The Health Classification of Pinus densata Typical Forest Ecosystem in Alpine Region of Northwestern Yunnan Based on Hyperion Data

The pure Pinus densata forest of the typical forest ecosystem in the alpine region of ShangriLa is selected as the research subject and Hyperion images are taken as the data source. Based on using the sensitivity analysis method to screen out the Pinus densata forest′s health evaluation index system, the forest health index comprehensive evaluation model of the research zone is set up based on image elements. By using the analytic hierarchy process method and the Delphi method, this paper determines various indexes′ weighted values and combines the investigated data of the ground surface to classify the research zone′s forest health indexes into four levels, including healthy, subhealthy, moderately healthy and unhealthy. Results have shown that the forest health index of the research zone′s remote images is in the range of -025~7534,and the average value is 3323, the research zone′s forest is generally in the subhealthy state. The unhealthy area, moderately healthy area,subhealthy area and healthy area occupies of the forest′s total area is 1624%, 3160%, 2580% and 2636%, respectively.