Internal lakes are considered as the ecological environments and the monitoring and evaluation of which can be considered as a matter in the national development and natural resources management(Rasouli et al.,2008).T...Internal lakes are considered as the ecological environments and the monitoring and evaluation of which can be considered as a matter in the national development and natural resources management(Rasouli et al.,2008).The changes in the water levels of lakes and the variations of the land use and cover along with the margins and the association of these changes with natural and human factors in Urmia Lake and Aral Lake are much-discussed and taken into consideration.In this study,a comparison was made between Urmia Lake and the Aral Lake over a period of 28 years,and the first one being suffered from declining water levels and drastic changes due to many factors,including,most specifically,human intrusion and mismanagement.For this purpose,Moderate Resolution Imaging Spectroradiometer(MODIS)and Landsat 5 and 8 satellite images were utilized for 1988 and 2018 and object-based image classification was applied in six classes,i.e.water body,arid,agriculture,salt marsh,salt wet and built with the acceptable accuracy of 89.23(1988)and 85.8(2018)percent for land use maps of the Urmia Lake and 92.46(1988)and 95.15(2018)percent for the Aral Lake.The overall accuracy of classification which represents the functioned credit classification for the maps should be more than 85%(Anderson et al.,1976).The classification showed that during the study period both of the lakes faced declination of water and increasing of salt levels.The trend of agricultural and built-in lands has been rising especially in Urmia Lake.Results indicated that the creation of the agricultural lands and human levels in recent decades has caused the increase in water consumption which in turn has reduced the amount of water entering to the Urmia Lake and has created hard conditions such as the dryness of the lake today.展开更多
Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO ...Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.展开更多
Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustaina...Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals(SDGs).Informal settlement indicators are used as a framework to carry out image analysis,and include vegetation extent,lacunarity of housing structures/vacant land,road segment type and materials,texture measures of built-up areas,roofing extent of built-up areas and dwelling size.Objectbased image analysis(OBIA)methods are recommended to identify informal settlements.This paper documents the application of OBIA to map informal settlements,drawing on the ontology of Kohli et al.(2012)and the indicators of Owen and Wong(2013)for a Middle Eastern city.Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah,Saudi Arabia.Vegetation extent was the most successful indicator detected,with 100% producer accuracy and over 84% user accuracy,followed by the road network,with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies.Lacunarity of housing structures/vacant land was detected well in informal settlements.The texture measure indicator was detected using GLCM_(Ent)(R)with low producer accuracy across all case studies.The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures.The dwellings size indicator generally failed to distinguish formal from informal settlements.Informal and formal were distinguished with an overall accuracy of 83%.This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities.However,a generic ruleset for mapping informal settlements remains elusive,and each indicator requires significant localised‘tuning’.展开更多
文摘Internal lakes are considered as the ecological environments and the monitoring and evaluation of which can be considered as a matter in the national development and natural resources management(Rasouli et al.,2008).The changes in the water levels of lakes and the variations of the land use and cover along with the margins and the association of these changes with natural and human factors in Urmia Lake and Aral Lake are much-discussed and taken into consideration.In this study,a comparison was made between Urmia Lake and the Aral Lake over a period of 28 years,and the first one being suffered from declining water levels and drastic changes due to many factors,including,most specifically,human intrusion and mismanagement.For this purpose,Moderate Resolution Imaging Spectroradiometer(MODIS)and Landsat 5 and 8 satellite images were utilized for 1988 and 2018 and object-based image classification was applied in six classes,i.e.water body,arid,agriculture,salt marsh,salt wet and built with the acceptable accuracy of 89.23(1988)and 85.8(2018)percent for land use maps of the Urmia Lake and 92.46(1988)and 95.15(2018)percent for the Aral Lake.The overall accuracy of classification which represents the functioned credit classification for the maps should be more than 85%(Anderson et al.,1976).The classification showed that during the study period both of the lakes faced declination of water and increasing of salt levels.The trend of agricultural and built-in lands has been rising especially in Urmia Lake.Results indicated that the creation of the agricultural lands and human levels in recent decades has caused the increase in water consumption which in turn has reduced the amount of water entering to the Urmia Lake and has created hard conditions such as the dryness of the lake today.
基金the Austrian Science Fund(FWF)through the Doctoral College GIScience(DK W1237-N23)Contributions of Dirk Tiede and Hannah Augustin were supported by the Austrian Research Promotion Agency(FFG)the Austrian Space Application Programme(ASAP)within the project Sen2Cube.at(project no.:866016).
文摘Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.
文摘Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals(SDGs).Informal settlement indicators are used as a framework to carry out image analysis,and include vegetation extent,lacunarity of housing structures/vacant land,road segment type and materials,texture measures of built-up areas,roofing extent of built-up areas and dwelling size.Objectbased image analysis(OBIA)methods are recommended to identify informal settlements.This paper documents the application of OBIA to map informal settlements,drawing on the ontology of Kohli et al.(2012)and the indicators of Owen and Wong(2013)for a Middle Eastern city.Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah,Saudi Arabia.Vegetation extent was the most successful indicator detected,with 100% producer accuracy and over 84% user accuracy,followed by the road network,with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies.Lacunarity of housing structures/vacant land was detected well in informal settlements.The texture measure indicator was detected using GLCM_(Ent)(R)with low producer accuracy across all case studies.The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures.The dwellings size indicator generally failed to distinguish formal from informal settlements.Informal and formal were distinguished with an overall accuracy of 83%.This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities.However,a generic ruleset for mapping informal settlements remains elusive,and each indicator requires significant localised‘tuning’.
