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Дэлхийджижиг мэрэгч амьтад тэдгээрийн тоо толгой, өсөлтийн динамик, амьдралын мөчлөгийн хэлбэлзэл, түүнийдалайц зэрэгтолон эрдэмтэн, судлаачийн анхаарал татагддаг.Хөлөнбуйрын тал нутаг болдэлхийн мал аж ахуйн үйлдвэрлэлийн чухал төвүүдийн нэг бөгөөд сүүлийн хэдэн арван жилд тус бүс нутаг тал хээрийн мэрэгч амьтад,тухайлбал үлийн цагаан оготно (Lasiopodymys brandtii)-ны тархалтаас үүдсэн экологийн хүнд сорилтуудтай тулгараад байна. Энэ мэрэгч нь улирлын чанартай үржин өсдөг бөгөөдтоо толгой нь эрс нэмэгдсэн.Жилд тэдний сөрөг нөлөөлөл, хор уршиг эрс нэмэгдэж тэднийг нэн хортон шавьжийн ангилалд оруулдаг. Өмнөх судалгаануудаас үзэхэд үлийн цагаан оготны газарзүйн тархалт нь орон зайн хувьд илүү өргөн бүс нутагт тархах хандлагатай байгаа чтэрхүү тархалтыг тодорхойлох үндсэн үзүүлэлт болтэдний нүхний бөөгнөрлийг (нүх) илрүүлэх явдал болохыг нотолсон.Энэхүү судалгаагаар 2021 он (Брандтын оготнын тоо толгой эрс өссөн)-ы 1000км²-аас дээш талбайг хамарсан GF-2 хиймэл дагуулын хоёр зургийг ашиглан Хөлөнбуйрын тал нутагт үлийн цагаан оготны үүрний бөөгнөрлийг илрүүлэхийг зорьсон. Үүнд зорилтот объектын Faster R-CNN загварыг гурван төрлийн илрүүлэх арга болох объектод суурилсан ангилал (object-based image classification), ургамлын индексийн ангилал (vegetation index classification) болон бүтцэд үндэслэсэн ангилал (texture classification)-тай хослуулан ашигласан.Үрдүнгээрүлийнцагаан оготны нүх илрүүлэхэд объектод суурилсан ангиллын аргабол хамгийн өндөр үзүүлэлтийг үзүүлж, хоёрзургийн дундаж F1 үзүүлэлтнь 0.722, дундаж нарийвчлал нь (AP) 63.80%-д хүрчээ. Бүтцэд үндэслэсэн ангиллаар арай бага дундаж нарийвчлал бүхий үр дүн буюу дундаж F1 үзүүлэлтнь 0,666; дундаж нарийвчлал нь 55.95%-тай тооцоологдсонболно. Харин ургамлын индексийн ангиллаар F1 үзүүлэлт нь дөнгөж 0.437, дундаж нарийвчлал нь 29.45% хувь болжээ. Энэ нь голдуу тухайн бүс нутгийн уур амьсгал, ургамлын ногооролтын хэмжээнээс шалтгаалсан үр дүн юм. Ерөнхийдөө тус судалгаа нь өндөр нарийвчлалтай хиймэл дагуулын зургийг гүн сургалтад суурилсан объект илрүүлэх аргатай хослуулан хэрэглэх нь хуурай буюу хагас хуурай бүс нутгийн тал хээрийн жижиг мэрэгч амьтны тоо толгойн мониторинг, менежментэд чухал ач холбогдолтойг харуулж байна.
