Urban growth can profoundly change the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical factor for sustainable development strategies and it improves the urban residential environment and living quality. In recent years, the migration from rural area to urban is major driver for expansion of Ulaanbaatar and city was urbanized a quite rapidly in the past twenty years. The aggregation of high temperature occurs particularly in the more constructed area as much as evidenced in the city center, meanwhile the reduction and fragmentation of low temperature is even more apparent in suburban areas of city. Urban Heat Island (UHI) is the most significant issue towards urbanization and sustainable development of urban around in the world. This study analyzed the Land Surface Temperature (LST) differences on time series (1995 - 2016) of Ulaanbaatar, Mongolia and the relationships between LST and Normalized Difference Vegetation Index (NDVI) (green spaces in the city) using statistical analysis, such as box-plotting and regression. The analysis basically focused on the relationship between LST and change of green space area influenced by human activities. The analytical methodologies used in this study could help to calculate the urban thermal environmental functions under conditions of urban expansion and explore the local climate change of this city. In addition, the satellite images data based on Landsat time series between 1995 and 2016 were analyzed for change detection mapping. Landsat series images were gathered by thermal mapper (TM), enhanced thermal mapper plus (ETM+) and operational land imager (OLI). The results show that relationships between each impact become an important determinant of changes in the urban thermal environment. Furthermore, seasonal factor significantly impacts to the strength of this relationship and it is being to the highest contribution indices values in summer.
Land degradation is a serious environmental issue in the world. Both space and ground-based observations could be used to define the land changes and develop the assessment of land degradation. This study assessed land degradation in the Orkhon sub-province, the best representation sample in the prominent agricultural zone of Mongolia, using Landsat Thermal Mapper (TM) and Landsat Operation Land Imager (OLI) satellite images during the periods of 1990, 1994, 2000, 2006, 2010, and 2015. The land degradation of a region could be detected by changes in spectral indices and correlation of these indices. The most frequently used spectral indices include Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). These indices were selected as indicators for representing land surface conditions vegetation biomass, landscape pattern, micrometeorology and human activities. The land degradation analysis was described by descriptive statistics, correlation distributions and correlation coefficients of changes in index outputs. In addition, the validations of these indices were also verified by comparing LST and NDWI index values with in-situ, realtime climate data from 1984 to 2010. The Land Degradation Risk Mapping (LDRM) analysis shows that the agricultural and urban areas experienced degradation due to human activities and this has led to decline in the soil moisture in this region.
Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.