In this article,an extensive inventory in the literature of water erosion modelling from a geospatial point of view is conducted.Concepts of scale,spatiality and complexity are explored and clarified in a theoretical ...In this article,an extensive inventory in the literature of water erosion modelling from a geospatial point of view is conducted.Concepts of scale,spatiality and complexity are explored and clarified in a theoretical background.Use of Geographic Information Systems(GIS)is pointed out as facilitating data mixing and model rescaling and thus increasing complexity in data-method relations.Spatial scale,temporal scale and spatial methodologies are addressed as the most determining geospatial properties underlying water erosion modelling.Setting these properties as classification criteria,82 water erosion models are identified and classified into eight categories.As a result,a complete overview of water erosion models becomes available in a single table.The biggest share of the models is found in the category of the mechanistic pathway-type event-based models for watershed to landscape scales.In parallel,geospatial innovations that could be considered as milestones in water erosion modelling are highlighted and discussed.An alphabetical list of all models is also listed in the Appendix.For manipulating scale efficiently,two promising spatial theories are suggested for further exploitation in the future such as hierarchy theory and fractals theory.Regarding erosion applications,uncertainty analysis within GIS is considered to be necessary for further improving performance of erosion models.展开更多
It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand ...It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.展开更多
Currently,many soil erosion studies at local,regional,national or continental scale use models based on the USLE-family approaches.Applications of these models pay little attention to seasonal changes,despite evidence...Currently,many soil erosion studies at local,regional,national or continental scale use models based on the USLE-family approaches.Applications of these models pay little attention to seasonal changes,despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology.This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing.The latter,together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000.This potential service has led to the establishment of a new modelling approach(called the G2 model)based on the inheritance of USLE-family models.The G2 model proposes innovative techniques for the estimation of vegetation and protection factors.The model has been applied in a 14,500 km 2 study area in SE Europe covering a major part of the basin of the cross-border river,Strymonas.Model results were verified with erosion and sedimentation figures from previous research.The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.展开更多
Historical soil survey paper maps are valuable resources that underpin strategies to support soil protection and promote sustainable land use practices,especially in developing countries where digital soil information...Historical soil survey paper maps are valuable resources that underpin strategies to support soil protection and promote sustainable land use practices,especially in developing countries where digital soil information is often missing.However,many of the soil maps,in particular those for developing countries,are held in traditional archives that are not easily accessible to potential users.Additionally,many of these documents are over 50 years old and are beginning to deteriorate.Realising the need to conserve this information,the Joint Research Centre(JRC)and the ISRIC-World Soil Information foundation have created the European Digital Archive of Soil Maps(EuDASM),through which all archived paper maps of ISRIC has been made accessible to the public through the Internet.The immediate objective is to transfer paper-based soil maps into a digital format with the maximum possible resolution and to ensure their preservation and easy disclosure.More than 6,000 maps from 135 countries have been captured and are freely available to users through a user-friendly web-based interface.Initial feedback has been very positive,especially from users in Africa,South America and Asia to whom archived soil maps were made available to local users,often for the first time.Link:http://eusoils.jrc.ec.europa.eu/library/maps/country_maps/list_countries.cfm.展开更多
Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have de...Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.展开更多
Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding task.However,for most applications,many of the images in the archive are redundant and do not contribute to the qual...Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding task.However,for most applications,many of the images in the archive are redundant and do not contribute to the quality of the final result.An optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing,while retaining the quality of the resulting output.As a case study,we focused on the creation of a cloud-free composite,covering the global land mass and based on all the images acquired from January 2016 until September 2017.The total amount of available images was 2,128,556.The selection of the optimal subset was based on quicklooks,which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy compressed.The selected subset contained 94,093 image tiles in total,reducing the amount of images to be processed to 4.42%of the full set.展开更多
基金supported by the Research Committee of the Aristotle University of Thessaloniki in the framework of the Geoland 2 project:towards an operational GMES Land Monitoring Core Service.EC Proposal Reference No FP-7-218795.
文摘In this article,an extensive inventory in the literature of water erosion modelling from a geospatial point of view is conducted.Concepts of scale,spatiality and complexity are explored and clarified in a theoretical background.Use of Geographic Information Systems(GIS)is pointed out as facilitating data mixing and model rescaling and thus increasing complexity in data-method relations.Spatial scale,temporal scale and spatial methodologies are addressed as the most determining geospatial properties underlying water erosion modelling.Setting these properties as classification criteria,82 water erosion models are identified and classified into eight categories.As a result,a complete overview of water erosion models becomes available in a single table.The biggest share of the models is found in the category of the mechanistic pathway-type event-based models for watershed to landscape scales.In parallel,geospatial innovations that could be considered as milestones in water erosion modelling are highlighted and discussed.An alphabetical list of all models is also listed in the Appendix.For manipulating scale efficiently,two promising spatial theories are suggested for further exploitation in the future such as hierarchy theory and fractals theory.Regarding erosion applications,uncertainty analysis within GIS is considered to be necessary for further improving performance of erosion models.
文摘It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.
文摘Currently,many soil erosion studies at local,regional,national or continental scale use models based on the USLE-family approaches.Applications of these models pay little attention to seasonal changes,despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology.This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing.The latter,together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000.This potential service has led to the establishment of a new modelling approach(called the G2 model)based on the inheritance of USLE-family models.The G2 model proposes innovative techniques for the estimation of vegetation and protection factors.The model has been applied in a 14,500 km 2 study area in SE Europe covering a major part of the basin of the cross-border river,Strymonas.Model results were verified with erosion and sedimentation figures from previous research.The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.
文摘Historical soil survey paper maps are valuable resources that underpin strategies to support soil protection and promote sustainable land use practices,especially in developing countries where digital soil information is often missing.However,many of the soil maps,in particular those for developing countries,are held in traditional archives that are not easily accessible to potential users.Additionally,many of these documents are over 50 years old and are beginning to deteriorate.Realising the need to conserve this information,the Joint Research Centre(JRC)and the ISRIC-World Soil Information foundation have created the European Digital Archive of Soil Maps(EuDASM),through which all archived paper maps of ISRIC has been made accessible to the public through the Internet.The immediate objective is to transfer paper-based soil maps into a digital format with the maximum possible resolution and to ensure their preservation and easy disclosure.More than 6,000 maps from 135 countries have been captured and are freely available to users through a user-friendly web-based interface.Initial feedback has been very positive,especially from users in Africa,South America and Asia to whom archived soil maps were made available to local users,often for the first time.Link:http://eusoils.jrc.ec.europa.eu/library/maps/country_maps/list_countries.cfm.
基金The research leading to these results benefited from funding by the European Union's Horizon 2020 Framework Programme research and innovation programme[under grant agreements:n.689443(ERA-PLANET),n.777536(EOSC-hub),n.776136(EDGE),n.34538(EO Value),n.101039118(GPP)]by the European Space Agency[under ESA Contracts:n.4000123005/18/IT/CGD(DAB4EDGE)and n.4000138128/22/I/AG(DAB4GPP)]European Commission CNECT(grant n.35713).
文摘Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.
文摘Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding task.However,for most applications,many of the images in the archive are redundant and do not contribute to the quality of the final result.An optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing,while retaining the quality of the resulting output.As a case study,we focused on the creation of a cloud-free composite,covering the global land mass and based on all the images acquired from January 2016 until September 2017.The total amount of available images was 2,128,556.The selection of the optimal subset was based on quicklooks,which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy compressed.The selected subset contained 94,093 image tiles in total,reducing the amount of images to be processed to 4.42%of the full set.
