Global ecological monitoring faces unprecedented challenges from accelerating climate change,biodiversity loss,urban expansion,and unsustainable resource use.Although existing scientific programs and monitoring platfo...Global ecological monitoring faces unprecedented challenges from accelerating climate change,biodiversity loss,urban expansion,and unsustainable resource use.Although existing scientific programs and monitoring platforms have improved data accessibility and methodological standards,they remain constrained by three critical bottlenecks:near-real-time performance,intelligent analysis,and comprehensive coverage.Overcoming these shortcomings is essential to advance global change science,climate governance,and sustainable development.This review synthesized recent progress in international ecological monitoring,including major scientific initiatives,thematic and integrated monitoring platforms,and emerging remote sensing cloud systems.These systems have collectively promoted open data sharing,standardized observation protocols,and cross-domain integration.However,critical challenges remain in achieving near-real-time observation,interpretable intelligent modeling,and globally balanced coverage,which limit the transition toward an intelligent and fully consistent monitoring framework.Building on this foundation,we introduce the Global Intelligent Ecological Horizon Project(GIEHP),an exploratory framework aimed at constructing the world’s first Digital Earth Atlas with long-term consistency and high-dimensional integration.GIEHP represents a pathway toward integrating multi-source observations,intelligent modeling,and cloud-cluster computation to achieve near-real-time,multi-scale,and globally consistent monitoring.Its pilot applications illustrate the potential for unified mapping of key ecological indicators and provide a methodological reference for advancing intelligent,data-driven environmental governance.This review not only summarized the progress and limitations of existing monitoring systems but also outlines a forward-looking framework for the next generation of global ecological monitoring.Achieving a truly intelligent,near-real-time,and globally consistent system requires sustained international collaboration,interdisciplinary integration,and open data sharing.We call on the global scientific community to advance this vision and provide robust knowledge support for ecological restoration,climate adaptation,and sustainable development.展开更多
Strategic selection and precise matching of climate-resilient tree species are crucial for maximizing the mitigation and adaptation potential of Climate-Smart Forestry.However,current forestation plans often overlook ...Strategic selection and precise matching of climate-resilient tree species are crucial for maximizing the mitigation and adaptation potential of Climate-Smart Forestry.However,current forestation plans often overlook species-specific environmental shifts,leading to suboptimal long-term carbon sequestration.Here we developed a climate-adaptive optimization framework to guide tree species selection and planting in China,based on projected habitat suitability and range shifts under future climate scenarios.Utilizing over 200,000 tree records from China’s National Forest Inventory(1999-2018),we quantified habitat suitability declines of 12.1%-42.9%for currently dominant plantation species by 2060 due to climate change.By optimizing species-site matching and strategically harvesting timber at peak carbon uptake,we identified 43.2 million hectares suitable for climate-resilient forestation between 2025 and 2060,enabling the planting of approximately 46 billion climate-adapted trees with a total sequestration potential of 3822.6 Tg of carbon-a 28.7%increase compared to unmanaged scenarios.Our study highlights the importance of optimizing adaptive forestation strategies to enhance carbon sequestration under future climate conditions,providing technical guidance for climate-resilient forest management in support of China’s net-zero commitment.展开更多
The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development...The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development.However,there is a paucity of knowledge on this cutting-edge topic.Given the extensive and rapid urbanization in the United States(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global implications.This study finds that although the 740 U.S.cities have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that time.The study also reveals the spatio-temporal heterogeneity of the gap.The gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city level.While cities in the Northwestern U.S.maintain UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS.Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of biomass.Hence,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the U.S.and globally.展开更多
