The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave...The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution.However,the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes.This work used Sentinel-1 synthetic aperture radar(SAR)data to derive high-resolution ice maps(about 6 days),then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP(e.g.Selin Co,Ayakekumu Lake,and Nam Co).The results showed that three lakes had an average annual ice period of 125-157 days and a complete ice cover period of 72-115 days,from 2018 to 2022.They exhibit different ice phenology patterns.Nam Co is characterized by repeated episodes of freezing,melting,and refreezing,resulting in a prolonged freeze-up period.Meanwhile,the break-up period of Nam Co lasts for a longer duration(about 19 days),and the break-up exhibits a smooth process.Similarly,Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period,with deviations of up to 28 days observed among different years.Compared to the other two lakes,Selin Co experienced a relatively short freeze-up and break-up period.In short,Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP.Particularly,this data facilitates quantification of the freeze-melt dynamics.展开更多
Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from...Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.展开更多
The Earth is experiencing unprecedented climate change.Vegetation phenology has already showed strong response to the global warming,which alters mass and energy fluxes on terrestrial ecosystems.With technology and me...The Earth is experiencing unprecedented climate change.Vegetation phenology has already showed strong response to the global warming,which alters mass and energy fluxes on terrestrial ecosystems.With technology and method developments in remote sensing,computer science and citizen science,many recent phenology-related studies have been focused on macrophenology.In this perspective,we 1)reviewed the responses of vegetation phenology to climate change and its impacts on carbon cycling,and reported that the effect of shifted phenology on the terrestrial carbon fluxes is substantially different between spring and autumn;2)elaborated how vegetation phenology affects ecohydrological processes at different scales,and further listed the key issues for each scale,i.e.,focusing on seasonal effect,local feedbacks and regional vapor transport for individual,watershed and global respectively);3)envisioned the potentials to improve current hydrological models by coupling vegetation phenology-related processes,in combining with machine learning,deep learning and scale transformation methods.We propose that comprehensive understanding of climate-macrophenology-hydrology interactions are essential and urgently needed for enhancing our understanding of the ecosystem response and its role in hydrological cycle under future climate change.展开更多
Since climate change shocks and stresses cannot be fully prevented, building resilient urban areas is gaining more attention in the global community. By building resilience, the negative impacts of climate change shoc...Since climate change shocks and stresses cannot be fully prevented, building resilient urban areas is gaining more attention in the global community. By building resilience, the negative impacts of climate change shocks and stresses can be alleviated. Several indices have been developed to measure urban resilience. Yet, most of these indices focus more on objective methods which require robust bio-physical and socio-economic data sets which are generally lacking in many developing countries. To reduce this challenge, the use of subjective methods has recently been suggested. This study proposed and tested a Municipality Resilience Index (MRI) which employed a subjective method to assess the resilience of Mbale municipality in Eastern Uganda against climate change shocks and stresses. The proposed MRI includes 46 variables describing the physical, social, economic and institutional dimensions. The MRI can be applied in any municipality in developing countries facing climate related shocks and stresses and with limited survey data. The application of this index to Mbale municipality shows that the municipality has a low resilience index of 0.2. Similarly, most variables in the four dimensions of resilience reflected very low resilience scores with other divisions being more resilient than the others. Furthermore, the social dimension has the lowest score as compared to the physical, economic and institutional dimensions. The findings indicate a spatial variability in the contribution of the resilience dimensions within this small geographic confine. Moreover, the findings show the strengths and weaknesses in the different dimensions of the proposed MRI. This can act as a guide for policy and practitioners on which sectors to target in order to enhance the resilience of Mbale municipality.展开更多
