Бидний тухай
Багш ажилтан
Some results of soil research in western Mongolia mountain In the scope of research on the mountain soil of Western Mongolia, rather than the characteristics of the soil cover of the high mountains, it is aimed to cover the characteristics of the soil of the foothills, lowlands, intermountain valleys, and low mountain regions, as well as the characteristics of the soil of the mountain barren steppe and desert region. In our research area, the Turgani Mountains and Tsagaan Shuvuut mountain, located between Uureg and Achit lakes, were mainly carried out in the barren field zone of the foothill system and light brown soil types of the dry field. will be selected. Also, since the distribution of mountain barren steppe zone is not recorded in the Altai Mountains, Uvs, and Khyargas lake basins, it was carried out in order to clarify the characteristics of the soil and clarify the morphological structure of the soil. Since the distribution of mountain steppe zone has not been recorded in the Altai Mountains, Uvs, and Khyargas Lake basins, this study was carried out in order to clarify the characteristics of the soil and the morphological structure of the soil. Түлхүүр үг: Баруун Монгол, уулын хөрс, хөрсний шинж чанар
The aim of this research is to study the relationship between the estimated aboveground forest biomass and spectral vegetation indices derived from the visible and infrared bands of multispectral Sentinel-2 satellite data. The study area is situated in Teshig soum of Bulgan aimag, the northern part of Mongolia and geographically it belongs to a forest-steppe natural zone. To calculate the aboveground biomass in sampling plots, forest stand parameters such as diameter at breast height (DBH) and tree height (H) have been measured, and allometric equations were used. In the final analysis, we investigated the relationship between the aboveground biomass measured at sampling plots and several predefined vegetation indices. The relationships between the aboveground biomass values and vegetation indices were explored by the use of a linear regression model. Of the outputs, the best results demonstrating the highest level of significance were obtained by the uses of the LAI (with R2=0.61) and SR (with R2=0.59).
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.
The Scots pine (Pinus sylvestris L.) forest is one of major forest types in Mongolia, and overharvesting has threatened its natural regeneration and forest ecosystem sustainability. In present study we examined the effect of different logging intensity on natural regeneration and soil properties 20 year after logging using the experimental research plots established with a randomized block design. Here, we considered four treatments, including low (27.5% removal of growing stock volume), medium (51.6%), high (74.3%) intensities of selective cutting with non-cutting as the reference stand. Our findings revealed that logging under low and medium intensities ensures a successful natural regeneration of Scots pine, and high intensity logging and clear-cuts showed a limiting effect to the regeneration for Scots pine and created more suitable preconditions for broad-life tree regeneration, and development of abundant herbal and shrub coverage.
In recent years, the sustainable forest management has been developing in many countries. There are sufficient support and initiatives from policy and legal environment from the government to sustainable utilization of Mongolia’s forest resources. In the past 8 years /2009-2016/ 732 thousand cubic meter wood on average was harvested per year. These included 76-245 thousand cubic meter wood or around 21.6% consumption and 428-702 thousand cubic meter wood or around 78.4% firewood.Based on the calculation, there is a need of approximately 3107901 cubic meter wood for statewide utilization, which will include 1047267 cubic meter wood or 33.7% for consumption and 2060634 cubic meter wood or 66,3% for firewood. To our knowledge, except [3], there are no mathematical models for Mongolian forest resource depletion and operation. Therefore, the main aim of this work is to develop and give decision results based on the use of mathematical modelling. In this study, we propose a dynamical model for interactions between Mongolian forest resource, the possible resource of forest operation and illegal logging. We investigate local stability conditions of the equilibrium points by using some harvesting parameters.
The study also summarizes the climate and weather conditions in soums (Mongolian provincial units) along the border and infrastructure of the BgayanUlgii, Dornod and Selenge provinces. In Dornod aimag: Kherlen, Bayantumen, Bayan-Uul, Gurvanzagal, Dashbalbar, Sergelen and Chuluunkhoroot soums; Selenge aimag: Altanbulag, Sukhbaatar and Shaamar soum. As key weather stations for analyzing the dynamics of temperature and regime humidification in the border area of Russia and Mongolia, stations were selected, the Kosh-Agach Irkutsk, Kyakhta, Borzya, which register climatic changes in conditional four border regions - Altai, Baikal region, Western and Eastern Transbaikalia (picture 1).