Forests are chiefly responsible for the terrestrial carbon sink that greatly re duces the buildup of CO_(2)concentrations in the atmosphere and alleviates climate change.Current predictions of terrestrial carbon sinks...Forests are chiefly responsible for the terrestrial carbon sink that greatly re duces the buildup of CO_(2)concentrations in the atmosphere and alleviates climate change.Current predictions of terrestrial carbon sinks in the future have so far ignored the variation of forest carbon uptake with forest age.Here,we predict the role of China's current forest age in future carbon sink capacity by generating a high-resolution(30 m)forest age map in 2019 over China's landmass using satellite and forest inventory data and deriving forest growth curves using measurements of forest biomass and age in 3,121 plots.As China's forests currently have large proportions of young and middle-age stands,we project that China's forests will maintain high growth rates for about 15 years.However,as the forests grow older,their net primary productivity will decline by 5.0%±1.4%in 2050,8.4%±1.6%in 2060,and 16.6%±2.8%in 2100,indicating weakened carbon sinks in the near future.The weakening of forest carbon sinks can be potentially mitigated by optimizing forest age structure through selective logging and implementing new or improved afforestation.This finding is important not only for the global carbon cycle and climate projections but also for developing forest management strategies to enhance land sinks by alleviating the age effect.展开更多
DEAR EDITOR,Ancient DNA(a DNA) from mollusc shells is considered a potential archive of historical biodiversity and evolution.However, such information is currently lacking for mollusc shells from the deep ocean, espe...DEAR EDITOR,Ancient DNA(a DNA) from mollusc shells is considered a potential archive of historical biodiversity and evolution.However, such information is currently lacking for mollusc shells from the deep ocean, especially those from acidic chemosynthetic environments theoretically unsuitable for longterm DNA preservation. Here, we report on the recovery of mitochondrial and nuclear gene markers by Illumina sequencing of a DNA from three shells of Archivesica nanshaensis – a hydrocarbon-seep vesicomyid clam previously known only from a pair of empty shells collected at a depth of 2626 m in the South China Sea.展开更多
The accuracy of existing forest cover products typically suffers from“rounding”errors arising from classifications that estimate the fractional cover of forest in each pixel,which often exclude the presence of large...The accuracy of existing forest cover products typically suffers from“rounding”errors arising from classifications that estimate the fractional cover of forest in each pixel,which often exclude the presence of large,isolated trees and small or narrow forest clearings,and is primarily attributable to the moderate resolution of the imagery used to make maps.However,the degree to which such high-resolution imagery can mitigate this problem,and thereby improve large-area forest cover maps,is largely unexplored.Here,we developed an approach to map tropical forest cover at a fine scale using Planet and Sentinel-1 synthetic aperture radar(SAR)imagery in the Google Earth Engine platform and used it to map all of Southeastern Asia’s forest cover.The machine learning approach,based on the Random Forests models and trained and validated using a total of 37,345 labels collected from Planet imagery across the entire region,had an accuracy of 0.937 and an F1 score of 0.942,while a version based only on Planet imagery had an accuracy of 0.908 and F1 of 0.923.We compared the accuracy of our resulting maps with 5 existing forest cover products derived from medium-resolution optical-only or combined optical-SAR approaches at 3,000 randomly selected locations.We found that our approach overall achieved higher accuracy and helped minimize the rounding errors commonly found along small or narrow forest clearings and deforestation frontiers where isolated trees are common.However,the forest area estimates varied depending on topographic location and showed smaller differences in highlands(areas>300 m above sea level)but obvious differences in complex lowland landscapes.Overall,the proposed method shows promise for monitoring forest changes,particularly those caused by deforestation frontiers.Our study also represents one of the most extensive applications of Planet imagery to date,resulting in an open,high-resolution map of forest cover for the entire Southeastern Asia region.展开更多
Cropland monitoring is a crucial component for a broad user community from Land Use and Land Cover Change study to food security policy making.Faced with the rich natural ecological environment and variable agricultur...Cropland monitoring is a crucial component for a broad user community from Land Use and Land Cover Change study to food security policy making.Faced with the rich natural ecological environment and variable agricultural production conditions of Mid-Spine Belt of Beautiful China(MSBBC),this study developed a novel operational assessment framework that combined the near real-time land cover mapping platform(i.e.,FROM-GLC Plus),the FAO Agricultural Stress Index System,and the land degradation monitoring method suggested by United Nations Convention to Combat Desertification for the timely monitoring of cropland extent change,cropland conditions,and cropland degradation.With integrated monitoring system,this framework can provide