A remote sensing approach to the quantification of local to global scale social-ecological impacts of anthropogenic landscape changes

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsLanduse and Landcover (LULC) is the common aspect that influences several ecological issues, environmental degradations, changes in Land Surface Temperature (LST), hydrological changes and ecosystem function at regional to global level. Research on the drivers and progressions of LULC change has been key to developing models that can project and predict future LULC extent, level and patterns under different assumptions of socioeconomic, ecological and environmental situations. Rapid and extensive urbanization and Urban Sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands, boosting mining, decrease in surface permeability and the emergence of Urban Heat Islands (UHI), and in turn, adversely affects the provision of ecosystem services. Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e., subsidence, and degradation of vegetation productivity. The greater changes in LULC, US, LST and vegetation dynamics due to increasing human population not only affects inland forest and wetland, it also directly influences coastal forest lands such as mangroves, peat swamps and riparian forest and threats to ecosystem services. Mangroves provide valuable provisioning (e.g. aquaculture, fisheries, fuel, medicine, textiles), regulation (e.g. shoreline protection, erosion control, climate regulation), supporting (nutrient cycling, nursery habitat), and cultural (recreation and tourism) ecosystem services with an important impact on human well-being. However, the mangrove forest is highly threatened due to climate changes, and human activities which ignore the ecological and economic value of these habitats, contributing to its degradation. There is an increasing number of studies about mangrove distribution, changes and re-establishment activities, denoting a growing attentiveness on the value of these coastal wetland ecosystems. Most of these studies address mangrove degradation drivers at regional or local levels. However, there has not been yet enough assessment on the drivers of mangrove degradation at global level. Thus, complexity of inland and coastal landscape degradation should be addressed using multidisciplinary methodology and conditions. Therefore, this dissertation aimed to assess the impact of LULC associated with vegetation, temperature and wetland changes. To understand the relation among three different types of landscape changes associated with anthropogenic activities: Urbanization, Geological changes and Forest degradation at local to global level, we have selected thirty-three global regions. In chapter 2, We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10km suburban buffer of Chennai city, Tamilnadu, India. The level of US was then quantified using Renyi’s entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services. In chapter 3, We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyse landuse and landcover dynamics, and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation productivity using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within four years, with 87.2% growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that the productivity of 66.5% of vegetation of the Kirchheller Heide was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide. In chapter 4, We demonstrated the advantage of fusing imageries from multiple sensors for LULC change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast-growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. South-eastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion. In chapter 5, We identified mangrove degradation drivers at regional and global levels resulted from decades of research data (from 1981 to present) of climate variations (seal-level rising, storms, precipitation, extremely high water events and temperature), and human activities (pollution, wood extraction, aquaculture, agriculture and urban expansion). This information can be useful for future research on mangroves, and to help delineating global planning strategies which consider the correct ecological and economic value of mangroves protecting them from further loss.O uso e a cobertura da Terra (UCT) são o aspeto comum que influencia várias questões ecológicas, degradações ambientais, mudanças na temperatura da superfície terrestre, mudanças hidrológicas, e de funções dos ecossistemas a nível regional e global. A investigação sobre os determinantes e progressão da mudança de UCT tem sido fundamental para o desenvolvimento de modelos que podem projetar e prever a extensão, o nível e os padrões futuros de UCT sob diferentes hipóteses de situações socioeconómicas, ecológicas e ambientais. A rápida e extensa urbanização e expansão urbana impulsionada pelo rápido crescimento populacional, levou ao encolhimento de terras agrícolas produtivas, impulsionando a mineração, a diminuição da permeabilidade da superfície e o surgimento de ilhas urbanas. Por outro lado, tem afetado negativamente a produção de serviços de ecossistemas. A mineração para extração de recursos pode levar a mudanças geológicas e ambientais devido a movimentos do solo, colisão com cavidades de mineração e deformação de aquíferos. As mudanças geológicas podem continuar numa área de mina recuperada, e os aquíferos deformados podem acarretar uma quebra de substratos e um aumento nos lençóis freáticos, causando a inundação na superfície. Consequentemente, uma área de mina recuperada pode sofrer um colapso à superfície, provocando o afundamento e a degradação da produtividade da vegetação. As mudanças na UCT, no crescimento urbano rápido, na temperatura da superfície terrestre e na dinâmica da vegetação devido ao aumento da população humana não afetam apenas a floresta interior e as zonas húmidas. Estas também influenciam diretamente as terras florestais costeiras, tais como mangais, pântanos e florestas ribeirinhas, ameaçando os serviços de ecossistemas. Os mangais proporcionam um aprovisionamento valioso (por exemplo, aquacultura, pesca, combustível, medicamentos, têxteis), a regulação (por exemplo, proteção da linha de costa, controlo da erosão, regulação do clima), os serviços