生态系统生产总值与区域经济耦合协调机制及协同发展——以大连市为例

以生态系统生产总值(Gross Ecosystem Product,GEP)为手段评估生态系统状况,引领地方绿色发展方向,是实现"绿水青山"向"金山银山"转化的有效路径。文章以滨海城市大连为研究对象,在阐述GEP和区域经济发展的耦合协调机制基础上,对大连市2005、2010和2015年三期GEP进行核算,从时空维度对GEP—经济两大系统耦合协调关系予以分析,并就推动生态系统与区域经济可持续发展提出对策建议。结果表明:①2005—2015年,大连市生态系统生产总值处于上升趋势,实际增值141.95亿元,增幅37.1%,但调节服务价值实际增值仅为10.9亿元,实际增幅5.68%;②2005—2015年,庄河市GEP综合指数上升程度最大,增长率达140.02%,只有长海县GEP综合指数有所降低,下降率为30.61%;③2005—2015年,大连市经济与GEP主要耦合协调发展类型由中度失调—经济滞后变为中度协调—生态滞后,耦合协调发展持续转好,但GEP发展滞后于经济发展。GEP核算可以体现人类活动对生态系统的影响程度,定量反映生态系对经济发展的作用,从而为实现经济社会同生态文明建设协同发展提供科学依据。

Spatio-temporal Distribution of Drought in the Belt and Road Area During 1998–2015 Based on TRMM Precipitation Data

Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Based on the Tropical Rainfall Measuring Mission Satellite（TRMM） 3 B43 precipitation data, we used the Precipitation Abnormity Percentage drought model to study the monthly spatio-temporal distribution of drought in south region of N50° of the Belt and Road area. It was observed that drought during winter was mainly distributed in Northeast Asia, Southeast Asia, and South Asia, while it was mainly distributed in Central Asia and West Asia during summer. The occurrence of historical droughts indicates an obvious seasonal cycle. The regional variations in drought were analyzed using the Breaks for Additive Season and Trend tool（BFAST） in six sub-regions according to the spatial distribution of six economic corridors in the Belt and Road area. The average drought conditions over the 18 years show a slight decreasing trend in Northeast Asia, West Asia, North Africa, South Asia, Central and Eastern Europe, and a slight increasing trend in Central Asia. However, it was a fluctuating pattern of first increasing and then decreasing in Southeast Asia. The results indicate that the total drought area in the Belt and Road region showed a general decreasing trend at a rate of 40,260 km^2 per year from 1998 to 2015.

Evaluation of Water Yield and Soil Erosion in the Three-River-Source Region under Different Land-Climate Scenarios

Exploring the water yield and soil conservation in the Three-River-Source region is of great significance for evaluating both the ecological stability of the Qinghai-Tibet Plateau,Yellow River basin,Yangtze River basin and Lancang River basin and the sustainable development of human society.The data sources for this study were land use/cover data from four phases(2000,2005,2010 and 2015),daily precipitation and temperature datasets,and the 1:1000000 Chinese soil database.These data were combined with vector data,such as data on settlements,roads,and rivers,along with population,economic raster datasets and CCSM4 common climate model prediction results.The Three-River-Source region was taken as the study area,and four land use/cover development scenarios and two climate change scenarios were designed based on the FLUS model and the downscaling correction method.The InVEST model was used to quantitatively simulate the water yield and soil erosion under different scenarios in the study area in 2030.The results showed the following:(1)Under different land use/cover development scenarios,grassland remained the dominant land use/cover type in the Three-River-Source region,and the area ratio was always greater than 67%.(2)Under the RCP4.5 climate scenario,the annual water yield and soil erosion increased by more than 7%and 3.9%,respectively.Under the RCP8.5 climate scenario,the annual water yield and soil erosion decreased by more than 3.3%and 1.3%,respectively.(3)Climate change played a leading role in the changes in water yield and soil erosion.Climate change contributed as much as 89.97%–98.00%to the change in water yield and 60.49%–95.64%to the change in the soil erosion modulus.However,the contribution of land use/cover changes to the change in regional water yield was only 2.00%–10.03%,and the contribution of the soil erosion modulus change was 4.36%–39.91%.Therefore,the land use development strategy in the Three-River-Source region should comprehensively consider issues such as regional development,the input of returning farmland to forest and grassland,and the resulting ecological benefits.

Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing

Mongolia is an important part of the Belt and Road Initiative"China-Mongolia-Russia Economic Corridor"and a region that has been severely affected by global climate change.Changes in grassland production have had a profound impact on the sustainable development of the region.Our study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial distributions.Three estimation models were established using a statistical analysis method based on EVI,MSAVI,NDVI,and PsnNet from Moderate Resolution Imaging Spectroradiometer(MODIS)remote sensing data and measured data.A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation(model accuracy 78%).This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to 2015.The results show that the grassland production in the study area had a significantly fluctuating trend for the decade study;a slight overall increasing trend was observed.For the first five years,the grassland production decreased slowly,whereas in the latter five years,significant fluctuations were observed.The grassland production(per unit yield)gradually increased from the southwest to northeast.In most provinces of the study area,the production was above 1000 kg ha with the largest production in Hentiy,at 3944.35 kg ha.The grassland production(total yield)varied greatly among the provinces,with Kent showing the highest production,2341.76x1〇4 t.Results also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied.

Estimation of Travel Climate Comfort Degree in the Cross-border Region between China and Russia based on GIS

The duration of travel climate comfort degree is an important factor that influences the length of the tourism season and the development of a tourism destination.In this study,we used the monthly average meteorological data for the last 10 years from 46 weather stations in Heilongjiang Province(China)and Primorsky Krai(Russia)to calculate the temperature-humidity index(THI)and wind chill index(WCI)based on ArcGIS software interpolation technology.We obtained the climate comfort charts of the study area with a grid size a 1 km2 grid size,and analyzed the spatial distribution of comfort for each month.The results show the following:1)The THI and WCI of the cross-border region gradually decrease from south to north and from low altitude to high altitude.The annual comfortable climate period is longer when analyzed in terms of the WCI rather\than the THI.2)The travel climate comfortable period of the study area shows significant regional difference and the length of the comfortable period in Heilongjiang Province is 4 to 5 months.Meanwhile,the period in Primorsky Krai decreases from south to north and the length of the comfortable period length in its southern region can reach 7 months.3)The predominant length of the climate comfortable period in the cross-border area is 5 months per year,and it covers 46.6%of the total area,while areas that have a climate comfortable period of 2 months are the most limited,covering less than 0.3%of the area.The results provide a scientific basis for the utilization and development of a meteorological tourism resources and touring arrangements for tourists in the cross-border region between China and Russia.

Predicting Potential Geographic Distribution of Tibetan Incarvillea younghusbandii Using the Maxent Model

Incarvillea younghusbandii is a well-known Tibetan medicinal plant with considerable development and research value distributed widely throughout the Tibetan plateau.It is important to study spatial distribution patterns of the plant in order to develop effective protection measures.Based on field survey work and environmental data, the potential geographic distribution of Incarvillea younghusbandii was delineated using a Maximum Entropy (Maxent)model with 28environmental variables that screened for climate,topography,human activity and biological factors.Our results showed that the main geographic range of Incarvillea younghusbandii included the valley between the Yarlung Zangbo river and the Duoxiong Zangbo river,the valley in the middle section of the Himalaya Mountains,and the area between the north side of the east section of the Himalayas and the south bank of the middle reach of the Yarlung Zangbo river.Distribution may spread to parts of the eastern Himalayas.The Jackknife test indicated that soil types,ratio of precipitation to air temperature,extreme atmospheric pressure differences and annual precipitation were the most important predictive factors for the model,while other variables made relatively small contributions.