The remote forcing from land surface changes in the Sahara is hypothesized to play a pivotal role in modulating the intensity of the East Asian summer monsoon(EASM)through ocean-atmospheric teleconnections.This modula...The remote forcing from land surface changes in the Sahara is hypothesized to play a pivotal role in modulating the intensity of the East Asian summer monsoon(EASM)through ocean-atmospheric teleconnections.This modulation has far-reaching consequences,particularly in facilitating societal shifts documented in northern China.Here,we present a well-dated lake-level record from the Daihai Lake Basin in northern China,providing quantitative assessments of Holocene monsoonal precipitation and the consequent migrations of the northern boundary of the EASM.Our reconstruction,informed by a water-and-energy balance model,indicates that annual precipitation reached700 mm during 8–5 ka,followed by a rapid decline to tween 5 and 4 ka.This shift coherently aligns with a signi550 mm be-ficant300 km northwestward movement of the EASM northern boundary during the Middle Holocene(MH),in contrast to its current position.Our findings underscore that these changes cannot be entirely attributed to orbital forcing,as corroborated by simulation tests.Climate model simulations deployed in our study suggest that the presence of the Green Sahara during the MH significantly strengthened the EASM and led to a northward shift of the monsoon rainfall belt.Conversely,the Sahara’s reversion to a desert landscape in the late Holocene was accompanied by a corresponding southward retraction of monsoon influence.These dramatic hydroclimate changes during5–4 ka likely triggered or at least contributed to a shift in Neolithic cultures and societal transformation in northern China.With decreasing agricultural productivity,communities transitioned from millet farming to a mixed rainfed agriculture and animal husbandry system.Thus,our findings elucidate not only the variability of the EASM but also the profound implications of a remote forcing,such as surface transformations of the Sahara,on climatic changes and cultural evolution in northern China.展开更多
Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly ...Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.展开更多
Northern peatlands store nearly one-third of terrestrial carbon(C)stocks while covering only 3%of the global landmass;nevertheless,the drivers of C cycling in these often-waterlogged ecosystems are different from thos...Northern peatlands store nearly one-third of terrestrial carbon(C)stocks while covering only 3%of the global landmass;nevertheless,the drivers of C cycling in these often-waterlogged ecosystems are different from those that control C dynamics in upland forested soils.To explore how multiple abiotic and biotic characteristics of bogs interact to shape microbial activity in a northern,forested bog,we added a labile C tracer(13C-labeled starch)to in situ peat mesocosms and correlated heterotrophic respiration with natural variation in several microbial predictor variables,such as enzyme activity and microbial biomass,as well as with a suite of abiotic variables and proximity to vascular plants aboveground.We found that peat moisture content was positively correlated with respiration and microbial activity,even when moisture levels exceeded total saturation,suggesting that access to organic matter substrates in drier environments may be limiting for microbial activity.Proximity to black spruce trees decreased total and labile heterotrophic respiration.This negative relationship may reflect the influence of tree evapotranspiration and peat shading effects;i.e.,microbial activity may decline as peat dries and cools near trees.Here,we isolated the response of heterotrophic respiration to explore the variation in,and interactions among,multiple abiotic and biotic drivers that influence microbial activity.This approach allowed us to reveal the relative influence of individual drivers on C respiration in these globally important C sinks.展开更多
Similarity measurement has been a prevailing research topic geographic information science.Geometric similarity measurement inin scaling transformation(GSM_ST)is critical to ensure spatial data quality while balancing...Similarity measurement has been a prevailing research topic geographic information science.Geometric similarity measurement inin scaling transformation(GSM_ST)is critical to ensure spatial data quality while balancing detailed information with distinctive features.However,GSM_ST is an uncertain problem due to subjective spatial cognition,global and local concerns,and geometric complexity.Traditional rule-based methods considering multiple consistent conditions require subjective adjustments to characteristics and weights,leading to poor robustness in addressing GSM_ST.This study proposes an unsupervised representation learning framework for automated GSM_ST,using a Graph Autoencoder Network(GAE)and drainage networks as an example.The framework involves constructing a drainage graph,designing the GAE architecture for GSM_ST,and using Cosine similarity to measure similarity based on the GAE-derived drainage embeddings in different scales.We perform extensive experiments and compare methods across 71 drainage networks duringfive scaling transformations.The results show that the proposed GAE method outperforms other methods with a satisfaction ratio of around 88%and has strong robustness.Moreover,our proposed method also can be applied to other scenarios,such as measuring similarity between geographical entities at different times and data from different datasets.展开更多