convenient access to high-spatiotemporalresolution cropland maps(30 m,dekadal)and instant(near real time)cropland dynamics.According to the monitoring results,we found that the abnormally high temperatures of summer 2022 adversely affected crop health in the southwest of MSBBC.Besides,our results suggested that China’s ecological restoration projects made remarkable achievement in MSBBC.The productivity of more than 70% of cropland in MSBBC has improved,and only~6% cropland(~3.69×10^(4) km^(2))has degraded since 2000,mainly distributed in cropland with steep slope,insufficient precipitation,and intensive use.Site-specific measures,such as conservation tillage,improved tillage systems,and cropland ecological projects,should be adopted for sustainable cropland use and further increase in land carrying capacity of MSBBC to achieve balanced east-west development in China.展开更多
Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cy...Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change,but a holistic understanding and assessment remains lacking.Here we hypothesized that V_(c,max)was dictated by both factors of temperature-associated enzyme kinetics(capturing instantaneous ecophysiological responses)and the amount of activated RuBisCO(indexed by V_(c,max)standardized at 25℃,V_(c,max25)),and compiled a comprehensive global dataset(n=7339 observations from 428 sites)for hypothesis testing.The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems.We found that a semi-empirical statistical model considering both factors explained 78%of global V_(c,max)variability,followed by 55%explained by enzyme kinetics alone.This statistical model outperformed the current theoretical optimality model for predicting global V_(c,max)variability(67%),primarily due to its poor characterization on global V_(c,max25)variability(3%).Further,we demonstrated that,in addition to climatic variables,belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of V_(c,max25)was a key missing mechanism for improving the theoretical modelling of global V_(c,max)variability.These findings improve the mechanistic understanding of global V_(c,max)variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.展开更多
Accurate,detailed,and up-to-date urban land use information plays a key role in understanding the urban environment,enhancing urban planning,and promoting sustainable urban development.Recent advancements have focused...Accurate,detailed,and up-to-date urban land use information plays a key role in understanding the urban environment,enhancing urban planning,and promoting sustainable urban development.Recent advancements have focused on refining urban land use classification methods and generating data prod-ucts at various scales.However,detailed parcel-level urban land use mapping across China remains insuf-ficient with low accuracy.To address this issue,we propose an enhanced mapping framework of essential urban land use categories by integrating multi-modal deep learning models and multi-source geospatial data.Utilizing complete,accurate land parcels derived from the combined OpenStreetMap and Tianditu road networks as the smallest classification units,we have developed an enhanced Essential Urban Land Use Categories(EULUC)map covering all cities in China for 2022,termed EULUC-China 2.0.The mapping results show that residential,industrial,and park and greenspace are the dominant land use categories,collectively accounting for nearly 78%of the urban area.Compared to its predecessor,EULUC-China 1.0,the updated 2.0 version offers more detailed,spatially explicit information that reveals distinct spatial patterns within diverse land use compositions of each city.Our evaluation demonstrates that the overall accuracies of Level-I and Level-II classification reach up to 79%and 72%,respectively,representing sub-stantial enhancements across all categories over the previous product.These improvements are primarily attributed to the effectiveness of deep learning in processing multi-modal inputs,particularly through the graph modeling of Point-of-interest(POI)data.The publicly accessible product(https://zenodo.org/records/15180905)and the insights derived from this study offer a valuable dataset and references for researchers and practitioners addressing critical challenges in urbanization.展开更多
Remote sensing and land resource surveys have been used in recent decades for land use/land cover(LULC)mapping;however,keeping the developed LULC up-to-date and consistent with land survey statistics remains challengi...Remote sensing and land resource surveys have been used in recent decades for land use/land cover(LULC)mapping;however,keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging.This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data.This study employed Landsat imagery time series,change detection algorithms,sample migration,and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022.The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products.Additionally,a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps,increasing the correlation coefficient(r)with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99.Thus,the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.展开更多
