| dc.description |
Tarawally, M., Department of Science Education, Ernest Bai Koroma University of Science and Technology, Makeni Campus, Makeni, Sierra Leone; Xu, W., School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 611731, China; Kursah, M.B., School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 611731, China, Department of Geography Education, University of Education, Winneba (UEW), Box 25, Winneba, Ghana; Kamara, A.B., Department of Basic Sciences, Ernest Bai Koroma University of Science and Technology, Makeni Campus, Makeni, Sierra Leone |
en_US |
| dc.description.abstract |
The use of a single date remotely sensed image to represent seasonal land surface temperature (LST) is a common practice whose reliability has not been tested, even though that might be unrepresentative of the season. Through remote sensing and geographic information system (GIS) techniques, this paper examined the effects of using a single date image to represent the whole season by quantifying the intra-seasonal (intra- and inter-month) LST variations in Freetown (coastal city) and Bo (inland city), Sierra Leone. Multi-date Landsat images within three months (two images per month) in the dry season were used to retrieve the LST using the Normalized Difference Vegetation Index (NDVI) threshold method. The results showed that the spatial structures of LST were not uniform on different dates during the same season in both cities. LST differed by as much as 2��C for scenes within the same month and as much as 4��C between scenes of different months. The results also showed that the highest intra- and inter-month LST variations were recorded in Freetown than in Bo. This is attributed to the combined influences of the�proximity to the ocean, the mountain ranges and surface characteristics in Freetown. Thus, within a season, urban surface temperature varies not just in space based on the surface characteristics but also the variations between two urban areas could be significantly high. This renders surface temperature analysis based on a single date image unrepresentative due to the inability to incorporate such variability. The use of multi-date images could be more representative and can improve studies on urban LST. � 2021, Korean Spatial Information Society. |
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