Дэлгэрэнгүй мэдээлэл

Mongolia has extremely fragile ecosystems and rich vegetation resources in arid and semi-arid zones. It is highly affected by extreme climatic events and is important in the global carbon cycle. In global warming, it is important to study its vegetation changes for ecological security. In this paper, based on the gross primary productivity (GPP) data with daily maximum temperature, daily minimum temperature and daily precipitation data of Mongolia from 2000 to 2023, the characteristics of spatial and temporal changes in GPP and 3 its response to climate extremes were analyzed by using Sen Slope + Mann Kendall trend analysis, MK mutation test, Pearson's correlation analysis method, and structural equation modeling (SEM). The main findings of the study are as follows: (1) The overall trend of GPP in Mongolia from 2000 to 2023 is upward. Most areas with high GPP values are located in the northern Sayanleng and Khangai Mountains, and the areas with significant increases account for 61% of the total area of the study area; (2) Extreme temperature indices (SU, TNx, TNn) and extreme precipitation index R20 show an increasing trend at most weather stations in Mongolia, while other extreme precipitation indices (R95P, SDII) show a decreasing trend; (3) Extreme precipitation indices contributed to the effect of GPP in the study area, but they all inhibited GPP in Zabkhan province. R20 is the main factor influencing vegetation growth. Extreme temperature indices TNx and SU inhibited GPP and promoted GPP excepting the cold all year round Khuvsgul province, where GPP was promoted due to summers became warmer; (4) R20 and R95P significantly affect GPP positively and negatively, respectively. This opposed relationship reflects the dual role of precipitation pattern and intensity in regulating vegetation productivity. Other indices may influence ecosystem dynamics through indirect pathways under extreme climate change scenarios. The study's results can provide scientific references for Mongolia's ecological environment protection and green sustainable development.

Abstract: Grassland ecosystems are essential components of the global ecosystem. They may efficiently reduce CO2 concentrations in the atmosphere and play a vital role in mitigating climate change. The objectives of this study were to reveal the spatial distribution features of net primary production (NPP) and net ecosystem productivity (NEP) under climate change in the Inner Mongolia grassland ecosystem, China, and to devise effective management strategies for grassland ecosystems. Based on the multiscale geographically weighted regression (MGWR) model, this study investigated the spatial variation features of NPP and NEP along with their driving factors. The results showed the following: (1) The annual average NPP in the Inner Mongolia grassland ecosystem was 234.22 gC · m−2 · a−1, and the annual average NEP was 60.31 gC · m−2 · a−1 from 2011 to 2022. Both measures showed a spatial pattern of high values in the northeast and low values in the southwest, as well as a temporal pattern of high values in summer and low values in winter. (2) The normalized difference vegetation index (NDVI) and solar radiation had promoting effects on NPP, where NDVI had the largest significant positive correlation area. In addition, precipitation and temperature on the influence of NPP were significantly negative with a larger area. (3) The area with a significant positive correlation of NDVI, solar radiation, and precipitation on NEP was larger than that with a significant negative correlation, while the area with significant negative correlation of temperature was larger. This study used the MGWR model to explore the relationship between NPP, NEP, and multiple factors. The results showed regional variation in NPP and NEP under the combined effect of various drivers. This contributes to a better understanding of carbon sinks under climate change in the Inner Mongolia grassland ecosystem.

Aim: Cold degree days (CDD) represent the heat deficit for vegetation leaf senescence in autumn and serve as a critical parameter in modelling leaf senescence. This study aimed to quantify the spatiotemporal patterns of CDD and its key accumulation processes and determinants. Location: At northern middle and high latitudes (> 30° N). Period: 2001–2022. Major Taxa Studied: Vegetation. Methods: We estimate CDD as the cumulative sum of the difference between the daily mean temperature and a threshold temperature (12.75°C) during the period from midsummer to the end of the growing season. To identify its crucial metric, we employ a combination of grey relational analysis, random forest model and partial correlation analysis. Results: The average CDD increases linearly with latitude at a rate of 5.9°C-days per degree. Higher latitudes exhibit larger CDD (> 300.0°C-days), longer accumulation periods (> 70 days) and faster accumulation rates (> 6.0°C/day), whereas lower latitudes show smaller CDD (< 60.0°C-days), shorter accumulation periods (< 30 days) and slower accumulation rates (< 1.0°C/day). Temporally, CDD tended to decrease from 2001 to 2022 with −1.3°C ± 4.0°C-days/year, largely attributed to climate warming. Precipitation frequency emerged as a significant climatic variable influencing CDD variations across > 46% of the study area, especially at high latitudes and on the Tibetan Plateau. While climate warming generally reduces CDD, an increase in precipitation frequency can counteract this trend and shape the relationship between precipitation amount and CDD. The effects of radiation and wind speed on CDD were less pronounced than those of precipitation frequency, with wind exerting a positive (cooling) effect that increases CDD accumulation and radiation producing a negative (heating) effect that decreases CDD accumulation. Main Conclusions: This study highlights the critical aspects of the CDD accumulation process and emphasises the importance of incorporating precipitation frequency into CDD-based autumn phenology models across northern latitudes

