TITLE:
Predicting the Seasonal NDVI Change by GIS Geostatistical Analyst and Study on Driver Factors of NDVI Change in Hainan Island, China
AUTHORS:
Shaojun Liu, Bin Wang, Jinghong Zhang, Daxin Cai, Guanhui Tian, Guofeng Zhang
KEYWORDS:
NDVI, GIS Geostatistical Analyst, MODIS, Driving Factors, Correlation Coefficients
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.4 No.6,
June
15,
2016
ABSTRACT: 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.