Understanding the spatial and temporal patterns of forest fires, along with the key factors influencing their occurrence, and accurately forecasting these events are crucial for effective forest management. In the Central-South region of China, forest fires pose a significant threat to the ecological system, public safety, and economic stability. This study employs Geographic Information Systems (GISs) and the LightGBM (Light Gradient Boosting Machine) model to identify the determinants of forest fire incidents and develop a predictive model for the likelihood of forest fire occurrences, in addition to proposing a zoning strategy. The purpose of the study is to enhance our understanding of forest fire dynamics in the Central-South region of China and to provide actionable insights for mitigating the risks associated with such disasters. The findings reveal the following: (i) Spatially, fire incidents exhibit significant clustering and autocorrelation, highlighting areas with heightened likelihood. (ii) The Central-South Forest Fire Likelihood Prediction Model demonstrates high accuracy, reliability, and predictive capability, with performance metrics such as accuracy, precision, recall, and F1 scores exceeding 85% and AUC values above 89%, proving its effectiveness in forecasting the likelihood of forest fires and differentiating between fire scenarios. (iii) The likelihood of forest fires in the Central-South region of China varies across regions and seasons, with increased likelihood observed from March to May in specific provinces due to various factors, including weather conditions and leaf litter accumulation. Risks of localized fires are noted from June to August and from September to November in different areas, while certain regions continue to face heightened likelihood from December to February.
Abstract: Understanding the spatial and temporal patterns of forest fires, along with the key fac-tors influencing their occurrence, and accurately forecasting these events are crucial for effective forest management. In the Central-South region of China, forest fires pose a significant threat to the ecological system, public safety, and economic stability. This study employs Geographic Informa-tion Systems (GISs) and the LightGBM (Light Gradient Boosting Machine) model to identify the determinants of forest fire incidents and develop a predictive model for the likelihood of forest fire occurrences, in addition to proposing a zoning strategy. The purpose of the study is to enhance our understanding of forest fire dynamics in the Central-South region of China and to provide actionable insights for mitigating the risks associated with such disasters. The findings reveal the following: (i) Spatially, fire incidents exhibit significant clustering and autocorrelation, highlighting areas with heightened likelihood. (ii) The Central-South Forest Fire Likelihood Prediction Model demonstrates high accuracy, reliability, and predictive capability, with performance metrics such as accuracy, precision, recall, and F1 scores exceeding 85% and AUC values above 89%, proving its effectiveness in forecasting the likelihood of forest fires and differentiating between fire scenarios.(iii) The likelihood of forest fires in the Central-South region of China varies across regions and seasons, with increased likelihood observed from March to May in specific provinces due to various factors, including weather conditions and leaf litter accumulation. Risks of localized fires are noted from June to August and from September to November in different areas, while certain regions continue to face heightened likelihood from December to February.
The Net Primary Productivity (NPP) of vegetation is a crucial metric for understanding plant dynamics and carbon sequestration capacity in a specific region. According to the Coupled Human and Natural Systems (CHANS) framework posits that human societies and natural ecosystems operate as cohesive subsystems intricately linked across spatial and temporal scales. This research aims to elucidate the dynamics of the CHANS in Mongolia by utilizing widely available indicators encompassing social, economic, and ecological dimensions, including NPP, Gross Domestic Product (GDP), population, livestock count, and their respective ratios. In this study, the ecosystem Carnegie-Ames-Stanford Approach (CASA) modelwas used to compute NPP from 2000 to 2021. The Mann-Kendall trend test and variance stability analysis were conducted to examine the spatiotemporal variability of NPPand its interplay with societal dynamics. Additionally, partial correlation analysis was performed to elucidate the association between economic factors across spatial and temporal series. Over the 22-year study period, Mongolia experienced increases in NPP, population, livestock count, and GDP (0.77 g C m-2 yr-1, 49,416 people/year, 2.0 million heads/year, and 2 trillion/year, respectively). Our investigation highlights the spatial influences impacting NPP, particularly the positive correlation between NPP and livestock count during periods of NPP escalation, which stimulates GDP growth. Interestingly, there is no discernible correlation between population expansion and either livestock count or GDP. Spatially, NPP exhibits a modest positive correlation with GDP (R=0.3, R=0.15) and a negative correlation (R=0.04) with livestock count. Recent years have witnessed a rise in NPP, precipitation and livestock numbers, despite considerable rural migration. Looking ahead, increasing carbon absorption and accumulation is crucial for enhancing agricultural productivity and fostering socioeconomic development. Therefore, a detailed exploration of these dynamics holds significance for pastoral Mongolia.
