基于时序遥感影像的西南山区地块尺度作物类型识别

Identification of Parcel-Scale Crop Types in Southwestern Mountainous Area based on Time Series Remote Sensing Images

西南山区地块破碎且复杂,种植类型多样,且该地区多云多雨,可利用遥感数据有限,利用单一的光谱特征或植被指数难以实现作物的有效识别。物候特征能够反映作物的生长周期规律,可用于复杂地区作物的精细监测。本研究以重庆市潼南区为例,基于多时相中分辨率的Sentinel-2和Landsat8遥感影像,对地块尺度的NDVI、EVI等6个植被指数进行时间序列构建并提取其物候特征,通过特征优选构建多维数据,利用随机森林分类模型对柠檬、油菜、水稻、玉米等作物进行识别。结合植被指数与物候特征进行作物分类,总体精度达94.52%,Kappa系数为0.90,柠檬、油菜、玉米、水稻的精度分别为89.88%、87.30%、84.98%和95.57%。研究表明,利用时序遥感数据的植被指数与物候特征能够有效进行地块尺度的作物识别,为复杂种植结构地区获取大范围作物分布制图提供参考。

The distribution of crop planting structure can directly reflect the form and location of farmland,which is of great significance for the development of precision agriculture and inventory of agricultural resources.In the southwestern mountainous area in China,the farmland parcel is small and fragmented,with complex planting types,and thus the image visual characteristics are fuzzy and diverse.Due to the complexity of climate and terrain surface,it is difficult to identify crop types in this area.The arrival of the era of highresolution remote sensing has promoted the rapid development of object-oriented crop extraction methods.Classification based on object segmentation combing spectral,contextual,and other features can effectively overcome the shortcomings of pixel-based classification methods and better serve the field farming management and land ownership management.Therefore,this study uses high-resolution remote sensing images to extract farmland parcels.Taking parcels as the spatial constraints,six time-series vegetation indices including NDVI,EVI,RVI,DVI,GNDVI,and SAVI,are constructed at the scale of parcels based on multi-temporal mediumresolution Sentinel-2 and Landsat8 OLI remote sensing images.Their phenological characteristics are also extracted using these remote sensing images.The multi-dimensional data are constructed through the importance evaluation of classification features,and the random forest classification model is used to identify crop types(i.e.,lemon,rape,rice,corn,and other crops)combined with field sampling data.By comparing the classification accuracy using vegetation indices or phenological characteristics alone and that combining vegetation indices and phenological characteristics together,we found that the classification accuracy combining vegetation indices and phenological characteristics is the highest.The overall accuracy is 94.52%,and the Kappa coefficient is0.90.The accuracy of lemon,rape,corn,rice,and others is 89.88%,87.30%,84.98%,95.57%,and 98.05%,respectively.There are 90348 lemon parcels,30173 rape parcels,271606 corn parcels,146113 rice parcels,and 16406 other parcels,accounting for 10.96%,36.40%,32.94%,17.72%,and 1.99%of the total farmland in Tongnan area,respectively.The planting area accounted for 14.82%,18.44%,35.37%,29.92%and 1.45%,respectively.Our results indicate that the vegetation indices and phenological characteristics extracted from timeseries remote sensing data can be used for crop identification at the parcel scale,which provides reference for obtaining large-scale crop mapping in the region with complex planting structures.