1.Introduction Planning and managing land resources requires the use of land use and land cover(LULC)maps,which provide vital information on the interactions between humans and the environment(Esfandeh et al.,2022;Pra...1.Introduction Planning and managing land resources requires the use of land use and land cover(LULC)maps,which provide vital information on the interactions between humans and the environment(Esfandeh et al.,2022;Pratic`o et al.,2021;Yao et al.,2022).The precision of LULC monitoring has increased due to developments in Earth observation and remote sensing,allowing for well-informed environmental management decision-making(Qian and Zhang,2022;Viana et al.,2019).展开更多
The climate change and Land Use/Land Cover(LULC)change both have an important impact on the rainfall-runoff processes.How to quantitatively distinguish and predict the impacts of the above two factors has been a hot s...The climate change and Land Use/Land Cover(LULC)change both have an important impact on the rainfall-runoff processes.How to quantitatively distinguish and predict the impacts of the above two factors has been a hot spot and frontier issue in the field of hydrology and water resources.In this research,the SWAT(Soil and Water Assessment Tool)model was established for the Jinsha River Basin,and the method of scenarios simulation was used to study the runoff response to climate change and LULC change.Furthermore,the climate variables exported from 7 typical General Circulation Models(GCMs)under RCP4.5 and RCP8.5 emission scenarios were bias corrected and input into the SWAT model to predict runoff in 2017-2050.Results showed that:(1)During the past 57 years,the annual average precipitation and temperature in the Jinsha River Basin both increased significantly while the rising trend of runoff was far from obvious.(2)Compared with the significant increase of temperature in the Jinsha River Basin,the LULC change was very small.(3)During the historical period,the LULC change had little effect on the hydrological processes in the basin,and climate change was one of the main factors affecting runoff.(4)In the context of global climate change,the precipitation,temperature and runoff in the Jinsha River Basin will rise in 2017-2050 compared with the historical period.This study provides significant references to the planning and management of large-scale hydroelectric bases at the source of the Yangtze River.展开更多
快速准确地获取土地利用信息,可为城市发展和生态环境保护提供参考依据。基于谷歌地球引擎(Google Earth Engine,GEE)平台的多时相Landsat图像密集时间叠加和随机森林算法对云南省的土地利用类型进行分类,分析云南省土地利用和土地覆盖(...快速准确地获取土地利用信息,可为城市发展和生态环境保护提供参考依据。基于谷歌地球引擎(Google Earth Engine,GEE)平台的多时相Landsat图像密集时间叠加和随机森林算法对云南省的土地利用类型进行分类,分析云南省土地利用和土地覆盖(Land use and Land cover,LULC)时空变化趋势,并使用地理探测器定量评估关键的驱动因素。结果表明,1)LULC分类平均总体精度和Kappa系数分别为88.64%、86.01%,精度较高,满足数据使用要求。2)云南省土地类型以林地、耕地、草地及稀疏灌草混交地为主,占比97.91%~98.38%,土地利用转移以林地和耕地互相转换、草地及稀疏灌草混交地转为耕地为主。3)云南省滇中和滇东部的土地利用强度总体高于其他地区,滇西北和滇西南地区的土地利用强度较低。4)不同驱动因素对LULC影响程度存在显著差异,植被类型、年均气温和土壤类型对LULC变化的影响程度相对较小,高程、坡度、坡向、年均降水、人口密度、GDP和人口城镇化率等对LULC变化的影响程度普遍较高,其中GDP、人口密度和人口城镇化率对LULC变化程度影响较高。研究结果可为云南省后续生态环境保护政策制定和区域可持续发展提供数据基础与支撑。展开更多
Understanding the spatial patterns of land-use and land-cover(LULC)and their driving forces in transnational areas is important for the sustainable development of these regions.However,the spatial patterns of LULC and...Understanding the spatial patterns of land-use and land-cover(LULC)and their driving forces in transnational areas is important for the sustainable development of these regions.However,the spatial patterns of LULC and their driving forces across multiple scales are poorly understood in transnational areas.In this study,we analyzed the spatial patterns of LULC and driving forces in the transnational area of Tumen River(TATR)in 2016 across two scales:the entire region and the sub-regions of China,the Democratic People’s Republic of Korea(DPRK),and Russia.Results showed that the LULC was dominated by broadleaf forest and dry farmland in the TATR in 2016,which accounted for 66.86%and 13.60%of the entire region,respectively.Meanwhile,the LULC in the three sub-regions exhibited noticeable differences.In the Chinese and the DPRK’s sub-regions,the area of broadleaf forest was greater than those for the other LULC types,while the Russian sub-region was dominated by broadleaf forest and grassland.The spatial patterns of LULC were mainly influenced by topography,climate,soil properties,and human activities.In addition,the driving forces of the spatial patterns of LULC in the TATR had an obvious scaling effect.Therefore,we suggest that effective policies and regulations with cooperation among China,the DPRK,and Russia are needed to plan the spatial patterns of LULC and improve the sustainable development of the TATR.展开更多
Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of researc...Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.展开更多
The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city an...The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.展开更多
Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and...Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.展开更多
