Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms

Development of long‐term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China

“…Compared to findings obtained by Ma, Xie et al (2022), our downscaled NDVI product shows higher accuracy and fewer errors. However, the differences between the two NDVI products are more significant in the alpine region and in some areas to the southeast of the plateau.…”

“…Furthermore, the spatial distribution of NDVI in alpine meadow types is subject to a combination of climatic factors and human activities, and with the instability of global climate change and the increasing intensity of human activities, this has led to highly abrupt spatial changes in grasslands and significant changes in NDVI (Sun, Gong, et al, 2023; Sun, Li, et al, 2023), which may also contribute to the high bias in the downscaled product. On the contrary, the Enhanced Vegetation Index (EVI), also used to monitor vegetation growth, performs well in vegetation types where NDVI saturates, and can clearly reflect the seasonal characteristics of vegetation growth (Bai, 2021; Lin et al, 2008; Ma, Xie, et al, 2022). However, the EVI series has a narrow temporal range, limiting its full potential (Fensholt et al, 2006; Li et al, 2021).…”

Remote Sensing of Watershed: Towards a New Research Paradigm

“…Many studies applied multisource data. For example, Duan et al (2022) aim to solve the missing data issues by integrating multiple satellite sources [40], and others rely on multisource data to gain a comprehensive understanding [51].…”

“…The advent of remote sensing has revolutionized watershed research, providing unprecedented insights into watershed dynamics and spatiotemporal patterns [10,11]. These techniques offer breakthrough advantages over traditional field-based methods, including covering large areas with low cost and high efficiency, monitoring remote and inaccessible regions, and obtaining data at different spatial, spectral, and temporal resolutions [12][13][14].…”

Spatial and Temporal Variation in Vegetation Cover and Its Response to Topography in the Selinco Region of the Qinghai-Tibet Plateau

“…The Selinco region is located in the northern Qinghai-Tibet Plateau, ranging from 29 • 56 to 36 • 28 N and 85 • 3 to 93 • 1 E. It encompasses five counties and one district in the western part of the Naqu region of the Tibet Autonomous Region, namely, Nima County, Shuanghu County, Shenzha County, Bange County, Anduo County, and Seni District (Figure 1) [9]. Covering a total area of approximately 300,000 km 2 , the region features rugged terrain and an altitude range of 4139 to 6941 m. The topography of the study area consists of a central lowland surrounded by higher elevations [29]. The climate in this region falls within the Plateau temperate and subarctic climate zones, characterized by intense radiation and prolonged sunshine, with annual average temperature for each county in the Selinco region ranging from 0.9 to 3.3 • C, and annual precipitation ranging between 100 and 200 mm [9].…”