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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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... a large number of urban areas were found in the developed world, however, in the recent past, the sizes and number of large urban areas in Asia and Africa has grown significantly (United Nations, 2014). In sub-Saharan Africa, for instance, urban population grew from 15% in the 1950s to 32% in the 1990s (Keiser et al. 2004). This population is expected to double to 4.9 billion (54–60% increase) by 2030, with majority of the people moving to existing cities. Globally, 17 of the 20 biggest cities are found along the coast (Timmerman & White, 1997). This is concurrent with the proportion of global coastal settlements that account for 60% of population within 100 km of shorelines (Hinrichsen, 1990; World Resources Institute, 1996). According to World Coast Conference (1994), the current coastal popu- lation is growing faster than the average global population; consequently coastal cities will continue to become influential nodes of human settlement. South Africa’s metropolitan areas (referred to as metros hereafter) are inhabited by over 36% of the country’s population and account for over 64% of the country’s economic output (CSIR, 2007). In the last decade, the country’s population grew by over 4.3 million people (CSIR, 2007). During this period, the average population growth in the metros was 2.9% compared to the national 1.8% (South African Cities Network, Department of Provincial and Local Government and Presidency, 2009). Currently, four of the country’s six metros, defined as urban areas with the highest concentration of people, are located at the coast. These metros (Buffalo City, eThekwini, Mandela Bay and Cape Town) account for over 44.6% of the population in the country’s eight major urban areas (Census 2011). Whereas these coastal metros occupy less than 2% of the country’s land mass, they account for almost half of the country’s economic activity. Three (Buffalo City, eThekwini, Mandela Bay) of the four coastal metros are situated on the country’s eastern seaboard (Figure 1). Increased population and landscape transformation associated with enhanced socio- economic activities concentrated in a limited geographic space significantly induce adverse urban socio-environmental change (Chen, Zhao, Li, & Yin, 2006; Huyssteen, Meiklejohn, Coetzee, Goss, & Oranje, 2010). The growth of residential, commercial and industrial devel- opments that typifies urbanization involves the conversion of natural green landscapes into impervious surfaces (Hung, Uchihama, Ochi, & Yasuoka, 2006; Odindi, Bangamwabo, & Mutanga, 2015). Such conversion, along with industrial and transportation waste heat sig- nificantly transform the landscape’s surface and near-surface thermal radiative and emission properties (Giannaros & Melas, 2012; Hung et al., 2006; Rizwan, Dennis, & Liu, 2008). These changes elevate surface and local air temperatures above the rural surroundings, referred to as the Urban Heat Island (UHI). Changes in coastal cities’ thermal microclimate arising from physical and natural transformations influence the cities themselves, the coastline, the hinterland and the marine environment (Timmerman & White, 1997). Urban thermal elevation may among others, deteriorate the environment by increasing ground-level ozone (Crutzen, 2004; Konopacki & Akbari, 2002; Rosenfeld, Akbari, & Romm, 1998), affect aquatic life, influence heat waves, wind and rainfall patterns, and necessitate further air conditioning and therefore more urban heat and CO 2 (Crutzen, 2004; Huang, Li, Zhao, & Zhu, 2008; Ngie, Abutaleb, Ahmed, Darwish, & Ahmed, 2014; Senanayake, Welivitiya, & Nadeeka, 2013; US Environmental Protection Agency, 2012). Typically, urbanization involves a transformation from natural thermal sinks to built-up and impervious thermal sources. Whereas it is widely acknowledged (by among others Hawkins, Brazel, Stefanov, Bigler, and Saffell (2004), Unger, (2004), Rizwan et al. (2008), Hart and Sailor (2009), Mirzaei, and Haghighat (2010)) that temperature vary at rural/ urban macro scales, urban micro land use–land cover (LULC) mosaics form the basis of rural/urban thermal difference. Determining levels of relative vulnerability in coastal cities based on their LULC matrix is therefore valuable in dealing with local, regional and global environmental change. Quantitative determination of relationship between surface heat and LULC is particularly critical in planning and mitigation of effects associated with UHI and climate change. Traditionally, in situ climatologic measurements and surveying techniques have been used to determine UHI and LULCs. Whereas these techniques are relatively accurate, have a high temporal resolution and can provide long historical records, they are often costly, time-consuming and may not effectively display thermal spatial distribution. The emergence of satellite remotely sensed data-sets in concert with Geographical Information Systems (GIS) provide a cost-effective and timely acquisition and