Information about the spatial distribution of urban surface emissivity is essential for surface temperature estimation. The latter is critical in many applications, such as estimation of surface sensible and latent heat fluxes, energy budget, urban canopy modeling, bio-climatic studies and urban planning. This study proposes an estimation of urban surface emissivity, which is primarily based on spectral mixture analysis. The urban surface is assumed to consist of three fundamental land cover components, namely vegetation, impervious and soil that refer to the urban environment. Due to the complexity of the urban environment, the impervious component is further divided into two land cover components: high-albedo and low-albedo impervious. Emissivity values are assigned to each component based on emissivity distributions derived from the Landsat8. Following the proposed method, by combining the fraction of each cover component with a respective emissivity value, an overall emissivity for a given pixel is estimated. The methodology is applicable to visible and near infrared satellite imagery. Therefore it could be used to derive emissivity maps from most multispectral satellite sensors. The proposed approach was applied to Landsat8 multispectral data for the city of Darkhan-Uul, Mongolia. Emissivity, as well as land surface temperature maps in the spectral region of 10.6 - 11.2 μm (Landsat8 band 10) and 11.5-12.5 (Landsat8 band 11) were derived.
The accumulation of 137Cs was determined in soil samples, which were
collected from Selenge and Selenge provinces in Mongolia, using HP-Ge gammaspectrometer.
It was determined the soil erosion by accumulation of 13Cs using
MODIS satellite information.
Based on the results of chemical analysis made in 2002 and 2011, Landsat 8 satellite data processing values of
reflection for at 2 points on The Khangal river (Below the bridge of the train station and Khangal train station)
and at 2 points on The Orkhon river (before and after The Khangal river flows into). Results of the reflection
value of the objects are shown similar to chemical analysis.
Bogd Khan Mountain all areas consist of 41129 hectares from 22992 hectares 55.9 of the forest about
20 rivers originate from mountain. Therefore Tuul river recourse depends on the flow of these water
resources. In this research paper for using Landsat-5 satellite estimation of forest resource of Bogd Khan
Mountain. How depending of Tuul river watering resource. This area estimation of vegetation index soil, soil
temperature, soil water supply is the index to how depends on each other. Result is relate of vegetation index
and water supply index directly but soil temperature undirectly reciprocal value. There for forest area, soil
to low and it’s possible to accumulate moisture.
The air quality indicator approximated by satellite measurements is known as an atmospheric particulate loading, which
is evaluated in terms of the columnar optical thickness of aerosol scattering. This paper is attempting and estimating
PM10 concentration by using Landsat 5 satellite data and validating these with air pollution measurements in
Ulaanbaatar, Mongolia. We have been used the empirical method which based on multispectral algorithm PM10 model.
Results from this research on concentration of PM10 in Ulaanbaatar city have been included.