de ecossistema de apoio (ciclo de nutrientes, habitats) e culturais (recreação e turismo) com um impacto importante no bem-estar humano. No entanto, a floresta de mangal é altamente ameaçada devido às mudanças climáticas e às atividades humanas que ignoram o valor ecológico e económico desses habitats, contribuindo para a sua degradação. Há um número crescente de estudos sobre distribuição, mudança e atividades de restabelecimento de mangais, denotando uma crescente atenção sobre o valor desses ecossistemas costeiros de zonas húmidas. A maioria desses estudos aborda os fatores de degradação dos mangais a nível regional ou local. No entanto, ainda não há avaliação suficiente sobre os determinantes da degradação dos mangais a nível global. Assim, a complexidade da degradação da paisagem interior e costeira deve ser abordada usando uma metodologia multidisciplinar. Portanto, esta dissertação teve, também, como objetivo avaliar o impacto do UCT associado à vegetação, temperatura e mudanças de zonas húmidas. Para compreender a relação entre a dinâmica da paisagem associada às atividades antrópicas a nível local e global, selecionámos quatro áreas de estudo, duas da Ásia, uma da Europa e outro estudo a nível global. No capítulo 2, empregamos a classificação Random Forest (RF) nas imagens Landsat de 1991, 2003 e 2016, e computamos seis métricas de paisagem para delinear a extensão das áreas urbanas numa área de influência suburbana de 10 km da cidade de Chennai, Tamil Nadu, Índia. O nível de crescimento urbano rápido foi quantificado usando a entropia de Renyi. Um modelo de UCT foi posteriormente usado para projetar a cobertura de terra para 2027. Uma expansão de 70,35% nas áreas urbanas foi observada principalmente para a periferia suburbana de Chennai entre 1991 e 2016. O valor de entropia do Renyi para 2016 foi de 0,9, exibindo uma duplicação do nível de crescimento urbano rápido quando comparado com 1991. Os valores das métricas espaciais indicam que as áreas urbanas existentes se tornaram mais densas e as terras agrícolas, florestas e terras particularmente áridas foram transformadas em assentamentos urbanos fragmentados. A previsão de cobertura da Terra para 2027 indica uma conversão de 13.670,33 ha (16,57% da paisagem total) de florestas e terras agrícolas existentes em áreas urbanas, com um aumento associado no valor de entropia para 1,7, indicando um tremendo nível de crescimento urbano rápido. O nosso estudo fornece métricas úteis para as autoridades de planeamento urbano para lidarem com as consequências socio-ecológicas do crescimento urbano rápido e para proteger os serviços de ecossistemas. No capítulo 3, estudamos a dinâmica da paisagem em Kirchheller Heide, Alemanha, que experimentou um movimento extensivo do solo devido à mineração, usando imagens Landsat entre 2013 e 2016. Uma técnica de classificação de imagem Random Forest foi aplicada para analisar dinâmicas de UCT e o crescimento das áreas de zonas húmidas foi avaliado usando uma Análise de Mistura Espectral. Também analisámos as mudanças na produtividade da vegetação usando um Índice de Vegetação por Diferença Normalizada (NDVI). Observámos um crescimento de 19,9% da área húmida em quatro anos, com um crescimento de 87,2% de dois principais corpos de água na área de mina recuperada. Valores de NDVI indicam que a produtividade de 66,5% da vegetação de Kirchheller Heide foi degradada devido a mudanças nos lençóis freáticos e inundações superficiais. Os resultados informam as autoridades de gestão ambiental e recuperação de mineração sobre os pontos de subsidência e áreas de mitigação prioritárias da degradação da superfície e da vegetação da terra em Kirchheller Heide. No capítulo 4, demonstramos a vantagem de fusionar imagens de múltiplos sensores para avaliações de mudanças de UCT, bem como para avaliar a permeabilidade, temperatura da superfície e a emergência do ilhas de calor numa cidade em rápido crescimento, Tirunelveli, Tamilnadu, Índia. As imagens IRS-LISSIII e Landsat-7 ETM + foram fusionadas para 2007 e 2017, e classificadas usando um algoritmo de Random Forest (RF). A permeabilidade de superfície e a temperatura foram então quantificadas usando-se o Índice de Vegetação Ajustada pelo Solo (SAVI) e o Índice de Temperatura da Superfície Terrestre (LST), respectivamente. Finalmente, avaliamos a relação entre SAVI e LST para Tirunelveli, bem como para cada zona de UCT, e também detetamos a emergência de pontos quentes de emergência usando uma métrica combinada de SAVI-LST. As nossas imagens fusionadas exibiram precisões de classificação mais altas, ou seja, valores globais do coeficiente kappa, do que as imagens não fusionadas. Observámos um aumento geral na cobertura de áreas urbanas (áreas de terrenos secos e construídas), e uma diminuição de áreas com vegetação (plantações e florestas) em Tirunelveli entre 2007 e 2017. Os valores de SAVI indicaram uma extensa diminuição na superfície de permeabilidade para Tirunelveli e também para quase todas as classes de UCT. Os valores de LST mostraram um aumento global da temperatura da superfície em Tirunelveli, sendo o maior aumento para as áreas urbanas entre 2007 e 2017. O LST também apresentou uma forte associação negativa com o SAVI. As áreas urbanas do Sudeste de Tirunelveli foram representadas como um potencial ponto quente, com uma chamada de atenção para a zona ribeirinha ocidental onde foi verificada a emergência de uma ilha de calor em 2017. Os nossos resultados fornecem métricas importantes sobre a permeabilidade da superfície, temperatura e monitoramento de ilhas de calor e informam as autoridades de planeamento sobre as vantagens da fusão de imagens de satélite. No capítulo 5, identificamos os fatores de degradação dos mangais a nível regional e global resultantes de décadas de dados de investigação (de 1981 até o presente) de variações climáticas (aumento do nível das águas do mar, tempestades, precipitação, eventos extremos de água e temperatura) e atividades humanas (poluição, extração de madeira, aquacultura, agricultura e expansão urbana). Estas informações podem ser úteis para investigações futuras sobre mangais e para ajudar a delinear estratégias de planeamento global que considerem o valor ecológico e económico dos mangais, protegendo-os de novas perdas

