基于GF-2影像对恰西国家森林公园的遥感分类

【目的】 选用面向对象的监督分类法,对研究区域的天山云杉林进行遥感分类,选取一种分类效果最佳的方法,为该区域的林地资源调查、动态监测评价提供依据。【方法】 基于高分二号(GF-2)遥感影像数据,借助ESP 尺度评价工具和目视解译相结合,筛选研究区各地物最优分割尺度,利用3种不同分类方法在此基础上进行遥感分类。【结果】 研究区内的水体、道路、其他用地、林地和草地的最优分割尺度,分别为390、372、316、296、246;其次在各地物最优分割尺度下,比较最邻近分类、结合矢量数据分类和阈值分类3种方法,经过精度评估发现,3种分类方法的 Kappa 系数和总体精度值分别为0.760 7、0.782 0、0.840 6和0.814 8、0.830 5、0.876 5。【结论】 阈值分类方法优于其他2种方法,选用更为优良的阈值分类方法引入解决该地区林地资源调查是可行的。

Study on the Classification of Qiaxi National Forest Park Based on the GF-2 Image

【Objective】 To select the object-oriented supervised classification method for remote sensing classification of spruce forest in Tianshan Mountain, and to select a method with the best classification effect in the hope of providing the basis for the investigation of forest land resources and dynamic monitoring and evaluation in this area. 【Methods】 Based on GF-2 remote sensing image data, the optimal segmentation scale of objects in the study area was screened by the combination of ESP scale evaluation tool and visual interpretation. On the basis of three different classification methods, remote sensing classification was conducted. 【Results】 The optimal segmentation scales of water body, road, other land, forest land and grassland in the study area were 390, 372, 316, 296 and 246, respectively. Secondly, under the optimal segmentation scale of local objects, compared with the nearest neighbor classification, combined with vector data classification and threshold classification, it was found that the Kappa coefficient and overall precision of the three classification methods were 0.760,7, 0.782,0, 0.840,6 and 0.814,8, 0.830,5 and 0.876,5, respectively. 【Conclusion】 The threshold classification method is superior to the other two classification methods, which indicates that it is feasible to select a better threshold classification method to solve the problem of forest land resources investigation in this area.