基于MODIS数据和BFAST方法的植被变化监测

Monitoring the changes of vegetation based on MODIS data and BFAST methods

植被是联结土壤、大气和水分的自然"纽带",在全球气候变化研究中具有"指示器"的作用。对归一化植被指数(normalized difference vegetation index,NDVI)时间序列分析,可以为相关部门的工作和决策提供更好的支持。使用MODIS NDVI数据结合BFAST(breaks for additive seasonal and trend)方法实现对老哈河流域及周边地区的植被变化监测,并确定其NDVI时间序列出现突变点的时间节点。结合气象数据以及数据本身的质量作为影响因子,分析出现突变点的主要原因。研究结果表明,降水量、相对湿度、温度、日照时数、流域蒸发量与NDVI变化趋势呈正相关,风速与NDVI变化趋势相关性很小。降水量对NDVI变化的影响具有滞后性,滞后时间与降水量大小有关。

Vegetation is a natural ＂link＂ which links soil, air and water and an ＂indicator＂ in global climate change research. Using normalized difference vegetation index （NDVI） time -series analyses, we can provide better support for the relevant researches and decision - making. Using MODIS NDVI data binding with BFAST （breaks for additive seasonal and trend） method, the authors implemented monitoring vegetation dynamics in the Laohahe River Basin and the surrounding areas, and identified its NDVI time -series abrupt change points occurring in time. The meteorological data and the quality of the data itself were also used as an influence factor analysis of the main reason for the breakpoints. It is found that precipitation, relative humidity, temperature, sunshine and water evaporation are positively correlated with NDVI trends, while wind speed is less correlated with NDVI trends. What＇ s more, the precipitation and sunshine hour impact on NDVI change has a certain lag.