An Evaluation of Surface Urban Heat Islands in Two Contrasting Cities

This thesis presents a comparative study on surface urban heat islands effects in Baghdad and Perth. The first part evaluates expansion of built-up areas and quantifies its effects on land surface temperature patterns. The second part examines the extent to which the urban thermal environment is influenced by spatial patterns of land use and land cover (LULC) categories. The final part investigates the thermophysical behaviour of various urban LULC categories using albedo and LST parameters

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Multisource Remote Sensing based Impervious Surface Mapping

Impervious surface (IS) not only serves as a key indicator of urbanization, but also affects the micro-ecosystem. Therefore, it is essential to monitor IS distribution timely and accurately. Remote sensing is an effective approach as it can provide straightforward and consistent information over large area with low cost. This thesis integrates multi-source remote sensing data to interpretate urban patterns and provide more reliable IS mapping results. Registration of optical daytime and nighttime lights (NTL) data is developed in the first contribution. An impervious surface based optical-to-NTL image registration algorithm with iterative blooming effect reduction (IS_iBER) algorithm is proposed. This coarse-to-fine procedure investigates the correlation between optical and NTL features. The iterative registration and blooming effect reduction method obtains precise matching results and reduce the spatial extension of NTL. Considering the spatial transitional nature of urban-rural fringes (URF) areas, the second study proposed approach for URF delineation, namely optical and nighttime lights (NTL) data based multi-scale URF (msON_URF).The landscape heterogeneity and development vitality derived from optical and NTL features are analyzed at a series of scales to illustrate the urban-URF-rural pattern. Results illustrate that msON_URF is effective and practical for not only concentric, but also polycentric urban patterns. The third study proposes a nighttime light adjusted impervious surface index (NAISI) to detect IS area. Parallel to baseline subtraction approaches, NAISI takes advantage of features, rather than spectral band information to map IS. NAISI makes the most of independence between NTL-ISS and pervious surface to address the high spectral similarity between IS and bare soil in optical image. An optical and NTL based spectral mixture analysis (ON_SMA) is proposed to achieve sub-pixel IS mapping result in the fourth study. It integrates characteristics of optical and NTL imagery to adaptively select local endmembers. Results illustrate the proposed method yields effective improvement and highlight the potential of NTL data in IS mapping. In the fifth study, GA-SVM IS mapping algorithm is investigated with introduction of the achieved urban-URF-rural spatial structure. The combination of optical, NTL and SAR imagery is discussed. GA is implemented for feature selection and parameter optimization in each urban scenario

