Understanding land use and land cover changes has become a necessity in managing and monitoring natural resources and development especially urban planning. Remote sensing and geographical information systems are proven tools for assessing land use and land cover changes that help planners to advance sustainability. Our study used remote sensing and geographical information system to detect and predict land use and land cover changes in one of the world’s most vulnerable and rapidly growing city of Kathmandu in Nepal. We found that over a period of 20 years (from 1990 to 2010), the Kathmandu district has lost 9.28% of its forests, 9.80% of its agricultural land and 77% of its water bodies. Significant amounts of these losses have been absorbed by the expanding urbanized areas, which has gained 52.47% of land. Predictions of land use and land cover change trends for 2030 show worsening trends with forest, agriculture and water bodies to decrease by an additional 14.43%, 16.67% and 25.83%, respectively. The highest gain in 2030 is predicted for urbanized areas at 18.55%. Rapid urbanization—coupled with lack of proper planning and high rural-urban migration—is the key driver of these changes. These changes are associated with loss of ecosystem services which will negatively impact human wellbeing in the city. We recommend city planners to mainstream ecosystem-based adaptation and mitigation into urban plans supported by strong policy and funds.

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.

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982-2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer-Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness and climate variables and analyzed trends during summer (April-October), winter (November-March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05-0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to -0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. Increases in both the mean annual signal and annual cycle occurred in the forest, herbaceous, and cropland areas of India, Northwest China, and eastern Kazakhstan. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R = 0.84-0.96). •Temperature is the main climatic variable affecting vegetation greenness.•A downward trend in vegetation greenness was observed during summer (April-October).•Temperature showed an upward trend across many areas of Asia during the study period.•In winter, rainfall showed downward and upward trends in different parts of Asia.