Predicting the Seasonal NDVI Change by GIS Geostatistical Analyst and Study on Driver Factors of NDVI Change in Hainan Island, ChinaReport as inadecuate




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

KEYWORDS

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

Cite this paper

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.





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

Source: http://www.scirp.org/



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