基于无人机遥感与随机森林的荒漠草原植被分类方法

Vegetation Classification of Desert Steppe Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest

荒漠草原是草原中最旱生的类型,属于草原的极限生态状态,也是气候变化和生态系统演变的预警区。利用无人机高光谱遥感技术快速、准确地提取荒漠草原草地植被类型,对动态监测草原生态安全和合理开发草地畜牧业具有重要意义。以无人机搭载高光谱成像系统采集内蒙古荒漠草原遥感图像,获得具有高空间分辨率和高光谱分辨率的图像;通过光谱连续统去除变换,增强草地植被之间的光谱差异,并构建植被指数;采用分步波段选择法选择荒漠草原植被的特征波段,实现高光谱数据降维;构建融合光谱特征、植被特征、地形特征和纹理特征等24个变量的随机森林分类模型,并与支持向量机(SVM)、K-最近邻(KNN)和最大似然分类(MLC)法进行比较。结果表明,在4种分类方法中随机森林分类算法分类效果最好,总体分类精度达到91.06%,比SVM、KNN和MLC等机器学习算法分别高7.9、15.61、18.33个百分点,Kappa系数达到0.90,比SVM、KNN和MLC算法分别高0.13、0.23和0.26。无人机高光谱低空遥感和随机森林算法的结合为荒漠草原草地植被分类提供了新途径。

Desert steppe is the most arid type of grassland.As the transition between grassland and desert,desert steppe constitutes the fragile zone of ecological environment,and it is also the early warning area of climate change and ecosystem evolution.Using unmanned aerial vehicle(UAV)hyperspectral remote sensing technology to extract grassland vegetation types more quickly and accurately is of great significance to the monitoring of grassland ecological security and the rational development of grassland animal husbandry.The HEX6 eight rotor UAV was utilized,on which the Pika XC2 hyperspectral imager(spectral wavelength:400~1000 nm,spectral resolution:1.3 nm)was mounted to collect remote sensing images of desert steppe in Inner Mongolia,China.The hyperspectral images with a spatial resolution of 2.1 cm were obtained by the UAV flying at a height of 30 m from the ground.Spectral difference was enhanced by spectral continuum removal transformation and vegetation indices were constructed by the spectra after continuum removal transformation.The step by step band selection method was used to select vegetation feature bands for reducing data dimension.A random forest classification model with 24 variables,including spectral features,vegetation features,terrain features and texture features was constructed and compared with support vector machine(SVM),K-nearest neighbor(KNN)and maximum likelihood classification(MLC).The random forest classification algorithm(SBS_RF)proposed had the best classification effect among the four classification methods.The overall classification accuracy was 91.06%,which was 7.9,15.61 and 18.33 percentage points higher than that of SVM,KNN and MLC,respectively.Kappa coefficient was 0.90,which was 0.13,0.23 and 0.26 higher than that of SVM,KNN and MLC,respectively.The results showed that the combination of UAV hyperspectral remote sensing and SBS_RF algorithm provided a technical means for rapid investigation of desert grassland vegetation types and quantitative indicators for grassland ecological monitoring and animal husbandry management.