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Transect showing progressive thermal variability from the urban core (a) to the furthest peripheral natural landscape (b) in eThekwini (A), Buffalo City (B), Nelson Mandela Bay (C). Source: Author.
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Coastal landscapes have historically attracted a larger number of settlements than inland. This trend is expected to continue. Commonly, increase in coastal settlements has been accompanied
by growth of existing urban areas. Such growth is characterized by transformation from natural landscapes to impervious surfaces associated with thermal elevati...
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Context 1
... normalization was deemed unnecessary. Based on temperature data from ground-based weather stations, an extensive body of literature that include Wan et al. (2004), Wan and Li (2008) and Wan, Zhang, Zhang, and Li (2002) have established high accuracy level (<1 K) of MODIS LST on varied surfaces and atmospheric conditions. However, to further validate the accuracy of the LST data used in this study, temperature values of impervious surfaces (weather stations located at three metros airports) at the time of the satellite overpass were compared to values extracted from similar locations on the MODIS image. Temperature values from weather stations and MODIS images for the three metros were similar. To determine the effect of landscape transformation within the three metros, thermal values from MODIS LST imagery corresponding to the major LULCs were extracted. The Landsat 8 (Operational Land Imager – OLI), used in this study to determine LULC types, was launched on 11/02/2013 and is the latest of the Landsat series. The OLI image is characterized by nine, 30 m multispectral bands and a 15 m panchromatic band (a detailed description of the Landsat 8 OLI can be found at The utility and reliability of the Landsat series in LULC mapping is widely documented. In this study, cloud-free Landsat 8-Level 1T images (radiometric, geometric and terrain cor- rected) detailed in Table 2 were used. A two-scene mosaic was required for the eThekwini metro (Table 2). Using the metros’ corresponding aerial photos, accuracy of the geo-rectified images was confirmed to be less than 0.025 of a pixel. Commonly, urban landscapes are made up of four major surface cover types: vegetation, impervious surfaces, water and bare surfaces. These LULCs can be regarded as either LST source or sink, based on their positive or negative LST contribution (Xu, 2009). The source/ sink distribution is particularly valuable in spatially determining LST contribution over varied LULCs within a landscape. The Landsat LULC classes were used to determine the metros landscape mosaics. Firstly, an Iterative Self-Organizing Data Analysis (ISODATA) was used to gain an insight into the metros major LULCs. With an aid of respective metros aerial photographs, ISODATA results of related LULCs were trained and re-classified into six major LULC types (Bare soil, Dense vegetation, Densely built-up, Low vegetation, Moderately built-up and Water). The classes were then smoothed using the majority filter rule to remove noisy pixels and to improve the classification accuracy. Based on 100 stratified randomly sampled points from each LULC type, the classification accuracy was assessed by comparing the LULC classes and corresponding covers identified on the three metros’ respective aerial photographs acquired at the same season as the image used. As described by Congalton and Green (2008), confusion matrices showing producer’s accuracy (PA), user’s accuracy (UA) and overall accuracy (OA) were generated. The PA provides an indication of the probability that the classification has been correctly matched to the image pixel. The UA indicates the probability that a randomly selected point is correctly classified on the map, while the OA indicates the probability of correct map classification of a randomly selected point. To determine the UHI effect within the three metros, transects, based on gradual visual built-up/natural landscape transition from the urban core to the periphery were generated. A Duncan post hoc one-way ANOVA was also used to test the hypothesis that there was no significant difference ( p ≤ 0.05) in temperature values for each of the LULC mosaics between the metros and within the respective metros LULC types. The LULCs thermal contribution to each of the metros was determined by calculating the average LST differences between the cover type and the entire metro. Using the Hawths random point generator, over 500 points within each of the LULC surfaces were generated and corresponding thermal values extracted from MODIS LST image. The thermal contribution of the LULCs to each of the metros were determined by multiplying the proportion of the LULC by the difference in the metros average temperature using Chen et al. (2006) Contribution Index (CI) expressed as: CI = D × S t where CI is the proportional LST contribution to the entire metro, D t is the average LST difference between the cover type and the entire metro and S is the proportional area. Results based on this equation show source (positive) and sink (negative) LULC thermal contributions. Based on a densely built-up/natural landscape transect, the