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
A vital component of fire detection from remote sensors is the accurateestimation of the background temperature of an area in fire’s absence,assisting in identification and attribution of fire activity. Newgeostation...A vital component of fire detection from remote sensors is the accurateestimation of the background temperature of an area in fire’s absence,assisting in identification and attribution of fire activity. Newgeostationary sensors increase the data available to describebackground temperature in the temporal domain. Broad area methodsto extract the expected diurnal cycle of a pixel using this temporally richdata have shown potential for use in fire detection. This paper describesan application of a method for priming diurnal temperature fitting ofimagery from the Advanced Himawari Imager. The BAT method is usedto provide training data for temperature fitting of target pixels, to whichthresholds are applied to detect thermal anomalies in 4 μm imageryover part of Australia. Results show the method detects positive thermalanomalies with respect to the diurnal model in up to 99% of caseswhere fires are also detected by Low Earth Orbiting (LEO) satellite activefire products. In absence of LEO active fire detection, but where aburned area product recorded fire-induced change, this method alsodetected anomalous activity in up to 75% of cases. Potentialimprovements in detection time of up to 6 h over LEO products are alsodemonstrated.展开更多
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’.
文摘A vital component of fire detection from remote sensors is the accurateestimation of the background temperature of an area in fire’s absence,assisting in identification and attribution of fire activity. Newgeostationary sensors increase the data available to describebackground temperature in the temporal domain. Broad area methodsto extract the expected diurnal cycle of a pixel using this temporally richdata have shown potential for use in fire detection. This paper describesan application of a method for priming diurnal temperature fitting ofimagery from the Advanced Himawari Imager. The BAT method is usedto provide training data for temperature fitting of target pixels, to whichthresholds are applied to detect thermal anomalies in 4 μm imageryover part of Australia. Results show the method detects positive thermalanomalies with respect to the diurnal model in up to 99% of caseswhere fires are also detected by Low Earth Orbiting (LEO) satellite activefire products. In absence of LEO active fire detection, but where aburned area product recorded fire-induced change, this method alsodetected anomalous activity in up to 75% of cases. Potentialimprovements in detection time of up to 6 h over LEO products are alsodemonstrated.
