基于GF-1遥感数据决策树与混合像元分解模型的冬小麦种植面积早期估算

Early Estimation of Winter Wheat Planting Area in Qingyang City by Decision Tree and Pixel Unmixing Methods based on GF-1 Satellite Data

我国西北地区耕地细碎,冬小麦种植面积提取时混合像元较多,所以将决策树和混合像元分解相结合可大大提高解译精度。以高时间分辨率及较高空间分辨率的GF-1卫星遥感数据为研究数据源。根据冬小麦和其他各类地物在不同时相数据上NDVI值的变化特性及特征值差异,建立决策树模型,快速高效地提取冬小麦像元。运用线性光谱混合模型,降低混合像元的影响,进一步精确提取冬小麦的种植面积。最后与实测样方的冬小麦种植面积数据进行比较,验证提取精度。结果表明:研究区内冬小麦种植面积提取精度达90%以上,Kappa系数接近0.8,可较为准确地反映出区域内冬小麦的分布情况。利用较高分辨率的遥感影像并结合决策树分类和混合像元分解可以较准确地提取耕地破碎地区作物种植面积,对开展早期农作物面积遥感监测有较大帮助。

In Northwest China,there are many mixed pixels in the winter wheat area,so the combination of decision tree and mixed pixel decomposition is of great significance to improve the interpretation accuracy. The data source of this result is GF-1 satellite data which excellent in the high temporal resolution and high spatial resolution.Based on the difference about variation characteristics and NDVI value for winter wheat and the other crops in different phase data,we build decision tree to extract winter wheat pixels preliminary.Then selected linear spectral mixture model,further analysis the previous data by mixed pixel decomposition,get the final planting area data more exactly.Compared with the winter wheat samples measurement data, calculate the extraction accuracy eventually. The result shows that the extraction accuracy of winter wheat planting area in the study area was more than 90%,Kappa coefficient is close to 0.8,can reflect the distribution of winter wheat in the region accurately. This study found that the method which combined with decision tree classification and pixel unmixing based on high resolution remote sensing image can extract the winter wheat planting area precisely,This is helpful for the development of crop area remote sensing monitoring.