基于CART决策树的自然保护区植被类型分布研究

【目的】针对保护区监测需求,充分发挥GF-1 WFV影像的宽幅特点和面向对象、机器学习算法在遥感影像分类中的优势,提高保护区植被类型遥感监测的精度,为保护区管理决策提供依据。【方法】以甘肃省白水江国家级自然保护区为研究区,主要数据源包括GF-1 WFV多光谱数据、Landsat-8 OLI遥感数据、DEM数据、野外调查数据等。首先,对GF-1WFV数据进行多尺度分割,将研究区划分为诸多区域性的分割对象;然后,以分割对象为基本单元,研究光谱特征、几何特征、纹理特征不同组合情况下,基于CART决策树分类的结果;最后,利用训练样本建立基于TTA的精度检验,并基于混淆矩阵对分类结果进行分析。【结果】在多尺度分割过程中,形状因子、紧致度分别设置为0.2和0.5时地物边界显示较好;当形状因子和紧致度固定时,研究区最佳分割尺度为40。精度检验结果表明,基于CART决策树的保护区植被类型分类结果整体精度均在83%以上,Kappa系数在0.80以上,优于最邻近分类法和支持向量机分类算法,其中基于光谱特征、几何特征、纹理特征的CART决策树分类结果精度最高,总体精度为85.18%,Kappa系数为0.832 2,优于光谱特征分类、光谱特征结合几何特征分类的方法。【结论】基于CART决策树算法和面向对象方法的GF-1遥感影像分类方法适用于保护区植被类型分布研究,可有效辅助保护区监测工作。

Vegetation type distribution in nature reserve based on CART decision tree

ObjectiveIn order to meet the needs of nature reserve monitoring, improve the precision of remote sensing inventory on vegetation types, an object-oriented classification method and machine learning algorithms were developed in vegetation classification by GF-1 WFV remote sensing data.MethodThe study site is located in Baishuijiang National Natural Reserve, Gansu Province of northwestern China. The GF-1 WFV multispectral data, Landsat-8 OLI remote sensing data, DEM data and field survey data were employed as the key data sources. Firstly, the multiresolution segmentation of GF-1 WFV data was carried out, and the research area was divided into many polygon objects. Then spectral features, geometric features and texture features from polygon objects were extracted to vegetation classification using CART decision tree. Finally, the accuracy of classification was analyzed by error matrix based on TTA mask.ResultIn the multiresolution segmentation process, the shape factor and compactness were set to 0.2 and 0.5, respectively, the boundary of the polygon objects was identical with ground objects. When the shape factor and compactness were fixed, the optimal segmentation scale was 40. The accuracy results showed that the overall accuracy and Kappa coefficient were exceed of 83% and 0.80 in three CART decision trees, which was superior to KNN algorithm and SVM algorithm. Overall accuracy and Kappa coefficient of CART decision tree reached 85.18% and 0.832 2 by spectral, geometric and texture features, which was better than CART decision tree by spectral features or spectral combined with geometric features.ConclusionThe image classification based on CART decision tree algorithm and object-oriented classification method were suitable for vegetation classification in nature reserve by GF-1 WFV image, which could effectively assist the nature reserve monitoring.