The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the ...The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.展开更多
Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a no...Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a novel method is proposed for streetlevel visualization based on visual saliency evaluation.The basic idea of the method is to preserve these salient buildings in a scene while removing those that are non-salient.The method can be divided into pre-processing procedures and real-time visualization.The first step in pre-processing is to convert 3D building models at higher Levels of Detail(Lo Ds) into LoD 1 models with simplified ground plans.Then,a number of index viewpoints are created along the streets; these indices refer to both the position and the direction of each street site.A visual saliency value is computed for each building,with respect to the index site,based on a visual difference between the original model and the generalized model.We calculate and evaluate three methods for visual saliency:local difference,global difference and minimum projection area.The real-time visualization process begins by mapping the observer to its closest indices.The street view is then generated based on the building information stored in those indexes.A user study shows that the local visual saliency method performs better than do the global visual saliency,area and image-based methods and that the framework proposed in this paper may improve the performance of 3D visualization.展开更多
The amount of volunteered geographic information(VGI)has increased over the past decade,and several studies have been conducted to evaluate the quality of VGI data.In this study,we evaluate the completeness of the roa...The amount of volunteered geographic information(VGI)has increased over the past decade,and several studies have been conducted to evaluate the quality of VGI data.In this study,we evaluate the completeness of the road network in the VGI data set OpenStreetMap(OSM).The evaluation is based on an accurate and efficient network-matching algorithm.The study begins with a comparison of the two main strategies for network matching:segment-based and nodebased matching.The comparison shows that the result quality is comparable for the two strategies,but the node-based result is considerably more computationally efficient.Therefore,we improve the accuracy of node-based algorithm by handling topological relationships and detecting patterns of complicated network components.Finally,we conduct a case study on the extended node-based algorithm in which we match OSM to the Swedish National Road Database(NVDB)in Scania,Sweden.The case study reveals that OSM has a completeness of 87%in the urban areas and 69%in the rural areas of Scania.The accuracy of the matching process is approximately 95%.The conclusion is that the extended node-based algorithm is sufficiently accurate and efficient for conducting surveys of the quality of OSM and other VGI road data sets in large geographic regions.展开更多
Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one ...Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.展开更多
The aim of this study is to estimate and compare soil erosion,in the Mount Elgon region,eastern Uganda,during the last decade.Possible trends and changes in erosion are linked to precipitation/climate change as well a...The aim of this study is to estimate and compare soil erosion,in the Mount Elgon region,eastern Uganda,during the last decade.Possible trends and changes in erosion are linked to precipitation/climate change as well as changes in land cover.Two different versions of the Revised Universal Soil loss Equation(RUSLE)are implemented and compared,one using slope length and the other using flow accumulation to estimate the slope length and steepness factor(LS).Comparisons of the modeled soil erosion vs.field data indicate that RUSLE based on flow accumulation is preferable.The modeling is carried out for the years 2000,2006,and 2012,and is based on ASTER remotely sensed data,digital elevation models,precipitation data from the study area,as well as existing soil maps.No significant trends in estimated soil erosion are found to be present during the last decade.Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation.Even if there are reports of more intense and increasing amounts of rainfall in the area,this could not be verified,neither through the analysis of climate data,nor by trends in the estimated soil loss.展开更多
Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a disaster.This study propo...Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a disaster.This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas.It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data.To precisely calculate the textual similarity,three state-of-theart text embedding methods of Word2vec,GloVe,and Fast Text were used to capture both syntactic and semantic similarities.The impact of selected embedding algorithms on the quality of the outputs was studied.Different combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes.The proposed method was applied to a case study related to 2018 Hurricane Florence.The method was able to precisely identify events of varied sizes and densities before,during,and after the hurricane.The feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed.The proposed method was also compared to an implementation based on the standard density-based spatial clustering of applications with noise algorithm,where it showed more promising results.展开更多