Sustainability in China: Bridging Global Knowledge with Local Action

China’s road to sustainability has attracted global attention. Since the “Reform & Opening Up” policy, China’s rapid pace of both urbanization and industrialization has made its being the second largest economy but meantime a heavy environmental price has been paid over the past few decades for addressing the economic developmental target. Today, as the biggest developing country, China needs to take more responsibilities for constructing its local ecological-civilization society as well as for addressing the global challenges such as climate change, resources scary and human beings well-fare; therefore, we need to have deeper understandings into China’s way to sustainability at very different levels, both spatially and structurally, concerns ranging from generating sustainable household livelihoods to global climate change, from developing technological applications to generate institutional changes. In this spirit, this publication, “Sustainability in China: Bridging Global Knowledge with Local Action” aims to investigate the intended and spontaneous issues concerning China’s road to sustainability in a combined top-down and bottom-up manner, linking international knowledge to local-based studies

Land Use Change from Non-urban to Urban Areas

This reprint is related to land-use change and non-urban and urban relationships at all spatiotemporal scales and also focuses on land-use planning and regulatory strategies for a sustainable future. Spatiotemporal dynamics, socioeconomic implication, water supply problems and deforestation land degradation (e.g., increase of imperviousness surfaces) produced by urban expansion and their resource requirements are of particular interest. The Guest Editors expect that this reprint will contribute to sustainable development in non-urban and urban areas

Dampak Pandemi terhadap Kehidupan Manusia (Ditinjau dari Berbagai Aspek)

Dampak Pandemi terhadap Kehidupan Manusia (Ditinjau dari Berbagai Aspek

Monitoring drought intensity in Illinois with a combined index

Many traditional drought assessments are conducted based on climate and hydrologic data. The availability and precision of data limit the spatial and temporal resolution and accuracy of derived drought indices. In this study, Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were generated from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The VCI was derived from Normalized Difference Vegetation Index (NDVI) that was calculated with near infrared and visible red band reflectance from MOD09Q1. The TCI was derived from land surface temperature (LST) product MOD11A2. The VCI and TCI were then combined with reference to the vegetation coverage information from MOD44B to generate the modified Vegetation Health Index (VHI). The modified VHI was applied to quantify the intensity of drought that took place in Illinois from 2000 to 2012. The results showed that the modified VHI identified the major droughts that occurred in Illinois from 2000 to 2012, especially the extreme one taking place in 2012. Moreover, the modified VHI led to the spatial distributions and temporal trends of drought severity, which were overall similar to those from the U.S. Drought Monitor (USDM) maps, but had more detailed spatial variability and much higher spatial resolution. The modified VHI also differentiated the drought impacts between the vegetated and non-vegetated areas, being a lack of the original VHI. Thus, the modified VHI takes advantage of spatially continuous and timely data from satellites and can be applied to conduct the monitoring and detection of drought intensity at local, regional, and national scales. The modified VHI can effectively synthesize the drought information of LST and NDVI to differentiate the effects of land use and land cover (LULC) types and provide the detailed spatial variability of drought intensity and thus enhance the understanding of relationship between drought condition and LULC types

Remote sensing of impervious surface area and its interaction with land surface temperature variability in Pretoria, South Africa

