Mao Xue Gang, Wei Jing Yu
School of Forestry, Northeast Forestry University, Harbin 150040, China.
Ying Yong Sheng Tai Xue Bao. 2017 Nov;28(11):3711-3719. doi: 10.13287/j.1001-9332.201711.012.
The recognition of forest type is one of the key problems in forest resource monitoring. The Radarsat-2 data and QuickBird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multi-source remote sensing data. In the process of object-based classification, three segmentation schemes (segmentation with QuickBird remote sensing image only, segmentation with Radarsat-2 data only, segmentation with combination of QuickBird and Radarsat-2) were adopted. For the three segmentation schemes, ten segmentation scale parameters were adopted (25-250, step 25), and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result, three forest types of Chinese fir, Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine (SVM) classifier with Radial Basis Foundation (RBF) kernel according to different feature combinations of topography, height, spectrum and common features. The results showed that the combination of Radarsat-2 data and QuickBird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or QuickBird remote sensing image only. The optimal scale parameter for QuickBirdRadarsat-2 segmentation was 100, and at the optimal scale, the accuracy of object-based forest type classification was the highest (OA=86%, Kappa=0.86), when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data, but also had a practical significance for forest resource investigation and monitoring.
森林类型识别是森林资源监测中的关键问题之一。利用Radarsat - 2数据和QuickBird遥感影像进行基于对象的分类，研究基于多源遥感数据组合的基于对象的森林类型分类与识别。在基于对象的分类过程中，采用了三种分割方案（仅用QuickBird遥感影像分割、仅用Radarsat - 2数据分割、QuickBird和Radarsat - 2组合分割）。对于这三种分割方案，采用了十个分割尺度参数（25 - 250，步长25），并进一步使用修正欧氏距离3指数来评估分割结果，以确定最优分割方案和分割尺度。基于最优分割结果，根据地形、高度、光谱和共同特征的不同特征组合，使用具有径向基核（RBF）的支持向量机（SVM）分类器对杉木、马尾松和阔叶林三种森林类型进行分类和识别。结果表明，与仅使用Radarsat - 2数据或QuickBird遥感影像相比，Radarsat - 2数据和QuickBird遥感影像的组合在基于对象的森林类型分类方面具有优势。QuickBird与Radarsat - 2组合分割的最优尺度参数为100，在最优尺度下，当使用从两种数据资源中提取的所有特征时，基于对象的森林类型分类精度最高（总体精度OA = 86%，Kappa系数 = 0.86）。本研究不仅可为利用多源遥感数据进行森林类型识别提供参考，而且对森林资源调查与监测具有实际意义。