Impact of Land Use Changes on Habitat Quality in Altay Region

Altay region is located in the northern part of Xinjiang,and has complex and diverse internal geomorphic types,undulating terrain and a fragile ecosystem.Studying the impact of land use changes on habitat quality is of great significance to regional ecological protection and development,rational planning and utilization,and ensuring the sustainable development of the ecological environment.Based on the InVEST model,combined with land use panel data and topographic relief data of the Altay region,this paper studied the habitat quality from 1995 to 2018.The results show that cultivated land,water area and construction land increased gradually from 1995 to 2018,while grassland and unused land decreased.Forestland remained stable in the first five periods,but increased significantly in 2018.During 1995-2018,all land use types were transferred,mainly between cultivated land,forestland,grassland and unused land in the flat and slightly undulating areas.Poor habitat quality was dominant during 1995-2018.Habitat quality decreased significantly in 2015,which was related to the rapid expansion of cultivated and construction land as threat sources,as well as the decrease of forest and grassland as sensitive factors.However,habitat quality improved significantly in 2018,because a large amount of cultivated land and unused land were converted into forest land and grassland with high habitat suitability.Land use type has an important influence on habitat quality.The distribution characteristics of habitat quality for topographic relief types from good to bad were:large undulating area>medium undulating area>small undulating area>flat area>slightly undulating area.The findings of this study are of great significance for coordinating social,economic,and ecological development in this region and in similar areas.

Spatial Structure and Development of Tourism Resources based on Point Pattern Analysis: A Case Study in Hainan Island, China

Tourism resources are the basic materials of tourism development, and they also provide the support for regional tourism spatial competition. The development of tourism depends on the degree to which tourism resources are utilized, and it is of great guiding significance for their development and utilization to study their spatial structure. Based on a large sample of data on tourism resources, and starting from the characteristics of multi-type,multi-level and multi-combination, this paper puts forward a framework and method for analyzing the spatial structure of tourism resources. Taking Hainan Island as an example, this paper describes the spatial structure of tourism resources in Hainan Island by using the method of point pattern analysis, identifies the tourism resource development zones, and puts forward some suggestions for the development of tourism resources. The results are as follows:(1) The characteristic scale of the spatial structure of tourism resources in Hainan Island is 30.5 km, and there are significant differences in the distributions of all kinds of tourism resources.(2) Through the spatial structure map of tourism resources, the tourism resource development zones are identified, including three tourist central city levels, “one horizontal and three vertical” tourist belts and four tourist combination areas.(3) By combing the distribution of tourism resources and the development zones in Hainan Island, the cross-border characteristics of the tourism resources and development zones are obvious. In order to give full play to the spatial combination and superposition effect of tourism resources, a change from a single isolated development mode to the overall combined development between regions is suggested. On the provincial scale, it is relatively accurate to describe the spatial structure of tourism resources for point data with a large sample size. However, the method of point pattern analysis can not only accurately describe the spatial structure of tourism resources, but it can also provide reference for other types of regional spatial analyses. The research results provide a scientific basis for the spatial planning of regional tourism resources and have practical significance for the development of regional tourism.

Spatial Distribution and Dynamic Changes in Research Hotspots for Desertification in China based on Big Data from CNKI

Desertification research plays a key role in the survival and development of all mankind.The Normalized Comprehensive Hotspots Index(NCH)is a comprehensive index that reveals the spatial distribution of research hotspots in a given research field based on the number of relevant scientific papers.This study uses Web Crawler technology to retrieve the full text of all Chinese journal articles spanning the 1980s-2018 in the Chinese Academic Journal full-text database(CAJ)from CNKI.Based on the 253,055 articles on desertification that were retrieved,we have constructed a research hotspot extraction model for desertification in China by means of the NCH Index.This model can reveal the spatial distribution and dynamic changes of research hotspots for desertification in China.This analysis shows the following:1)The spatial distribution of research hotspots on desertification in China can be effectively described by the NCH Index,although its application in other fields still needs to be verified and optimized.2)According to the NCH Index,the research hotspots for desertification are mainly distributed in the Agro-Pastoral Ecotone and grassland in Inner Mongolia,the desertification areas of Qaidam Basin in the Western Alpine Zone and the Oasis-Desert Ecotone in Xinjiang(including the extension of the central Tarim Basin to the foothills of the Kunlun Mountains,the sporadic areas around the Tianshan Mountains and the former hilly belt of the southern foothills of the Altai Mountains).Among these three,the Agro-Pastoral Ecotone in the middle and eastern part of Inner Mongolia includes the most prominent hotspots in the study of desertification.3)Since the 1980s,the research hotspots for desertification in China have shown a general downward trend,with a significant decline in 219 counties(10.37%of the study area).This trend is dominated by the projects carried out since 2002.The governance of desertification in the eastern part of the Inner Mongolia-Greater Khingan Range still needs to be strengthened.The distribution of desertification climate types reflects the distribution of desertification in a given region to some extent.The Normalized Comprehensive Hotspots Index provides a new approach for researchers in different fields to analyze research progress.

