Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades.Despite the high sensitivity of push-broom hyperspectral imagers,they experience limited swath and wavelengt...Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades.Despite the high sensitivity of push-broom hyperspectral imagers,they experience limited swath and wavelength coverage.In this study,we report the development of a push-broom airborne multimodular imaging spectrometer(AMMIS)that spans ultraviolet(UV),visible near-infrared(VNIR),shortwave infrared(SWIR),and thermal infrared(TIR)wavelengths.As an integral part of China's HighResolution Earth Observation Program,AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads.It has been mounted on aircraft platforms such as Y-5,Y-12,and XZ-60.Since 2016,AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data.This study describes the system design,calibration techniques,performance tests,flight campaigns,and applications of the AMMIS.The system integrates UV,VNIR,SWIR,and TIR modules,which can be operated in combination or individually based on the application requirements.Each module includes three spectrometers,utilizing field-of-view(FOV)stitching technology to achieve a 40°FOV,thereby enhancing operational efficiency.We designed advanced optical systems for all modules,particularly for the TIR module,and employed cryogenic optical technology to maintain optical system stability at 100 K.Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data.The AMMIS features more than 1400 spectral bands,with spectral sampling intervals of 0.1 nm for UV,2.4 nm for VNIR,3 nm for SWIR,and 32 nm for TIR.In addition,the instantaneous fields of view(IFoVs)for the four modules were 0.5,0.25,0.5,and 1 mrad,respectively,with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode.This study reports on land-cover surveys,pollution gas detection,mineral exploration,coastal water detection,and plant investigations conducted using AMMIS,highlighting its excellent performance.Furthermore,we present three hyperspectral datasets with diverse scene distributions and categories suitable for developing artificial intelligence algorithms.This study paves the way for next-generation airborne and spaceborne hyperspectral payloads and serves as a valuable reference for hyperspectral sensor designers and data users.展开更多
Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their...Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.展开更多
The advancement of effective spatial planning to support sustainable development and interregional cooperation has become an issue of serious concern for regional authorities.Spatial planning research helps to identif...The advancement of effective spatial planning to support sustainable development and interregional cooperation has become an issue of serious concern for regional authorities.Spatial planning research helps to identify economic clusters and analyze their changing spatial patterns,which is important for understanding regional economic space dynamics and poten-tial inter-regional cooperation.To support decision-makers in the development of efficient plans of spatial development encompassing the identification of the best-suited territories,a combined Geographic Information System(GIS)based approach to interpret qualitatively expressed multiple socio-economic scenarios in quantitative map-based terms of graded suitability,and a formalized approach to the socio-economic evaluation of the territory is offered.Based on GIS technology coupled with integrated cellular automata decision analysis techniques,the study provides a method that performs socio-economic assessment of the study area according to the generated scenarios of regional spatial and socio-economic development.The proposed method is applied to Primorsky and Khabarovsk Krais,located in the Russian Far East.Socio-economic scenarios of spatial development initiated by investors and regional authorities were assessed and evaluated.The generated socio-economic scenar-ios illustrate how the unified set of spatial and socio-economic variables can be linked and used to gain insights into inter-regional socio-economic and spatial development.The application results demonstrate the advantage of the proposed method in identifying the best-suited unit areas for targeted regional development.展开更多
This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were proce...This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.展开更多
With growing demand on multi-purpose or multi-modal navigation,the route calculation needs to traverse semantically enriched road networks for different transportation modes.Currently,operational route planning algori...With growing demand on multi-purpose or multi-modal navigation,the route calculation needs to traverse semantically enriched road networks for different transportation modes.Currently,operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the under-lying geo-data that are separately maintained in different spatial databases.To overcome this limitation,a novel approach has been proposed to integrate the routing-relevant information from different data sources,which involves three processes:(1)automatic matching to identify the corresponding road objects between different datasets;(2)interaction to refine the automatic matching result;and(3)transferring the routing-relevant information from one data-set to another.In process(1),the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets,which has revealed a high matching rate and certainty.However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent.The remaining unmatched or wrongly matched objects are treated in process(2),with the help of a series of interaction tools.On the basis of refined matching results after the interaction,process(3)is dedicated to automatic integration of the routing-relevant information from different data sources.展开更多