Most small rodent populations worldwide exhibit fascinating population dynamics, capturing the attention of numerous scholars due to their multiyear cyclic fluctuations in population size and the astonishing amplitude of these fluctuations. Hulunbuir steppe stands as a crucial global hub for livestock production, yet in recent decades, the area has faced recurring challenges from steppes rodent invasions, with Brandt’s vole (Lasiopodomys brandtii, BV) being particularly rampant among them. They not only exhibit seasonal reproduction but also strong social behavior, and are generally considered pests, especially during population outbreak years. Prior studies suggest that BV population outbreaks tend to occur across a wider geographic area, and a strong indicator for identifying rodent outbreaks is recognizing their burrow clusters (burrow systems). Hence, this paper conducts target object detection of BV burrow clusters in the typical steppes of Hulunbuir using two GF-2 satellite images from 2021 (the year of the BV outbreak). This task is accomplished by incorporating the Faster R-CNN model in combination with three detection approaches: object-based image classification (OBIC), based on vegetation index classification (BVIC), and based on texture classification (BTC). The results indicate that OBIC demonstrated the highest robustness in BV burrow cluster detection, achieving an average AP of 63.80% and an F1 score of 0.722 across the two images. BTC exhibited the second-highest level of accuracy, achieving an average AP of 55.95% and an F1 score of 0.6660. Moreover, this approach displayed a strong performance in BV burrow clusters localization. In contrast, BVIC achieved the lowest level of accuracy among the three methods, with an average AP of only 29.45% and an F1 score of 0.4370. Overall, this study demonstrates the crucial role of utilizing high-resolution satellite imagery combined with DL-based object detection techniques in effectively monitoring and managing the potential outbreaks of steppe rodent pests across larger spatial extents.
The innovative normal form, named deep learning-couple-physical and statistical methods (DL-C-PS), has been developed for synchronously retrieving land surface temperature and emissivity (LST&E) from Japan's geostationary meteorological satellite Himawari-8 carrying the Advanced Himawari Imager (AHI). First, geophysical logical reasoning (GLR) and expert knowledge were used to formulate radiative transfer equations (RTEs). Then, a hybrid approach integrating physical and statistical methods was employed to derive the solution, with deep learning (DL) optimizing the solution process. Three band combination schemes were designed to assess the effectiveness of the DL-C-PS normal form. Simulation data from the MODerate spectral resolution atmospheric TRANsmittance mode (MODTRAN) yielded promising results during validation. Root mean square error (RMSE) values were below 1 K for LST and below 0.008 for LSE when using band combinations of four thermal infrared (TIR) bands or at least three TIR bands combined with water vapor information. Cross-validation and in situ validation showed consistent findings with simulation validation. Compared to MODIS LST&E products (MYD21), in most cases, the RMSE values for LST&E were approximately 2 K and less than 0.015 during daytime, and below 1.3 K and 0.017 during night, respectively. Validated against in situ observations, nighttime RMSE values for LST were approximately 1.5 K with correlation coefficient (R) values better than 0.93. The higher RMSE observed in daytime compared to nighttime can be attributed to the influence of the sun's illumination angle on satellite scanning imaging. Overall, this research presented a normal form for multi-parameter estimation by leveraging the optimal calculation of DL and incorporating physical interpretations.
Due to the intensification of climate change around the world, the incidence of natural disasters is increasing year by year, and monitoring, forecasting, and detecting evolution using satellite imaging technology are important methods for remote sensing. This study aimed to monitor the occurrence of fire disasters using Sentinel-2 satellite imaging technology to determine the burned-severity area via classification and to study the recovery process to observe extraordinary natural phenomena. The study area that was sampled was in the southeastern part of Mongolia, where most wildfires occur each year, near the Shiliin Bogd Mountain in the natural steppe zone and in the Bayan-Uul sub-province in the forest-steppe natural zone. The normalized burn ratio (NBR) method was used to map the area of the fire site and determine the classification of the burned area. The Normalized Difference Vegetation Index (NDVI) was used to determine the recovery process in a timely series in the summer from April to October. The results of the burn severity were demonstrated in the distribution maps from the satellite images, where it can be seen that the total burned area of the steppe natural zone was 1164.27 km2, of which 757.34 km2 (65.00 percent) was classified as low, 404.57 km2 (34.70 percent) was moderate-low, and the remaining 2.36 km2 (0.30 percent) was moderate-high, and the total burned area of the forest-steppe natural zone was 588.35 km2, of which 158.75 km2 (26.98 percent) was classified as low, 297.75 km2 (50.61 percent) was moderate-low, 131.25 km2 (22.31 percent) was moderate-high, and the remaining 0.60 km2 (0.10 percent) was high. Finally, we believe that this research is most helpful for emergency workers, researchers, and environmental specialists.