The abrupt outbreak of coronavirus disease in 2019,also known as COVID-19,has led to an unprecedented global public healthcrisis.Current studies have paid immense attention to the impacts of COVID-19 posed to the atmo...The abrupt outbreak of coronavirus disease in 2019,also known as COVID-19,has led to an unprecedented global public healthcrisis.Current studies have paid immense attention to the impacts of COVID-19 posed to the atmosphere and the land-based sectors in areas such as air quality,carbon emission,economic senti ment,educational and social equality,etc.It is depicted that carbon emission had dropped about 8.8%in the first half of 2020 compared to 2019,'significant reduc-tion of air pollutants such as PM25 and NO2 were moreover reported at national,regional,and global levels.On the flip side,the amount of attention paid to the ocean during this pandemic has been nearly negligible despite its prominent functions of supporting livelihoods for 40%of the global population,absorbing~30%of anthropogenic CO_(2)emissions,and processing over 90%of excess heat in our climate system.Both direct and indirect effects of the pandemicare insufficiently understood in the ocean,which include their key roles in blue carbon sequestration,ocean-atmosphere and ocean-land interactions,sealevel changes,and their impacts to human beings.展开更多
Understanding the distribution and land history of old urban areas(OUAs)and renewed urban areas(RUAs)has become the key point of urban management.However,it is hard to acquire adequate information for lack of pertinen...Understanding the distribution and land history of old urban areas(OUAs)and renewed urban areas(RUAs)has become the key point of urban management.However,it is hard to acquire adequate information for lack of pertinent detection methods.Here,we established a complete mapping framework on Google Earth Engine(GEE)platform to identify OUAs and RUAs and detect the temporal information of urban renewal,which was implemented in Beijing during 2000-2020.We used Landsat imagery and LandTrendr algorithm to fit the spectral trajectories of 14 bands/indices with specific segment attributes as the feature inputs for Random Forest classification.We produced the maps of OUAs and RUAs with an overall accuracy of 95.36%.On this basis,we further utilized LandTrendr to detect the start year,end year,and duration of urban renewal with the accuracies within the±5-year difference of 85.52%,80.97%,and 74.53%,respectively.These maps all present informative spatiotemporal patterns.Furthermore,the urban renewal process is likely to be influenced by major national or international events.The study answers the issues about urban renewal from multiple angles and provides scientific support for future urban planning.展开更多
基金Under the auspices of Shaanxi Land Construction-Xi’an Jiaotong University Land Engineering and Human Settlement Environment Technology Innovation Center Open Fund Project(No.2024WHZ2059)。
文摘Global ecological monitoring faces unprecedented challenges from accelerating climate change,biodiversity loss,urban expansion,and unsustainable resource use.Although existing scientific programs and monitoring platforms have improved data accessibility and methodological standards,they remain constrained by three critical bottlenecks:near-real-time performance,intelligent analysis,and comprehensive coverage.Overcoming these shortcomings is essential to advance global change science,climate governance,and sustainable development.This review synthesized recent progress in international ecological monitoring,including major scientific initiatives,thematic and integrated monitoring platforms,and emerging remote sensing cloud systems.These systems have collectively promoted open data sharing,standardized observation protocols,and cross-domain integration.However,critical challenges remain in achieving near-real-time observation,interpretable intelligent modeling,and globally balanced coverage,which limit the transition toward an intelligent and fully consistent monitoring framework.Building on this foundation,we introduce the Global Intelligent Ecological Horizon Project(GIEHP),an exploratory framework aimed at constructing the world’s first Digital Earth Atlas with long-term consistency and high-dimensional integration.GIEHP represents a pathway toward integrating multi-source observations,intelligent modeling,and cloud-cluster computation to achieve near-real-time,multi-scale,and globally consistent monitoring.Its pilot applications illustrate the potential for unified mapping of key ecological indicators and provide a methodological reference for advancing intelligent,data-driven environmental governance.This review not only summarized the progress and limitations of existing monitoring systems but also outlines a forward-looking framework for the next generation of global ecological monitoring.Achieving a truly intelligent,near-real-time,and globally consistent system requires sustained international collaboration,interdisciplinary integration,and open data sharing.We call on the global scientific community to advance this vision and provide robust knowledge support for ecological restoration,climate adaptation,and sustainable development.
