Бидний тухай
Багш ажилтан
Mongolian forests have low productivity and growth and are vulnerable to disturbances. Additionally, it is difficult to control and evaluate the forested areas. Therefore, satellite data and surveillance methods are needed to study mountain forests. This study aimed to determine the changes in the main forest cover classes of Khangal soum using remote sensing and geographical information system datasets. A spectral forest index (SFI) using Sentinel-2 imagery was developed for forest cover estimations and applied to the study area during 2015–2020. The SFI was based on the forest index (FI) and the concept of Dark Objects. Each SFI was compared to existing vegetation indices (ratio vegetation index, normalized difference vegetation index, leaf area index, and forest index) for forest data analysis. The highest correlation was with SFI2. The SFI2 data agreed with the national forest inventory (NFI) 2018 data. The SFI2 of the forest area was set at 1.2, which was confirmed with 90.4% confidence. Overall, SFI2 is suitable for land cover/land use changes and forest classification, monitoring, and management in Mongolia and could be crucial for estimating the boundary of forested areas depending on the forest cover and species in the region.
Монгол орны ойг хамгаалах, нөхөн сэргээх, зохистой ашиглах, ойг цогц байдлаар судлах нь бүс нутаг, засгийн газар, экологи, нийгэм эдийн засгийн ашиг сонирхолд бүрэн нийцнэ. Орчин үед ойг огтолж ашиглах, өсгөн үржүүлж нөөц баялгийг нь нэмэгдүүлэх гэсэн хоёр асуудлыг зохистойгоор шийдвэрлэж байгалийн тэнцлийг алдагдуулахгүй байх нь чухал. Байгаль орчинд сөрөг нөлөөтэй мод бэлтгэлийн технологи нь ойн экосистемийг өөрчилсөөр байгаа гол хүчин зүйлийн нэг. Иймд энэ чиглэлийн судалгааг нарийвчлан явуулж, тухайн бүс нутагт тохирсон ойн экосистемд сөрөг нөлөөгүй арга технологи буй болгох шаардлага бодит байдлаас урган гарч байна. Янз бүрийн арга, технологиор мод бэлтгэсэн шинэсэн ойн сэргэн ургалт, хөрс, ургамлан бүрхэвчийн өөрчлөлт, модны өсөлтийн онцлогыг судалж, уулын ойн огтлолтын зохистой арга, технологийг сонгон тогтоох боломжит хувилбарыг тогтоохМод бэлтгэх үйл ажиллагааны оновчгүй байдал нь ойн экосистемийн хэвийн нөхцлийн алдагдуулж байгаа нь бидний судалгаанаас тодорхой болж байна. Бид судалгаандаа харилцан ялгаатай технологиор (1.Модыг хавтгарйруулан огтолж трактораар цагаалсан, 2.Модыг нарийн зурвасаар огтолж татлагат төхөөрөмжөөр цагаалсан) мод бэлтгэсэн талбайнуудыг сонгож аваад хөрс, ургамлан бүрхэвчийн өөрчлөлтийн хэмжилтүүд, өсвөр модны тооллого, Lansat хиймэл дагуулын мэдээний анализ хийсэн дүнгээс харахад уламжлалт байдлаар хэрэглэж байгаа одоогийн мод бэлтгэх технологийг өөрчлөх шаардлагатай болох нь тодорхой байна. Экосистемийн өөрчлөгдлийн мониторинг судалгааг 5-10 жилийн хугацаанд тасралтгүй хийх шаардлага байгаа нь судалгаанаас харагдаж байна. Бид мониторинг судалгаандаа орчин үеийн сансарын хиймэл дагуулын мэдээлэл авч ашиглах нь цаг хугацаа, эдийн засаг, мониторинг судалгааны үр дүнг сайжруулах ач холбогдолтой гэж үзэж байна. Ойн боломжит нөөцөд түшиглэн ойгоос мод бэлтгэх технологийг сонгохдоо ойн экосистемд сөрөг нөлөөгүй байдлыг нэн тэргүүнд харгалзан үзэх шаардлагатай байна. Бодлого шийдвэр гаргагчид болон ойн үйлдвэрлэл эрхлэгчид шинэ технологи иноваци нэвтрүүлэхдээ экосистемийн сөрөг нөлөөллийн инженерийн тооцоог заавал хийж байх нь зүйтэй гэж үзэж байна.
Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 0–20 cm and p < 0.005 at 0–50 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (2010–2019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.
Agriculture is one of the most critical sectors of the Mongolian economy. In Mongolia, land degradation is increasing in the cropland region, especially in a cultivated area. The country has challenges to identify new croplands with sufficient capacity for cultivation, especially for local decision-makers. GIS applications tremendously help science in making land assessments. This study was carried out in Bornuur soum, Mongolia. The goal of this study to estimate that best suitable area for supporting crop production in Bornuur soum, using a GIS-based multi-criteria analysis (MCA) and remote sensing. GIS-based multi-criteria analysis (MCA) has been widely used in land suitability analyses in many countries. In this research, the GIS-based spatial MCA among the Analytical Hierarchy Process (AHP) method has employed. The approach was enhanced for each criterion which as soil, topography and vegetation. The opinions of agronomist experts and a literature review helped in identifying criteria (soil data, topography, water and vegetation data) that are necessary to determine areas suitable for crops. The detailed cropland suitability maps indicate that 46.12 % is highly suitable for cropland, 34.68 % is moderate suitable, 13.64 % is marginal suitable and 5.56 % is not suitable. The MCA and AHP tools play an essential role in the multi-criteria analysis. Therefore, the results of these methods allow us to estimate an appropriate area for cultivation in Bornuur soum, Tuv province. The crop suitability method implies significant decisions on different levels and the result will be used for cropland management plan to make a decision. It is an integral role in agricultural management and land evaluation. Future research should further develop this method by including socio-economic (potential citizens for agriculture, current crop growth, water resource, etc.) and environmental variables (rainfall, vegetation types, permafrost distribution, etc.) to obtain specific results. However, it could be also be applied for a single crop type (mainly barley, wheat and potato) in Mongolia.
The forest biomass is one of the most important parameters for the global carbon stock. Information on the forest volume, coverage and biomass are important to develop the global perspective on the CO2 concentration changes. Objective of this research is to estimate forest biomass in the study area. The study area is Hangal sum, Bulgan province, Mongolia. Backscatter coefficients for vertical transmit and vertical receive (VV), for vertical transmit and horizontal receive (VH) from Sentinel data and Leaf Area Index (LAI) from Landsat data were used in the study area. We developed biomass estimation approach using ground truth data which is DBH, height and soil moisture. The coefficient α, β, δ, ɣ were found from the approach. The output map from the approach was compared with VV and VH, LAI data. The relationship between output map and VH data shows a positive result R2=0.61. This study suggests that the biomass estimation using Remote sensing data can be applied in forest region in the North.
Abstract. Promoting the recovery of forest management has been identified as a key priority by the Government of Mongolia. The objective of this paper is to define land cover classification and land cover change in Khandgait valley between 2000 and 2019. The study area is located in the North central part of Mongolia in Bulgan province. Landsat satellite images with 30m resolution were applied. For the validation, we used ground truth measurements. Maximum-likelihood method was applied in this study. The output map of land cover classification was analyzed and compared with the ground truth measurements. The results showed an overall accuracy of 86.5% and 89.0% for the 2000 and 2019 images, respectively. Land cover changes were quantitatively presented with the results of accuracy assessments between 2000 and 2019. In the future, we need to improve forest monitoring and analyze forest management using satellite images.
Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30′′N and 106°15′55′′E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.
Abstract. Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.
Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.
Abstract The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r2 = 0.58) and at a 50-cm depth (r2 = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region. Keywords: PET, soil moisture content, climate station, NDVI, Mongolia