Since the estimate of moisture stress coefficients (MSC) in the current Carnegie-Ames-Stanford-Approach (CASA) model still requires considerable inputs from ground meteorological data and many soil parameters, here we present a modified CASA model by introducing the land-surface water index (LSWI) and scaled precipitation to model the vegetation net primary productivity (NPP) in the arid and semiarid climate of the Mongolian Plateau. The field-observed NPP data and a previously proposed model (the Yu-CASA model) were used to evaluate the performance of our LSWI-based CASA model. The results show that the NPP predicted by both the LSWI-based CASA model and the Yu-CASA model showed good agreement with the observed NPP in the grassland ecosystems in the study area, with coefficients of determination of 0.717 and 0.714, respectively. The LSWI-based CASA model also performed comparably with the Yu-CASA model at both biome and per-pixel scales when keeping other inputs unchanged, with a difference of approximately 16 g C in the growing-season total NPP and an average value of 2.3 g C bias for each month. This indicates that, unlike an earlier method that estimated MSC based entirely on climatic variables or a soil moisture model, the method proposed here simplifies the model structure, reduces the need for ground measurements, and can provide results comparable with those from earlier models. The LSWI-based CASA model is potentially an alternative method for modelling NPP for a wide range of vegetation types in the Mongolian Plateau.
Дэлхий дахинаа байгаль, нийгэмцаашлаад нийгэм-эдийн засагт асар их хохирол үзүүлж байдаг олон янзын байгалийн гамшиг тохиолддог бөгөөд тэдгээрийн нэг нь ой,хээрийн түймэр юм.Байгалийн гамшигт үзэгдэл, байгалийн нөөцийн судалгаанд зайнаас тандан судалгааны аргыг өргөн ашиглаж, хяналт, мониторинг хийж байна. Ой,хээрийн түймрийн голомтыг цаг тухайд нь зөв тооцоолох нь түймрийн дараах менежмент, шийдвэр гаргахад онцгой ач холбогдолтой. Энэхүү судалгаагаар Европын Сансар Судлалын Агентлагийн(ESA) ‘Sentinel-2’хиймэл дагуулын зургийг ашиглан Дорнод аймгийн Баян-Уулболон Баяндун сумдын нутагт гарсан ой,хээрийн түймэрт өртсөн талбайг тооцоолох, шатсан талбарын зэрэглэлийг тогтоох,хүчин зүйлүүдтэй харьцуулан тодорхойлохявдал юм. Судалгаанд нормчилсон шаталтын харьцаа буюу ‘NBR’, ‘NBR+’ индексүүдийг түймэр гарахын өмнө болон дараах хиймэл дагуулын зурагт суурилан тооцоолон гаргасан. Нийт 58,131.6 га талбай түймэрт өртсөнбөгөөдтухайн шатсан талбайгАНУ-ын Геологийн албанаас санал болгосон шаталтын зэрэглэлийн ангиллаартооцоход шаталтын бага зэрэглэлтэй 15,423.7 га буюу 26.3%, дундаас доогуурзэрэглэлтэй 29,529.4 га буюу 50.4%, дундааж дээгүүрзэрэглэлтэй 13,160.2 га буюу 22.5%, өндөрзэрэглэлтэй 18.3га буюу 0.03%-ийг эзэлж байна.Нийт шатсан талбайн 7,181.5 га буюу 12.4%-ийг ойн талбайн эзэлж байна. Түймэрт өртсөн нийт талбайн 87.6% нь Монгол улсад, 12.4%нь ОХУ-ын нутагдэвсгэртбайна. Түймрийн шаталтын зэрэглэлднөлөөлж болох байгаль, газарзүйн 10 хүчин зүйлийг харьцуулан хамаарлыг Пирсоны корреляцийн коэффициентоор тооцоход 4 хүчин зүйл эерэг сул хамааралтай, 6 хүчин зүйл сул сөрөг хамааралтай гарсан. Түймрийн шаталтын зэрэглэлдбусад хүчин зүйлүүдээсээ хамгийн өндөрнөлөө үзүүлсэн хүчин зүйл ургамлын нормчилсон ялгаврын индекс‘NDVI’0.4 буюу сул эерэг хамааралтай байсан бол өндөршил 0.23 буюу хамаарал маш сул байна. Харин хур тунадас -0.22 буюу сөрөн сул хамааралтай байна.Түймрийн тархалтад салхи хүчтэй нөлөөлдөг хэдий ч түймрийн шаталттай харьцуулахад нөлөөлөөгүй сөрөг хамаарлыг үзүүлсэн байна.Энэ нь түймэр тархахад салхи нөлөөдөгч шатах материал удаан шатах нөхцөлд салхи эсрэг нөлөө үзүүлдэг нь харагдаж байна.Түймрийн дараа ургамлын төрөл, нөөц хомсдох, ургамлын бүтцэд өөрчлөлт орох, бэлчээрийн нөөц хомсдох, ховор амьтан ургамал устаж үгүй болох, ойн нөөц багасах, хүн болон мал амьтны амь эрсдэх,агаарыг их хэмжээгээр бохирдуулах зэрэг нийгэм-эдийн засагболонэкологид нөхөж баршгүй сөрөг үр дагавар гардаг учир түймрийн шаталт, тархалт, хамрах талбайг судалж, цаашид гарч болох эрсдэлийг тооцоолох, урьдчилсан сэргийлэх нөхцөлийг бүрдүүлэх юм.