Rapid urbanization creates complexity,results in dynamic changes in land and environment,and influences the land surface temperature(LST)in fast-developing cities.In this study,we examined the impact of land use/land ...Rapid urbanization creates complexity,results in dynamic changes in land and environment,and influences the land surface temperature(LST)in fast-developing cities.In this study,we examined the impact of land use/land cover(LULC)changes on LST and determined the intensity of urban heat island(UHI)in New Town Kolkata(a smart city),eastern India,from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands.This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis(Getis–Ord Gi^(*) method)to examine the impact of LULC changes on urban thermal environment.This study noticed that the area percentage of built-up land increased rapidly from 21.91%to 45.63%during 1991–2021,with a maximum positive change in built-up land and a maximum negative change in sparse vegetation.The mean temperature significantly increased during the study period(1991–2021),from 16.31℃to 22.48℃in winter,29.18℃to 34.61℃in summer,and 19.18℃to 27.11℃in autumn.The result showed that impervious surfaces contribute to higher LST,whereas vegetation helps decrease it.Poor ecological status has been found in built-up land,and excellent ecological status has been found in vegetation and water body.The hot spot and cold spot areas shifted their locations every decade due to random LULC changes.Even after New Town Kolkata became a smart city,high LST has been observed.Overall,this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment,and appropriate planning is needed to reduce LST.This study can help policy-makers create sustainable smart cities.展开更多
Background Investigating the influencing factors of groundwater drought offers critical insights for the sustainable management of groundwater-dependent ecosystems(GDEs).The Upper Zambezi Catchment hosts a large-scale...Background Investigating the influencing factors of groundwater drought offers critical insights for the sustainable management of groundwater-dependent ecosystems(GDEs).The Upper Zambezi Catchment hosts a large-scale alluvial aquifer system,which is vulnerable to the effects of climate change to sustain GDEs.The study aims to:(a)characterize the spatial-temporal distribution of groundwater drought in the catchment,(b)identify hydrological and terrestrial drivers affecting groundwater drought,(c)rank the drivers according to their impact on the groundwater distribution/system,and(d)explore groundwater management actions under drought conditions i.e.disaster risk management.Methods Influencing factors,which include meterological drought indicators(such as Standardized Precipitation Evapotranspiration Index,SPEI),teleconnection factors(ENSO,PDO and AMO),and anthropogenic factors(land use and land cover(LULC)),were investigated and quantitatively compared based on Spearman correlation analysis and a decision tree machine learning model(extreme gradient boosting,XGBoost).Structural Equation Modelling(SEM)was then used to explain latent(important)factors in the nexus of climate variability—LULC dynamics to groundwater response.Results The study reveals that LULC types,particularly water bodies,cropland and bare land,exert the greatest influence on groundwater drought responses under teleconnection patterns attributed to ENSO,rather than through changes in the net water balance.This highlights the critical role of surface cover dynamics in shaping subsurface hydrological responses,with significant implications for the sustainability of groundwater-dependent ecosystems.Conclusions This study is novel in its application of XGBoost and SEM to unravel the complex nexus between climate variability,LULC,and groundwater dynamics within an ecosystem context,under data-scarcity conditions.This understanding is not only critical for sustaining groundwater availability but also for preserving the integrity and functioning of groundwater-dependent ecosystems.展开更多
Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate ...Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction.This paper examined effects of pansharpening and atmospheric correction on LULC classification.Object-Based Support Vector Machine(OB-SVM)and Pixel-Based Maximum Likelihood Classifier(PB-MLC)were applied for LULC classification.Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image.Nevertheless,pansharpening plays much more important roles on the classification accuracy than the atmospheric correction.It can help to increase classification accuracy by 12%on average compared to the ones without pansharpening.PB-MLC and OB-SVM achieved similar classification rate.This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.展开更多