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 erosion and land degradation assessment became serious environment problem in Mongolia. There isn’t enough soil erosion study in in Mongolia and most of them to focusing on the soil distribution in cultivated areas and anthropogenic disturbed areas. The Mongolian has continental climate conditions and vulnerable ecosystem with transition areas with Siberian taiga to Gobi areas. The ecosystem was very sensitive with soil erosion induced by water and wind activities due to the climate change impacts. In this study, we used RUSLE modelling to estimate the soil erosion rate in northern Mongolia. The study area covered by transboundary area of Russia and Mongolia and based on administrative boundaries. The result has showed that higher erosion was marked in highest mountain areas of northern Mongolia with steep slopes and more annual precipitations. Lower values are occurred in plate areas with wide steppes in eastern Mongolia and bigger rivers. Water induced soil erosion depending the rainfall intensity, slope length and soil particles etc. Keyword: Soil erosion. RUSLE, transboundary area.
Depletion and deforestation of forest resources are mainly due to industrialization, population, pollution, forest fire, improper commercial logging, and illegal logging in the world. In this paper, we consider two dynamic models. A mathematical Model 1 is proposed considering the forest biomass density urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0001, the density of wood-based industries urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0002 with unknown parameter urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0003. Model 2 is an extension of Model 1 with the density of illegal logging urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0004 with unknown parameter urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0005. It is assumed that the density of forest biomass grows logistically in the absence of wood-based industries and illegal logging. In the proposed models, the controlling parameters urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0006 and urn:x-wiley:08908575:media:nrm12333:nrm12333-math-0007 are crucial parameters for the local stable conditions of the equilibrium points and system control. We also show in this paper that it is possible to control illegal logging by increasing the level of logging by selecting system parameters efficiently and effectively.
Desertification and land degradation are one of the biggest challenges in the world as well as the ecological integrity of nature, society, and economy. In recent years, this process is being considered as significant and serious as changes in climate change, air pollution, and biodiversity. Land degradation and desertification are occurring at almost all levels in the world, regional and regional levels. According to the United Nations Convention to Combat Desertification, "Desertification is a land degradation of the natural and subsequent climate (Dash D., Janchivdorj L., 1998) in drought. " (UNCCD, 1994). From this point, over 90 percent of Mongolia's grasslands are affected by desertification. Therefore, it is imperative that the policy and management of desertification be assessed. The study also summarizes the climate and weather conditions in soums (Mongolian provincial units) along the border and infrastructure of the Dornod and Selenge provinces. In Dornod aimag: Kherlen, Bayantumen, Bayan-Uul, Gurvanzagal, Dashbalbar, Sergelen and Chuluunkhoroot soums; Selenge aimag: Altanbulag, Sukhbaatar and Shaamar soum. The data was taken from the https://globalweather.tamu.edu database developed by the University of Texas at the meteorological level in 1979-2014 with a 37 km step (Table 1) Keywords: socio-economic, pasture degradation, vegetation cover, population migration, and desertification
Deforestation and forest degradation in the forest-steppe zone is one of the most pressing issues in the world, involving territory of southern boreal forests in Northern Mongolia. The changes in forest cover between 1999 and 2016 and driving factors to deforestation and forest degradation in the Khustai nuruu mountains of the Northern Mongolia were analyzed. Forest monitoring was carried out in mature and over-mature flat-leaved birch Betula platyphilla Sukacz. forests with an admixture of aspen Populus tremula L. using the combined method of remote sensing and ground based field measurements. We found an accelerated deforestation trend between 2006 and 2009, which amounted to 463 ha (23.2 %) since deforestation in the Khustai nuruu mountains was started. Overall 17-year forest monitoring revealed that a total of 675 ha of forests were completely converted to non-forest area. As urgent measures to mitigate the effects and limit rapid deforestation in study area, it is recommended to improve the sustainable forest management via establishing optimum head of livestock and wild animals, strengthening prevention and control measures against pests, and reforestation on deforested areas using seedling of native tree species taken from forest nurseries in the region.
