Remote Sensing Identification and Stability Change of Alpine Grasslands in Guoluo Tibetan Autonomous Prefecture, China
Xingsheng Xia (),
Wei Liang,
Shenghui Lv,
Yaozhong Pan and
Qiong Chen
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Xingsheng Xia: Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
Wei Liang: Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
Shenghui Lv: Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
Yaozhong Pan: Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
Qiong Chen: Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
Sustainability, 2024, vol. 16, issue 12, 1-19
Abstract:
Alpine grasslands, a crucial component of the Qinghai–Tibet Plateau, play a vital role in maintaining ecological barriers and facilitating sustainable development, and the exact stability change is also the key to coping with climate change and implementing ecological protection projects. The purpose of this study was to identify the spatial and temporal distribution of multi-stage alpine grassland and explore its inter-annual distribution and growth stability. The Guoluo Tibetan Autonomous Prefecture, China (hereinafter referred to as Guoluo), where alpine grassland is widely distributed, was selected as the research area. Long-term stable grassland samples constructed using the Mann–Kendall–Sneyers mutation test method were analyzed alongside random forest classification to identify multi-stage grassland distribution trends from 1990 to 2020. Based on the Fractional Vegetation Cover (FVC) and coefficient of variation ( C v ), spatial and temporal changes in grassland quality and their driving factors were discussed. The results show the following: (1) Remote sensing grassland extraction, based on the establishment of long-term stable grassland samples and random forest classification, demonstrated high accuracy and reliability, with OA and Kappa coefficients consistently above 0.89 and 0.77, and PA and UA maintained consistently at approximately 0.9. (2) The distribution of grassland in Guoluo corresponded to the spatial patterns determined by the natural geographical environment, showing a gradual trend from high-cover grassland in the southeast to low-cover grassland in the northwest. The proportion of medium and high-cover grasslands slightly increased, indicating an improvement in grassland quality. However, the encroachment and degradation caused by human activities and climate change resulted in a slight decrease in the proportion of grassland area compared with 1990. (3) Despite the overall grassland ecosystem still having relative stability, local grassland quality changes dramatically, mainly in the north of Maduo County. And significant fluctuations in the area of grassland quality were noted over the last two decades, suggesting potential degradation in ecosystem stability. Climate change and human activities were identified as primary drivers of these changes. Climate change is dominant in the alpine region. The low-warming region is dominated by human activities. These findings offer essential insights for the planning and implementation of alpine grassland ecosystem protection and restoration initiatives and also have important value for exploring the evolution law of alpine grassland ecosystems.
Keywords: alpine grassland; remote sensing identification; Google Earth Engine (GEE); random forest; spatiotemporal variation; geographic detector; Qinghai–Tibet plateau (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)
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