Author(s)

Shaojun Liu^*, Bin Wang, Jinghong Zhang, Daxin Cai, Guanhui Tian, Guofeng Zhang

South China Sea Meteorology and Disaster Mitigation Research Key Laboratory, Hainan Institute of Meteorological Sciences, Haikou, China.

As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.

NDVI, GIS Geostatistical Analyst, MODIS, Driving Factors, Correlation Coefficients

Liu, S. , Wang, B. , Zhang, J. , Cai, D. , Tian, G. and Zhang, G. (2016) Predicting the Seasonal NDVI Change by GIS Geostatistical Analyst and Study on Driver Factors of NDVI Change in Hainan Island, China. Journal of Geoscience and Environment Protection, 4, 92-100. doi: 10.4236/gep.2016.46008.