three metros showed higher thermal values at the coastline that formed the metros urban cores than inland (Figure 2). Whereas the eThekwini and Buffalo City metro’s MODIS imagery showed more significant decline in thermals values towards inland, the Mandela Bay imagery showed a relatively lower thermal decline from the urban core towards the periphery (Figure 2). There was significant ( p ≤ 0.05) thermal variability between the three metros (Figure 3). The eThekwini metro was characterized by significantly higher mean temperature values and lower variability ( M = 15.17, SD = 1.4) than the Nelson Mandela Bay ( M = 11.15, SD = 1.5) and Buffalo City ( M = 10.18, SD = 2.3) metros (Figure 3). As aforementioned, to avoid redundancy and to effectively relate LULCs generated from Landsat 8 to MODIS with 30 m and 1 km spatial resolutions, respectively, existing LULCs within the three metros were amalgamated into six major LULCs. Table 3 provides a sum- mary of LULC classification accuracy in the three metros. There was a significant variability in spatial coverage for the LULCs in the three metros (Figure 4). Generally, the eThekwini metro had a larger area covered by Densely built-up LULC category than the Buffalo City and Mandela Bay metros, respectively (Figure 4). The area and proportions covered by Low and Dense vegetation were higher in Buffalo City and Nelson Mandela Bay metro areas (Table 4). Within the three metros, there were small spatial extents covered by water bodies (Table 4). Based on the MODIS LST values extracted from different LULC types, except Densely built and Water bodies in the Nelson Mandela Bay metro, the difference in all the LULCs thermal values were statistically significant ( p ≤ 0.05) (Figure 5). Generally, the built-up areas in the three metros were characterized by higher thermal values than dense vegetation areas (Figure 5). Whereas the LST value trends for Bare soil, Dense vegetation, Densely built-up, and Low vegetation were relatively consistent, values for the Moderately built-up and Water bodies were inconsistent in three cities (Figure 5). The Densely built LULC had a higher thermal contribution in eThekwini than in Buffalo City and Nelson Mandela Bay, correspondingly (Figure 6). There were positive thermal contributions by Low vegetation, Moderately built-up and Water in eThekwini, Bare surfaces Moderately built-up and Water in Buffalo City and Moderately built-up and Water in Nelson Mandela Bay (Figure 6). The highest negative contributions were by the Dense vegetation LULC types in Buffalo City, Nelson Mandela Bay and eThekwini. There were also negative thermal contributions by the Bare surfaces in eThekwini, Low vegetation in Buffalo City and Bare surfaces, Low vegetation and Water in Nelson Mandela Bay (Figure 6). In consistency with literature (e.g. Adebayo, 1987; Arnfield, 2003; Voogt & Oke, 2003), the three metros showed a general thermal decline from the urban core to the periphery (Figure 2). Based on transects generated from highest/lowest visual thermal differences, the eThekwini, Buffalo City and Nelson Mandela Bay had R 2 values of 0.88, 0.88 and 0.68, respectively. It can therefore be concluded that the eThekwini and Mandela Bay metros had the highest and lowest thermal variability, respectively. This study showed varied contribution of respective LULC types to the metros thermal properties. The Densely built-up and Dense vegetation LULCs had the highest and lowest thermal values in the three metros (Figure 5). This is consistent with findings by Kjelgren and Montague (1997) and Oke (1982), who attribute the impervious surface’s high thermal contribution to among others, urban canyon geometry, reduced net long wave radiation and limited sensible heat loss resulting from the urban impervious roughness. The thermal values for Low vegetation were lower than Moderately built-up areas in the three metros. Based on this finding, it can be concluded that areas with Low vegetation like crop land and open urban public parks are more efficient heat sinks than Moderately built areas, often charac- terized by a mosaic of impervious surfaces and grown trees. This finding contradicts Walz and Hwang (2007), who showed how grown trees around impervious surfaces ameliorate surface heat by shading and transpiration and therefore acting as solar radiation barriers and heat absorbers. However, according to Walz and Hwang (2007), there is an inverse rela- tionship between the amount of vegetation and surface temperature. Areas characterized by impervious surfaces within greenery often exhibit thermal hotspots. Consequently, a land- scape’s overall thermal positive or negative thermal balance is determined by the proportion of such hotspots to the available greenery sink (Townsend-Small & Czimczik 2010). The higher thermal values for the Moderately built-up areas seen in this study can therefore be attributed to a possible higher number of hotspots within a lower spatial resolution MODIS LST pixel. The Low vegetation, on the other hand, transpires and therefore acts as benign thermal sink. Furthermore, the partial ground shading by the Low vegetation may increase near-surface soil moisture, further acting as surface’s thermal sink. As shown in this study, the Landsat OLI imagery (30 m spatial resolution) can be used to ...