基金supported by the Shanghai Industrial Collaborative Innovation Fund(HCXBCY-2021-001)the Academy of Finland(349229)。
文摘Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades.Despite the high sensitivity of push-broom hyperspectral imagers,they experience limited swath and wavelength coverage.In this study,we report the development of a push-broom airborne multimodular imaging spectrometer(AMMIS)that spans ultraviolet(UV),visible near-infrared(VNIR),shortwave infrared(SWIR),and thermal infrared(TIR)wavelengths.As an integral part of China's HighResolution Earth Observation Program,AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads.It has been mounted on aircraft platforms such as Y-5,Y-12,and XZ-60.Since 2016,AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data.This study describes the system design,calibration techniques,performance tests,flight campaigns,and applications of the AMMIS.The system integrates UV,VNIR,SWIR,and TIR modules,which can be operated in combination or individually based on the application requirements.Each module includes three spectrometers,utilizing field-of-view(FOV)stitching technology to achieve a 40°FOV,thereby enhancing operational efficiency.We designed advanced optical systems for all modules,particularly for the TIR module,and employed cryogenic optical technology to maintain optical system stability at 100 K.Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data.The AMMIS features more than 1400 spectral bands,with spectral sampling intervals of 0.1 nm for UV,2.4 nm for VNIR,3 nm for SWIR,and 32 nm for TIR.In addition,the instantaneous fields of view(IFoVs)for the four modules were 0.5,0.25,0.5,and 1 mrad,respectively,with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode.This study reports on land-cover surveys,pollution gas detection,mineral exploration,coastal water detection,and plant investigations conducted using AMMIS,highlighting its excellent performance.Furthermore,we present three hyperspectral datasets with diverse scene distributions and categories suitable for developing artificial intelligence algorithms.This study paves the way for next-generation airborne and spaceborne hyperspectral payloads and serves as a valuable reference for hyperspectral sensor designers and data users.
基金funded by Erasmus+ICM programme for a 3-month and 5-month stay at Lund University,Lund,Sweden,and thank the European Union.
文摘Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.
基金supported by the Ministry of Science and Higher Education of Russia[grant number 075-15-2020-804].
文摘The advancement of effective spatial planning to support sustainable development and interregional cooperation has become an issue of serious concern for regional authorities.Spatial planning research helps to identify economic clusters and analyze their changing spatial patterns,which is important for understanding regional economic space dynamics and poten-tial inter-regional cooperation.To support decision-makers in the development of efficient plans of spatial development encompassing the identification of the best-suited territories,a combined Geographic Information System(GIS)based approach to interpret qualitatively expressed multiple socio-economic scenarios in quantitative map-based terms of graded suitability,and a formalized approach to the socio-economic evaluation of the territory is offered.Based on GIS technology coupled with integrated cellular automata decision analysis techniques,the study provides a method that performs socio-economic assessment of the study area according to the generated scenarios of regional spatial and socio-economic development.The proposed method is applied to Primorsky and Khabarovsk Krais,located in the Russian Far East.Socio-economic scenarios of spatial development initiated by investors and regional authorities were assessed and evaluated.The generated socio-economic scenar-ios illustrate how the unified set of spatial and socio-economic variables can be linked and used to gain insights into inter-regional socio-economic and spatial development.The application results demonstrate the advantage of the proposed method in identifying the best-suited unit areas for targeted regional development.
文摘This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.
文摘With growing demand on multi-purpose or multi-modal navigation,the route calculation needs to traverse semantically enriched road networks for different transportation modes.Currently,operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the under-lying geo-data that are separately maintained in different spatial databases.To overcome this limitation,a novel approach has been proposed to integrate the routing-relevant information from different data sources,which involves three processes:(1)automatic matching to identify the corresponding road objects between different datasets;(2)interaction to refine the automatic matching result;and(3)transferring the routing-relevant information from one data-set to another.In process(1),the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets,which has revealed a high matching rate and certainty.However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent.The remaining unmatched or wrongly matched objects are treated in process(2),with the help of a series of interaction tools.On the basis of refined matching results after the interaction,process(3)is dedicated to automatic integration of the routing-relevant information from different data sources.
