Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

编号 030029804

推送时间 20210705

研究领域 森林经理 

年份 2021 

类型 期刊 

语种 英语

标题 Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

来源期刊 REMOTE SENSING

期 第298期

发表时间 20210626

关键词 mixed forests;  very-high-resolution imagery;  object-based image analysis;  multiresolution segmentation;  semi-automatic classification;  forest mapping;  Italy; 

摘要 The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

服务人员 付贺龙

服务院士 唐守正

PDF文件 浏览全文