基于物候模型的作物种植面积变化监测方法

在利用多时相数据进行作物变化监测中，通常根据日历选取同一天或相近日期的遥感数据来减小植物季相变化所导致辐射差异所带来的“伪变化”。但是物候存在着年际变化，且与日历日期并不完全一致，不同年份的作物的生长发育状况可能不同。该文利用气候数据建立了作物物候指标模型，利用该模型计算出的熟度指数作为选择季相差别最小的两时期遥感数据依据，选择了用于顺义地区冬小麦播种面积变化信息提取的两时期数据，并利用NOAA AVHRR/NDVI序列数据对选择结果进行了验证。结果表明利用该物候模型选择的两时期遥感数据季相匹配较好。该方法为消除或减小变化监测过程中植被季相差异所导致的辐射误差提供了新思路并进行了有益探索。该文还利用该方法，以北京顺义地区为研究区，采用图像差值法提取1999～2000年冬小麦播种面积变化信息，其中变化像元提取精度达到90%。

Crop acreage change detection based on phenology model

Conventionally, land cover change detection with remote sensing was performed between images matched in Julian calendar dates. The phenology ofplants often brings errors into the final results, and it may make this methodfail in some cases. Detected changes may contain difference in phenology which is not the real change of land cover. To detect accurately crop acreage change, the fluctuation of crop phenology must be excluded. In the paper, the authors deviseda phenological index and applied it in the crop acreage change detection. Matching crop phonological stages allows the users to more strategically choose TM images for the analysis of crop acreage change. This methodology was applied in Shunyi,Beijing as a case study using NOAA AVHRR/NDVI time series data to validate the selection of TM images. The results prove that the method is efficient. In addition, the authors also adopt image difference method to extract the acreage change information of winter wheat from 1999 to 2000, using the theory of the method. The extractionprecision of changing pixel can reach 90%.