基金supported by the National Key Research and Development Program of China(2021YFD2200405)support from a NASA GEDI award(#80NSSC24K0600)a NASA Carbon Cycle award(#80NSSC21K1705)。
文摘Strategic selection and precise matching of climate-resilient tree species are crucial for maximizing the mitigation and adaptation potential of Climate-Smart Forestry.However,current forestation plans often overlook species-specific environmental shifts,leading to suboptimal long-term carbon sequestration.Here we developed a climate-adaptive optimization framework to guide tree species selection and planting in China,based on projected habitat suitability and range shifts under future climate scenarios.Utilizing over 200,000 tree records from China’s National Forest Inventory(1999-2018),we quantified habitat suitability declines of 12.1%-42.9%for currently dominant plantation species by 2060 due to climate change.By optimizing species-site matching and strategically harvesting timber at peak carbon uptake,we identified 43.2 million hectares suitable for climate-resilient forestation between 2025 and 2060,enabling the planting of approximately 46 billion climate-adapted trees with a total sequestration potential of 3822.6 Tg of carbon-a 28.7%increase compared to unmanaged scenarios.Our study highlights the importance of optimizing adaptive forestation strategies to enhance carbon sequestration under future climate conditions,providing technical guidance for climate-resilient forest management in support of China’s net-zero commitment.
基金supported by the National Natural Science Foun-dation of China(Grants No.42330103,42271469)the Ningbo Science and Technology Bureau(Grant No.2022Z081).
文摘The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development.However,there is a paucity of knowledge on this cutting-edge topic.Given the extensive and rapid urbanization in the United States(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global implications.This study finds that although the 740 U.S.cities have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that time.The study also reveals the spatio-temporal heterogeneity of the gap.The gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city level.While cities in the Northwestern U.S.maintain UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS.Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of biomass.Hence,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the U.S.and globally.
基金supported by Major Program of the National Natural Science Foundation of China(42090015 and 72091514)the National Natural Science Foundation of China(42071400)+3 种基金the University of Hong Kong HKU-100 Scholars FundURC Seed Fund for Strategic Interdisciplinary Research SchemeSeed Fund for Basic ResearchTsinghua-Toyota Joint Research Fund。
基金National Natural Science Foundationof China(grant nos.42101367 to R.S.and 42201360 to M.X.)Natural Science Foundation of Fujian Province(grant no.2021J05041 to R.S.)+1 种基金Fujan Forestry Science and Technology Key Project(grant no.2022FKJ03 to R.S)Open Fund Project of the Academy of Carbon Neutrality of Fujian Normal University(grant no.TZH2022-02 to R.S).
