文献类型：期刊文献

中文题名：Integrating vegetation phenological characteristics and polarization features with object-oriented techniques for grassland type identification

作者：Bin Sun[1,2] Pengyao Qin[1,2] Changlong Li[3] Zhihai Gao[1,2] Alan Grainger[4] Xiaosong Li[5] Yan Wang[6] Wei Yue[1,2]

第一作者：Bin Sun;孙斌

机构：[1]Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing,China;[2]Key Laboratory of Forestry Remote Sensing and Information System,NFGA,Beijing,China;[3]School of Information Technology and Engineering,Guangzhou College of Commerce,Guangzhou,China;[4]School of Geography,University of Leeds,Leeds,UK;[5]Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,China;[6]Shandong Geographical Institute of Land Spatial Data and Remote Sensing Technology,Jinan,China

年份：2024

卷号：27

期号：3

起止页码：794-810

中文期刊名：Geo-Spatial Information Science

外文期刊名：地球空间信息科学学报(英文)

收录：CSCD:【CSCD2023_2024】；

基金：supported by the National Natural Science Foundation of China[grant number 42001386,42271407];within the ESA-MOST China Dragon 5 Cooperation(ID:59313).

语种：英文

中文关键词：Grassland types;vegetation phenological characteristics;polarization feature;integrated active and passive remote sensing;object-oriented classification

分类号：S812;TP79

摘要：Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.