There exist major challenges in accelerating the spatial data infrastructure(SDI)planning process in the developing countries as well as advocating for politicians to support the development of SDI,due to the high com...There exist major challenges in accelerating the spatial data infrastructure(SDI)planning process in the developing countries as well as advocating for politicians to support the development of SDI,due to the high complexity of SDI,lack of knowledge and experience,and limited insight in the benefits.To address these challenges,a methodology for SDI planning in Tanzania,based on the system dynamics technique and the communities of practice concept,was adopted and applied within a community consisting of experts from stakeholder organizations.The groups gathered to develop an SDI plan,while they shared their knowledge and discussed their ideas that helped their understanding of SDI.By running the system dynamics model,the development of SDI over time could be simulated that gave the planning community an insight about the future effects of today’s plans and decisions.Finally,an optimum model could be developed by refinements and improvements done with the consensus of the SDI stakeholders.This model included the components and policies that are essential for a successful SDI implementation in Tanzania and can be used as a basis for SDI planning and help to gain political support.Lessons learnt from this research were promising regarding the usability of the methodology for SDI planning in comparable countries.展开更多
Climate warming leads to vast changes in the land cover types and plant biomass in the northern high-latitude regions.The overall trend is of shrubland and tree lines moving northwards,while changes in different land ...Climate warming leads to vast changes in the land cover types and plant biomass in the northern high-latitude regions.The overall trend is of shrubland and tree lines moving northwards,while changes in different land cover types and vegetation growth in response to climate change are largely unknown.Here,we selected land areas with latitudes higher than 50°N as the study area.We compared the land cover type changes and explored relationships between the normalized difference vegetation index(NDVI)values of different land cover types,air temperature,and precipitation during 1982-2015 based on dynamic grid.The results indicated that forest and shrubland areas increased as a large area of grassland shifted to forest and shrubland.The snow/ice,tundra and grassland largely have decreased from 1982 to 2015.Although approximately 277.3×103 km2 of barren land(6.2%of the total barren land area in 1982)changed to tundra,the tundra area still decreased because some tundra shifted to forest and grassland.The NDVI values of tundra significantly increased,but the shrubland showed a decreasing trend.Temperature in the growing season(June to September)showed the largest positive correlation coefficients with the NDVI values of forest,tundra,grassland,and cropland.However,due to shrubification processes and plant mortality in shrubland areas,the shrubland NDVI showed negative relationship with annual temperature but positively correlated with monthly t.Taken together,although there is large room for improvement of the land cover type data accuracy,our results suggested that the land cover types in high-latitude regions changed significantly,while the NDVI values of the different land cover types showed different responses to climate change.展开更多
基金supported financially by the National Nature Science Foundation of China(No.41901129)the University Natural Sciences Research Project of Anhui Educational committee(KJ2020JD06)DUAN Zheng acknowledges the support from the Joint China-Sweden Mobility Grant funded by NSFC and STINT(CH2019-8250).
文摘The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution.However,the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes.This work used Sentinel-1 synthetic aperture radar(SAR)data to derive high-resolution ice maps(about 6 days),then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP(e.g.Selin Co,Ayakekumu Lake,and Nam Co).The results showed that three lakes had an average annual ice period of 125-157 days and a complete ice cover period of 72-115 days,from 2018 to 2022.They exhibit different ice phenology patterns.Nam Co is characterized by repeated episodes of freezing,melting,and refreezing,resulting in a prolonged freeze-up period.Meanwhile,the break-up period of Nam Co lasts for a longer duration(about 19 days),and the break-up exhibits a smooth process.Similarly,Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period,with deviations of up to 28 days observed among different years.Compared to the other two lakes,Selin Co experienced a relatively short freeze-up and break-up period.In short,Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP.Particularly,this data facilitates quantification of the freeze-melt dynamics.