Patna is among the cities high populated at risk of ecological and environmental deterioration due to a variety of human activities,such as poor land cover management.One of the most crucial elements of a successful l...Patna is among the cities high populated at risk of ecological and environmental deterioration due to a variety of human activities,such as poor land cover management.One of the most crucial elements of a successful land resource management plan is the evaluation of Land Use Land Cover(LULC).Over the past 20 years,our planet’s land cover resources have undergone substantial changes due to rapid development.The Land Use Land Cover(LULC)categories of the Patna Urban Agglomeration(PUA),including water bodies,agricultural land,barren land,built-up areas,and vegetation,were identified using Geographic Information System(GIS)techniques.Three multi-temporal images were analyzed and classified through supervised classification using the maximum likelihood method.By comparing three separately created LULC categorized maps from 1990 and 2024,temporal changes were analyzed.In order to update land cover or manage natural resources,it is vital to use change detection as a tool to identify changes in LULC over time in PUA,Patna between 1990,2010 and 2024.According to their respective Kappa coefficients,the accuracy rates for 1990,2010 and 2024 LULC are 91.66 and 94.93,respectively.An accuracy evaluation was conducted to determine the correctness of the classification system and to determine the efficacy of the LULC classification maps.One hundred reference test pixels were identified.There have been found significant changes in the LULC were built up area has increased doubled in last thirty-four years of timeline.展开更多
Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact ...Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact on the change of ecosystem.The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values(ESVs)in the upper Gilgel Abbay watershed,Ethiopia.Changes in LULC types were studied using three Landsat images representing 1986,2003,and 2021.The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System(ERDAS)Imagine 2014.We classified ESs in this study into four categories(including provisioning,regulating,supporting,and cultural services)based on global ES classification scheme.The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs.Five LULC types were identified in this study,including cultivated land,forest,shrubland,grassland,and water body.The result revealed that the area of cultivated land accounted for 64.50%,71.50%,and 61.50%of the total area in 1986,2003,and 2021,respectively.The percentage of the total area covered by forest was 9.50%,5.90%,and 14.80%in 1986,2003,and 2021,respectively.Result revealed that the total ESV decreased from 7.42×10^(7) to 6.44×10^(7) USD between 1986 and 2003.This is due to the expansion of cultivated land at the expense of forest and shrubland.However,the total ESV increased from 6.44×10^(7) to 7.76×10^(7) USD during 2003-2021,because of the increment of forest and shrubland.The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003.Nevertheless,the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021.Therefore,the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.展开更多
Eritrea faces significant environmental and agricultural challenges due to human activities, rugged terrain, and fluctuating climates like recurrent droughts and erratic rainfall. Desertification, deforestation, and s...Eritrea faces significant environmental and agricultural challenges due to human activities, rugged terrain, and fluctuating climates like recurrent droughts and erratic rainfall. Desertification, deforestation, and soil erosion are major concerns affecting soil quality, water resources, and vegetation, especially in areas like the Alla catchment. Recent assessments reveal declining vegetation and precipitation levels over four decades, alongside rising temperatures, linked to increased desertification and land degradation driven by climate variations and prolonged droughts. The urgent need for sustainable land management practices is explained by reduced productivity, biodiversity, and ecosystem health. This study focused on modelling land degradation in Eritrea’s Alla catchment using advanced geospatial techniques. Vegetation indices and soil erosion models were used to evaluate critical factors such as rainfall Erosivity, soil erodibility, slope characteristics, and land cover management. The resulting model highlighted varying levels of susceptibility to land degradation, highlighting widespread vulnerability characterized by high and very high susceptibility hotspots. Areas with minimal degradation were found in the northern vegetation-covered regions. Soil loss in the catchment is primarily influenced by inadequate land cover, steep slopes, soil erosion susceptibility, erosive rainfall patterns, and insufficient support practices. The study underscores the urgency of addressing deforestation and unsustainable agricultural practices to mitigate soil erosion. Recommendations include enhancing community capacity for effective land management, promoting climate adaptation strategies, and aligning national efforts with the global Sustainable Development Goals to achieve Land Degradation Neutrality.展开更多
文摘1.Introduction Planning and managing land resources requires the use of land use and land cover(LULC)maps,which provide vital information on the interactions between humans and the environment(Esfandeh et al.,2022;Pratic`o et al.,2021;Yao et al.,2022).The precision of LULC monitoring has increased due to developments in Earth observation and remote sensing,allowing for well-informed environmental management decision-making(Qian and Zhang,2022;Viana et al.,2019).
