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 n...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.展开更多
This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi In...This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi Inthanon National Park. To mitigate the devastating impacts of these wildfires, effective monitoring and management strategies are necessary. Remote sensing technology provides a promising approach for mapping burnt areas and understanding fire regimes at a regional scale. The primary focus of this research is to employ the MODIS Aqua/Terra satellite system for obtaining historical remote sensing data on hotspots. The advantages of remote sensing include accurate identification and mapping of burnt areas, regular monitoring, rapid data acquisition, and historical data analysis. The MODIS sensor, specifically designed for fire monitoring, offers enhanced fire detection and diagnosis, multiple channels for qualitative and quantitative analysis, and precision positioning capabilities. The research results presented in the analysis contribute to the understanding of fire incidents and hotspot occurrences within the four national parks studied. This paper suggests the optimization of early detection of forest and land fires through the utilization of Artificial Intelligence (AI), presenting it as a recommendation for future endeavors. The research emphasizes the significance of implementing efficient policies and management strategies to effectively tackle the challenges associated with fires in these ecologically significant areas.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 42061004)the Joint Special Project of Agricultural Basic Research of Yunnan Province (Grant No. 202101BD070001093)the Youth Special Project of Xingdian Talent Support Program of Yunnan Province
文摘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.
文摘This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi Inthanon National Park. To mitigate the devastating impacts of these wildfires, effective monitoring and management strategies are necessary. Remote sensing technology provides a promising approach for mapping burnt areas and understanding fire regimes at a regional scale. The primary focus of this research is to employ the MODIS Aqua/Terra satellite system for obtaining historical remote sensing data on hotspots. The advantages of remote sensing include accurate identification and mapping of burnt areas, regular monitoring, rapid data acquisition, and historical data analysis. The MODIS sensor, specifically designed for fire monitoring, offers enhanced fire detection and diagnosis, multiple channels for qualitative and quantitative analysis, and precision positioning capabilities. The research results presented in the analysis contribute to the understanding of fire incidents and hotspot occurrences within the four national parks studied. This paper suggests the optimization of early detection of forest and land fires through the utilization of Artificial Intelligence (AI), presenting it as a recommendation for future endeavors. The research emphasizes the significance of implementing efficient policies and management strategies to effectively tackle the challenges associated with fires in these ecologically significant areas.