The northern foothills of the Yinshan Mountains lie at the intersection of arid and semi-arid regions, where vegetation is predominantly composed of desert steppes. The ecological environment in this area is highly fragile, and vegetation dynamics serve as sensitive indicators of regional climate change. This study analyzes changes in vegetation net primary productivity (NPP) and its response to climatic factors from 2001 to 2020, employing Theil-Sen trend analysis and statistical methods to assess spatial and temporal patterns. Additionally, it investigates the relationship between NPP variability and climate variables. The results indicate the following:(1) The 20-year average NPP in the region is 152.3 gC/(m²·a), with interannual variation ranging from 103.9 to 194.7 gC/(m²·a). The spatial trend of NPP generally shows an upward trajectory, with slopes between 1 and 3. (2) There is a strong positive correlation between annual precipitation and annual NPP across 95.62% of the area, while no significant correlation is observed between annual NPP and annual mean temperature. Seasonally, spring temperature shows a significant positive correlation with annual NPP over 28.93% of the area. In contrast, summer temperature exhibits a significant negative correlation with NPP in 20.57% of the region, while autumn temperature shows no notable relationship. Regarding precipitation, spring precipitation has little influence, with only 0.5% of the area showing a significant positive correlation with annual NPP. Summer precipitation, however, displays a strong positive correlation across 95.66% of the region. Autumn precipitation has limited influence, with a significant positive correlation found in just 2.86% of the area.

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought event, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm event, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks.

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought events, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm events, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks.

first_pagesettingsOrder Article Reprints Open AccessArticle Utilizing Deep Learning and Spatial Analysis for Accurate Forest Fire Occurrence Forecasting in the Central Region of China by Youbao Guo 1,2,Quansheng Hai 1,3,4,* andSainbuyan Bayarsaikhan 1,4,5 1 Department of Geography, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar 14200, Mongolia 2 Department of Railway Engineering, Baotou Railway Vocational and Technical College, Baotou 014060, China 3 Department of Ecology and Environment, Baotou Teacher’s College, Baotou 014030, China 4 Laboratory of Geoinformatics (GEO-iLAB), Graduate School, National University of Mongolia, Ulaanbaatar 14200, Mongolia 5 Research Institute of Urban and Regional Development, National University of Mongolia, Ulaanbaatar 14200, Mongolia * Author to whom correspondence should be addressed. Forests 2024, 15(8), 1380; https://doi.org/10.3390/f15081380 Submission received: 24 June 2024 / Revised: 17 July 2024 / Accepted: 27 July 2024 / Published: 7 August 2024 (This article belongs to the Section Natural Hazards and Risk Management) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Forest fires in central China pose significant threats to ecosystem health, public safety, and economic stability. This study employs advanced Geographic Information System (GIS) technology and Convolutional Neural Network (CNN) models to comprehensively analyze the factors driving the occurrence of these fire events. A predictive model for forest fire occurrences has been developed, complemented by targeted zoning management strategies. The key findings are as follows: (i) Spatial analysis reveals substantial clustering and spatial autocorrelation of fire points, indicating high-density areas of forest fire occurrence, primarily in Hunan and Jiangxi provinces, as well as the northeastern region. This underscores the need for tailored fire prevention and management approaches. (ii) The forest fire prediction model for the central region demonstrates exceptional accuracy, reliability, and predictive power. It achieves outstanding performance metrics in both training and validation sets, with an accuracy of 86.00%, precision of 88.00%, recall of 87.00%, F1 score of 87.50%, and an AUC value of 90.50%. (iii) Throughout the year, the occurrence of forest fires in central China varies by location and season. Low-occurrence periods are observed in summer and winter, particularly in Hunan and Hubei provinces, due to moderate weather conditions, agricultural practices, and reduced outdoor activities. However, spring and autumn also present localized risks due to uneven rainfall and dry climates. This study provides valuable insights into the dynamics of forest fire occurrences in central China, offering a solid framework for proactive fire management and policy formulation to effectively mitigate the impacts of these events.

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.