基金National Key Research and Development Program of China,N.2017YFA0603702National Natural Science Foundation of China,No.51539009,No.51339004。
文摘The climate change and Land Use/Land Cover(LULC)change both have an important impact on the rainfall-runoff processes.How to quantitatively distinguish and predict the impacts of the above two factors has been a hot spot and frontier issue in the field of hydrology and water resources.In this research,the SWAT(Soil and Water Assessment Tool)model was established for the Jinsha River Basin,and the method of scenarios simulation was used to study the runoff response to climate change and LULC change.Furthermore,the climate variables exported from 7 typical General Circulation Models(GCMs)under RCP4.5 and RCP8.5 emission scenarios were bias corrected and input into the SWAT model to predict runoff in 2017-2050.Results showed that:(1)During the past 57 years,the annual average precipitation and temperature in the Jinsha River Basin both increased significantly while the rising trend of runoff was far from obvious.(2)Compared with the significant increase of temperature in the Jinsha River Basin,the LULC change was very small.(3)During the historical period,the LULC change had little effect on the hydrological processes in the basin,and climate change was one of the main factors affecting runoff.(4)In the context of global climate change,the precipitation,temperature and runoff in the Jinsha River Basin will rise in 2017-2050 compared with the historical period.This study provides significant references to the planning and management of large-scale hydroelectric bases at the source of the Yangtze River.
文摘快速准确地获取土地利用信息,可为城市发展和生态环境保护提供参考依据。基于谷歌地球引擎(Google Earth Engine,GEE)平台的多时相Landsat图像密集时间叠加和随机森林算法对云南省的土地利用类型进行分类,分析云南省土地利用和土地覆盖(Land use and Land cover,LULC)时空变化趋势,并使用地理探测器定量评估关键的驱动因素。结果表明,1)LULC分类平均总体精度和Kappa系数分别为88.64%、86.01%,精度较高,满足数据使用要求。2)云南省土地类型以林地、耕地、草地及稀疏灌草混交地为主,占比97.91%~98.38%,土地利用转移以林地和耕地互相转换、草地及稀疏灌草混交地转为耕地为主。3)云南省滇中和滇东部的土地利用强度总体高于其他地区,滇西北和滇西南地区的土地利用强度较低。4)不同驱动因素对LULC影响程度存在显著差异,植被类型、年均气温和土壤类型对LULC变化的影响程度相对较小,高程、坡度、坡向、年均降水、人口密度、GDP和人口城镇化率等对LULC变化的影响程度普遍较高,其中GDP、人口密度和人口城镇化率对LULC变化程度影响较高。研究结果可为云南省后续生态环境保护政策制定和区域可持续发展提供数据基础与支撑。
基金Under the auspices of National Natural Science Foundation of China(No.41771094,41871185,41801184)。
文摘Understanding the spatial patterns of land-use and land-cover(LULC)and their driving forces in transnational areas is important for the sustainable development of these regions.However,the spatial patterns of LULC and their driving forces across multiple scales are poorly understood in transnational areas.In this study,we analyzed the spatial patterns of LULC and driving forces in the transnational area of Tumen River(TATR)in 2016 across two scales:the entire region and the sub-regions of China,the Democratic People’s Republic of Korea(DPRK),and Russia.Results showed that the LULC was dominated by broadleaf forest and dry farmland in the TATR in 2016,which accounted for 66.86%and 13.60%of the entire region,respectively.Meanwhile,the LULC in the three sub-regions exhibited noticeable differences.In the Chinese and the DPRK’s sub-regions,the area of broadleaf forest was greater than those for the other LULC types,while the Russian sub-region was dominated by broadleaf forest and grassland.The spatial patterns of LULC were mainly influenced by topography,climate,soil properties,and human activities.In addition,the driving forces of the spatial patterns of LULC in the TATR had an obvious scaling effect.Therefore,we suggest that effective policies and regulations with cooperation among China,the DPRK,and Russia are needed to plan the spatial patterns of LULC and improve the sustainable development of the TATR.
