Browsing by Author "Kowe, Pedzisai"

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    Effect of landscape pattern and spatial configuration of vegetation patches on urban warming and cooling in Harare metropolitan city, Zimbabwe
    (Bellweather Publishing, 2021) Kowe, Pedzisai; Mutanga, Onisimo; Dube, Timothy
    The spatial configuration of vegetation patches in the landscape has implications for the provision of ecosystem services, human adaptation to climate change, enhancement, or mitigation of urban heat island. Until recently, the effect of spatial configuration of vegetation to enhance or mitigate urban heat island has received little consideration in urban thermal assessments. This study examines the impact of spatial configuration of vegetation patches on urban thermal warming and cooling in Harare metropolitan city, Zimbabwe. The study used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat and Sentinel 2 data acquired between 1994 and 2017 to derive detailed information on vegetation patches, landscape metrics, and land surface temperature LST(°C). The spatial configuration of urban vegetation patterns was analyzed using landscape metrics in Fragstats program. Getis Ord Gi* as a Local Indicator of Spatial Association (LISA) was used to characterize the spatial clustering and dispersion of urban vegetation patches. Results of the Getis Ord Gi* showed that clustered vegetation lowers surface temperatures more effectively than dispersed and fragmented patterns of vegetation. The size, density, shape complexity, and cohesion of vegetation patches conferred different levels of cooling but Patch Cohesion Index had the strongest negative relationship with LST(°C) at three spatial resolutions of 10 m (Sentinel 2), 15 m (ASTER) and 30 m (Landsat 8). The Spatial Lag Regression model performed better than the Ordinary Least Squares regression analysis in exploring the relationship between LST(°C) and landscape metrics. Specifically, the Spatial Lag Regression model showed higher R2 values and log likelihood, lower Schwarz criteria, and Akaike information criterion, and reduced spatial autocorrelations. The overall information provides important insights into the provision of larger, connected, and less fragmented urban vegetation patches to derive maximum and higher cooling effects which is critical for urban planning and design approaches for mitigating increasing surface temperatures in cities.
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    Impacts of eco-environmental quality, spatial configuration, and landscape connectivity of urban vegetation patterns on seasonal land surface temperature in Harare metropolitan city, Zimbabwe
    (Taylor and Francis Group, 2022) Kowe, Pedzisai; Mutanga, Onisimo; Dube, Timothy
    The study examined the impact of eco-environmental quality conditions, spatial configurations and landscape connectivity of urban vegetation on seasonal land surface temperature (LST) in Harare, Zimbabwe between May and October 2018. The results showed that densely built-up areas with sparse vegetation experienced extremely poor eco-environmental conditions. Clustered and highly connected were more beneficial in decreasing LST. These findings have important urban and landscape planning implications regarding how the spatial configuration and land-scape connectivity patterns of urban vegetation can be optimized to mitigate Urban Heat Island (UHI) effects and to improve the thermal comfort conditions in rapidly urbanizing cities.
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    Impacts of the spatial configuration of built-up areas and urban vegetation on land surface temperature using spectral and local spatial autocorrelation indices
    (Remote Sensing Letters, 2022) Dube, Timothy; Kowe, Pedzisai; Mushore, Terence Darlington; Ncube, Amos
    Understanding how the spatial configuration of land cover patterns of built-up areas and urban vegetation affect urban surface temperatures is crucial for improving the sustainability of cities as well as optimizing urban design and landscape planning. Because of their capability to detect distinct surface thermal features, satellite data have proved useful in exploring the impacts of spatial configuration of land cover on land surface temperature (LST). In this study, we examine how the spatial configuration of built-up and urban vegetation affects the LST in the Harare metropolitan city, Zimbabwe. In order to achieve this objective, we combined the LST, local spatial statistics of Getis-Ord Gi* and local Moran’s I statistic, Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI) derived from multi-date Landsat satellite data (1994, 2001 and 2017)