基于无人机高光谱遥感的典型草原退化指示种识别

Identification of Typical Grassland Degradation Indicator Species based on UAV Hyperspectral Remote Sensing

利用无人机高光谱遥感数据技术快速、准确地提取典型草原植被类型,对动态监测草原生态安全具有重要的意义。为此,在退化较严重的白音锡勒牧场典型草原区,采集了空间分辨率为1.8 cm、光谱分辨率为4 nm,共有125个波段(450~950 nm)的高光谱影像。选主要退化指示种—冷蒿为识别目标,经微分变换和包络线去除等光谱变换处理,进行光谱特征差异分析发现冷蒿与其他绿色植被和背景土壤在500、550、670 nm处有明显的光谱差异,因此选择以上3个波段作为特征波段,构建了支持向量机(SVM)和随机森林(RF)的退化指示物种识别模型,并进行了精度验证。结果显示:SVM和RF的识别精度分别为96.92%和97.34%,Kappa系数分别为0.95和0.96。从结果可知,随机森林模型的识别精度更高,退化指示种的像元空间分布更接近自然状况,可以为监测典型草原退化指示种提供技术支持。

The use of UAV hyperspectral remote sensing data technology to quickly and accurately extract typical grassland vegetation types is of great significance for dynamic monitoring of grassland ecological security.In the typical grassland area of Baiyinxile pasture with severe degradation,hyperspectral images with a spatial resolution of 1.8 cm and a spectral resolution of 4 nm,with a total of 125 bands(450 nm to 950 nm)were collected.The main degradation indicator species,Artemisia cholerae,was selected as the identification target,and after differential transformation,envelope removaland other spectral transformations,the differences in spectral characteristics were analyzed.There are obvious spectral differences at 500 nm、550 nm、670 nm,so the above three bands were selected as characteristic bands,and the degradation indicator species identification model of Support Vector Machine(SVM)and Random Forest(RF)was constructed,and the accuracy was verified.The results show that the recognition accuracy of SVM and RF are 96.92%和97.34%,respectively,and the Kappa coefficients are 0.95 and 0.96,respectively.It can be seen from the results that the identification accuracy of the random forest model is higher,and the pixel spatial distribution of degraded indicator species is closer to the natural state,which can provide technical support for monitoring typical grassland degradation indicator species.