Nighttime light data provides an important method for monitoring urbanization and regional development, but its specific applicability needs to be further explored. Nighttime light data provide a new way to obtain urbanization information. Based on GIS and RS technology, this paper obtains Mongolian nighttime light data from 1992 to 2018 by correcting DMSP/OLS and NPP/VIIRS data, aiming to analyze the applicability of nighttime light data to less developed countries. The research results show that the agglomeration effect of nighttime lights in the capital city cluster of Mongolia is obvious, presenting a spatial pattern of “concentration in the north central regions, and rapid growth along the railway line”; The total value of nighttime light showed a significant growth trend after 2010, which indicates that the power supply has achieved rapid and stable growth firstly; in second, when the spatial scale is larger, the more applicable the nighttime light data is, and the higher the accuracy of reflecting social and economic activities. At the same provincial scale, only when the urbanization rate is greater than 30% and the population cannot be lost in large quantities, the nighttime light data has a positive correlation with the number of people, otherwise it is negatively correlated or irrelevant; in third, NPP/VIIRS has detected more settlements than DMSP/OLS, and stable power supply is key to whether settlements are detected.
Using GIMMS NDVI growing season (April-October) data from 1982-2015, mean temperature and monthly precipitation data from 60 meteorological station observations for the same period, the vegetation cover change and climate change in the Mongolian region and its response were studied with the help of linear trend analysisand Pearson correlation method the relationship between vegetation cover change and climate change in Mongolia and its response was studied using linear trend analysis, Mann-Kendall trend analysis and Pearson correlation. The results show that the average NDVI of the growing season in Mongolia has gradually increased spatially from south to north over the past 34 years. Seasonally, there was an overall increasing trend of NDVI in all three seasons, and the influence of precipitation on vegetation NDVI was greater than the influence of temperature on it. Different vegetation types responded more to precipitation than to temperature, and alpine grassland vegetation significantly affected air temperature and precipitation.
Using the 1982–2015 GIMMS NDVI growing season (April — October) data, the average temperature and monthly precipitation data of 60 meteorological stations in the same period, the linear trend analysis method, the Mann-Kendall trend analysis method, the Pearson correlation we have studied the relationship between vegetation cover change and climate change in Mongolia and their response relationship. The results show that in the past 34 years, the average NDVI of the growing season in Mongolia has gradually increased from south to north in space. From the seasonal point of view, the NDVI showed an increasing trend in all three seasons. From the impact of vegetation NDVI, the impact of precipitation on vegetation NDVI was greater than that of temperature. The response of different vegetation types to precipitation is greater than that of air temperature, and alpine grassland vegetation has a significantly effect on air temperature and precipitation.