Quantitative Assessment of the Effects of Climate Change and Human Activities on Grassland NPP in Altay Prefecture

Grassland degradation in Altay Prefecture is of considerable concern as it is a threat that hinders the sustainable development of the local economy and the stable operation of the livestock industry.Quantitative assessment of the relative contributions of climate change and human activities,which are considered as the dominant triggers of grassland degradation,to grassland variation is crucial for understanding the grassland degradation mechanism and mitigating the degraded grassland in Altay Prefecture.In this paper,the Carnegie-Ames-Stanford Approach model and the Thornthwaite memorial model were adopted to simulate the actual net primary productivity(NPP_(A))and potential net primary productivity(NPP_(P))in the Altay Prefecture from 2000 to 2019.Meanwhile,the difference between potential NPP and actual NPP was employed to reflect the effects of human activities(NPP_(H))on the grassland.On this basis,we validated the viability of the simulated NPP using the Pearson correlation coefficient,investigated the spatiotemporal variability of grassland productivity,and established comprehensive scenarios to quantitatively assess the relative roles of climate change and human activities on grassland in Altay prefecture.The results indicate three main points.(1)The simulated NPP_(A) was highly consistent with the MOD17 A3 dataset in spatial distribution.(2)Regions with an increased NPP_(A) accounted for 70.53% of the total grassland,whereas 29.47% of the total grassland area experienced a decrease.At the temporal scale,the NPP_(A) presented a slightly increasing trend(0.83 g C m^(-2) yr^(-1))over the study period,while the trends of NPP_(P) and NPP_(H) were reduced(-1.31 and-2.15 g C m^(-2) yr^(-1)).(3)Compared with climate change,human activities played a key role in the process of grassland restoration,as 66.98% of restored grassland resulted from it.In contrast,inter-annual climate change is the primary cause of grassland degradation,as it influenced 55.70% of degraded grassland.These results could shed light on the mechanisms of grassland variation caused by climate change and human activities,and they can be applied to further develop efficient measures to combat desertification in Altay Prefecture.

Spatial and Temporal Pattern Changes and Driving Forces:Analysis of Salinization in the Yellow River Delta from 2015 to 2020

China’s Yellow River Delta represents a typical area with moist semi-humid soil salinization,and its salinization has seriously affected the sustainable use of local resources.The use of remote sensing technology to understand changes in the spatial and temporal patterns of salinization is key to combating regional land degradation.In this study,a feature space model was constructed for remote sensing and monitoring land salinization using Landsat 8 OIL multi-spectral images.The feature parameters were paired to construct a feature space model;a total of eight feature space models were obtained.An accuracy analysis was conducted by combining salt-loving vegetation data with measured data,and the model demonstrating the highest accuracy was selected to develop salinization inversion maps for 2015 and 2020.The results showed that:(1)The total salinization area of the Yellow River Delta displayed a slight upward trend,increasing from 4244 km^(2) in 2015 to 4629 km^(2) in 2020.However,the area’s salting degree reduced substantially,and the areas of saline soil and severe salinization were reduced in size;(2)The areas with reduced salinization severity were mainly concentrated in areas surrounding cities,and primarily comprised wetlands and some regions around the Bohai Sea;(3)Numerous factors such as the implementation of the“Bohai Granary”cultivation engagement plan,increase in human activities to greening local residential living environments,and seawater intrusion caused by the reduction of sediment contents have impacted the distribution of salinization areas in the Yellow River Delta;(4)The characteristic space method of salinization monitoring has better applicability and can be promoted in humid-sub humid regions.

