Extracting Natural and Artificial Forest Information Based on High Resolution Remote Sensing Data
高分辨率遥感影像天然林与人工林植被覆盖信息提取

The recognition of forest type provides an important basis for forest monitoring and ecological protection. Forest land can be easily distinguished from other land cover types in the multiple spectral bands of satellite images, but traditional methods based on single pixel and pure spectra, common vegetation indexes (NDVI, SAVI, EVI) or image texture feature values (HOMO, ENT, Con, DISS, SEC and VAR) cannot effectively distinguish between different types of forest vegetation. Although there are some differences between natural forest and plantation in GLCM texture feature value of RG, GB, the differences of GB is only 0.2 between natural forest and plantation and it is very difficult to classify natural forest and plantation. The objective of this study was to raise a new method for artificial-natural forest classification using multiresolution object-oriented effective segmentation based on optimal split scales, selecting samples, sobel edge detection and an extracting skeleton. We then construct a new feature index, the Texture Line Density Index (TLDI). The performance of the new method was tested with several geo-statistical texture measures from IKONOS multiple spectral images in South China. Compared with commonly used vegetation indexes and the GLCM texture index, the discrete degree of TLDI and classification result are superior. When TLDI>0.1, the area is natural forest vegetation coverage;when 0