基金supported by the National Key Research and Development Program of China(2022YFB3903503)the National Natural Science Foundation of China(U1901601)the Science and Technology Project of the Department of Education of Jiangxi Province,China(GJJ210541)。
文摘Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.
文摘The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.
基金partly funded by the National Key Research and Development Program of China(NK2023190801)the National Foreign Experts Program of China(G2023041024L)the Key Scientific Research Program of Shaanxi Provincial Education Department,China(21JT028)。
文摘Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.
基金the University Grants Commission,New Delhi,India,for providing financial support in the form of the Junior Research Fellowship。
文摘Rapid urbanization creates complexity,results in dynamic changes in land and environment,and influences the land surface temperature(LST)in fast-developing cities.In this study,we examined the impact of land use/land cover(LULC)changes on LST and determined the intensity of urban heat island(UHI)in New Town Kolkata(a smart city),eastern India,from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands.This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis(Getis–Ord Gi^(*) method)to examine the impact of LULC changes on urban thermal environment.This study noticed that the area percentage of built-up land increased rapidly from 21.91%to 45.63%during 1991–2021,with a maximum positive change in built-up land and a maximum negative change in sparse vegetation.The mean temperature significantly increased during the study period(1991–2021),from 16.31℃to 22.48℃in winter,29.18℃to 34.61℃in summer,and 19.18℃to 27.11℃in autumn.The result showed that impervious surfaces contribute to higher LST,whereas vegetation helps decrease it.Poor ecological status has been found in built-up land,and excellent ecological status has been found in vegetation and water body.The hot spot and cold spot areas shifted their locations every decade due to random LULC changes.Even after New Town Kolkata became a smart city,high LST has been observed.Overall,this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment,and appropriate planning is needed to reduce LST.This study can help policy-makers create sustainable smart cities.
基金SASSCAL(Southern Africa Science Service Centre for Adaptive Land Management and Climate Change)through the Tipping Points Explained by Climate Change(TIPPECC)the ACEWATER III Project
文摘Background Investigating the influencing factors of groundwater drought offers critical insights for the sustainable management of groundwater-dependent ecosystems(GDEs).The Upper Zambezi Catchment hosts a large-scale alluvial aquifer system,which is vulnerable to the effects of climate change to sustain GDEs.The study aims to:(a)characterize the spatial-temporal distribution of groundwater drought in the catchment,(b)identify hydrological and terrestrial drivers affecting groundwater drought,(c)rank the drivers according to their impact on the groundwater distribution/system,and(d)explore groundwater management actions under drought conditions i.e.disaster risk management.Methods Influencing factors,which include meterological drought indicators(such as Standardized Precipitation Evapotranspiration Index,SPEI),teleconnection factors(ENSO,PDO and AMO),and anthropogenic factors(land use and land cover(LULC)),were investigated and quantitatively compared based on Spearman correlation analysis and a decision tree machine learning model(extreme gradient boosting,XGBoost).Structural Equation Modelling(SEM)was then used to explain latent(important)factors in the nexus of climate variability—LULC dynamics to groundwater response.Results The study reveals that LULC types,particularly water bodies,cropland and bare land,exert the greatest influence on groundwater drought responses under teleconnection patterns attributed to ENSO,rather than through changes in the net water balance.This highlights the critical role of surface cover dynamics in shaping subsurface hydrological responses,with significant implications for the sustainability of groundwater-dependent ecosystems.Conclusions This study is novel in its application of XGBoost and SEM to unravel the complex nexus between climate variability,LULC,and groundwater dynamics within an ecosystem context,under data-scarcity conditions.This understanding is not only critical for sustaining groundwater availability but also for preserving the integrity and functioning of groundwater-dependent ecosystems.
基金The authors would like to thank Aerial Survey Office,Forest Bureau of TaiwanROC for their supports in both financial and data collection under the project 102AS-13.3.1-FB-e3.