Дэлхий дээр жил болгон олон янзын байгалийн гамшиг нүүрлэж, байгаль, хүн, ан амьтан цаашлаад нийгэмд асар их хор хөнөөл учруулж байна. Үүний нэг нь ойн болон хээрийн түймэр. Байгалийн гамшигт үзэгдэл, байгалийн нөөцийн судалгаанд зайнаас тандан судалгааны аргыг өргөн ашиглаж, хяналт, мониторинг хийж байна. Ой хээрийн түймрийн голомтыг цаг тухайд нь зөв тооцоолох нь түймрийн дараах менежмент, шийдвэр гаргахад онцгой ач холбогдолтой. Энэхүү судалгаагаар Европын Сансар Судлалын Агентлаг (ESA) -ийн Sentinel-2 хиймэл дагуулын зургийг ашиглан Дорнод аймгийн Баян-Уул, Баяндун сумдын нутагт гарсан ой хээрийн түймэрт өртсөн өртсөн талбайг тооцоолох, шатсан талбарын зэрэглэлийг ангилах, байгалийн нөхөн сэргэлтийн үйл явцыг тодорхойлох явдал юм. Бид судалгаандаа нормчилсон шаталтын харьцаа буюу ‘NBR’, ‘NBR+’ болон индексүүдийг түймэр гарахын өмнө болон гарсны дараах хиймэл дагуулын зураг дээр тооцон гаргасан. Нийт 58834.47 га талбай шатсан ба шатсан талбайд шаталтын зэргийг тус тус тооцсон. Үүнээс бага зэрэглэлтэй 15875.41 га буюу 26.9 хувь, бага-дунд зэрэглэлтэй 29775.28 га буюу 50.61 хувь, их-дунд зэрэглэлтэй 13124.97 га буюу 22.31 хувь, өндөр зэрэглэлтэй 58.8 га буюу 0.1 хувийг эзэлж байна. Түймэрт өртсөн нийт талбайн 87.6 хувь Монгол улсад, 12.4 хувь нь ОХУ-ын нутагт гарсан ба түймрийн улмаас 7181.52 га ой шатсан байна. Түймрийн дараа ургамлын төрөл, нөөц хомсдох, ургамлын бүтцэд өөрчлөлт орох, бэлчээрийн нөөц хомсдох, ховор амьтан ургамал устаж үгүй болох, ойн нөөц багасах, хүн болон мал амьтны амь эрсдэх агаарыг их хэмжээгээр бохирдуулах зэрэг нийгэм-эдийн засаг, экологид нөхөж баршгүй сөрөг үр дагавар гардаг учир түймрийн шаталт, тархалт, хамрах талбайг судалж, цаашид гарч болох эрсдэлийг урьдчилсан сэргийлэх нөхцөлийг бүрдүүлэх юм.
Ой, хээрийн түймэр нь байгалийн экосистемийг сүйрэлд хүргэж болзошгүй юм. Гэхдээ энэ нь зарим талаараа өрөөсгөл ойлголт ч байж болох бөгөөд байгаль өөрөө өөрийгөө цэвэрлэж буйн ямар нэгэн хэлбэр гэж ойлгож болох юм. Зайнаас тандан судлах хиймэл дагуулууд нь тодорхой хэмжээний түймрийг хянах зардал багатай аргыг санал болгох ба сүүлийн жилүүдэд дэлхийг ажиглах зайнаас тандан судлалын зорилготой орон зайн дунд болон өндөр нарийвчлалтай оптик хиймэл дагуулын мэдээллийн хүртээмж нэмэгдэж байгаа нь судлаачид болон түймрийн удирдлагын ажилтнуудад түймрийн дараах нарийвчилсан үнэлгээ хийх боломжийг олгож байна. Энэхүү судалгаагаар ‘NBR’ нь түймэрт өртсөн газар нутгийг тодорхойлох, ‘Sentinel-2’ хиймэл дагуулын өгөгдлүүдийг ашиглан шаталтын талбайн үнэлгээ хийхэд хамгийн чухал индексийн нэг болохыг харуулсан болно. Дүгнэж хэлэхэд зайнаас тандан судлал болон ГМС-ийн техник нь байгалийн экосистем дэх ой, хээрийн түймрийн нөлөөллийн талаарх амин чухал ойлголтыг өгч чаддаг. Энэ нь дэлхий даяар гарч буй томоохон болон жижиг хэмжээний ой, хээрийн түймрүүдийг үр дүнтэй илрүүлэх, хянах, төлөвлөх, урьдчилан сэргийлэх менежментэд чухал ач холбогдолтой юм.
