【作者】 施拥军；

【导师】 王珂；

【作者基本信息】 浙江大学 ， 农业遥感与信息技术， 2003， 硕士

【摘要】 森林是国家可持续发展的重要物质基础，是经济建设和生态环境建设中不可多得的、可更新的再生资源，具有保持水土、涵养水源、防风固沙、净化空气、调节气候等生态功能，森林资源状况及其消长变化，不仅影响区域经济的持续发展，而且还影响地区乃至全球环境的变化，因此倍受人们关注。 遥感技术具有宏观、动态、便捷、可周期重复和成本低等诸多优点，非常吻合森林资源辽阔性、复杂性、通达性差等特点，已成为研究森林资源状况的理想手段。目前，在森林遥感应用中，多源遥感数据的提供能力越来越强，但由于遥感信息的综合性、复杂性，使得遥感信息处理技术却相对落后。基于不同区域、不同季相和不同背景特征的森林遥感分类技术远未成熟，高分辨率IKONOS卫星影像在林业上的应用研究更是少见报道。 本文以浙西北山区(淳安和临安两个研究点)为研究对象，利用IKONOS和TM遥感数据针对森林遥感分类中的影像处理、波段组合选择、多源影像融合、影像分类方法和森林遥感分类效益等问题开展研究，对推进浙江地区森林遥感应用具有重要意义。 研究的主要内容和结论归纳如下： (1)分别对研究区TM和IKONOS影像进行了各波段信息量、标准差、相关性等统计特征及森林类型光谱特征分析。通过定性分析和最佳指数因子OIF值计算，表明，TM541、IKONOS421是一种最佳的三波段组合方式，具有最大的信息量和最少的信息冗余度。研究还对两种影像做了主成分分析，提取出主成分特征影像。 (2)研究进行了HIS、Brovey、主成分变换等多种影像融合试验，通过融合前后影像的目视比较和相关系数定量分析得出，主成分变换法是一种优良的影像融合方法。融合后的IKONOS影像，既具有丰富的色彩，又具有清晰的纹理结构，有利于森林类型分类。 （3）在 GIS数据支持下，进行了多种森林遥感分类试验，精度分析表明：对于TM影像来说，基于原始六波段影像（热波段除外）的监督分类效果最佳，但若分至四级森林类型，其分类总体精度只能达 70．67％，面积相对精度仅为60．13％，与实用要求相去甚远，如果分至二级，则面积精度可以达到 88．63％。在监督分类中，研制了一种专题矢量图件与种子像元扩展紧密结合的训练样本优选法，可使训练样本得以优化和纯化。对于 IKONOS高分辨率融合影像，人机交互目视解译是一个最佳方法，森林四级类型的目视解译面积精度可达 97．60％。 门）研究表明 IKONOS适宜用于森林面积资源本底清查，完全满足制作．：5000 山林现状图的精度要求，并具有良好的经济效益，每平方公里林业用地可节省经费约70元，整个浙江省完成一次森林资源调查，可节省经费约458万元（不考虑遥感数据的其它综合利用价值）。更多还原

【Abstract】 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更多还原