Remote sensing applications in monitoring urban growth impacts on in-and-out door thermal conditions: A review

- Review shows that urban growth is raising land- and near-surface temperatures.
- Resultant warming may cause a range of socio-economic challenges.
- Medium resolution sensors such as Landsat and MODIS have become valuable in mapping urban thermal conditions, particularly in developing countries.
- There is paucity in predictive studies.
- Lower temporal resolution that characterizes the commonly used medium resolution thermal sensors as a major limitation to urban thermal analysis.

Urban growth and the consequent expansion of impervious surfaces influence a landscape's thermal characteristics by raising Land Surface Temperatures (LST). Resultant warming may cause among others, thermal discomfort, high prevalence of heat related health conditions, air pollution, increased water usage and energy demand for air-conditioning. Recently, studies aimed at understanding the impacts of urbanization and subsequent landscape transformation on in-and-out door temperature have increased significantly. This review therefore provides synthesis on the progress of space-borne remote sensing in monitoring the implications of urban growth on thermal characteristics. It was observed that despite the relative coarse spatial properties; medium resolution sensors (i.e. Landsat and MODIS) have become valuable in characterizing urban thermal conditions, especially in data-limited areas. More importantly, literature shows that thermal assessments have been confined to examination of historical and current conditions, without considering current research studies. This work identifies low temporal resolution that characterizes the commonly used medium spatial resolution thermal sensors as a major limitation to mapping urban surface temperature. There is therefore need for future studies to shift towards integrating new crop of high resolution satellite data with existing high temporal and low spatial resolution sensors. Such techniques can lead to the development of robust spatial datasets suitable for improved seasonal and long term monitoring of urban thermal patterns.