文献类型：期刊文献

英文题名：Integrating Otsu Thresholding and Random Forest for Land Use/Land Cover (LULC) Classification and Seasonal Analysis of Water and Snow/Ice

作者：Sun, Xuexia[1,2] Li, Xiaoyao[1,2] Tan, Bingxiang[1,2] Gao, Jian[3] Wang, Lei[3] Xiong, Shimei[1,2]

第一作者：Sun, Xuexia

通信作者：Li, XY[1];Li, XY[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Xinjiang Acad Forestry Sci, Inst Modern Forestry, Urumqi 830063, Peoples R China

年份：2025

卷号：17

期号：5

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20251118051456);Scopus(收录号：2-s2.0-86000793048);WOS:【SCI-EXPANDED(收录号:WOS:001442456800001)】；

基金：This research was supported by the Key R&D Task Special Project of the Xinjiang Uygur Autonomous Region for the establishment and demonstration of the application of the meteorological disaster monitoring and early warning service platform for forestry and fruit (Grant No. 2023B02004-3) and the Research Fund of the Chinese Academy of Forestry for the study on the remote sensing response mechanism of carbon sources and sinks in the forest management cycle (Grant No. CAFYBB2023MA013).

语种：英文

外文关键词：LULC; random forest; Otsu thresholding; combined model; seasonal dynamics

摘要：Accurate land use/land cover (LULC) classification and the detection of seasonal dynamics are crucial for effective environmental monitoring and resource management. To improve the precision and temporal resolution of regional LULC classification products, this study combined the Otsu threshold method and Random Forest algorithm to generate a 10 m-resolution land cover classification map for Wensu County based on Sentinel-2 imagery, with a particular focus on orchard categories, and investigated the seasonal dynamics of LULC between winter and summer. The results show that the overall accuracy (OA) of the water and snow/ice models was 85.50%, with a Kappa coefficient of 0.8088; for the vegetation model, the OA was 93.77%, with a Kappa coefficient of 0.8755. Feature importance analysis indicated that terrain features were key factors in improving classification performance. Seasonal dynamics analysis showed that the snow/ice coverage area in winter increased by 6379.18 square kilometers compared to that in summer, with 5252.85 km2 of bare land and 910.66 km2 of grassland being covered by snow/ice. Meteorological data analysis revealed that land cover changes caused by winter snowfall were primarily concentrated in areas where temperatures exceeded -8 degrees C, while land cover changes were smaller in areas with either low or high precipitation. These findings provide valuable data support for regional resource management and agricultural development.