In this study, we have developed a novel method for generating forest type map using airborne laser scanner data. An object-based approach is implemented for forest type classification, and feature images extracted from laser data are utilized for image segmentation and classification. Four types of feature images, namely, Digital Height Model, Reflectance Intensity, Ratio of First Pulse to Total Pulse, and Binary Reflectance Intensity are generated from the laser data. The first three ones are selected for image segmentation, and all the four feature images are used for classification. To assess the effectiveness of the proposed method, the classified map has been verified by comparing against a visual interpretation map. Our evaluation confirmed that by utilizing the proposed method we could achieve classification results close to the result of visual interpretation results.

Identifying the vulnerable area is important for forest resource management. In this research, with support of remote sensing and GIS, an environmental vulnerability index is constructed to describe the vulnerability status in Daxing'anling area, China. Thirteen variables related to exposure, sensitivity and adaptive capacity of ecosystem were integrated to a comprehensive index using spatial principal component analysis. The results show that the degree of vulnerability distributed unevenly through the whole area. The highest value of environment vulnerability index (EVI) is approximately 0.8 which appeared in the southern and central part while the lowest is about 0.012 that located in the eastern region. According to the numerical results, the vulnerability is classified into five levels : potential, slight, light, medium and heavy level. The distribution map of environmental vulnerability might provide a more rational decision-making basis for effective forest management.