文摘Forests are chiefly responsible for the terrestrial carbon sink that greatly re duces the buildup of CO_(2)concentrations in the atmosphere and alleviates climate change.Current predictions of terrestrial carbon sinks in the future have so far ignored the variation of forest carbon uptake with forest age.Here,we predict the role of China's current forest age in future carbon sink capacity by generating a high-resolution(30 m)forest age map in 2019 over China's landmass using satellite and forest inventory data and deriving forest growth curves using measurements of forest biomass and age in 3,121 plots.As China's forests currently have large proportions of young and middle-age stands,we project that China's forests will maintain high growth rates for about 15 years.However,as the forests grow older,their net primary productivity will decline by 5.0%±1.4%in 2050,8.4%±1.6%in 2060,and 16.6%±2.8%in 2100,indicating weakened carbon sinks in the near future.The weakening of forest carbon sinks can be potentially mitigated by optimizing forest age structure through selective logging and implementing new or improved afforestation.This finding is important not only for the global carbon cycle and climate projections but also for developing forest management strategies to enhance land sinks by alleviating the age effect.
基金supported by the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)(SMSEGL20SC02)University Grants Committee of Hong Kong(GRF12102222)。
文摘DEAR EDITOR,Ancient DNA(a DNA) from mollusc shells is considered a potential archive of historical biodiversity and evolution.However, such information is currently lacking for mollusc shells from the deep ocean, especially those from acidic chemosynthetic environments theoretically unsuitable for longterm DNA preservation. Here, we report on the recovery of mitochondrial and nuclear gene markers by Illumina sequencing of a DNA from three shells of Archivesica nanshaensis – a hydrocarbon-seep vesicomyid clam previously known only from a pair of empty shells collected at a depth of 2626 m in the South China Sea.
基金supported by the National Natural Science Foundation of China(grant no.42071022)the startup fund provided by the Southern University of Science and Technology(grant no.29/Y01296122)+1 种基金the China Postdoctoral Science Foundation(grant no.2022M711472)upported by the Hung Ying Physical Science Research Fund 2021-22 and the Innovation and Technology Fund(funding support to State Key Laboratories in Hong Kong of Agrobiotechnology)of the HKSAR,China.
文摘The accuracy of existing forest cover products typically suffers from“rounding”errors arising from classifications that estimate the fractional cover of forest in each pixel,which often exclude the presence of large,isolated trees and small or narrow forest clearings,and is primarily attributable to the moderate resolution of the imagery used to make maps.However,the degree to which such high-resolution imagery can mitigate this problem,and thereby improve large-area forest cover maps,is largely unexplored.Here,we developed an approach to map tropical forest cover at a fine scale using Planet and Sentinel-1 synthetic aperture radar(SAR)imagery in the Google Earth Engine platform and used it to map all of Southeastern Asia’s forest cover.The machine learning approach,based on the Random Forests models and trained and validated using a total of 37,345 labels collected from Planet imagery across the entire region,had an accuracy of 0.937 and an F1 score of 0.942,while a version based only on Planet imagery had an accuracy of 0.908 and F1 of 0.923.We compared the accuracy of our resulting maps with 5 existing forest cover products derived from medium-resolution optical-only or combined optical-SAR approaches at 3,000 randomly selected locations.We found that our approach overall achieved higher accuracy and helped minimize the rounding errors commonly found along small or narrow forest clearings and deforestation frontiers where isolated trees are common.However,the forest area estimates varied depending on topographic location and showed smaller differences in highlands(areas>300 m above sea level)but obvious differences in complex lowland landscapes.Overall,the proposed method shows promise for monitoring forest changes,particularly those caused by deforestation frontiers.Our study also represents one of the most extensive applications of Planet imagery to date,resulting in an open,high-resolution map of forest cover for the entire Southeastern Asia region.
基金supported by the National Key R&D Program of China(2019YFA0606601)the Tsinghua University Initiative Scientific Research Program(20223080017)the National Natural Science Foundation of China(42201367).
