基于Sentinel-2时序多特征的植被分类

植被分类是研究森林资源状况和动态变化规律的基础，利用遥感手段可以更加快速、准确地识别植被类型。以位于内蒙古赤峰市喀喇沁旗西南部的旺业甸实验林场为研究对象进行植被分类。采用分层分类的思想，首先根据植被物候特征选取植被生长旺盛时期的影像，计算归一化植被指数（normalized difference vegetation index，NDVI）并设定合适的阈值将研究区内的植被提取出来，剩余部分归为非植被。然后选取NDVI时间序列、最佳时相的Sentinel-2数据中10个波段的光谱反射率特征和主成分分析前3个分量的纹理特征作为分类特征，利用支持向量机分类器将研究区内的植被类型分为耕地、草地、常绿针叶林、落叶针叶林和落叶阔叶林五大类，并将分类结果与最大似然法、NDVI时序+光谱特征的分类结果进行对比分析。NDVI时序+光谱特征+纹理特征的多特征植被分类总体精度达87.64%，Kappa系数为0.85，分别比最大似然法和结合NDVI时序+光谱特征的分类总体精度提高了15.73%和14.61%，Kappa系数提高了0.20和0.18。其中常绿针叶林和耕地的分类结果与实地调查情况高度一致，分类精度分别达到95.65%和92.31%。从而得出:①基于多特征的分类方法有助于提高分类精度；②NDVI时序特征对于植被的区分具有很大帮助；③采用分层分类的思想，首先将研究区内的植被提取出来，可以排除非植被因素的干扰，有效提高植被类型的分类精度。

Vegetation classification based on a multi-feature Sentinel-2 time series

To provide a statistical basis for biological characteristics, ecological characteristics, and management value of different woodlands, and to provide effective data support for rational management of forests, a forest resources survey was conducted. Vegetation classification, using a hierarchical classification, was the basis for studying the status and dynamics of forest resources with vegetation types being identified quickly and accurately by means of remote sensing. The investigated site was Wangye Forest Farm in southwest Harqin Banner, Inner Mongolia. First, according to the phenological characteristics of the vegetation, images of vigorously growing vegetation were selected to calculate an NDVI and to set appropriate thresholds to extract the vegetation. Then using the NDVI time series, spectral reflectance characteristics of 10 bands in the best time of the Sentinel-2 data as well as textural features of the first three components from a principal component analysis were selected as classification features. The vegetation types in the study area were divided into the five categories of cultivated land, grassland, evergreen coniferous forest, deciduous coniferous forest, and deciduous broadleaf forest using a support vector machine classifier. Classification results were compared with the maximum likelihood method and the method of combining NDVI time series and spectral characteristics. Results showed that the overall accuracy of vegetation classification based on Sentinel-2 time series multi-features reached 87.64%. This was an increase of 15.73% compared to the maximum likelihood method and an increase of 14.61% compared to the method of combining NDVI time series and spectral characteristics. The Kappa coefficient was 0.85, which was an increase of 0.20 compared to the maximum likelihood method and an increase of 0.18 compared to the method of combining NDVI time series and spectral characteristics. Classification accuracy for the evergreen coniferous forest (95.65%) and cultivated land (92.31%) were highly consistent with the field survey. Thus, (1) combining multiple features was helpful for improving classification accuracy; (2) temporal characteristics of NDVI greatly helped to distinguish vegetation; and (3) by using the idea of stratified classification, the vegetation could be extracted first, thereby eliminating the disturbance of non-vegetation factors and effectively improving the classification precision of vegetation types.