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Vegetation plays a very important role conserving the natural environment and improving the living environment for humans. Thus, investigating spatio-temporal changes in vegetation is a crucial indicator to understand natural environmental changes. We explored the spatio-temporal trends of changing vegetation cover in Mongolia from 2002 to 2010 by investigating changes in the normalized difference vegetation index (NDVI) with rainfall. The 16-day composite NDVI time series Terra MODIS (MOD13A2) and the gridded rainfall data of the Inter-governmental Panel on Climate Change (IPCC) were used to identify the relationship between vegetation dynamics and rainfall. Residual NDVI trends were computed. Following our general procedure, monthly NDVI and annual rainfall were correlated for each pixel. The resulting regression equation represented the statistical association between observed NDVI and rainfall, and the model predicted NDVI according to rainfall. Residuals of NDVI (differences between the observed and predicted NDVI) for each pixel were calculated, and the trends in these residuals was analyzed by linear regression. From the 12 months NDVI and rainfall values they determine a linear regression line for each pixel. The positive or negative slope of this line is considered to reflect an increase or decrease in green biomass. Our results show that vegetation cover changes in Mongolia were caused by both natural factors and human activities. The natural vegetation change, such as those in forests was influenced by climate change, whereas human activities were the main reason for the change in vegetation planted.

Abstract:In this paper, we used Landsat thematic mapper (TM) and enhanced thematic mapper (ETM) data from 1990, 2002, and 2011 to analyze the spatial and temporal patterns of desertification using seven factors; the normalized difference vegetation index (NDVI), the topsoil grain size index (TGSI), land surface albedo, the topographic wetness index (TWI), land surface temperature (LST), the perpendicular drought index (PDI), and the elevation of Hogno Khaan, which lies in a semiarid region of central Mongolia. We normalized the indicators, determined their weights, and defined five levels of desertification; none, low, medium, high, and severe. Sets of rules were constructed, and a multi-criteria evaluation (MCE) approach was used to assess desertification and test the correlations between the seven variables in comparison to the different levels of desertification, with field and reference data used for accuracy. We provide a review of the literature on MCE applied to desertification assessment issues based on satellite data. At the first step, major desertification factors were computed for satellite data. The next step was the construction of pairwise comparison matrix. Then, the weight of each factor was determined by the contribution of an analytical hierarchy process. Finally, the susceptible areas to desertification in the study area were identified using a multi-criteria evaluation method. We found that more than 15% of the total land area in Hogno Khaan suffered from severe desertification in 2011, increasing from 7% in 1990. Our analysis showed that the highest correlations were between TGSI and albedo, PDI and TGSI, and PDI and albedo at all levels of desertification. LST was less strongly correlated with TGSI, albedo, and PDI. The correlation of TWI with PDI and NDVI in the non-and low desertification areas produced R values of 0.15 and 0.58, respectively. The correlation analysis indicated a significant positive correlation between TWI and both NDVI and PDI for all years in non-and low desertification areas. Comparing elevation and NDVI, the highest correlation was found for severe desertification in 2002, although correlations for severe desertification were lower in 1990 and 2011.

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