文摘Cropland monitoring is a crucial component for a broad user community from Land Use and Land Cover Change study to food security policy making.Faced with the rich natural ecological environment and variable agricultural production conditions of Mid-Spine Belt of Beautiful China(MSBBC),this study developed a novel operational assessment framework that combined the near real-time land cover mapping platform(i.e.,FROM-GLC Plus),the FAO Agricultural Stress Index System,and the land degradation monitoring method suggested by United Nations Convention to Combat Desertification for the timely monitoring of cropland extent change,cropland conditions,and cropland degradation.With integrated monitoring system,this framework can provide convenient access to high-spatiotemporalresolution cropland maps(30 m,dekadal)and instant(near real time)cropland dynamics.According to the monitoring results,we found that the abnormally high temperatures of summer 2022 adversely affected crop health in the southwest of MSBBC.Besides,our results suggested that China’s ecological restoration projects made remarkable achievement in MSBBC.The productivity of more than 70% of cropland in MSBBC has improved,and only~6% cropland(~3.69×10^(4) km^(2))has degraded since 2000,mainly distributed in cropland with steep slope,insufficient precipitation,and intensive use.Site-specific measures,such as conservation tillage,improved tillage systems,and cropland ecological projects,should be adopted for sustainable cropland use and further increase in land carrying capacity of MSBBC to achieve balanced east-west development in China.
基金supported by National Natural Science Foundation of China(31922090 and 31901086)Hong Kong Research Grant Council Early Career Scheme(27306020)+4 种基金HKU Seed Fund for Basic Research(201905159005 and 202011159154)supported by the Innovation and Technology Fund(funding support to State Key Laboratories in Hong Kong of Agorobiotechnology)of the HKSAR,Chinasupported by the Carbon Mitigation Initiative of the Princeton Universitysupport from the National Science Foundation(DEB-2045968)Texas Tech University.
文摘Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change,but a holistic understanding and assessment remains lacking.Here we hypothesized that V_(c,max)was dictated by both factors of temperature-associated enzyme kinetics(capturing instantaneous ecophysiological responses)and the amount of activated RuBisCO(indexed by V_(c,max)standardized at 25℃,V_(c,max25)),and compiled a comprehensive global dataset(n=7339 observations from 428 sites)for hypothesis testing.The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems.We found that a semi-empirical statistical model considering both factors explained 78%of global V_(c,max)variability,followed by 55%explained by enzyme kinetics alone.This statistical model outperformed the current theoretical optimality model for predicting global V_(c,max)variability(67%),primarily due to its poor characterization on global V_(c,max25)variability(3%).Further,we demonstrated that,in addition to climatic variables,belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of V_(c,max25)was a key missing mechanism for improving the theoretical modelling of global V_(c,max)variability.These findings improve the mechanistic understanding of global V_(c,max)variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.
基金supported by the National Natural Science Foun-dation of China Young Scientists Fund(42201373)Major Pro-gram of the National Natural Science Foundation of China(42090015)+4 种基金the National Key Research and Development Program of China(2022YFB3903703)the Guangdong Natural Science Foun-dation of General Program(260842044)the National Natural Science Foundation of China(NSFC)/Research Grants Council(RGC)Joint Research Scheme(N_HKU722/23)The University of Hong Kong HKU-100 Scholars Fundthe Seed Fund for Basic Research,Strategic Interdisciplinary Research Scheme Fund,and the Croucher Foundation(CAS22902/CAS22HU01).
文摘Accurate,detailed,and up-to-date urban land use information plays a key role in understanding the urban environment,enhancing urban planning,and promoting sustainable urban development.Recent advancements have focused on refining urban land use classification methods and generating data prod-ucts at various scales.However,detailed parcel-level urban land use mapping across China remains insuf-ficient with low accuracy.To address this issue,we propose an enhanced mapping framework of essential urban land use categories by integrating multi-modal deep learning models and multi-source geospatial data.Utilizing complete,accurate land parcels derived from the combined OpenStreetMap and Tianditu road networks as the smallest classification units,we have developed an enhanced Essential Urban Land Use Categories(EULUC)map covering all cities in China for 2022,termed EULUC-China 2.0.The mapping results show that residential,industrial,and park and greenspace are the dominant land use categories,collectively accounting for nearly 78%of the urban area.Compared to its predecessor,EULUC-China 1.0,the updated 2.0 version offers more detailed,spatially explicit information that reveals distinct spatial patterns within diverse land use compositions of each city.Our evaluation demonstrates that the overall accuracies of Level-I and Level-II classification reach up to 79%and 72%,respectively,representing sub-stantial enhancements across all categories over the previous product.These improvements are primarily attributed to the effectiveness of deep learning in processing multi-modal inputs,particularly through the graph modeling of Point-of-interest(POI)data.The publicly accessible product(https://zenodo.org/records/15180905)and the insights derived from this study offer a valuable dataset and references for researchers and practitioners addressing critical challenges in urbanization.