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... the classification accuracy was assessed by comparing the LULC classes and corresponding covers identified on the three metros’ respective aerial photographs acquired at the same season as the image used. As described by Congalton and Green (2008), confusion matrices showing producer’s accuracy (PA), user’s accuracy (UA) and overall accuracy (OA) were generated. The PA provides an indication of the probability that the classification has been correctly matched to the image pixel. The UA indicates the probability that a randomly selected point is correctly classified on the map, while the OA indicates the probability of correct map classification of a randomly selected point. To determine the UHI effect within the three metros, transects, based on gradual visual built-up/natural landscape transition from the urban core to the periphery were generated. A Duncan post hoc one-way ANOVA was also used to test the hypothesis that there was no significant difference ( p ≤ 0.05) in temperature values for each of the LULC mosaics between the metros and within the respective metros LULC types. The LULCs thermal contribution to each of the metros was determined by calculating the average LST differences between the cover type and the entire metro. Using the Hawths random point generator, over 500 points within each of the LULC surfaces were generated and corresponding thermal values extracted from MODIS LST image. The thermal contribution of the LULCs to each of the metros were determined by multiplying the proportion of the LULC by the difference in the metros average temperature using Chen et al. (2006) Contribution Index (CI) expressed as: CI = D × S t where CI is the proportional LST contribution to the entire metro, D t is the average LST difference between the cover type and the entire metro and S is the proportional area. Results based on this equation show source (positive) and sink (negative) LULC thermal contributions. Based on a densely built-up/natural landscape transect, the three metros showed higher thermal values at the coastline that formed the metros urban cores than inland (Figure 2). Whereas the eThekwini and Buffalo City metro’s MODIS imagery showed more significant decline in thermals values towards inland, the Mandela Bay imagery showed a relatively lower thermal decline from the urban core towards the periphery (Figure 2). There was significant ( p ≤ 0.05) thermal variability between the three metros (Figure 3). The eThekwini metro was characterized by significantly higher mean temperature values and lower variability ( M = 15.17, SD = 1.4) than the Nelson Mandela Bay ( M = 11.15, SD = 1.5) and Buffalo City ( M = 10.18, SD = 2.3) metros (Figure 3). As aforementioned, to avoid redundancy and to effectively relate LULCs generated from Landsat 8 to MODIS with 30 m and 1 km spatial resolutions, respectively, existing LULCs within the three metros were amalgamated into six major LULCs. Table 3 provides a sum- mary of LULC classification accuracy in the three metros. There was a significant variability in spatial coverage for the LULCs in the three metros (Figure 4). Generally, the eThekwini metro had a larger area covered by Densely built-up LULC category than the Buffalo City and Mandela Bay metros, respectively (Figure 4). The area and proportions covered by Low and Dense vegetation were higher in Buffalo City and Nelson Mandela Bay metro areas (Table 4). Within the three metros, there were small spatial extents covered by water bodies (Table 4). Based on the MODIS LST values extracted from different LULC types, except Densely built and Water bodies in the Nelson Mandela Bay metro, the difference in all the LULCs thermal values were statistically significant ( p ≤ 0.05) (Figure 5). Generally, the built-up areas in the three metros were characterized by higher thermal values than dense vegetation areas (Figure 5). Whereas the LST value trends for Bare soil, Dense vegetation, Densely built-up, and Low vegetation were relatively consistent, values for the Moderately built-up and Water bodies were inconsistent in three cities (Figure 5). The Densely built LULC had a higher thermal contribution in eThekwini than in Buffalo City and Nelson Mandela Bay, correspondingly (Figure 6). There were positive thermal contributions by Low vegetation, Moderately built-up and Water in eThekwini, Bare surfaces Moderately built-up and Water in Buffalo City and Moderately built-up and Water in Nelson Mandela Bay (Figure 6). The highest negative contributions were by the Dense vegetation LULC types in Buffalo City, Nelson Mandela Bay and eThekwini. There were also negative thermal contributions by the Bare surfaces in eThekwini, Low vegetation in Buffalo City and Bare surfaces, Low vegetation and Water in Nelson Mandela Bay (Figure 6). In consistency with literature (e.g. Adebayo, 1987; Arnfield, 2003; Voogt & Oke, 2003), the three metros showed a general thermal decline from the urban core to the periphery (Figure 2). Based on transects generated from highest/lowest visual thermal differences, the eThekwini, Buffalo City and Nelson Mandela Bay had R 2 values of 0.88, 0.88 and 0.68, respectively. It can therefore be concluded that the eThekwini and Mandela Bay metros had the highest and lowest thermal variability, respectively. This study showed varied contribution of respective LULC types to the metros thermal properties. The