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.
Highlights
During the last years, powerful and versatile geostatistical tools have been developed for geoscience applications
ArcGIS Geostatistical Analyst is an extension to ArcGIS Desktop (ArcInfo, ArcEditor, and ArcView) that provides a variety of tools for spatial data exploration, anomaly identification, optimum prediction, uncertainty evaluation, and surface creation
By far the most extended method for multitemporal compositing is the Maximum Value Composite (MVC) [10], which is computed by selecting the image values of the day when the Normalized Difference Vegetation Index (NDVI) is maximum in the time series
Summary
Powerful and versatile geostatistical tools have been developed for geoscience applications. The NDVI predicting was accomplished by the ArcGIS Geostatistical Analyst model. ArcGIS Geostatistical Analyst was applied to analyze the seasonal NDVI change trend by the Kriging method, based on the MODIS data of limited NDVI values. NDVI is a good indicator of the ability for vegetation to absorb photo synthetically active radiation Environmental factors such as soil, geomorphology and vegetation can influence NDVI values. Using the temperature, precipitation, and land-cover to discuss the relation of NDVI in Hainan Island is useful to confirm the influenced role on vegetation index change, which will benefit to study the relationship between vegetation with climate change. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island and check the predicting method. The correlation of temperature, precipitation, and land cover with NDVI change was explored and discussed
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