Analyzing The Effect Of Optimizing The Spatial Locations Of Green Infrastructure On Land Surface Temperature: The Case Of Dire Dawa City

Abstract

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Green infrastructure (GI) can mitigates warming, providing cooling benefits important to reducing energy consumption and improving human health. Lack of proper identifications of the most critical area or temperature hotspot location and the arrangement of GI is one of the most important problems in Dire Dawa City Administration, especially considering the spatial location of urban heat island effects. Surprisingly, methods of identifying the hotspot location that support the strategic placement of green infrastructure in the context of identified urban heat island locations are lacking. In order to overcome these problems, the integrations of remote sensing and geographic information systems were developed as framework to identify the hotspot areas, examines the relationship between the urban GI and LST and finally proposing the optimal locations of new GI with respect to cooling benefits. To achieve the objective of the study, Land-use/land-cover, LST and NDVI were extracted from Landsat 5 TM (1999) and Landsat 8 OLI/TIRS (2019). The result of LU/LC change indicated for the last 20 years there has been a radical increase in areal coverage of settlements by more than 6% and on the other side, sparse vegetation was decreased by more than 4%. The study indicated that most areas having lower LST in 1999 were changed to higher LST in 2019. The results show that average LST in Dire Dawa City was increased from 24.61°C to 28.77°C. This due to the increased in settlements LU/LC, especially attributed to the decreasing of vegetation cover in the study area. This shows that, the correlation between LST and NDVI for both study years (1999 and 2019) have strong negative correlation with the R² values of 0.88 and 0.94, respectively. The study also found that, more than 13% of LU/LC was identified as hotspot location with mean LST more than 33°C. As a result of optimal GI placement, significant cooling benefits can be achieved. The results showed that, the distance from the park was positively correlated with the LST. The mean LST increased by 0.53°C, as the distance increases by 500m from the park. Also, this study found that, increasing the GI rate on this hotspot area by 13%, the mean LST drops approximately by 0.79°C, from the local hotspots area and 1.55 –1.64°C regionally, this can be achieved by the addition of new GI. The study conclude that, optimizing the spatial locations of GI in the proximity of most heat load or hotspot areas such as built-up areas and bare lands should receive primary attention in the process of planning and designing. The optimization based framework can effectively inform planning decisions with regard to GI allocation to improve excessive heat. This study showed that geospatial tools and techniques can give fast and reliable results of LST variability and its impact on the environment in a shorter analysis and evaluation.