Forests in Mongolia yield low productivity and are vulnerable to disturbances from drought, fire, pests, and illegal logging. Such forests can quickly lose their ecological balance. Logging activities in these areas are limited in monitoring and controls. This study assesses two different logging operations for their natural regeneration capacity by comparing the composition of the soil, soil organisms, physical and chemical properties, and forest cover change after the completion of logging operations. The logging operations were analyzed in two different regions, the Khartsai and Tariakhtai threshold in Selenge soum, Bulgan province. A skyline logging operation was undertaken on Khartsai threshold in 1983 and a tractor logging operation (clear-cutting) on Tariakhtai threshold in 1987. After the completion of the logging, the forests were naturally regenerated. In 2002, soil samples were collected and soil organisms and physical and chemical properties were examined. Satellites images were also used to evaluate forest cover changes after the end of the logging operations. Significant differences in the naturally regenerated tree species in the skyline logging, tractor logging, and natural forest areas were observed. Average tree ring growth was 0.9 mm in the skyline logging site, 0.6 mm in the tractor logging site, and 1.2 mm in the natural forest. Based on forest cover changes observed in satellite images, the density of naturally regenerated tree species in the natural forest area was higher than that in the skyline logging area. In contrast, the latter recorded a higher density than that in the tractor logging area. Therefore, processing of satellite images of forest cover changes with high-resolution data provides valuable information for the local forest community and helps decision-makers in their further actions.
Агаар бохирдоход дэлхийн уур амьсгалын өөрчлөлт, манай орны нийгмийн амьдралын өөрчлөлт зэрэг хүчин зүйлүүд нөлөөлж байна. Улаанбаатар хот орчмын агаарын найрлага дахь SO2 ба NO2-ын жилийн дундаж агууламжууд нэмэгдсээр байна. Бохирдсон агаар хүчиллэг тунадас байдлаар ойн хөрсөнд нэвчин бохирдуулж ойн экосистемд ихээхэн хэмжээний сөрөг нөлөө үзүүлнэ. Судалгааг агаарын бохирдол хамгийн их нөлөөлөх боломжтой ойн талбай, нөлөөлөлгүй ойн талбай байх зарчмыг баримтлав. Улаанбаатар хотын цахилгаан станцууд, тээвэр, гэр хорооллын утааны зонхилох чигт орших Богд хан уулын нүхт, Жаргалантын аманд судалгааны 1 ба 2 дугаар талбайг, энэхүү дээж талбайнуудыг жиших зорилгоор агаарын бохирдлын эх үүсвэрээс хол орших /100 км-т/ Төв аймгийн Батсүмбэр сумын Өртөө мухарт судалгааны 3 дугаар талбайг байгуулав. Дээж талбайнуудаас авсан хөрсний дээжүүдийн чийгийн агууламж, PH, солигдох катионууд (EX-Ca, Mg, K, Na), хүнд металлууд (Cd, Ni, Pb, Co) зэргийг (Atomic absorption spectrometry) төхөөрөмжийг ашиглан тодорхойлов. Хөрсний шинжилгээний үр дүнгээс үзэхэд солигдох катионууд (Ex-Ca, Mg,K, Na) ба хүнд металлууд (Cd, Ni, Pb, Co)-ын агууламж хавар намрын улиралд харилцан адилгүй байгаа нь ажиглагдлаа. Агаарын бохирдолгүй гэж сонгон авсан 3 дугаар талбайн хөрсөнд хар тугалга, никелийн агууламж зөвшөөрөгдөх хэмжээнээс их гарч байна.
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.
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, γ 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.
This paper aims to apply Forest Index (FI) and to determine forest coverage in the study area. The study area (49° 15ʹ to 49° 10ʹ N and 104° 05ʹ to 104° 15ʹ E) is located in the northern region of Mongolia and consist of mixed forest. Larch forest (86.12%) is dominating in the study area. The Sentinel-2 satellite data for the years 2015–2019 were used in the research. The land surface temperature (LST) was produced from Landsat-8 OL. FI methodology was applied for the Sentinel data in order to estimate larch forest coverage. The output map of forest coverage was compared with ground truth measurements and thematic map. The agreement between FI map and ground measurement was 85%. LST from Landsat and FI from Sentinel were sampled in to same size. The relationship between LST (Landsat-8) and FI (Sentinel-2) was reasonable (R = 0.5). FI index and LST is applicable for different forest type in the region.