Grassland ecosystem dominates in the Mongolian Plateau, mostly located in its arid and semi-arid regions. Although the ecosystem is an important source for agriculture, it is also a fragile system ecologically. This system is one of the most sensitive areas to global climate change. Precipitation (PPT) and soil moisture (SM) are important water sources in the grassland ecosystem, and their changes would greatly affect vegetation growth. This paper generates the precipitation use efficiency (PUE) and soil water use efficiency (SWUE) of Mongolian Plateau grassland based on multi-source remote sensing data to investigate the spatio-temporal distribution pattern and identify the driving factors. Results showed four main findings. Firstly, two water use efficiency (WUE) indicators show a generally increasing trend from 2000 to 2018, with average PUE and SWUE 1.07 gC⋅m− 2⋅mm− 1 and 1.03 gC/kg⋅H2O, respectively. They have similar spatial distribution patterns, consistent with the available water resources, decreasing from northeast to southwest. However, grassland vegetation growth is more sensitive to soil moisture than precipitation, and the dynamic change of SWUE is smoother and more significant than PUE. Secondly, due to the higher species richness, better vegetation biological characteristics and less severe growth environment, meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Thirdly, PUE and SWUE are relatively low in extremely arid and humid regions. In areas with relatively moderate water conditions (PPT in 148–360 mm, SM in 0.14–0.35 cm3/cm3), two indicators increase with the abundance of moisture conditions and reach the maximum. Fourthly, there is a positive linear relationship between PUE (SWUE) with precipitation and a unimodal correlation between PUE (SWUE) with temperature across the entire grassland. However, a varying correlation exists in different grassland ecosystems, especially meadow grassland. Together with analyzing past and future trends, this study provides strong evidence to reflect the impact of global climate change and the management and protection of the grassland ecosystems in arid and semi-arid regions.
Ikh Nuuruudyn khotgor in western Mongolia which is characterized by large vertical extend of the sand and area with intensity of desertification in Mongolia. Therefore, it is required to do scientific basis of preservation and usage after examining sand shifting, type, extents of sand in this area. The main objective is to detect the dune sand distribution and its movement in study area, including 1) to determine the geographical conditions 2) to mapping dune sand distribution from topographic map and satellite images, 3) to calculate correlation with climate factors. The variations of spatial and time series of the sand distribution area in this area were delineated using Landsat, NOAA, DEM, MODIS data from 1985-1990-1995-2000-2005-2010-2015-2020 and variations of precipitation and temperature is estimated between these periods. Correlation matrix by Pearson’s correlation coefficient (2) and the ordinary least square estimation of regression analysis was applied in this study to analyze time series of field study area. The result of the study showed sand accumulation area as 1457722.3 hectares (12.7% of total study area) and 1889957.9 hectares (16.5% of total study area) in 1985 and 2020 respectively in this area. Compared to 1985, the sandy area increased by 432235.6 ha in 2020. It is increased by 29.07 % as relative variation percent for three decades in study area. The results showed that the sand distribution changes are negatively correlated with NDVI and precipitation as Pearson’s correlation coefficient. Besides, sand distribution changes are linear correlated with temperature between 1985 and 2020 in study area.
