://www.mdpi.com/2072-4292/16/24/4807alpine meadowgrass quality indicatorsgrassland carrying capacityDangqu basin |
| spellingShingle | Xuejun Cheng Maoxin Liao Shuangyin Zhang Siying Wang Yiyun Chen Teng Fei Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images Remote Sensing alpine meadow grass quality indicators grassland carrying capacity Dangqu basin |
| title | Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images |
| title_full | Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images |
| title_fullStr | Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images |
| title_full_unstemmed | Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images |
| title_short | Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images |
| title_sort | optimization of grassland carrying capacity with grass quality indicators through gf5b hyperspectral images |
| topic | alpine meadow grass quality indicators grassland carrying capacity Dangqu basin |
| url | https://www.mdpi.com/2072-4292/16/24/4807 |
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