Densely built-up and Dense vegetation LULCs had the highest and lowest thermal values in the three metros (Figure 5). This is consistent with findings by Kjelgren and Montague (1997) and Oke (1982), who attribute the impervious surface’s high thermal contribution to among others, urban canyon geometry, reduced net long wave radiation and limited sensible heat loss resulting from the urban impervious roughness. The thermal values for Low vegetation were lower than Moderately built-up areas in the three metros. Based on this finding, it can be concluded that areas with Low vegetation like crop land and open urban public parks are more efficient heat sinks than Moderately built areas, often charac- terized by a mosaic of impervious surfaces and grown trees. This finding contradicts Walz and Hwang (2007), who showed how grown trees around impervious surfaces ameliorate surface heat by shading and transpiration and therefore acting as solar radiation barriers and heat absorbers. However, according to Walz and Hwang (2007), there is an inverse rela- tionship between the amount of vegetation and surface temperature. Areas characterized by impervious surfaces within greenery often exhibit thermal hotspots. Consequently, a land- scape’s overall thermal positive or negative thermal balance is determined by the proportion of such hotspots to the available greenery sink (Townsend-Small & Czimczik 2010). The higher thermal values for the Moderately built-up areas seen in this study can therefore be attributed to a possible higher number of hotspots within a lower spatial resolution MODIS LST pixel. The Low vegetation, on the other hand, transpires and therefore acts as benign thermal sink. Furthermore, the partial ground shading by the Low vegetation may increase near-surface soil moisture, further acting as surface’s thermal sink. As shown in this study, the Landsat OLI imagery (30 m spatial resolution) can be used to effectively delineate major LULCs. However, due to the limited spatial extents that charac- terize Water bodies and Bare soil within urban landscapes, values extracted from the lower spatial resolution (1 km) MODIS LST data are influenced by the adjoining LULC thermal values. Water bodies for instance are known to be better thermal sinks than any other LULC type (Ramachandra & Uttam, 2009), however, in this study, water bodies thermal values in three metros were inconsistent and higher than expected (Figure 5). Thus, whereas MODIS LST data offer great potential in determining thermal values of large and homogeneous LULC types, their low spatial resolution may not be ideal for smaller cover types or highly heterogeneous landscapes. In such circumstances, higher spatial resolution imagery like the Landsat thermal band, with 100 m pixel resolution, that can be re-sampled to 30 m may be suitable for determining urban thermal variability. According to Zoran (2011), such higher spatial resolution imagery provide time-synchronized dense grid thermal data valuable in determining thermal variability of the often complex urban landscape mosaics. However, it should be noted that literature on Landsat thermal bands validation is limited. Depending on their respective built-up/natural landscape matrix, thermal characteristics between urban areas may vary significantly (Kabat et al., 2004). In this study, eThekwini and Buffalo City metros had the highest and lowest average thermal values, respectively (Figure 3). The higher thermal values in eThekwini can be attributed to the larger propor- tions of Densely and Moderately built-up LULC types that constitute major heat sources. Conversely, the lower average thermal values in the Buffalo City metro can be attributed to the larger proportions of Dense and Low vegetation (Figure 3). The eThekwini metro had a higher average CI value than the Buffalo City and Nelson Mandela Bay metros. Generally, there was a higher positive contribution by the impervious surfaces in eThekwini than Buffalo City and Nelson Mandela Bay (Figure 6). Therefore, based on the higher average thermal contribution by eThekwini metro’s heat sources and a larger proportion of Built-up areas (Table 4), the eThekwini metro can be regarded as most vulnerable to changes in microclimate and associated impacts. Conversely, due to a larger spatial extent and higher negative contribution by Dense and Low vegetation LULC types in Buffalo City than in eThekwini and Nelson Mandela Bay (Figure 6), the metro can be regarded as least vulnerable to microclimate change and associated impacts. The ...
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... image thermal values were converted from kelvins to degrees centigrade. Since the archived MOD11A2 scenes are geo-rectified, the image was not subjected to a geo- rectification process. All the metros were within a single scene, therefore, radiometric normalization was deemed unnecessary. Based on temperature data from ground-based weather stations, an extensive body of literature that include Wan et al. (2004), Wan and Li (2008) and Wan, Zhang, Zhang, and Li (2002) have established high accuracy level (<1 K) of MODIS LST on varied surfaces and atmospheric conditions. However, to further validate the accuracy of the LST data used in this study, temperature values of impervious surfaces (weather stations located at three metros airports) at the time of the satellite overpass were compared to values extracted from similar locations on the