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Purpose – The purpose of this paper is to demonstrate a spatial model of population vulnerability (VI) capable of identifying areas of high emergency service demand (ESD) during extreme heat events (EHE). Design/methodology/approach – An index of population vulnerability to EHE was developed from a literature review. Threshold temperatures for EHE were defined using local temperatures, and indicators of increased morbidity. Spearman correlations determined the strength of the relationship between the VI and morbidity during EHE. The VI was mapped providing a visual guide of risk during EHE. Future changes in population vulnerability based on future population projections (2020-2030) were mapped. Findings – The VI can be used to explain the spatial distribution of ESD during EHE. Mapping future changes in population density/demography indicated several areas currently showing high risk will continue to show increased risk. Research limitations/implications – The limitations include using outdoor temperatures to determine health-related thresholds. Due to data restrictions three different measures of morbidity were used and aggregated to postal areas. Practical implications – Identifying areas of increased service demand during EHE allows the development of proactive as-well-as reactive responses to heat. The model uses readily available data, is replicable in larger urban areas. Social implications – The model allows emergency service providers to work with high risk communities to build resilience to heat exposure and subsequently save lives. Originality/value – To the authors’ knowledge this triangulated approach using heat thresholds, ESD and projected changes in risk in a spatial framework has not been presented to date.

Monitoring of rice growth is a requirement for high quality rice production. In addtion to plant height, number stem and rice leaf color, vegetation coverage (VC) which represents for percentage of ground covered by rice plant is also considered as an important index to validate rice growth. Thus, the study is to estimate rice vegetation coverage from difference vegetation index (DVI) calculated from reflectance of near-infrared and red band of Landsat 7 and 8 images. The field observations of the reflectance and the VC were carried out in two paddy rice varieties in 2013. Paddy field reflectance was observed by spectrometer Ocean Optics SD2000. The photos of paddies were taken from the height of 1 m by a digital camera in order to calculate the VC. The reflectances of paddy field corresponding to red and near-infrared bands of Landsat 7 and 8 were calculated from the field observation data. Satellite reflectance was also converted from pixel value of Landsat images. According to the data analysis, VC rapidly increased in two fields and got saturation status (VC>90%) at 65 days after transplanting (DAT) in the early July. DVI was approximately 25% when VC saturated. Additionally, DVI had strong correlation with VC with high determination coefficient (r2 =0.9) when VC was less than 90%. Thus, VC were computed from DVI, calculated from reflectances of Landsat images, using a regression model of VC and DVI. From the result of comparison between the estimated and computed VC, the possibility of estimating VC from DVI calculated from Landsat reflectance is confirmed.