This paper aims to apply Forest Index (FI) and to determine forest coverage in the study area. The study area (490 15ʹ to 490 10ʹ N and 1040 05ʹ to 1040 15ʹ E) is located in the northern region of Mongolia and consist of mixed forest. Larch forest (86.12%) is dominating in the study area. The Sentinel-2 satellite data for the years 2015-2019 were used in the research. The land surface temperature (LST) was produced from Landsat-8 OL. FI methodology was applied for the Sentinel data in order to estimate larch forest coverage. The output map of forest coverage was compared with ground truth measurements and thematic map. The agreement between FI map and ground measurement was 85%. LST from Landsat and FI from Sentinel were sampled in to same size. The relationship between LST (Landsat-8) and FI (Sentinel-2) was reasonable (R=0.5). FI index and LST is applicable for different forest type in the region.
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.
Desertification and land degradation are one of the biggest challenges in the world as well as ecological integrity of nature, society and economy. In recent years, this process is being considered as significant and serious as changes in climate change, air pollution, and biodiversity. Land degradation and desertification are occurring at almost all levels in the world, regional and regional levels. According to the United Nations Convention to Combat Desertification, "Desertification is a land degradation of the natural and subsequent climate (Dash D., Janchivdorj L., 1998) in drought. " (UNCCD, 1994). From this point, over 90 percent of Mongolia's grasslands are affected by desertification. Therefore, it is imperative that the policy and management of desertification be assessed. The study also summarizes the climate and weather conditions in soums (Mongolian provincial units) along the border and infrastructure of the Dornod and Selenge provinces. In Dornod aimag: Kherlen, Bayantumen, Bayan-Uul, Gurvanzagal, Dashbalbar, Sergelen and Chuluunkhoroot soums; Selenge aimag: Altanbulag, Sukhbaatar and Shaamar soum.
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
Scots pine (Pinus sylvestris L.) forests are one of the main vegetation types in the Asian forest-steppe zone. However, over-harvesting currently threatens the natural regeneration and sustainability of these forests. In this study, we examine the long-term effects of different logging intensities on soil properties and natural regeneration in a natural Scots pine forest in the West Khentii Mountains (Mongolia), 19 years after selective logging. Our experimental design included five treatments: clear cut (CC), treatments with high (HI), medium (MI), low (LI) intensities, and a reference parcel with no logging impact at all (RE). We described and quantified the harvest events and applied ANOVA and LMM modeling to analyze and explain the long-term impacts of the logging intensities on soil properties and natural regeneration. We found that logging has a significant negative influence on the physical and chemical properties of the soil because it increases soil compaction and reduces soil nutrients. The most critical impacts of logging were on soil bulk density, total porosity, organic matter, and total nitrogen and phosphorus. The LMM modeling showed that organic matter (OgM), total nitrogen (TN), available K (AK) and pH values are especially impacted by logging. Our study revealed that the values for all of these variables show a linear decrease with increasing selective logging intensity and have a level of significance of p < 0.05. Another finding of this study is that selective logging with low and medium intensities can promote natural regeneration of Scots pine to numbers above those of the reference site (RE). High intensity logging and clear-cuts, however, limit the regeneration of Scots pine, reduce overall seedling numbers (p < 0.05), and create conditions that are suitable only for the regeneration of deciduous tree species. This underlines the risk of Scots pine forest degradation, either by replacement by broad-leaf trees or by conversion into non-forest ecosystems.