基金supported by the National Natural Science Foundation of China (41975044)the Open Research Fund of the State Laboratory of Information Engineering in Surveying,Mapping,Remote Sensing,Wuhan University (20R02)+2 种基金the Fundamental Research Funds for the Central Universities,China University of Geosciences (Wuhan)(111-G1323520290)funded by SNSA (Dnr 96/16)the EU-Aid funded CASSECS Project。
文摘Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.
基金the National Science Fund for Distinguished Young Scholars(Grant No.42025101)International Cooperation and Exchanges NSFC-STINT(Grant No.42111530181).
文摘The Earth is experiencing unprecedented climate change.Vegetation phenology has already showed strong response to the global warming,which alters mass and energy fluxes on terrestrial ecosystems.With technology and method developments in remote sensing,computer science and citizen science,many recent phenology-related studies have been focused on macrophenology.In this perspective,we 1)reviewed the responses of vegetation phenology to climate change and its impacts on carbon cycling,and reported that the effect of shifted phenology on the terrestrial carbon fluxes is substantially different between spring and autumn;2)elaborated how vegetation phenology affects ecohydrological processes at different scales,and further listed the key issues for each scale,i.e.,focusing on seasonal effect,local feedbacks and regional vapor transport for individual,watershed and global respectively);3)envisioned the potentials to improve current hydrological models by coupling vegetation phenology-related processes,in combining with machine learning,deep learning and scale transformation methods.We propose that comprehensive understanding of climate-macrophenology-hydrology interactions are essential and urgently needed for enhancing our understanding of the ecosystem response and its role in hydrological cycle under future climate change.
基金the generous funding of this research by the Swedish International Development Agency(SIDA)under the Building Resilient Ecosystems and Livelihoods to Climate Change and Disaster Risks(BREAD)project,grant Number 331.
文摘Since climate change shocks and stresses cannot be fully prevented, building resilient urban areas is gaining more attention in the global community. By building resilience, the negative impacts of climate change shocks and stresses can be alleviated. Several indices have been developed to measure urban resilience. Yet, most of these indices focus more on objective methods which require robust bio-physical and socio-economic data sets which are generally lacking in many developing countries. To reduce this challenge, the use of subjective methods has recently been suggested. This study proposed and tested a Municipality Resilience Index (MRI) which employed a subjective method to assess the resilience of Mbale municipality in Eastern Uganda against climate change shocks and stresses. The proposed MRI includes 46 variables describing the physical, social, economic and institutional dimensions. The MRI can be applied in any municipality in developing countries facing climate related shocks and stresses and with limited survey data. The application of this index to Mbale municipality shows that the municipality has a low resilience index of 0.2. Similarly, most variables in the four dimensions of resilience reflected very low resilience scores with other divisions being more resilient than the others. Furthermore, the social dimension has the lowest score as compared to the physical, economic and institutional dimensions. The findings indicate a spatial variability in the contribution of the resilience dimensions within this small geographic confine. Moreover, the findings show the strengths and weaknesses in the different dimensions of the proposed MRI. This can act as a guide for policy and practitioners on which sectors to target in order to enhance the resilience of Mbale municipality.
基金This research was supported by the National Key Research and Development Program of China(no.2022YFF0801103)the Strategic Priority Research Program of the Chinese Academy of Sciences(no.XDB40010200)+1 种基金the National Natural Science Foundation of China(no.41977381)the Youth Innovation Promotion Association CAS(grant no.Y201959).The EC-Earth and LPJ-GUESS simulations were performed on the Swedish National Infrastructure for Computing at the National Super-computer Center,partially funded by the Swedish Research Council through grant agreement no.2018-05973.We thank Lili Liu for the laboratory work,Yingxiao Yu for the field sampling,and Yunkai Zhou for providing the meteorological data.