MODIS image. Temperature values from weather stations and MODIS images for the three metros were similar. To determine the effect of landscape transformation within the three metros, thermal values from MODIS LST imagery corresponding to the major LULCs were extracted. The Landsat 8 (Operational Land Imager – OLI), used in this study to determine LULC types, was launched on 11/02/2013 and is the latest of the Landsat series. The OLI image is characterized by nine, 30 m multispectral bands and a 15 m panchromatic band (a detailed description of the Landsat 8 OLI can be found at The utility and reliability of the Landsat series in LULC mapping is widely documented. In this study, cloud-free Landsat 8-Level 1T images (radiometric, geometric and terrain cor- rected) detailed in Table 2 were used. A two-scene mosaic was required for the eThekwini metro (Table 2). Using the metros’ corresponding aerial photos, accuracy of the geo-rectified images was confirmed to be less than 0.025 of a pixel. Commonly, urban landscapes are made up of four major surface cover types: vegetation, impervious surfaces, water and bare surfaces. These LULCs can be regarded as either LST source or sink, based on their positive or negative LST contribution (Xu, 2009). The source/ sink distribution is particularly valuable in spatially determining LST contribution over varied LULCs within a landscape. The Landsat LULC classes were used to determine the metros landscape mosaics. Firstly, an Iterative Self-Organizing Data Analysis (ISODATA) was used to gain an insight into the metros major LULCs. With an aid of respective metros aerial photographs, ISODATA results of related LULCs were trained and re-classified into six major LULC types (Bare soil, Dense vegetation, Densely built-up, Low vegetation, Moderately built-up and Water). The classes were then smoothed using the majority filter rule to remove noisy pixels and to improve the classification accuracy. Based on 100 stratified randomly sampled points from each LULC type, the classification accuracy was assessed by comparing the LULC classes and corresponding covers identified on the three metros’ respective aerial photographs acquired at the same season as the image used. As described by Congalton and Green (2008), confusion matrices showing producer’s accuracy (PA), user’s accuracy (UA) and overall accuracy (OA) were generated. The PA provides an indication of the probability that the classification has been correctly matched to the image pixel. The UA indicates the probability that a randomly selected point is correctly classified on the map, while the OA indicates the probability of correct map classification of a randomly selected point. To determine the UHI effect within the three metros, transects, based on gradual visual built-up/natural landscape transition from the urban core to the periphery were generated. A Duncan post hoc one-way ANOVA was also used to test the hypothesis that there was no significant difference ( p ≤ 0.05) in temperature values for each of the LULC mosaics between the metros and within the respective metros LULC types. The LULCs thermal contribution to each of the metros was determined by calculating the average LST differences between the cover type and the entire metro. Using the Hawths random point generator, over 500 points within each of the LULC surfaces were generated and corresponding thermal values extracted from MODIS LST image. The thermal contribution of the LULCs to each of the metros were determined by multiplying the proportion of the LULC by the difference in the metros average temperature using Chen et al. (2006) Contribution Index (CI) expressed as: CI = D × S t where CI is the proportional LST contribution to the entire metro, D t is the average LST difference between the cover type and the entire metro and S is the proportional area. Results based on this equation show source (positive) and sink (negative) LULC thermal contributions. Based on a densely built-up/natural landscape transect, the three metros showed higher thermal values at the coastline that formed the metros urban cores than inland (Figure 2). Whereas the eThekwini and Buffalo City metro’s MODIS imagery showed more significant decline in thermals values towards inland, the Mandela Bay imagery showed a relatively lower thermal decline from the urban core towards the periphery (Figure 2). There was significant ( p ≤ 0.05) thermal variability between the three metros (Figure 3). The eThekwini metro was characterized by significantly higher mean temperature values and lower variability ( M = 15.17, SD = 1.4) than the Nelson Mandela Bay ( M = 11.15, SD = 1.5) and Buffalo City ( M = 10.18, SD = 2.3) metros (Figure 3). As aforementioned, to avoid redundancy and to effectively relate LULCs generated from Landsat 8 to MODIS with 30 m and 1 km spatial resolutions, respectively, existing LULCs within the three metros were amalgamated into six major LULCs. Table 3 provides a sum- mary of LULC classification accuracy in the three metros. There was a significant variability in spatial coverage for the LULCs in the three metros (Figure 4). Generally, the eThekwini metro had a larger area covered by Densely built-up LULC category than the Buffalo City and Mandela Bay metros, respectively (Figure 4). The area and proportions covered by Low and Dense vegetation were higher in Buffalo City and Nelson Mandela Bay metro areas (Table 4). Within the three