Spatial Analysis and Biogeochemical Cycles: A Comparative Study of Kashin-Beck Disease Villages and Non-disease Villages in Linzhou County, Tibet

This study investigated Linzhou County in Tibet, which currently hosts the most serious outbreak of Kashin-Beck disease(KBD) in China. This study uses the geographical detector(GeoDetector) algorithm to measure the influences that several risk factors have on KBD prevalence and validates the spatial analysis results with environmental chemistry. Based on a comprehensive examination of 10 potentially related spatial factors and an environmental chemistry analysis of the soil-water-grain-human biogeochemical cycle in the local KBD and non-KBD villages, four main conclusions are drawn.(1) KBD in Linzhou County is a consequence of multiple interrelated environmental factors, of which the most important controlling factor is the stratum factor.(2) The concentrations of selenium(Se) in all environmental media(soil, water, and food) and human tissue in the KBD villages in Linzhou County are lower than those of the non-KBD villages.(3) The intake of Se and chromium(Cr) by local residents is seriously insufficient, especially the average daily dose by ingestion(ADD) for Se in the KBD village, which is only about 4% of the World Health Organization(WHO) recommended lower limit for adult elemental intake.(4) We speculate that the main cause for the local KBD outbreak is a lack of Se in the stratum. This absence leads to a serious Se deficiency in the local population through ecosystem migration and transformation, which will eventually lead to an endemic biogeochemical Se deficiency.

Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development.In the past 20 years,the ecological environment in Altay Prefecture has changed significantly due to global warming.Meanwhile,with increasing human activities,the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess.Hence,we quantified the vegetation growth by using the Normalized Difference Vegetation Index(NDVI)on the Google Earth Engine(GEE).Then,the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale.Finally,significance threshold segmentation was performed using meteorological data based on the correlation analysis results,and the contributions of climate change and human activities to vegetation variation were quantified.The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north.The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture,respectively.Moreover,spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature,precipitation and sunlight hours accounted for 3.3%,6.9% and 20.3% of Altay Prefecture,respectively.In the significant restoration area,18.94% was dominated by multiple factors,while 3.4% was dominated by human activities,and 1.74% was dominated by climate change.Within the significant degradation area,abnormal degradation and climate change controlled 1.07% and 0.17%,respectively.This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture,and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture.

Geographical and Botanical Variation in Concentrations of Molybdenum in Natural Pasture Plants and Surface Water and Yak Molybdenum Ingestion in North Tibet,China

The North Tibet plateau is the world highest plateau with a unique alpine grassland and water environment. To obtain a better understanding of the correct supply of Molybdenum（Mo） to livestock in north Tibet,we investigated the content and geographical variation of Mo in different families of pasture plants（n=1017） and water（n=40）,then discuss the Cuprum（Cu）：Mo ratio in different plant families,and calculate the total Mo intake of the yak in north Tibet. The average Mo concentration in five families preferred for grazing are： Compositae（2.71 μg g^（-1））,Leguminosae（2.70 μg g^（-1））,Gramineae（2.48 μg g^（-1））,Cyperaceae（1.63 μg g^（-1））,and Rosaceae（1.51 μg g^（-1））. There was a strong geographical variation in Mo concentration（p 0.001）. The mean value of Mo in north Tibet surface water from 15 sites is 0.89 μg L^（-1）. The Mo ingestion by yak through these plants and water in north Tibet is about 9586 μg day^（-1） which means the toxicity of Mo does not exist in the average daily diet. However,the large geographical variation found may cause some toxicity of Mo in the average daily intake of north Tibet pasture plants in some areas. The Cu：Mo ratio of 2：60 is considered the limit for risk of Mo hyperactivity,while extremely high Cu：Mo ratios may lead to chronic copper poisoning. Our survey of plant samples found 43.29% below and 29.3% above the limiting Cu：Mo ratio of 60 indicating some risk to north Tibet livestock.

