期刊
REMOTE SENSING
卷 13, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/rs13132508
关键词
mixed forests; very-high-resolution imagery; object-based image analysis; multiresolution segmentation; semi-automatic classification; forest mapping; Italy
类别
资金
- ERA-Net SUMFOREST project REFORM Mixed species forest management. Lowering risk, increasing resilience (Italian ministry of agricultural food and forestry policies) [31950/7303/16]
The study evaluates 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. The findings demonstrate that very high resolution images can be reliably used to detect the fine-grained pattern of rare mixed forests, supporting the monitoring and management of forest resources on fine spatial scales.
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 m(2). 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.
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