| Forest resource is the important material foundation of national sustainable development. It is the rare , renewable resources in the economic construction and ecological environment construction, having the ecological function of water and soil conserving,wind-defensing,sand-binding, air purifying and weather egulating, etc. The forest resource state of growth and decline influence not only the sustainable development of the regional economy , but also the changes of region and even global environment.Therefore,it has attracted attention extremely.Possessing the advantages of macroscopic information, realtime,convenience,periodicity and low cost, remote sensing is suitable for forestry with the features of extensity,complexity and difficulty to access, and has already become the ideal means to study forest reserves state. At present, in the application of forest, the ability to gain multi-sources remote sensing data is stronger and stronger, but because of the integrity of remotely sensed information and the mechanism complexity of remote sensing, information process technology lag behind information acquisition technology relativly. It is far away ripe for the forest types classification by remote sensing basis of different areas , seasonal aspect and different background characteristics. The application study on the forestry of the high-resolution IKONOS satellite image has been reported rarely.So, this research regards mountain area of the northwest of Zhejiang( two cases in Chunan and Linan are clicked) as the research objects, some key problems are discussed about image processing, band combination selection, image merge, image interpretation, and the efficiency of forest classification by remote sensing. It will be significant effect to promote the forest remote sensing application in Zhejiang.The main content and conclusion of this research is summed up as follows:(1) Analyzing TM and IKONOS images amount,standard deviation and the correction between bands. Meantime, analyzing spectrum features of main forest types. Based on various qualitative analysis and quantitative calculation of the value of OIF , the different band combination means have been discussed and TM541 and IKONOS421 have been proposed in forest classification because of possessing the largest amount of information and the least redundancy .The feature images were extracted through principal component transformation.(2) Different merge methods such as HIS, Brovey, and principal component transformation have been discussed. Based on correlation coefficient quantitative analysising, conclution was given that principal component transformation is a kind of fine merge method .The merged IKONOS image posses both spectrum information of the multi-spectrum image and structure information of the high resolution image, which is benefit to forest types classification.(3) With the support of GIS data ,various forest classification technology by remote sensing have been tested . The results indicate that supervised classification based on original six bands images ( except the hot wave band) have the highest accuracy for TM image .But if forest types were classified to the fourth level, The overall accuracy can only be up to 70.67% and the area relative precision is only 60.13%. If were classified to the second level, the area relative precision may up to 88.63% then. In supervised classification, an optimal selection method of training sample that combine thematic vector and seed pixel expanding tightly has been proposed , which can optimize and purify the training sample. As to the IKONOS image ,the human- computer interaction interpretation is most effective, the area relative precision of which can be up to 97.60%.(4) Research indicate IKONOS image is suitable for using in forest resource inventory , which meets the precision request totally of producing 1:5000 forest map and will be good economic benefits. It can save about 70 yuan every sq. km forest land and about 4,580,000 yuan in the whole Zhejia...
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