Spatiotemporal Variability of Surface Urban Heat Island Intensity in Kumasi from 1986 to 2022

Research Hypothesis. The study hypothesised that biophysical variables such as normalised difference vegetation index (NDVI) normalised difference vegetation index (NDVI), normalised built-up index (NDBI), dry barrenness index (DBSI), and elevation affect the intensity of urban heat island effect. Data Representation. The study was divided into two using a 15km buffer from the urban core. The division of the study area into urban and rural helps with the segregation of the extracted data. This enables the study to analyse the temperature difference between urban and rural areas. Data was collected from United States Geological Survey: Landsat and Modis respectively for three years. The digital value numbers of the satellite data were extracted using Arc GIS tools. Thus, the processed values of the satellite data depict the pixel values of the data extracted. These values give a representation of temperature variance between urban and rural areas. Findings. Notable findings include the determination of progressive increase in urban surface heat intensity from 1984 to 2022 for both Landsat data and Modis data. Also, biophysical variables: NDVI, DBSI, and NDBI resulted high positive variation with land surface temperature.研究假设 本研究提出如下假设：归一化差分植被指数（Normalised Difference Vegetation Index, NDVI）、归一化建筑指数（Normalised Built-up Index, NDBI）、干旱裸地指数（Dry Barrenness Index, DBSI）以及海拔等生物物理变量，会对城市热岛效应的强度产生影响。 数据采集与表征 本研究以城市核心区15km缓冲区为界，将研究区域划分为城区与乡村两部分，此举便于对提取的数据进行分类，进而可开展城乡温度差异的对比分析。 研究分别从美国地质调查局（United States Geological Survey, USGS）获取了三年期的Landsat与Modis卫星数据，并借助Arc GIS工具提取了卫星数据的数字数值。经处理后的卫星数据数值即为所提取的像素值，这些数值可表征城乡区域间的温度差异。 研究发现 核心研究结果如下：其一，1984年至2022年间，基于Landsat数据与Modis数据所得的城市地表热强度均呈逐步上升趋势；其二，NDVI、DBSI与NDBI等生物物理变量与地表温度呈现显著正相关关系。