Volunteered geographic information(VGI)is becoming an important source of geospatial big data that support many applications.The application semantics of VGI,i.e.how well VGI reflects the real-world geographic phenome...Volunteered geographic information(VGI)is becoming an important source of geospatial big data that support many applications.The application semantics of VGI,i.e.how well VGI reflects the real-world geographic phenomena of interest to the application,is essential for any VGI applications.VGI observations often are spatially biased(e.g.spatially clustered).Spatial bias poses challenges on VGI application semantics because it may impede the quality of inferences made from VGI.Using species distribution modeling(SDM)as an example application,this article argues that spatial bias impedes VGI application semantics,as gauged by SDM model performance,and accounting for bias enhances application semantics.VGI observations from eBird were used in a case study for modeling the distribution of the American Robin(Turdus migratorius)in U.S.T.migratorius observations from the North American Breeding Bird Survey were used as independent validation data for model performance evaluation.A grid-based strategy was adopted to filter eBird species observations to reduce spatial bias.Evaluations show that spatial bias in species observations degrades SDM model performance and filtering species observations improves model performance.This study demonstrates that VGI application semantics can be enhanced by accounting for the spatial bias in VGI observations.展开更多
Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss...Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services.Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions.Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation.In this study,the possibility of mapping the distribution of invasive Prosopis juliflora(Swartz)DC.was shown using present background data in Khuzestan Province,Iran.After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset,we applied six modelling algorithms(generalized additive model(GAM),classification tree analysis(CTA),random forest(RF),multivariate adaptive regression splines(MARS),maximum entropy(Max Ent)and ensemble model)to predict invasion distribution of the species under current and future climate conditions for both optimistic(RCP2.6)and pessimistic(RCP8.5)scenarios for the years 2050 and 2070,respectively.Predictor variables including weighted mean of CHELSA(climatologies at high resolution for the Earth’s land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables(1979–2020),physiographic variables extracted from shuttle radar topography mission(SRTM)and some human factors were used in modelling process.To avoid causing a biased selection of predictors or model coefficients,we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors.As in a conventional receiver operating characteristic(ROC),the area under curve(AUC)is calculated using presence and absence observations to measure the probability and the two error components are weighted equally.All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix.Sensitivity analysis showed that mean diurnal range(Bio2)and annual precipitation(Bio12)explained more than 50% of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P.juliflora.At all thresholds,the ensemble model showed a significant difference in comparison with single model.However,Max Ent and RF outperformed the others models.Under climate change scenarios,it is predicted that suitable areas for this invasive species will increase in Khuzestan Province,and increasing climatically suitable areas for the species in future will facilitate its future distribution.These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.展开更多
本文针对相位偏差对精密单点定位(Precise Point Positioning,PPP)的影响,分析了PPP模型中相位偏差估计的原则,并给出了宽巷和L1相位偏差的估计方法;对相位偏差小数提取过程中整数不一致以及偏差小数值存在正负差异的两个问题进行了修...本文针对相位偏差对精密单点定位(Precise Point Positioning,PPP)的影响,分析了PPP模型中相位偏差估计的原则,并给出了宽巷和L1相位偏差的估计方法;对相位偏差小数提取过程中整数不一致以及偏差小数值存在正负差异的两个问题进行了修正。算例从相位偏差时间和空间一致性两方面进行了分析,结果表明宽巷相位偏差具有较高的时间和空间一致性,单个站点相位偏差与其均值最大差值为0.050周;L1相位偏差受无电离层模糊度影响较为严重,其空间一致性较宽巷相位偏差稍差些,但基本控制在0.180周以内,可用于PPP模糊度的相位偏差改正。展开更多
Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this ...Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.展开更多
Rapid and accurate acquisition and analysis of information is crucial for emergency management,but traditional methods have limitations such as incomplete information acquisition and slow processing speed.The natural ...Rapid and accurate acquisition and analysis of information is crucial for emergency management,but traditional methods have limitations such as incomplete information acquisition and slow processing speed.The natural language oriented spatial scene reconstruction method provides a new solution for emergency management,but existing generative models have limited understanding of spatial relationships and lack high-quality training samples.To address these issues,this paper proposes a novel spatial scene reconstruction framework.Specifically,the BERT based spatial information knowledge graph extraction method is used to encode the input text,label and classify the encoded text,identify spatial objects and relationships in the text,and accurately extract spatial information.Additionally,a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition,and based on the obtained biases,a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph.Finally,use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints.In addition,a high-quality training sample set of“text-scene-knowledge graph”was constructed.展开更多
基金The work reported here was supported by the Faculty Research Fund[grant number 84363-145015]the Faculty Startup Fund at the University of Denver.
