Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan
Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan作者机构：Faculty of Geography and Environmental Sciences Northwest Normal University Lanzhou China Faculty of Agriculture University of Khartoum Khartoum Sudan Faculty of Agriculture Omdurman Islamic University Omdurman Sudan
出 版 物：《Journal of Geographic Information System》 (地理信息系统（英文）)
年 卷 期：2024年第16卷第2期
页 面：136-147页
学科分类：0710[理学-生物学] 071001[理学-植物学] 07[理学]
主 题：Climate Variables MODIS NPP Climate Change Correlation Coefficient Gadarif State Remote Sensing GIS Applications
摘 要：Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.
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