文摘The remote forcing from land surface changes in the Sahara is hypothesized to play a pivotal role in modulating the intensity of the East Asian summer monsoon(EASM)through ocean-atmospheric teleconnections.This modulation has far-reaching consequences,particularly in facilitating societal shifts documented in northern China.Here,we present a well-dated lake-level record from the Daihai Lake Basin in northern China,providing quantitative assessments of Holocene monsoonal precipitation and the consequent migrations of the northern boundary of the EASM.Our reconstruction,informed by a water-and-energy balance model,indicates that annual precipitation reached700 mm during 8–5 ka,followed by a rapid decline to tween 5 and 4 ka.This shift coherently aligns with a signi550 mm be-ficant300 km northwestward movement of the EASM northern boundary during the Middle Holocene(MH),in contrast to its current position.Our findings underscore that these changes cannot be entirely attributed to orbital forcing,as corroborated by simulation tests.Climate model simulations deployed in our study suggest that the presence of the Green Sahara during the MH significantly strengthened the EASM and led to a northward shift of the monsoon rainfall belt.Conversely,the Sahara’s reversion to a desert landscape in the late Holocene was accompanied by a corresponding southward retraction of monsoon influence.These dramatic hydroclimate changes during5–4 ka likely triggered or at least contributed to a shift in Neolithic cultures and societal transformation in northern China.With decreasing agricultural productivity,communities transitioned from millet farming to a mixed rainfed agriculture and animal husbandry system.Thus,our findings elucidate not only the variability of the EASM but also the profound implications of a remote forcing,such as surface transformations of the Sahara,on climatic changes and cultural evolution in northern China.
基金Supported by the National Key Research and Development Program of China(2016YFA0601504)National Natural Science Foundation of China(51979069)+1 种基金Fundamental Research Funds for the Central Universities(B200204029)Program of Introducing Talents of Discipline to Universities by the Ministry of Education and State Administration of Foreign Experts Affairs,China(B08048)。
文摘Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.
基金the United States Department of Energy,Office of Science,Office of Biological and Environmental Research,Terrestrial Ecosystem Sciences Program(No.DE-SC0010562).
文摘Northern peatlands store nearly one-third of terrestrial carbon(C)stocks while covering only 3%of the global landmass;nevertheless,the drivers of C cycling in these often-waterlogged ecosystems are different from those that control C dynamics in upland forested soils.To explore how multiple abiotic and biotic characteristics of bogs interact to shape microbial activity in a northern,forested bog,we added a labile C tracer(13C-labeled starch)to in situ peat mesocosms and correlated heterotrophic respiration with natural variation in several microbial predictor variables,such as enzyme activity and microbial biomass,as well as with a suite of abiotic variables and proximity to vascular plants aboveground.We found that peat moisture content was positively correlated with respiration and microbial activity,even when moisture levels exceeded total saturation,suggesting that access to organic matter substrates in drier environments may be limiting for microbial activity.Proximity to black spruce trees decreased total and labile heterotrophic respiration.This negative relationship may reflect the influence of tree evapotranspiration and peat shading effects;i.e.,microbial activity may decline as peat dries and cools near trees.Here,we isolated the response of heterotrophic respiration to explore the variation in,and interactions among,multiple abiotic and biotic drivers that influence microbial activity.This approach allowed us to reveal the relative influence of individual drivers on C respiration in these globally important C sinks.
基金supported by the National Natural Science Foundation of China[grant number 41531180]the National Natural Science Foundation of China[grant number 42071450]the China Scholarship Council(CSC)[grant number 202206270076].