基金supported by Fundamental National Key R&D Program of China(grant number 2019YFA0606601)Tsinghua University Initiative Scientific Research Program(grantnumber 20223080017)+1 种基金National Natural Science Foundation of China(grant number 42201367)Fundamental ResearchFunds for the Central Universities(grant number DUT23RC(3)064.
文摘Remote sensing and land resource surveys have been used in recent decades for land use/land cover(LULC)mapping;however,keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging.This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data.This study employed Landsat imagery time series,change detection algorithms,sample migration,and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022.The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products.Additionally,a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps,increasing the correlation coefficient(r)with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99.Thus,the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.
基金supported by the National Natural Scierce Foundation ofChina(Grant Nos 42101249 and 42022061)the Hui Oi-Chow Trust Fund(gant#263690561.114525.30900.400.01)of the University of Hong Kong.
文摘The abrupt outbreak of coronavirus disease in 2019,also known as COVID-19,has led to an unprecedented global public healthcrisis.Current studies have paid immense attention to the impacts of COVID-19 posed to the atmosphere and the land-based sectors in areas such as air quality,carbon emission,economic senti ment,educational and social equality,etc.It is depicted that carbon emission had dropped about 8.8%in the first half of 2020 compared to 2019,'significant reduc-tion of air pollutants such as PM25 and NO2 were moreover reported at national,regional,and global levels.On the flip side,the amount of attention paid to the ocean during this pandemic has been nearly negligible despite its prominent functions of supporting livelihoods for 40%of the global population,absorbing~30%of anthropogenic CO_(2)emissions,and processing over 90%of excess heat in our climate system.Both direct and indirect effects of the pandemicare insufficiently understood in the ocean,which include their key roles in blue carbon sequestration,ocean-atmosphere and ocean-land interactions,sealevel changes,and their impacts to human beings.
基金supported by the National Key R&D Program of China(grant number 2019YFA0606601)the Tsinghua University Initiative Scientific Research Program(grant numbers 2021Z11GHX002 and 20223080017)+1 种基金the National Natural Science Foundation of China(grant numbers 42090015 and 42071400)the Study on the Harmonious Symbiosis Model of Green Steel and Modern City(grant number 20202000575).
文摘Understanding the distribution and land history of old urban areas(OUAs)and renewed urban areas(RUAs)has become the key point of urban management.However,it is hard to acquire adequate information for lack of pertinent detection methods.Here,we established a complete mapping framework on Google Earth Engine(GEE)platform to identify OUAs and RUAs and detect the temporal information of urban renewal,which was implemented in Beijing during 2000-2020.We used Landsat imagery and LandTrendr algorithm to fit the spectral trajectories of 14 bands/indices with specific segment attributes as the feature inputs for Random Forest classification.We produced the maps of OUAs and RUAs with an overall accuracy of 95.36%.On this basis,we further utilized LandTrendr to detect the start year,end year,and duration of urban renewal with the accuracies within the±5-year difference of 85.52%,80.97%,and 74.53%,respectively.These maps all present informative spatiotemporal patterns.Furthermore,the urban renewal process is likely to be influenced by major national or international events.The study answers the issues about urban renewal from multiple angles and provides scientific support for future urban planning.
