WANG Yanxia, YANG Lisha, HUANG Xiaoyuan, ZHOU Ruliang. 2024: Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM. Journal of Mountain Science, 21(6): 2025-2042. DOI: 10.1007/s11629-023-8447-1
Citation: WANG Yanxia, YANG Lisha, HUANG Xiaoyuan, ZHOU Ruliang. 2024: Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM. Journal of Mountain Science, 21(6): 2025-2042. DOI: 10.1007/s11629-023-8447-1

Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

  • Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system. Fast and accurate measurements of the locations, intensity, and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues. This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase (NSHTI), one of the lesser-attended changes. First, raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data. It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size. Second, a threshold selection was performed to identify the NSHTI cells using a threshold of -0.65 ℃/100 m. Then, the NSHTI strips were parameterized through raster vectorization and spatial analysis. Taking Yunnan, a mountainous province in southwestern China, as the study area, the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys, and the strips are almost parallel to the altitude contours with a slight northward uplift. Also, they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors, where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth. Additionally, the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend, and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m. The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains, providing support for the modeling of weather and climate systems and the development of mountain resources.
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