文摘Similarity measurement has been a prevailing research topic geographic information science.Geometric similarity measurement inin scaling transformation(GSM_ST)is critical to ensure spatial data quality while balancing detailed information with distinctive features.However,GSM_ST is an uncertain problem due to subjective spatial cognition,global and local concerns,and geometric complexity.Traditional rule-based methods considering multiple consistent conditions require subjective adjustments to characteristics and weights,leading to poor robustness in addressing GSM_ST.This study proposes an unsupervised representation learning framework for automated GSM_ST,using a Graph Autoencoder Network(GAE)and drainage networks as an example.The framework involves constructing a drainage graph,designing the GAE architecture for GSM_ST,and using Cosine similarity to measure similarity based on the GAE-derived drainage embeddings in different scales.We perform extensive experiments and compare methods across 71 drainage networks duringfive scaling transformations.The results show that the proposed GAE method outperforms other methods with a satisfaction ratio of around 88%and has strong robustness.Moreover,our proposed method also can be applied to other scenarios,such as measuring similarity between geographical entities at different times and data from different datasets.
基金supported by the National Natural Science Foundation of China(42001314)the Open Research Fund of the State Laboratory of Information Engineering in Surveying,Mapping,and Remote Sensing,Wuhan University(grant number 20R02)+1 种基金Torbern Tagesson was additionally funded by the Swedish National Space Agency(SNSA 2021-00144)FORMAS(Dnr.2021-00644).
文摘The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.
基金supported by the National Natural Science Foundation of China(Grant No.41201486)the National Key Technologies R&D Program of China(Grant No.SQ2013GX07E00985)the project of the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) in the Collaborative Innovation Center of Modern Grain Circulation and Security,Nanjing University of Finance and Economics
文摘Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a novel method is proposed for streetlevel visualization based on visual saliency evaluation.The basic idea of the method is to preserve these salient buildings in a scene while removing those that are non-salient.The method can be divided into pre-processing procedures and real-time visualization.The first step in pre-processing is to convert 3D building models at higher Levels of Detail(Lo Ds) into LoD 1 models with simplified ground plans.Then,a number of index viewpoints are created along the streets; these indices refer to both the position and the direction of each street site.A visual saliency value is computed for each building,with respect to the index site,based on a visual difference between the original model and the generalized model.We calculate and evaluate three methods for visual saliency:local difference,global difference and minimum projection area.The real-time visualization process begins by mapping the observer to its closest indices.The street view is then generated based on the building information stored in those indexes.A user study shows that the local visual saliency method performs better than do the global visual saliency,area and image-based methods and that the framework proposed in this paper may improve the performance of 3D visualization.
文摘The amount of volunteered geographic information(VGI)has increased over the past decade,and several studies have been conducted to evaluate the quality of VGI data.In this study,we evaluate the completeness of the road network in the VGI data set OpenStreetMap(OSM).The evaluation is based on an accurate and efficient network-matching algorithm.The study begins with a comparison of the two main strategies for network matching:segment-based and nodebased matching.The comparison shows that the result quality is comparable for the two strategies,but the node-based result is considerably more computationally efficient.Therefore,we improve the accuracy of node-based algorithm by handling topological relationships and detecting patterns of complicated network components.Finally,we conduct a case study on the extended node-based algorithm in which we match OSM to the Swedish National Road Database(NVDB)in Scania,Sweden.The case study reveals that OSM has a completeness of 87%in the urban areas and 69%in the rural areas of Scania.The accuracy of the matching process is approximately 95%.The conclusion is that the extended node-based algorithm is sufficiently accurate and efficient for conducting surveys of the quality of OSM and other VGI road data sets in large geographic regions.
基金This work was supported by China Scholarship Council and Lund University.
文摘Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.
文摘The aim of this study is to estimate and compare soil erosion,in the Mount Elgon region,eastern Uganda,during the last decade.Possible trends and changes in erosion are linked to precipitation/climate change as well as changes in land cover.Two different versions of the Revised Universal Soil loss Equation(RUSLE)are implemented and compared,one using slope length and the other using flow accumulation to estimate the slope length and steepness factor(LS).Comparisons of the modeled soil erosion vs.field data indicate that RUSLE based on flow accumulation is preferable.The modeling is carried out for the years 2000,2006,and 2012,and is based on ASTER remotely sensed data,digital elevation models,precipitation data from the study area,as well as existing soil maps.No significant trends in estimated soil erosion are found to be present during the last decade.Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation.Even if there are reports of more intense and increasing amounts of rainfall in the area,this could not be verified,neither through the analysis of climate data,nor by trends in the estimated soil loss.