Intelligent Identification of Building Patches and Assessment of Roof Greening Suitability in High-density Urban Areas:A Case Study of Chengdu

With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment.To provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society environment.The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis methods.Taking the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment.The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu.This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.

Evolution of Landscape Pattern and Tourism Service Value in Zhangjiakou City

Zhangjiakou is a northern Chinese city that hosted the Beijing 2022 Winter Olympic Games. As an important ice and snow tourist destination, it is essential to investigate Zhangjiakou’s rate of landscape pattern change,the landscape ecological security level, and ecosystem service value, particularly the tourism ecological service value during its construction. With land use data from 2000 to 2020, this study comprehensively analyzed the dynamic changes in Zhangjiakou, including land use dynamics, the land use transfer matrix, landscape vulnerability,landscape disturbance, ecosystem service value, tourism ecological service value, and other aspects. The results show that the landscape pattern in Zhangjiakou was greatly disturbed from 2015 to 2018, and the landscape ecological security was threatened in the process of landscape pattern adjustment. By 2020, after the landscape pattern was adjusted and stabilized, the landscape ecological security was restored, and the ecosystem service value was significantly improved, especially the tourism ecological service value. The results of this study will play an important role in promoting the optimization of Zhangjiakou’s ice and snow landscape pattern and the improvement of tourism ecological value. In addition, it provides important lessons for the development of other ice and snow tourist destinations.

Changes in the Geographical Distributions of Global Human Settlements

In recent decades,the continuous growth in the population has significantly changed the area of human settlements across the globe.The change of human settlements has brought great challenges to human development,environmental change,resource allocation,and disaster prediction and prevention.In the current paper,we integrate data products provided by the European Commission,Joint Research Centre with multi-source remote sensing data to analyze the changing trends of global human settlements under varying geographical distributions from 1990 to 2014.The results demonstrate that on the global scale,human settlements are generally distributed in Europe,East Asia,Southeast Asia,South Asia,the eastern United States,the Gulf Coast and the coast of Oceania,with most of them distributed in urban agglomerations and coastal areas.Global human settlements have continued to grow over the past 25 years,mainly in East Asia,Western Europe and the United States.The area of human settlements in eastern Europe has been slightly reduced.The distribution of human settlements is affected by climate,water and terrain conditions.Humans were more likely to have settled in temperate regions with wetter climates,and most of the human settlements are located within 500 km of the coastline and 30 km of land-based water sources.Our results can provide insights into further investigations of the spatio-temporal dynamics of human settlements and its connections to ecological and environmental issues in a changing world.

Quantitative Analysis of Land Degradation in Mongolia from the Perspective of Geographical Zone

Natural and anthropogenic disturbances accelerate land degradation(LD)in arid,semi-arid,and dry sub-humid areas,leading to reduced land quality and productivity,loss of biodiversity,degradation of ecosystem services,and a decline in the quality of life of local people.To address this issue,the United Nations Convention to Combat Desertification(UNCCD)has set a target for LD neutrality(LDN).However,quantifying and comparing the status of LD at global or regional scales remains challenging due to the lack of coherent quantitative methods and tools.In this study,we focused on Mongolia,a region with significant LD problems,to examine patterns of LD and changes from 2015 to 2020,accounting for regional differences.Trends.Earth was used,as recommended by the UNCCD.The main findings are as follows:(1)Overall,the degraded land area in Mongolia accounted for 12.11%of the total land area,predominantly located in the southwest desert and desert steppe,gradually spreading to the northeast steppe.(2)The areas showing improvement in the land productivity index and degradation were 17.62%and 11.79%,respectively,with the most severely degraded areas concentrated in the southern desert and desert steppe regions.(3)The areas of improvement and degradation in the land cover index were 1.80%and 0.16%,respectively,with degraded areas scattered across regions of steppe,high mountains,and mountain taiga.(4)The areas of improvement and degradation in the land organic carbon index were 1.54%and 0.22%,respectively,with degradation primarily observed in adjacent areas of mountain taiga,steppe,and desert steppe.(5)The improved area(2.999×10^(5)km^(2))of LDN are more than the degraded area(1.895×10^(5)km^(2)),indicating a positive trend toward LDN in Mongolia.