Ikh Nuuruudyn khotgor in western Mongolia which is characterized by large vertical extend of the sand and area with intensity of desertification in Mongolia. Therefore, it is required to do scientific basis of preservation and usage after examining sand shifting, type, extents of sand in this area. The main objective is to detect the dune sand distribution and its movement in study area, including 1) to determine the geographical conditions 2) to mapping dune sand distribution from topographic map and satellite images, 3) to calculate correlation with climate factors. The variations of spatial and time series of the sand distribution area in this area were delineated using Landsat, NOAA, DEM, MODIS data from 1985-1990-1995-2000-2005-2010-2015-2020 and variations of precipitation and temperature is estimated between these periods. Correlation matrix by Pearson’s correlation coefficient (2) and the ordinary least square estimation of regression analysis was applied in this study to analyze time series of field study area. The result of the study showed sand accumulation area as 1457722.3 hectares (12.7% of total study area) and 1889957.9 hectares (16.5% of total study area) in 1985 and 2020 respectively in this area. Compared to 1985, the sandy area increased by 432235.6 ha in 2020. It is increased by 29.07 % as relative variation percent for three decades in study area. The results showed that the sand distribution changes are negatively correlated with NDVI and precipitation as Pearson’s correlation coefficient. Besides, sand distribution changes are linear correlated with temperature between 1985 and 2020 in study area.
Дэлхий уур амьсгалын өөрчлөлтийн шууд болон дам үр дагаврын нөлөөллөөр олон төрлийн байгалийн гамшигт үзэгдлүүд нэмэгдэж, тэдгээрийг судлах зорилгоор орчин үеийн судалгааны арга техникийг өргөн ашиглах боллоо. Үүний нэг тод илрэл нь зайнаас тандан судлалын арга бөгөөд түүний зураглалын технологийн тусламжтайгаар байгаль орчны өөрчлөлтийг хянах, урьдчилан таамаглах, үнэлэх, илрүүлэх боломжууд нэмэгдэж, судалгаа шинжилгээний хувьд ач холбогдол өндөртэй болж байгаа юм. Энэхүү судалгааны үндсэн зорилго нь ‘Sentinel-2’ хиймэл дагуулын зураглалын технологийн ашиглан түймрийн гамшигт үзэгдлийг хянах, түймэрт өртсөн талбайг тодорхойлох, шатсан талбарын зэрэглэлийг ангилах, байгалийн нөхөн сэргэлтийн үйл явцыг тодорхойлох явдал юм. Судалгааны нутаг дэвсгэрийг Монгол орны зүүн хэсэг, тал хээрийн бүсэд орших Шилийн Богд уул орчим дахь түймэр гарсан газар нутгийг сонгон авсан болно. Судалгааны аргазүйн хувьд нормчилсон шаталтын харьцаа буюу ‘NBR’ индексийг ашиглан түймэрт өртсөн талбарын талбай, шатсан талбарын зэрэглэлийг шатаагүй, бага, бага-дунд, их-дунд, өндөр гэсэн 5-н зэрэглэлд зураглан, нөхөн сэргэлтийн явцыг ургамлын нормчилсон индекс буюу ‘NDVI’ үзүүлэлт, хээрийн ажиглалтын үйл явцаар тодорхойлсон болно. Судалгааны үр дүнд сансрын зургаас түймэрт шатсан талбайг ялган зураглаж, нийт шатсан талбайн хэмжээг 1164.27 км2 гэж тооцоолсон. Үүний 65 хувь буюу 757.34 км2 талбай сул, 34.7 хувь буюу 404.57 км2 талбай бага-дунд, үлдсэн 0.3 хувь буюу 2.36 км2 талбай өндөр-дунд шаталтын зэрэглэлийн ангилалд багтаж байгааг тодорхойлсон. Энэ судалгааны ажил нь онцгой байдлын газрын албан хаагчид, судлаачид, байгаль орчны мэргэжилтнүүдэд түймэр гарсан газар нутгийн шаталтын зэргийг тодорхойлох, нөхөн сэргэлтийн байдлыг илрүүлэх, гамшгийн зэргийг үнэлэх, дүн шинжилгээ хийх зэрэг ажлуудад аргазүйн хувьд дэмжлэг болохуйц ач холбогдолтой юм.
