基于NDVI时间序列数据的土地覆盖变化检测指标设计

Abstract

大中尺度土地覆盖格局及其变化检测是研究全球变化和能量平衡的重要 内容. NDVI时间序列数据在土地覆盖变化动态遥感监测中占据着重要地位. 针 对 NDVI时间序列数据 ,现有的土地覆盖变化检测方法和指标存在许多不足之 处. 本文在分析现有土地覆盖变化检测指标的基础上 ,设计了一个新的基于交叉 相关光谱匹配 (CCSM)和兰氏距离的变化检测指标. 该指标充分考虑了 NDVI时 间序列曲线形状和数值两个变化特征. 理论与实例检验结果表明本文设计的指 标能够较好的抑制各种干扰噪音的影响 ,正确检测真实的土地覆盖变化 ,是一种 较为理想的检测指标.

The normalized difference vegetation index (NDVI) time-series database, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, is increasingly being recognized as a valuable data source for extracting land cover and its change information at global, continental and large regional scale. However, existing approaches, such as principal component analysis (PCA) and change vector analysis (CVA) present considerable difficulties in taking full advantage the NDVI dataset for land cover change detection. Based on the assumptions that different land cover types have different NDVI temporal profiles and that the NDVI profile curve can be regarded as a spectrum in which an NDVI value for a certain date corresponds to on band value of this spectrum, we analyzed the existing change detection indexes and develop a new land cover change detection method based on Lance distance and a cross correlogram spectral matching (CCSM) technique. The new method was validated in the simulation experiments and a case study area of Beijing. From the results, we have demonstrated that the new method takes the shape and value features of NDVI profile curve into consideration. The relatively better performance of the new method can be attributed to two advantages: (1) the new method can discriminate long-term land cover changes form other changes by excluding false changes caused by vegetation phenology changes, climate events, atmospheric variability and sensor noise; (2) it is similarly sensitive to all kinds of land cover changes no matter where the changes have occurred. The better results compared with the CVA method suggest that the new method is effective and has potential for land cover change detection using an NDVI time-series dataset. Furthermore, it is worth noting that the method can not only be applied to NDVI datasets but also to other index datasets reflection surface conditions sampled at different time interval. It can also be applied to datasets for different satellites without the need to normalized sensor differences.