Бидний тухай
Багш ажилтан
The purpose of this study, we use vegetation index with time series analysis and determine the predicted future prospects and forecasting for an agricultural area. The study area is situated in the steppe region Dornod province, north-eastern part of Mongolia. In this research, we choose the ARIMA (Autoregressive integrated moving average) model, one of the best-known methods of time series analysis using the NDVI (Normalized Difference Vegetation Index) vegetation index from MODIS remote sensing satellite data between 2010 to 2020. The analysis was performed by Python Jupyter Notebook, ArcGIS, and Envi classic. Validations of this model were issued every four season and the average of agreement is 62 percent.
The vegetation is the most important factor of the biomass. It is also an important factor in cropland suitability and agriculture. In this paper, we aim to study vegetation, which related to several factors in steppe region. The study area is located in the northeast of Mongolia. The satellite data NDVI is commonly used for vegetation. In this study, we use the Normalized Difference Vegetation Index (NDVI) which depended on Land Surface Temperature (LST), The Normalized Difference Water Index (NDWI) from MODIS satellite data and Elevation, Slope from ASTER GDEM satellite data. This research focuses on two machine learning methods: Multiple linear and Random forest regression. We used MODIS and ASTER GDEM satellite data’s from 2001 to 2010 (July to August). The analysis was performed in Python Jupyter Notebook and ArcGIS. The result of both proposed models was compared with MODIS NDVI value. The validation results of these two methods are 71 and 91, respectively
This study investigates the spatiotemporal variations of Mongolian steppe and proposes a model for analyzing the relationship between grassland and biophysical parameters such as Land Surface Temperature (LST), Normalized Difference Water Index (NDWI), Elevation, and Slope. The Dornod province, which is one of the largest steppes in Mongolia, is selected as the study area, and data from 10 selected locations over this area during the growing season (April to September) from 2003 to 2018 are analyzed. The Normalized Difference Vegetation Index (NDVI) is used to characterize the grassland variations over the selected locations. NDVI and LST are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data products. NDWI is estimated from MODIS spectral reflectance measurements. The Slope and Elevation are obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model. A panel regression analysis method is applied to derive an empirical relationship between the grassland and the biophysical parameters. Statistical significance tests show that random effects model coefficients represent the relationships better. According to the results, NDWI has a strong positive dependence on NDVI, whereas the LST has a weak and inverse effect. Though the Slope had little effect, the Elevation showed a weak positive dependence. A validation of the model estimated NDVI with observations yielded a correlation coefficient of 0.83 at 5% significance level. The results suggest that the proposed model is suitable for for monitoring Mongolian steppe, and to examine the factors that contribute to the grassland variations.
Бутархай программчлалын минимумчлах бодлогыг бодохдоо шийдийг нь локаль шийдэнд хүргэдэг Dinkelbach алгоритмыг ашиглан зарим жишээ бодлого дээр туршилтуудыг хийв. Туршилтыг Dinkelbach алгоритмыг бутархай программчлалын шууд бодлоготой нь харьцуулах хэлбэрээр хийв. Энэхүү туршилтыг Python программ дээр ажиллуулав. Түлхүүр үгс: Бутархай программчлал, Dinkelbach алгоритм
The Normalized Difference Vegetation Index (NDVI) is a graphical pixel indicator which used and analysed by remote sensing technology whether or not the target being observed contains live green vegetation. In this paper we estimated crop suitability using Normalized Difference Vegetation Index (NDVI) which depended on Land Surface Temperature (LST), The Normalized Difference Moisture Index (NDMI or water) from MODIS satellite data and Elevation, Slope form ASTER DEM satellite data. NDVI is used for several sector, especially in agriculture for cropland, precision farming and to measure biomass. Agriculture is one of the crucial and traditional sectors of Mongolia that produces approximately 15 % gross domestic production (GDP). This research focuses on estimation for crop suitability based on a statistical method and NDVI. The study area is situated in the steppe region Dornod province, eastern part of Mongolia. NDVI MODIS data (April to September) from 2003 to 2018 were applied for the estimation. We used multiple linear regression analysis with python in order to develop crop suitability model using NDVI. The result of proposed model was compared with MODIS NDVI value. The agreement is positive which 71percent.
