In the wake of large earthquake disasters, governments, international agencies, and large nongovernmental organizations scramble to conduct impact and damage assessments that help them understand the nature and scale of the emergency in order to orchestrate a complex series of emergency, response, and recovery activities. Using the Gorkha earthquake as a case study, this research seeks to provide greater clarity into the types of post-disaster damage assessments, their purposes, and their potential as catalysts for critical recovery activities. We argue that damage assessment methodologies need to be tailored to the diverse information needs in post-disaster contexts, which vary by user group and change over time. This research builds upon the authors’ direct experience supporting the government of Nepal in the Post-Disaster Needs Assessment (PDNA) process, support with the rapid visual inspections conducted by the National Engineering Association, and interviews with humanitarian organizations who conducted damage assessment in Nepal.

Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (Tair) and thermal comfort. We estimate LST based on Landsat-8 measurements, Tair and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean Tair and the compatibility of LST and Tair as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy Tair and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and Tair alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with “very hot” classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people’s well-being and comfort.

The urban heat island (UHI) effect has become a significant focus of research in today’s era of climate change, and a key consideration for the next generation of urban planning focused on green and livable cities. UHI has traditionally been measured using in situ data and ground-based measurements. However, with the increased availability of satellite-based thermal observations of the Earth, remotely sensed observations are increasingly being utilized to estimate surface urban heat island (SUHI), using land surface temperature (LST) as a critical indicator, due to its spatial coverage. In this study, we estimated LST based on Landsat-8 observations to demonstrate the relationship between LST and the characteristics of the land use and land cover on the campus of King Abdulaziz University (KAU), Jeddah, Saudi Arabia. We found a consistent variation of between 7 and 9 degrees Celsius for LST across campus, spanning all summer and winter seasons between 2014 and 2019. The LST correlates strongly with both green vegetation and built-up land cover, with a slightly stronger correlation with the latter. The relationship between LST and green vegetation has a notable seasonality, with higher correlation in the summer seasons compared to the winter seasons. Our study also found an overall increase in LST between 2014 and 2019, due to intentional changes in the built-up land cover, for example from the conversion of natural green surfaces to artificial surfaces. The findings of this study highlight the utility of the remotely sensed observation of LST to assess the SUHI phenomenon and can be used to inform future planning aimed at securing green and livable urban areas in the face of a changing climate.

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