metros, there were small spatial extents covered by water bodies (Table 4). Based on the MODIS LST values extracted from different LULC types, except Densely built and Water bodies in the Nelson Mandela Bay metro, the difference in all the LULCs thermal values were statistically significant ( p ≤ 0.05) (Figure 5). Generally, the built-up areas in the three metros were characterized by higher thermal values than dense vegetation areas (Figure 5). Whereas the LST value trends for Bare soil, Dense vegetation, Densely built-up, and Low vegetation were relatively consistent, values for the Moderately built-up and Water bodies were inconsistent in three cities (Figure 5). The Densely built LULC had a higher thermal contribution in eThekwini than in Buffalo City and Nelson Mandela Bay, correspondingly (Figure 6). There were positive thermal contributions by Low vegetation, Moderately built-up and Water in eThekwini, Bare surfaces Moderately built-up and Water in Buffalo City and Moderately built-up and Water in Nelson Mandela Bay (Figure 6). The highest negative contributions were by the Dense vegetation LULC types in Buffalo City, Nelson Mandela Bay and eThekwini. There were also negative thermal contributions by the Bare surfaces in eThekwini, Low vegetation in Buffalo City and Bare surfaces, Low vegetation and Water in Nelson Mandela Bay (Figure 6). In consistency with literature (e.g. Adebayo, 1987; Arnfield, 2003; Voogt & Oke, 2003), the three metros showed a general thermal decline from the urban core to the periphery (Figure 2). Based on transects generated from highest/lowest visual thermal differences, the eThekwini, Buffalo City and Nelson Mandela Bay had R 2 values of 0.88, 0.88 and 0.68, respectively. It can therefore be concluded that the eThekwini and Mandela Bay metros had the highest and lowest thermal variability, respectively. This study showed varied contribution of respective LULC types to the metros thermal properties. The Densely built-up and Dense vegetation LULCs had the highest and lowest thermal values in the three metros (Figure 5). This is consistent with findings by Kjelgren and Montague (1997) and Oke (1982), who attribute the impervious surface’s high thermal contribution to among others, urban canyon geometry, reduced net long wave radiation and limited sensible heat loss resulting from the urban impervious roughness. The thermal values for Low vegetation were lower than Moderately built-up areas in the three metros. Based on this finding, it can be concluded that areas with Low vegetation like crop land and open urban public parks are more efficient heat sinks than Moderately built areas, often charac- terized by a mosaic of impervious surfaces and grown trees. This finding contradicts Walz and Hwang (2007), who showed how grown trees around impervious surfaces ameliorate surface heat by shading and transpiration and therefore acting as solar radiation barriers and heat absorbers. However, according to Walz and Hwang (2007), there is an inverse rela- tionship between the amount of vegetation and surface temperature. Areas characterized by impervious surfaces within greenery often exhibit thermal hotspots. Consequently, a land- scape’s overall thermal positive or negative thermal balance is determined by the proportion of such hotspots to the available greenery sink (Townsend-Small & Czimczik 2010). The higher thermal values for the Moderately built-up areas seen in this study can therefore be attributed to a possible higher number of hotspots within a lower spatial resolution MODIS LST pixel. The Low vegetation, on the other hand, transpires and therefore acts as benign ...
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... In consistency with literature e.g. Abutaeb et al (2015), Mushore et al., (2017) and Odindi et al., (2017), results in this study showed that the UHI's spatial distribution within the study area is highly dependent on LULC. Furthermore, the findings showed an occurrence of the UHI phenomenon in both seasons, however, intensities and contributions of LULCs were varied. ...
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... According to Dube et al. (2016), the popularity of medium-to-coarse spatial resolution sensors such as Landsat could be explained by the large volumes of archived information dating back to 1972 during the first launch of Earth Resource Technology Satellite (ERTS-1), commonly referred to as Landsat. Hence, numerous urban ecosystem service studies in sub-Saharan Africa have been conducted using aforementioned satellite sensors, with reasonable accuracies (Dieye et al., 2012;Feyisa et al., 2014;Mushore et al., 2017;Odindi et al., 2017;Wangai et al., 2019). Feyisa et al. (2014), for instance, used Landsat-7 ETM + derived bio-geophysical variables such as normalised difference vegetation index (NDVI) spectral properties to quantify land surface temperature in the parks-vegetation of Addis Ababa, Ethiopia. ...
... Using thermal values derived from the mid-infrared spectroscopic reflectance of MODIS imagery, Odindi et al. (2017) for instance successfully estimated the implications of land use land cover change on urban thermal characteristics (R 2 : 0.68-0.88) in eThekwini, Buffalo and Nelson Mandela Bay urban municipalities of South Africa. The study concluded that eThekwini Municipality was more vulnerable to increasing urban heat and climate change due to its high proportion of impervious surfaces. ...