analysis of bioclimatic variables over large spatial extents (Senanayake et al., 2013). The value of satellite remotely sensed data-sets has further gained popularity due to advances in sensor technology with better data quality and proliferation of remote sensing, GIS software and processing algorithms (Johnson, 2009; Rogan & Chen, 2004). To date, adoption of remotely sensed data-sets in understating UHI and urban LULCs has been extensively studied (e.g. Hung et al., 2006; Jin, Dickson, & Zhang, 2005; Klok, Zwart, Verhagen, & Mauri, 2012; Pongracz, Baertholy, & Dezso, 2006; Senanayake et al., 2013; Weng, 2001; Xu, 2009; Yuan & Bauer, 2007). However, these studies have been lim- ited to individual cities (Hung et al., 2006). According to Timmerman and White (1997) and Yeung (2001), whereas there has been an increased proliferation of literature on urban landscapes and coastal zones, research on ‘coastal cities’ remain scarce. Furthermore, com- parative qualitative and quantitative studies on UHI and relative vulnerability arising from anthropogenic effects between different coastal cities remain limited. In South Africa, to the best of our knowledge, no study that compares urban thermal variability using remotely sensed data-sets in general and Moderate Resolution Imaging Spectroradiometer (MODIS) data in particular exists. Therefore, this study sought to determine the role of existing LULC mosaics on thermal variability between three South African coastal metros using remotely sensed MODIS data-set. Three metros (Nelson Mandela Bay, Buffalo City and eThekwini) located on the country’s eastern seaboard were selected for the study (Figure 1). The choice was motivated by their comparable sea-facing locations and therefore possibility for identification of near-similar microclimatic conditions as influenced by the ocean. To reliably compare the three metros, an archived MODIS (MOD11A2) scene with closest possible meteorological and atmospheric data collected at weather stations based ...
Context 2
... account for over 44.6% of the population in the country’s eight major urban areas (Census 2011). Whereas these coastal metros occupy less than 2% of the country’s land mass, they account for almost half of the country’s economic activity. Three (Buffalo City, eThekwini, Mandela Bay) of the four coastal metros are situated on the country’s eastern seaboard (Figure 1). Increased population and landscape transformation associated with enhanced socio- economic activities concentrated in a limited geographic space significantly induce adverse urban socio-environmental change (Chen, Zhao, Li, & Yin, 2006; Huyssteen, Meiklejohn, Coetzee, Goss, & Oranje, 2010). The growth of residential, commercial and industrial devel- opments that typifies urbanization involves the conversion of natural green landscapes into impervious surfaces (Hung, Uchihama, Ochi, & Yasuoka, 2006; Odindi, Bangamwabo, & Mutanga, 2015). Such conversion, along with industrial and transportation waste heat sig- nificantly transform the landscape’s surface and near-surface thermal radiative and emission properties (Giannaros & Melas, 2012; Hung et al., 2006; Rizwan, Dennis, & Liu, 2008). These changes elevate surface and local air temperatures above the rural surroundings, referred to as the Urban Heat Island (UHI). Changes in coastal cities’ thermal microclimate arising from physical and natural transformations influence the cities themselves, the coastline, the hinterland and the marine environment (Timmerman & White, 1997). Urban thermal elevation may among others, deteriorate the environment by increasing ground-level ozone (Crutzen, 2004; Konopacki & Akbari, 2002; Rosenfeld, Akbari, & Romm, 1998), affect aquatic life, influence heat waves, wind and rainfall patterns, and necessitate further air conditioning and therefore more urban heat and CO 2 (Crutzen, 2004; Huang, Li, Zhao, & Zhu, 2008; Ngie, Abutaleb, Ahmed, Darwish, & Ahmed, 2014; Senanayake, Welivitiya, & Nadeeka, 2013; US Environmental Protection Agency, 2012). Typically, urbanization involves a transformation from natural thermal sinks to built-up and impervious thermal sources. Whereas it is widely acknowledged (by among others Hawkins, Brazel, Stefanov, Bigler, and Saffell (2004), Unger, (2004), Rizwan et al. (2008), Hart and Sailor (2009), Mirzaei, and Haghighat (2010)) that temperature vary at rural/ urban macro scales, urban micro land use–land cover (LULC) mosaics form the basis of rural/urban thermal difference. Determining levels of relative vulnerability in coastal cities based on their LULC matrix is therefore valuable in dealing with local, regional and global environmental change. Quantitative determination of relationship between surface heat and LULC is particularly critical in planning and mitigation of effects associated with UHI and climate change. Traditionally, in situ climatologic measurements and surveying techniques have been used to determine UHI and LULCs. Whereas these techniques are relatively accurate, have a high temporal resolution and can provide long historical records, they are often costly, time-consuming and