Vegetation Changes from 2014 to 2023 in the Mongolian Plateau Permafrost Region under Climate Change

The permafrost region is one of the most sensitive areas to climate change.With global warming,the Mongolian Plateau permafrost is rapidly degrading,and its vegetation ecosystem is seriously threatened.To address this challenge,it is essential to understand the impact of climate change on vegetation at different permafrost degradation stages on the Mongolian Plateau.Based on the general permafrost distribution,in this study,we divided different permafrost regions and explored the response of vegetation to climate change at different stages of permafrost degradation by the idea of“space instead of time”from 2014 to 2023.The results of the study showed that:(1)Normalized difference vegetation index(NDVI)values showed a decreasing trend,and the proportion of the decreasing region was in the order of sporadic permafrost region>isolated and sparse permafrost region>continuous and discontinuous permafrost regions.(2)The main controlling factors of vegetation growth in permafrost regions are different,air temperature is the main controlling factor of vegetation growth in isolated and sparse permafrost region(r=-0.736)and sporadic permafrost regions(r=-0.522),and precipitation is the main controlling factor of vegetation growth in continuous and discontinuous permafrost region(r=-0.498).(3)The response of NDVI to climate change varies at different stages of permafrost degradation.In the early stages of permafrost degradation,increased land surface temperature(LST)and air temperature favored vegetation growth and increased vegetation cover,whereas increased precipitation impeded vegetation growth;as the permafrost degraded,increased LST and air temperature impeded vegetation growth,whereas increased precipitation promoted vegetation growth.

Characteristics of Spatial and Temporal Vegetation Index Variability and Its Responses to Temperature and Precipitation in Mongolia

The Mongolian Plateau,a vital ecological barrier in northern China,is of great importance for studying vegetation dynamics in Mongolia against the background of climate warming.Such studies can enhance our understanding of regional vegetation responses to global warming and contribute to the establishment of a stronger ecological barrier in northern China.Here,we analyzed the spatial and temporal characteristics of the NDVI(normalized difference vegetation index)in Mongolia using 8 km resolution GIMMS NDVI3g data from 1990 to 2022,along with temperature,precipitation,and elevation data.Trend analysis and correlation methods were used to examine the relationships between the NDVI and temperature,as well as precipitation.The results showed four important aspects of these relationships.(1)The NDVI in Mongolia increased significantly from 1990 to 2022 at a rate of 0.0015 yr^(-1)(P<0.05).(2)Mongolia’s NDVI increased from 1990 to 2022 in 60.73%of the country.Of this total,the area with a significant increase accounted for 31.67%and was concentrated on the eastern and western edges.The area experiencing a significant decrease accounted for 15.67%and was mainly located on the southwestern edges.(3)The NDVI analysis revealed significant increasing trends in all regions except for those at elevations of 1500-2000 m.The greatest rate of increase was observed between 500 and 1000 m,and the increasing trend weakened as elevation continued to increase before gradually becoming significant again.Additionally,the NDVI increased significantly across different slopes,and the rate of increase decreased as the slope increased.(4)From 1990 to 2022,Mongolia’s NDVI was mostly negatively correlated with temperature.This occurred over 66.75%of the total land area,with 17.21%of the region exhibiting a significant negative correlation,mainly in the southwest.Conversely,the NDVI demonstrated a positive correlation with precipitation,encompassing 86.71%of the total land area.Approximately 40.44%of the region had a significant positive correlation,primarily in the southwest.In conclusion,throughout the experimental period,the vegetation state in Mongolia improved.However,due to the warming and drying climate,more attention should be paid to vegetation degradation in the south-central region.