文摘Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a disaster.This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas.It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data.To precisely calculate the textual similarity,three state-of-theart text embedding methods of Word2vec,GloVe,and Fast Text were used to capture both syntactic and semantic similarities.The impact of selected embedding algorithms on the quality of the outputs was studied.Different combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes.The proposed method was applied to a case study related to 2018 Hurricane Florence.The method was able to precisely identify events of varied sizes and densities before,during,and after the hurricane.The feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed.The proposed method was also compared to an implementation based on the standard density-based spatial clustering of applications with noise algorithm,where it showed more promising results.
基金This study is part of the project“Geodata Infrastructure Development”implemented in collaboration between University of Dar es Salaam and Lund University and funded by the Swedish International Development Cooperation Agency(SIDA)[grant number 7500051503].
文摘There exist major challenges in accelerating the spatial data infrastructure(SDI)planning process in the developing countries as well as advocating for politicians to support the development of SDI,due to the high complexity of SDI,lack of knowledge and experience,and limited insight in the benefits.To address these challenges,a methodology for SDI planning in Tanzania,based on the system dynamics technique and the communities of practice concept,was adopted and applied within a community consisting of experts from stakeholder organizations.The groups gathered to develop an SDI plan,while they shared their knowledge and discussed their ideas that helped their understanding of SDI.By running the system dynamics model,the development of SDI over time could be simulated that gave the planning community an insight about the future effects of today’s plans and decisions.Finally,an optimum model could be developed by refinements and improvements done with the consensus of the SDI stakeholders.This model included the components and policies that are essential for a successful SDI implementation in Tanzania and can be used as a basis for SDI planning and help to gain political support.Lessons learnt from this research were promising regarding the usability of the methodology for SDI planning in comparable countries.
基金the National Key Research and Development Program of China(2020YFA0608501,2019YFA0607003)the National Natural Science Foundation of China(41941015,32061143032,41961144021)+3 种基金the State Key Laboratory of Cryospheric Science(SKLCS-ZZ-2021)the West Light Foundation of the Chinese Academy of SciencesZhang Wen-Xin acknowledged grants of Swedish Research Council FORMAS 2016-01201 and Swedish National Space Agency 209/19This work was also supported by Russian Fund for Basic Research grant N18-05-60080 and State topic N AAAA-A16-116032810095-6.
文摘Climate warming leads to vast changes in the land cover types and plant biomass in the northern high-latitude regions.The overall trend is of shrubland and tree lines moving northwards,while changes in different land cover types and vegetation growth in response to climate change are largely unknown.Here,we selected land areas with latitudes higher than 50°N as the study area.We compared the land cover type changes and explored relationships between the normalized difference vegetation index(NDVI)values of different land cover types,air temperature,and precipitation during 1982-2015 based on dynamic grid.The results indicated that forest and shrubland areas increased as a large area of grassland shifted to forest and shrubland.The snow/ice,tundra and grassland largely have decreased from 1982 to 2015.Although approximately 277.3×103 km2 of barren land(6.2%of the total barren land area in 1982)changed to tundra,the tundra area still decreased because some tundra shifted to forest and grassland.The NDVI values of tundra significantly increased,but the shrubland showed a decreasing trend.Temperature in the growing season(June to September)showed the largest positive correlation coefficients with the NDVI values of forest,tundra,grassland,and cropland.However,due to shrubification processes and plant mortality in shrubland areas,the shrubland NDVI showed negative relationship with annual temperature but positively correlated with monthly t.Taken together,although there is large room for improvement of the land cover type data accuracy,our results suggested that the land cover types in high-latitude regions changed significantly,while the NDVI values of the different land cover types showed different responses to climate change.