may not effectively display thermal spatial distribution. The emergence of satellite remotely sensed data-sets in concert with Geographical Information Systems (GIS) provide a cost-effective and timely acquisition and analysis of bioclimatic variables over large spatial extents (Senanayake et al., 2013). The value of satellite remotely sensed data-sets has further gained popularity due to advances in sensor technology with better data quality and proliferation of remote sensing, GIS software and processing algorithms (Johnson, 2009; Rogan & Chen, 2004). To date, adoption of remotely sensed data-sets in understating UHI and urban LULCs has been extensively studied (e.g. Hung et al., 2006; Jin, Dickson, & Zhang, 2005; Klok, Zwart, Verhagen, & Mauri, 2012; Pongracz, Baertholy, & Dezso, 2006; Senanayake et al., 2013; Weng, 2001; Xu, 2009; Yuan & Bauer, 2007). However, these studies have been lim- ited to individual cities (Hung et al., 2006). According to Timmerman and White (1997) and Yeung (2001), whereas there has been an increased proliferation of literature on urban landscapes and coastal zones, research on ‘coastal cities’ remain scarce. Furthermore, com- parative qualitative and quantitative studies on UHI and relative vulnerability arising from anthropogenic effects between different coastal cities remain limited. In South Africa, to the best of our knowledge, no study that compares urban thermal variability using remotely sensed data-sets in general and Moderate Resolution Imaging Spectroradiometer (MODIS) data in particular exists. Therefore, this study sought to determine the role of existing LULC mosaics on thermal variability between three South African coastal metros using remotely sensed MODIS data-set. Three metros (Nelson Mandela Bay, Buffalo City and eThekwini) located on the country’s eastern seaboard were selected for the study (Figure 1). The choice was motivated by their comparable sea-facing locations and therefore possibility for identification of near-similar microclimatic conditions as influenced by the ocean. To reliably compare the three metros, an archived MODIS (MOD11A2) scene with closest possible meteorological and atmospheric data collected at weather stations based at each of the metros airports was acquired (Table 1). A comparison of all the available 2013 archived MODIS images showed that 25 May–1 June provided the closest and the most comparable factors that influence thermal characteristics like mean temperature, humidity, wind direction and wind speed (Table 1). The archived images also coincided with relatively higher vegetation density after the summer rains, and therefore a more distinctive thermal variability between natural and impervious surfaces. In this study, the Terra MODIS was preferred due to its twice daily coverage and therefore data abundance, adequate spatial resolution and large swath-width, covering the three met- ros. The sensor’s high radiometric resolution and accurate calibrations at the visible, near and thermal infrared sections of the electromagnetic spectrum make it popular for most remote-sensing applications (Wan, Zhang, Zhang, & Li, 2004). To minimize the influence of cloud noise, the best-quality cloud-free 1 km 8-day land surface temperature (LST)/ emissivity (MOD11A2) image was used. According to Rigo, Parlow, and Oesh (2006), the Terra -MODIS night mode is known to be more reliable in determining LST than the Terra daytime mode, therefore, in this study, the night mode was used. The MODIS data has gained popularity in landscape thermal research. Currently, the Terra and Aqua MODIS instruments that monitor terrestrial and aquatic processes are in use. Launched on 18 December 1999, the Terra MODIS (used in this study) is character- ized by seven optical and three thermal spectral bands that measure surface radiation at 10:30 and 22:30 ascending and descending local times (Jin & Dickson 2000). The sensor determines surface temperature ( T skin ) based on split window algorithm from thermal and mid-infrared bands at 1-km resolution at nadir. The LST values are generated from clear sky thermal bands, atmospheric temperature and water vapour. Bands 3–7, 13 and 16–19 are used to determine landscape emissivity, 26 for cloud detection and 20, 22, 23, 29, 31 and 32 to correct for atmospheric temperature and water vapour (Wan & Dozier, 1996). Results are corrected for atmospheric and surface effects to approximately 1 K root mean square error and cloud-free emissivity quality flag values converted to broadband values using moderate resolution transmittance (Jin et al. 2005; Wan et al., 2004). See Wan et al. (2004) for detailed description of MODIS data characteristics. Previous work using remotely sensed LST data include validation of global meteorological data (Diak & Whipple, 1993), LULC analysis (Lambin & Ehrlich, 1997), drought and soil moisture monitoring (Feldhake, Glenn, & Peterson, 1996; McVicar & Jupp, 1998), water use in wheat farms (Jackson, Reginator, & Idso, 1977) and frost mapping in citrus farms (Caselles & Sobrino, 1989). The 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 ...