Scots pine (Pinus sylvestris L.) forests are one of the main vegetation types in the Asian forest-steppe zone. However, over-harvesting currently threatens the natural regeneration and sustainability of these forests. In this study, we examine the long-term effects of different logging intensities on soil properties and natural regeneration in a natural Scots pine forest in the West Khentii Mountains (Mongolia), 19 years after selective logging. Our experimental design included five treatments: clear cut (CC), treatments with high (HI), medium (MI), low (LI) intensities, and a reference parcel with no logging impact at all (RE). We described and quantified the harvest events and applied ANOVA and LMM modeling to analyze and explain the long-term impacts of the logging intensities on soil properties and natural regeneration. We found that logging has a significant negative influence on the physical and chemical properties of the soil because it increases soil compaction and reduces soil nutrients. The most critical impacts of logging were on soil bulk density, total porosity, organic matter, and total nitrogen and phosphorus. The LMM modeling showed that organic matter (OgM), total nitrogen (TN), available K (AK) and pH values are especially impacted by logging. Our study revealed that the values for all of these variables show a linear decrease with increasing selective logging intensity and have a level of significance of p < 0.05. Another finding of this study is that selective logging with low and medium intensities can promote natural regeneration of Scots pine to numbers above those of the reference site (RE). High intensity logging and clear-cuts, however, limit the regeneration of Scots pine, reduce overall seedling numbers (p < 0.05), and create conditions that are suitable only for the regeneration of deciduous tree species. This underlines the risk of Scots pine forest degradation, either by replacement by broad-leaf trees or by conversion into non-forest ecosystems.
Desertification and land degradation are one of the biggest challenges in the world and are the ecological integrity of nature, society and economy. In recent years, this process is being considered as the same as changes in climate change, air pollution, and biodiversity. The study also summarizes the climate and weather conditions in soums along the border and infrastructure of the Dornod and Selenge provinces. In Dornod aimag: Kherlen, Bayantumen, Bayan-Uul, Gurvanzagal, Dashbalbar, Sergelen and Chuluunkhoroot soums; Selenge aimag: Altanbulag, Sukhbaatar and Shaamar soum. A total of 73 households were selected from 8358 households in Selenge aimag, Sukhbaatar and Shaamar soums of Selenge aimag and conducted a socio-economic survey. A total of 240 households were randomly selected from 19,148 households in the Kherlen, Bayandun, Bayantumen, Bayan-Uul, Gurvanzagal, Dashbalbar, Sergelen, Choibalsan and Chuluunhoroot soums of Dornod aimag. Key words: socio-economic, population migration, desertification, education, health, mining
Abstract. The estimation of forest biomass using satellite data has received increasing attention for several reason in Mongolia. Since forest in Mongolia is decreasing and it is important to estimate forest resources using satellite data. This research aims to apply recently launched Sentinel-1B Synthetic Aperture Radar (SAR) C-band and optical Sentinel-2B satellite data for estimation forest biomass and coverage and develop model for the study area. The study area is small scale forestry area named by Khanbuyan community, Bulgan province is situated in the Northern part of Mongolia. Boreal and montane forest belts of larch is dominated in this area. Sentinel-1B was used for estimation forest biomass and multispectral bands of Sentinel-2B applied for forest classification map. We used regression analysis to develop the model using Sentinel-1B and Sentinel-2B VV and VH polarizations for Sentinel-1B and Normalized Difference Vegetation Index (NDVI) for Sentinel-2B were applied in this research. Ground truth data was collected in July 2016 and September 2016 for forest coverage and biomass measurements. NDVI and backscatter coefficients for polarizations VV and VH of Sentinel-1B 2016 were related to ground truth biomass for modeling. Comparison of the model and ground truth measurements for above ground biomass have a good agreement.
In this study, we assessed the effects of different harvesting intensities on the natural regeneration capacity and soil moisture and temperature regime of natural Scots pine (Pinus sylvestris L.) forests. This study was carried out as a follow-up study, in July 2017 on experimental plots previously established in 1998 (after 19 years). We inventoried tree seedlings, measured the moisture and temperature of the soils, and also collected soil samples from different depths of the soil at each treatment. Treatment were consisted of low, medium, high intensity thinning and clear-cutting except for reference stand. Data was collected from 15 subplots (with 3 replications). Natural regeneration was varied (F=3.29, p=0.05) among treatments, and relatively better natural regeneration was observed on treatments, harvested with low, medium harvesting intensities. The poorest regeneration density and intensified forest succession were found in clear-cuts. However, decreased critical low water content and increased soil temperature involved not only topsoil, but also subsoil. These findings suggested that logging intensity is a main driving factor of forest soil compaction, drainage and water availability in forest-steppe ecosystems in Mongolia.
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.
