Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI & TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

Aim of the research is to test multi-criteria method for suitability valuation with GIS method in cropland of

Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.

The plants in the Gobi desert region are sparsely distributed on a vast bare field, it is extremely difficult to accurately observe from the satellite. For the reason, the reflectance of dry soil is very high and the reflectance of slightly distributed plants is eliminated by soil reflection. As a result, the pixel’s NDVI value of desert plants shows a smaller value than the ground measurement. In this study, we succeeded to analyze Turing pattern of vegetation abundance using the method of spectral un-mixing for satellite data of the Gobi plants. It is shown that the fraction of the vegetation endmember after pixel un-mixing has a remarkably high correlation (R2=0.51 in Landsat 8 and R2=0.41 in Sentinel 2) with the ground true value of vegetation coverage. Гандуу бүсийн ургамлын орон зайн тархалт: тюрингийн хэлбэршил ус гачиг нөхцөлд илрэх нь Говь цөлийн ургамалшил нь асар уудамгазар дээр алаг цоог мөртлөө тачир сийрэг тархдаг тул хиймэл дагуулаас нарийвчлан ажиглахад хэцүү байдаг. Гол шалтгаан ньзайнаас тандсанцацраг туяа нь хуурай нүцгэн хөрсний маш өндөр ойлтын нөлөөгөөртачир сийрэг ургамлын цацрагийн ойлт сул, шингээлт нь дарагдаж илэрдэггүй байдал юм. Үүний үр дүнд цөлийн ургамлын хйимэл дагуулын зураг дээрх утга нь газрын гадаргуу дээр шууд хийсэн хэмжилтээс бага утгыг харуулж байдаг. Энэхүү судалгаанд бид хиймэл дагуулын мэдээг спектр үл холих аргыг ашиглан боловсруулжговийн ургамалшил Тюрингийн хэлбэршлийн дагуу тархаж байгаа зүйтогтлыг батлав. Спектр үл холих аргаар тогтоосон ургамлан бүрхэвчийн зайнаас тандсан мэдээ нь газрын бодит хэмжилтийн утгатай харьцангуй сайн хамааралтай (Ландсат 8-д R2 = 0.51, Сентинел 2-т R2 = 0.41) байна. Түлхүүр үг: Спектр үл холих арга,Говь цөлийн ургамлын тархац

The paper addresses planning issues of hay land based on new methods of suitability and need’s assessment of local area.  In order to develop the hay land future use and planning for 2023 of Erdenetsagaan soum, Sukhbaatar aimag, the assessment of the haymaking situation, the number of livestock growth and needs of nutritional resources for animal husbandry were calculated. According to the tested new suitability assessment of hay land, 4.37 percent or 74164 ha of land are most suitable, and 29.21 percent or 496014.3 ha are unsuitable. In order to calculate the nutritional needs of livestock in the future, the demand of green fodder for hay cut is estimated at 6712 t in 2023. The need for this fodder supply requires 8391 ha hay fields in 2023. Thus, demand (6712 t hay from 8391 ha of field) of green fodder will be supplied after proper implementation of plan (74164 ha suitable field to hay land usage).