文摘Volunteered geographic information(VGI)is becoming an important source of geospatial big data that support many applications.The application semantics of VGI,i.e.how well VGI reflects the real-world geographic phenomena of interest to the application,is essential for any VGI applications.VGI observations often are spatially biased(e.g.spatially clustered).Spatial bias poses challenges on VGI application semantics because it may impede the quality of inferences made from VGI.Using species distribution modeling(SDM)as an example application,this article argues that spatial bias impedes VGI application semantics,as gauged by SDM model performance,and accounting for bias enhances application semantics.VGI observations from eBird were used in a case study for modeling the distribution of the American Robin(Turdus migratorius)in U.S.T.migratorius observations from the North American Breeding Bird Survey were used as independent validation data for model performance evaluation.A grid-based strategy was adopted to filter eBird species observations to reduce spatial bias.Evaluations show that spatial bias in species observations degrades SDM model performance and filtering species observations improves model performance.This study demonstrates that VGI application semantics can be enhanced by accounting for the spatial bias in VGI observations.
文摘Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services.Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions.Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation.In this study,the possibility of mapping the distribution of invasive Prosopis juliflora(Swartz)DC.was shown using present background data in Khuzestan Province,Iran.After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset,we applied six modelling algorithms(generalized additive model(GAM),classification tree analysis(CTA),random forest(RF),multivariate adaptive regression splines(MARS),maximum entropy(Max Ent)and ensemble model)to predict invasion distribution of the species under current and future climate conditions for both optimistic(RCP2.6)and pessimistic(RCP8.5)scenarios for the years 2050 and 2070,respectively.Predictor variables including weighted mean of CHELSA(climatologies at high resolution for the Earth’s land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables(1979–2020),physiographic variables extracted from shuttle radar topography mission(SRTM)and some human factors were used in modelling process.To avoid causing a biased selection of predictors or model coefficients,we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors.As in a conventional receiver operating characteristic(ROC),the area under curve(AUC)is calculated using presence and absence observations to measure the probability and the two error components are weighted equally.All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix.Sensitivity analysis showed that mean diurnal range(Bio2)and annual precipitation(Bio12)explained more than 50% of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P.juliflora.At all thresholds,the ensemble model showed a significant difference in comparison with single model.However,Max Ent and RF outperformed the others models.Under climate change scenarios,it is predicted that suitable areas for this invasive species will increase in Khuzestan Province,and increasing climatically suitable areas for the species in future will facilitate its future distribution.These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.
文摘本文针对相位偏差对精密单点定位(Precise Point Positioning,PPP)的影响,分析了PPP模型中相位偏差估计的原则,并给出了宽巷和L1相位偏差的估计方法;对相位偏差小数提取过程中整数不一致以及偏差小数值存在正负差异的两个问题进行了修正。算例从相位偏差时间和空间一致性两方面进行了分析,结果表明宽巷相位偏差具有较高的时间和空间一致性,单个站点相位偏差与其均值最大差值为0.050周;L1相位偏差受无电离层模糊度影响较为严重,其空间一致性较宽巷相位偏差稍差些,但基本控制在0.180周以内,可用于PPP模糊度的相位偏差改正。
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)of China(XDA2004030202)Shanghai Cooperation and the Organization Science and Technology Partnership of China(2021E01019)。
文摘Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.
基金supported in part by the Fundamental Research Funds for the Central Universities of Beijing University of Chemical Technology(Grant No.BUCTRC202132)the National Natural Science Foundation of China(Grant Nos.42371476 and 41971366).
文摘Rapid and accurate acquisition and analysis of information is crucial for emergency management,but traditional methods have limitations such as incomplete information acquisition and slow processing speed.The natural language oriented spatial scene reconstruction method provides a new solution for emergency management,but existing generative models have limited understanding of spatial relationships and lack high-quality training samples.To address these issues,this paper proposes a novel spatial scene reconstruction framework.Specifically,the BERT based spatial information knowledge graph extraction method is used to encode the input text,label and classify the encoded text,identify spatial objects and relationships in the text,and accurately extract spatial information.Additionally,a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition,and based on the obtained biases,a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph.Finally,use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints.In addition,a high-quality training sample set of“text-scene-knowledge graph”was constructed.