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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. ...
... In agreement with existing literature, the study showed that high and low density buildings and bare LULCs had higher thermal values than Water, Dense vegetation and Grass and shrubs LULCs. Studies by Mushore et al. (2017) for Harare, Zimbabwe and Odindi et al. (2017) for South African coastal metropolitan cities for instance showed that high-density built-up areas were warmer than any other LULCs. Also, in agreement with Abutaleb et al. (2015) for the city of Cairo, this study showed that the UHI effect in the city of Pietermaritzburg intensifies in summer than winter, and that the UHI hot and cold spots remain on similar LULCs in the two seasons. ...
Urbanisation has been identified as a major threat to the environment as it increases demand for urban spaces and transforms natural landscapes to impervious surfaces, leading to the Urban Heat Island (UHI) phenomenon. Natural landscapes such as vegetation and water bodies act as thermal sinks that absorb heat while impervious surfaces such as buildings and concrete pavements act as thermal sources that retain and emit heat. The thermal emission results in several negative effects such as temperature inversion, compromised human health, pollution, species loss, high energy consumption and climate change at a local, regional and global scales. Whereas studies on UHI are abound, there is paucity in literature on the influence of seasonal urban Land Use Land Cover (LULC) transformation on urban thermal characteristics. Specifically, the proportional seasonal variability and contribution of individual LULCs to urban heat is often poorly understood. Using the freely available Landsat 8 optical and thermal bands, this study examined the seasonal characteristics of the UHI phenomenon in relation to LULCs in the Pietermaritzburg city, South Africa. Results in this study revealed that UHIs exist in both winter and summer, but with more intensity in summer. The study also established that LULCs varied with seasons. Bare surfaces and dense vegetation had the most thermal influence during winter while dense vegetation and low density buildings had the most thermal influence during summer. These findings provide a better understanding of thermal distribution based on LULC seasonality changes, valuable for sustainable urban planning and climate change mitigation.
... It is considered an essential parameter in environmental monitoring, urban heat island research and modelling of agricultural, ecological, meteorological, and biological processes on the earth's surface [2]. The collection of LST data using groundbased methods is costly and time-consuming and it is difficult to capture the temporal and spatial changes in large areas [3]. The advent of remotely-sensed satellite data has provided cost-effective and dependable data acquired regularly at varying spatial resolutions and extents [1]. ...
... Mo [5] conducted a bibliometric analysis on the progress of LST reconstruction research from January 2010 to June 2021 on the Web of Science (WoS) database across the globe. The results indicated that the highest number of publications were from China (36), the United States (12), Spain (4) and Portugal (3). The study highlighted that a low number of studies have been conducted in Africa. ...
... The rise in LST has negative impacts on the local microclimate, public health, thermal discomfort and hydrological behaviour [6]. The rise in LST causes the emergence of the UHI which causes, inter alia, increased mortality, changes in rainfall and wind patterns and more urban heat [3]. The rise of LST in Africa is not well understood based on the number of research publications over the years. ...
... 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). ...
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... 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 . ...
