Abstract
Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P- and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer’s accuracy of 96% and user’s accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring.
Highlights
Woodland is defined in Chinese woodland management regulations as low-density forest with an understory of shrubs and herbaceous plants
In the X-band interferometric elevation map, the edge of the large areas of land cover are continuous and the texture is uniform, e.g., the woodland and farmland, but some classes hardly exist in the classification result, e.g., buildings, and bare land are excluded from the result
This paper has explored the woodland extraction capability of Dempster-Shafer evidence fusion theory with P- and X-band polarimetric SAR (PolSAR) and interferometric SAR (InSAR) data
Summary
Woodland is defined in Chinese woodland management regulations as low-density forest with an understory of shrubs and herbaceous plants. Differing from forest with higher densities and areas of trees, woodland generally refers to low-density arbor and shrub forest with a smaller canopy closure [1]. Mapping and monitoring of woodland resources have been the subject of significant attention, as it offers an effective means for the measurement of carbon storage, which is highly relevant to climate change [2]. Many studies have been conducted using SAR techniques, e.g., woodland classification, extraction and mapping [3,4,5,6,7,8]. An increasing number of spaceborne and airborne SAR systems are being launched, which can provide abundant multi-frequency (X, C, S, L, P), quad-polarization, multi-interferometric mode data for woodland resource research
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