基于多时相协同变化检测的耕地撂荒遥感监测

针对地表覆被复杂、地块破碎等原因导致的撂荒地提取精度较低问题，提出一种基于多时相协同变化检测的耕地撂荒信息提取方法。以河北省石家庄市鹿泉区为研究区，采用Sentinel-2A和Landsat 7多光谱影像，在野外样本的支持下，分析耕地各种覆盖类型的归一化植被指数（Normalized Difference Vegetation Index,NDVI）季相变化规律，以季节性撂荒、常年性撂荒、冬小麦、多年生园地为分类体系，构建多时相协同变化检测模型，开展研究区耕地撂荒状态遥感监测。研究结果表明：基于Sentinel-2A影像的季节性撂荒和常年撂荒耕地的分类精度分别为95.83%和96.55%；基于Landsat 7影像的季节性撂荒和常年撂荒耕地的分类精度分别为91.67%和93.10%;2019年鹿泉区季节性撂荒占耕地面积的4.7%，常年撂荒耕地占7.1%。利用该方法能够快速、准确地获取研究区耕地空间分布、面积等信息，对于不同分辨率的影像均具有较好的撂荒地提取精度。

Remote Sensing Monitoring of Cultivated Land Abandonment based on Multi-temporal Collaborative Change Detection

Aiming at the problem of low precision of abandoned land extraction caused by complex land cover and broken land， a method of abandoned land information extraction based on multi temporal collaborative change detection was proposed. Taking Luquan District， Shijiazhuang City， Hebei Province as the research area， the Normalized Difference Vegetation Index （NDVI） of various types of cultivated land cover was analyzed by using sentinel 2a and Landsat 7 multispectral images and supported by field samples Based on the classification system of seasonal abandonment， perennial abandonment， winter wheat and perennial garden， a multi temporal collaborative change detection model was constructed to carry out remote sensing monitoring of cultivated land abandonment in the study area. The results show that the classification accuracy of seasonal and perennial abandoned farmland based on Sentinel 2A image is 95.83% and 96.55% respectively； the classification accuracy of seasonal and perennial abandoned farmland based on Landsat 7 image is 91.67% and 93.10% respectively； the seasonal abandoned farmland accounts for 4.7% and perennial abandoned farmland accounts for 7.1% in Luquan District in 2019. This method can quickly and accurately obtain the spatial distribution and area information of cultivated land in the study area， and has good extraction accuracy for abandoned land in different resolution images.