北京林业大学学报2018，Vol.40Issue(12)：38-48,11.DOI:10.13332/j.1000-1522.20180269
多时相高分辨率遥感影像的森林可燃物分类和变化分析
Classification and change analysis of forest fuels by multi-temporal high resolution remote sensing images
摘要
Abstract
[Objective]The classification of forest fuels based on high resolution remote sensing images is very important for the modern management of forest fire, but the study for classification by multitemporal high resolution remote sensing images is scanty at home and abroad. This study explored theclassification method of high-resolution images, the differences in classification results of multi-temporal forest fuels, and researched the relationship with altitude and slope change. [Method]According to the vegetation status and previous research results in the Jiufeng Forest Farm of Beijing, the fuels were classified by plant community, forest types and combustion characteristics. Then we studied and compared the spectral characteristic curves of different forest fuel types, and established the connection between remote sensing images and forest fuels. The remote sensing images of May, August and October of GF-1 were used as the original data. The classification of forest fuels was carried out by the support vector machine (SVM) algorithm, random forest (RF) and the decision tree method based on CART of EnMAP-box in the Jiufeng Forest Farm, and the classification results were as follows: coniferous forest, broadleaved forest, coniferous and broadleaved mixed forest, shrub forest and non-forest land. After describing their characteristics separately, the optimal classification method was applied into multitemporal remote sensing images, and the change detection algorithm was used to determine the changes among the types of forest fuels during non-fireproof period (May to November) . At the same time, we divided the digital elevation model (DEM) into four categories (1 (＜ 250 m) , 2 (250-500 m) , 3 (500-750 m) and 4 (＞ 750 m) ) . Similarly, we divided slope into three types: gentle slope (＜ 15°) , slope (15°-35°) , steep slope (＞ 35°) , and used the Jenks method to calculate the percentage of land area change for each category of elevation and slope, respectively. Then we studied the changes in the classification results of forest fuels with changes of altitude and slope. [Result]The results showed that the spectral characteristics of the five forest fuel categories were well differentiated. The SVM classification was the most accurate. The penalty parameter (C) was 1 000 and the kernel parameter (g) was 10, which made the SVM classification model optimal. The overall classification accuracy was91. 88％, the kappa coefficient was 0. 89. And the accuracy was improved relative to RF and CART.The classification accuracy was 2. 72％ and 9. 36％ higher than RF and CART, respectively. The types of forest fuels during non-fireproof period (May to November) had certain change regularity, and there were no significant changes in coniferous and mixed forest which belong to moderate stable types, keeping93. 74％ and 94. 87％, respectively. In contrast, broadleaved and shrub forest changed greatly by14. 64％ and 13. 36％, respectively; with the increase of altitude and the change of slope, the land area of forest fuels had also changed. The area with altitude above 500-750 m and slope of 16°-35° had the largest change, reaching more than 20％. [Conclusion] In the classification of forest fuels with multitemporal high-resolution remote sensing images, the SVM classification method can classify fuels better, and with the change of time, altitude and slope, the change of forest fuel area has certain regularity.From May to October, broadleaved forests and shrubs vary most at altitudes of 500-750 m and slopes of 16°-35°.关键词
森林可燃物分类/支持向量机/遥感/多时相Key words
forest fuel classification/support vector machine/remote sensing/multi-temporal分类
农业科技引用本文复制引用
陈冀岱,牛树奎..多时相高分辨率遥感影像的森林可燃物分类和变化分析[J].北京林业大学学报,2018,40(12):38-48,11.基金项目
国家林业局科技推广项目(2015-04) （2015-04）
森林培育与保护省部共建重点实验室建设项目 （）