... slope length and steepness, soil moisture, elevation and vegetation indices) to predict and map soil organic carbon. Their results showed high coefficient of determination (R 2 : 0.909) and low RMSE (2.47 g kg −1 ), Table 3 Sensor specifications and their integration with ancillary data for the assessment of urban ecosystem services in sub-Saharan Africa Rainfall, NDVI, temperature, slope, EVI Forage area, natural hazard control, air quality, air temperature and soil nutrient cycling (Boiyo et al., 2017;Fahr et al., 2015;Odindi et al., 2017;Winkler et al., 2017) Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
A dearth of information on urban ecosystem services in the past decades has led to little consolidation of such information for informed planning, decision-making and policy development in sub-Saharan African cities. However, the increasing recognition of the value of urban ecological processes and services as well as their contribution to climate change adaptation and mitigation has recently become an area of great research interest. Specifically, the emerging geospatial analytical approaches like remote sensing have led to an increase in the number of studies that seek to quantify and map urban ecosystem services at varying scales. Hence, this study sought to review the current remote sensing trends, challenges and prospects in quantifying urban ecosystem services in sub-Saharan Africa cities. Literature shows that consistent modelling and understanding of urban ecosystem services using remotely sensed approaches began in the 1990s, with an average of five publications per year after around 2010. This is mainly attributed to the approach’s ability to provide fast, accurate and repeated spatial information necessary for optimal and timely quantification and mapping of urban ecosystem services. Although commercially available high spatial resolution sensors (e.g. the Worldview series, Quickbird and RapidEye) with higher spatial and spectral properties have been valuable in providing highly accurate and reliable data for quantification of urban ecosystem services, their adoption has been limited by high image acquisition cost and small spatial coverage that limits regional assessment. Thus, the newly launched sensors that provide freely and readily available data (i.e. Landsat 8 and 9 OLI, Sentinel-2) are increasingly becoming popular. These sensors provide data with improved spatial and spectral properties, hence valuable for past, current and future urban ecosystem service assessment, especially in developing countries. Therefore, the study provides guidance for future studies to continuously assess urban ecosystem services in order to achieve the objectives of Kyoto Protocol and Reducing Emissions from Deforestation and forest Degradation (REDD +) of promoting climate-resilient and sustainable cities, especially in developing world.
... In many countries, meteorological departments measure air temperature at weather stations or airports and through small-scale physical models and in-situ retrievals (Aina et al., 2017). However, changes in the instruments and sites for in-situ measurements have limited the use of data from such sources and the data collection procedure is more costly and time-consuming for large areas (Odindi et al., 2015b). In addition, the arrangement of landcover structures and different land uses in urban environments makes it difficult to obtain accurate temperature results using data from weather stations and airports. ...
... Therefore, thermal intraurban distribution is better described using remote sensing satellite data (Alexander, 2020). The advent of remotely-sensed satellite data has provided cost-effective and dependable data acquired regularly at varying spatial resolutions and extents (Odindi et al., 2015b). Remote sensing data have been used in various studies such as forest ecology (Lechner et al., 2020), biology (Nimit et al., 2020) and thermal remote sensing (Guha and Govil, 2020) over large spatial extents. ...
... Remote sensing data have been used in various studies such as forest ecology (Lechner et al., 2020), biology (Nimit et al., 2020) and thermal remote sensing (Guha and Govil, 2020) over large spatial extents. The popularity of remote sensing data emanates from its good quality and availability, accompanied by continual improvements in processing algorithms (Odindi et al., 2015b). ...
Urban trees play a critical role in alleviating land surface temperatures in cities. In remote sensing studies, vegetation indices are widely used to examine the relationship between Land Surface Temperature (LST) and vegetation cover. The vegetation cover can be measured using the Normalised Difference Vegetation Index (NDVI). In this study, the LST-NDVI relationship was assessed in each of the seven city-regions (A-G) in Johannesburg using Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets to provide a basis for urban ecological planning and environmental protection. This study's specific objective was to determine the intraurban differences in vegetation coverage and LST in the seven city-regions over 19 years. The relationship between LST and NDVI was also examined over the years of study. The results showed a significant intraurban difference in LST and NDVI values in the city-regions with a negative correlation between them, ranging from −0.03 to −0.76. The LST values increased in all the city-regions with the highest value of 20.1°C in city-region G, followed by 19.6°C in city-region E. The vegetation cover decreased over the years, with the lowest NDVI value in city-region G (0.39), followed by city-regions F (0.43) and D (0.48). The city-regions with high LST and low vegetation cover include the city-centre and highly populated suburbs. This indicates that areas with greater vegetation cover have low LSTs and vice versa. These findings provide useful information for municipal authorities and other stakeholders to undertake appropriate decisions to tackle Urban Heat Island (UHI) effects by adopting effective urban planning and management interventions.
... Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) derived from Terra and Aqua Satellites can be used to investigate surface urban heat island intensity based on varying spatial resolutions [47,52]. ...
... Scientific and reasonable E-UHI mitigation is of great significance to sustainable city development [30]. Accurate land survey accounts show that temperature is greatly affected by the land-use structure [31,32]. Some factors that have a significant impact on factory E-UHIs include high industrial emissions during different industrial stages, material transfer storage within and outside industrial premises, and the high-temperature environment required for ore smelting [28]. ...