文摘Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction.This paper examined effects of pansharpening and atmospheric correction on LULC classification.Object-Based Support Vector Machine(OB-SVM)and Pixel-Based Maximum Likelihood Classifier(PB-MLC)were applied for LULC classification.Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image.Nevertheless,pansharpening plays much more important roles on the classification accuracy than the atmospheric correction.It can help to increase classification accuracy by 12%on average compared to the ones without pansharpening.PB-MLC and OB-SVM achieved similar classification rate.This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.
文摘Patna is among the cities high populated at risk of ecological and environmental deterioration due to a variety of human activities,such as poor land cover management.One of the most crucial elements of a successful land resource management plan is the evaluation of Land Use Land Cover(LULC).Over the past 20 years,our planet’s land cover resources have undergone substantial changes due to rapid development.The Land Use Land Cover(LULC)categories of the Patna Urban Agglomeration(PUA),including water bodies,agricultural land,barren land,built-up areas,and vegetation,were identified using Geographic Information System(GIS)techniques.Three multi-temporal images were analyzed and classified through supervised classification using the maximum likelihood method.By comparing three separately created LULC categorized maps from 1990 and 2024,temporal changes were analyzed.In order to update land cover or manage natural resources,it is vital to use change detection as a tool to identify changes in LULC over time in PUA,Patna between 1990,2010 and 2024.According to their respective Kappa coefficients,the accuracy rates for 1990,2010 and 2024 LULC are 91.66 and 94.93,respectively.An accuracy evaluation was conducted to determine the correctness of the classification system and to determine the efficacy of the LULC classification maps.One hundred reference test pixels were identified.There have been found significant changes in the LULC were built up area has increased doubled in last thirty-four years of timeline.
文摘Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact on the change of ecosystem.The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values(ESVs)in the upper Gilgel Abbay watershed,Ethiopia.Changes in LULC types were studied using three Landsat images representing 1986,2003,and 2021.The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System(ERDAS)Imagine 2014.We classified ESs in this study into four categories(including provisioning,regulating,supporting,and cultural services)based on global ES classification scheme.The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs.Five LULC types were identified in this study,including cultivated land,forest,shrubland,grassland,and water body.The result revealed that the area of cultivated land accounted for 64.50%,71.50%,and 61.50%of the total area in 1986,2003,and 2021,respectively.The percentage of the total area covered by forest was 9.50%,5.90%,and 14.80%in 1986,2003,and 2021,respectively.Result revealed that the total ESV decreased from 7.42×10^(7) to 6.44×10^(7) USD between 1986 and 2003.This is due to the expansion of cultivated land at the expense of forest and shrubland.However,the total ESV increased from 6.44×10^(7) to 7.76×10^(7) USD during 2003-2021,because of the increment of forest and shrubland.The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003.Nevertheless,the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021.Therefore,the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.
文摘Eritrea faces significant environmental and agricultural challenges due to human activities, rugged terrain, and fluctuating climates like recurrent droughts and erratic rainfall. Desertification, deforestation, and soil erosion are major concerns affecting soil quality, water resources, and vegetation, especially in areas like the Alla catchment. Recent assessments reveal declining vegetation and precipitation levels over four decades, alongside rising temperatures, linked to increased desertification and land degradation driven by climate variations and prolonged droughts. The urgent need for sustainable land management practices is explained by reduced productivity, biodiversity, and ecosystem health. This study focused on modelling land degradation in Eritrea’s Alla catchment using advanced geospatial techniques. Vegetation indices and soil erosion models were used to evaluate critical factors such as rainfall Erosivity, soil erodibility, slope characteristics, and land cover management. The resulting model highlighted varying levels of susceptibility to land degradation, highlighting widespread vulnerability characterized by high and very high susceptibility hotspots. Areas with minimal degradation were found in the northern vegetation-covered regions. Soil loss in the catchment is primarily influenced by inadequate land cover, steep slopes, soil erosion susceptibility, erosive rainfall patterns, and insufficient support practices. The study underscores the urgency of addressing deforestation and unsustainable agricultural practices to mitigate soil erosion. Recommendations include enhancing community capacity for effective land management, promoting climate adaptation strategies, and aligning national efforts with the global Sustainable Development Goals to achieve Land Degradation Neutrality.