Urban heat islands (UHIs) have caused radical changes in urban climates. However, the extreme UHI (E-UHI) formed in factory areas deserves more attention. To mitigate the E-UHI, machine learning is used for simulating and quantifying the marginal utility of the scale, shape, type, stage, and structure of the factory on the land surface temperature (LST), factory LST (LSTf), surrounding LST (LSTs) and increase value (ΔLST) level. The results show that the scale of all types of factories affects LSTf and LSTs, and the shape of steel factories affects LSTs and ΔLST. The LST in factories that require high-temperature environments (e.g., smelters) is significantly higher than that in other factories (e.g., sales plants). The ΔLST of green space (GS), staff activity ground (SG), material transfer ground (MG), material storage area (MA), factory building (FB), smelting area (SA) and casting building (CB) are 3.95 °C, 4.01 °C, 5.08 °C, 5.15 °C, 5.24 °C, 5.49 °C and 7.32 °C, and their optimal ranges are 8.84%–15.09%, 16.65%–25.52%, 3.91%–35.91%, 0.00%–8.70%, 5.06%–13.60%, 23.33%–48.02%, and 0.00%–5.73%, respectively. Appropriately standardizing the scale and shape, controlling the temperature of the high-temperature generation stage, reducing the proportion of CB, MG and MA, and increasing the proportion of GS and SG are effective ways to alleviate the E-UHI. The findings provide theoretical guidance for resource-based cities to mitigate E-UHIs.
... A major manifestation of an increase in LST is the urban heat island (UHI). The UHI phenomenon basically highlights significant differences between temperature variations in urban and rural areas (Aina et al. 2017a) and urbanization as well as industrialization have been identified as some of the main factors responsible for increase in UHI (El-Nahry and Rashash 2013; Assiri 2017; Odindi et al. 2017;Ye et al. 2017). Transformation of urban landscape causes significant alteration in the structure and fluctuation of UHI (Hereher 2016). ...
... Li et al. (2017) concluded that there is an association between land-cover composition and LST at daytime and nighttime. Odindi et al. (2017), Aina et al. (2017b) and Xiao et al. (2018) found variations in LST of different categories of urban density in Riyadh, Madrid, Vienna and three metropolitan areas in South Africa. In a study that evaluates the influence of land-use zoning plan on LST, Keeratikasikorn and Bonafoni (2018) identified the LST variability in 10 different land-use categories including commercial, industrial and residential land-use types. ...
This article explores using satellite images to monitor spatiotemporal variations in temperature related to urban form. Land surface temperatures (LST) were estimated from Landsat images (1986–2016) and the land cover and urban form LST were extracted by using samples representing different urban forms/cover types. A transect of 20 km was taken across the city to derive the LST across the different land cover types. Urban heat island index and statistical analysis were carried out to understand the influence of urban form and cover on changes in surface temperature. The results are compared with temperature regimes of an industrial city (Yanbu) to depict differences in the two cities. The analysis of variance (ANOVA) shows variations, at 0.01 level of significance, in the LST values of the city centre, high-rise, low-density, vegetation, desert and industrial land-use types. The outcome of the study is valuable for decision-makers in achieving sustainable urban development.
... It links spatial structure and long-term changes in land cover to LST intensities. The equation for calculating the CI is given by Eq. (8) (Odindi et al. 2015, Odindi et al. 2017, Tarawally et al. 2018. ...
The Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.
... Multispectral Imaging (MSI) was originally developed for remote sensing applications [1], such as environmental monitoring [2], [3], but has since been used across a variety of scientific applications including medical imaging [4][5][6][7], agriculture and horticulture [8][9][10], food science technology [11][12][13][14], and astronomy [15][16][17]. A major advantage of MSI for cultural heritage is that it does not require samples to be taken from the object. ...
... Therefore, the current pipeline for MSI needs to be overviewed. 2 Principal Component Analysis is a statistical procedure commonly used to reduce the dimension of a set of data [190]- [194] covered more fully below. ...
Although multispectral imaging (MSI) of cultural heritage, such as manuscripts, documents and artwork, is becoming more popular, a variety of approaches are taken and methods are often inconsistently documented. Furthermore, no overview of the process of MSI capture and analysis with current technology has previously been published. This research was undertaken to determine current best practice in the deployment of MSI, highlighting areas that need further research, whilst providing recommendations regarding approach and documentation. An Action Research methodology was used to characterise the current pipeline, including: literature review; unstructured interviews and discussion of results with practitioners; and reflective practice whilst undertaking MSI analysis. The pipeline and recommendations from this research will improve project management by increasing clarity of published outcomes, the reusability of data, and encouraging a more open discussion of process and application within the MSI community. The importance of thorough documentation is emphasised, which will encourage sharing of best practice and results, improving community deployment of the technique. The findings encourage efficient use and reporting of MSI, aiding access to historical analysis. We hope this research will be useful to digitisation professionals, curators and conservators, allowing them to compare and contrast current practices.
... water body as stated by Peng et al. (2014), which suggests that deforestation can modify surface thermal signal characteristics (Piao et al., 2015;Zhou et al., 2015;Mohamed et al., 2017;Mushore et al., 2017c;Odindi et al., 2017). In the year 1996, the spatial distribution of land surface temperature was evident, and it was revealed that the LST in most parts of the study area was above 25 °C . ...