Includes summary for chapter 1-5Pretoria, City of Tshwane (COT), Gauteng Province, South Africa is one of the cities that continues to experience rapid urban sprawl as a result of population growth and various land use, leading to the change of natural vegetation lands into impervious surface area (ISA). These are associated with transportation (paved roads, streets, highways, parking lots and sidewalks) and cemented buildings and rooftops, made of completely or partly impermeable artificial materials (e.g., asphalt, concrete, and brick). These landscapes influence the micro-climate (e.g., land surface temperature, LST) of Pretoria City as evidenced by the recent heat waves characterized by high temperature. Therefore, understanding ISA changes will provide information for city planning and environmental management. Conventionally, deriving ISA information has been dependent on field surveys and manual digitizing from hard copy maps, which is laborious and time-consuming. Remote sensing provides an avenue for deriving spatially explicit and timely ISA information. Numerous methods have been developed to estimate and retrieve ISA and LST from satellite imagery. There are limited studies focusing on the extraction of ISA and its relationship with LST variability across major cities in Africa. The objectives of the study were: (i) to explore suitable spectral indices to improve the delineation of built-up impervious surface areas from very high resolution multispectral data (e.g., WorldView-2), (ii) to examine exposed rooftop impervious surface area based on different colours, and their interplay with surface temperature variability, (iii) to determine if the spatio-temporal built-up ISA distribution pattern in relation to elevation influences urban heat island (UHI) extent using an optimal analytical scale and (iv) to assess the spatio-temporal change characteristics of ISA expansion using the corresponding surface temperature (LST) at selected administrative subplace units (i.e., local region scale). The study objectives were investigated using remote sensing data such as WorldView-2 (a very high-resolution multispectral sensor), medium resolution Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) at multiple scales. The ISA mapping methods used in this study can be grouped into two major categories: (i) the classification-based approach consisting of an object-based multi-class classification with overall accuracy ~90.4% and a multitemporal pixel-based binary classification. The latter yielded an area under the receiver operating characteristic curve (AUROC) = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015. (ii) the spectral index-based approach such as a new built-up extraction index (NBEI) derived in this study which yielded a high AUROC = ~0.82 compared to Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The multitemporal built-up Index (BUI) also estimated with AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. This indicates that all these methods employed, mapped ISA with high predictive accuracy from remote sensing data. Furthermore, the single-channel algorithm (SCA) was employed to retrieve LST from the thermal infrared (TIR) band of the Landsat images. The LST overall retrieval error for the entire study generally was quite low (overall root mean square RMSE ≤ ~1.48OC), which signifies that the Landsat TIR used provided good results for further analysis. In conclusion, the study showed the potential of multispectral remote sensing data to quantify ISA and evaluate its interaction with surface temperature variability despite the complex urban landscape in Pretoria. Also, using impervious surface LST as a complementary metric in this research helped to reveal urban heat island distribution and improve understanding of the spatio-temporal developing trend of urban expansion at a local spatial scale.Rapid urbanization because of population growth has led to the conversion of natural lands into large man-made landscapes which affects the micro-climate. Rooftop reflectivity, material, colour, slope, height, aspect, elevation are factors that potentially contribute to temperature variability. Therefore, strategically designed rooftop impervious surfaces have the potential to translate into significant energy, long-term cost savings, and health benefits. In this experimental study, we used the semi-automated Environment for Visualizing Images (ENVI) Feature Extraction that uses an object-based image analysis approach to classify rooftop based on colours from WorldView-2 (WV-2) image with overall accuracy ~90.4% and kappa coefficient ~0.87 respectively. The daytime retrieved surface temperatures were derived from 15m pan-sharpened Landsat 8 TIRS with a range of ~14.6OC to ~65OC (retrieval error = 0.38OC) for the same month covering Lynwood Ridge a residential area in Pretoria. Thereafter, the relationship between the rooftops and surface temperature (LST) were examined using multivariate statistical analysis. The results of this research reveal that the interaction between the applicable rooftop explanatory features (i.e., reflectance, texture measures and topographical properties) can explain over 22.10% of the variation in daytime rooftop surface temperatures. Furthermore, analysis of spatial distribution between mean daytime surface temperature and the residential rooftop indicated that the red, brown and green roof surfaces show lower LST values due to high reflectivity, high emissivity and low heat capacity during the daytime. The study concludes that in any study related to the spatial distribution of rooftop impervious surface area surface temperature, effect of various explanatory variables must be considered. The results of this experimental study serve as a useful approach for further application in urban planning and sustainable development.Evaluating changes in built-up impervious surface area (ISA) to understand the urban heat island (UHI) extent is valuable for governments in major cities in developing countries experiencing rapid urbanization and industrialization. This work aims at assessing built-up ISA spatio-temporal and influence on land surface temperature (LST) variability in the context of urban sprawl. Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) were used to quantify ISA using built-up Index (BUI) and spatio-temporal dynamics from 1993-2013. Thereafter using a suitable analytical sampling scale that represents the estimated ISA-LST, we examined its distribution in relation to elevation using the Shuttle Radar Topography Mission (SRTM) and also create Getis-Ord Gi* statistics hotspot maps to display the UHI extent. The BUI ISA extraction results show a high predictive accuracy with area under the receiver operating characteristic curve, AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. The ISA spatio-temporal changes within ten years interval time frame results revealed a 14% total growth rate during the study year. Based on a suitable analytical scale (90x90) for the hexagon polygon grid, the majority of ISA distribution across the years was at an elevation range of between >1200m – 1600m. Also, Getis-Ord Gi* statistics hotspot maps revealed that hotspot regions expanded through time with a total growth rate of 19% and coldspot regions decreased by 3%. Our findings can represent useful information for policymakers by providing a scientific basis for sustainable urban planning and management.Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ~1.44OC, 1.40OC, ~0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data is the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 to improve built-up material mapping irrespective of material type, age and colour. The new index was derived from spectral bands such as Green, Red edge, NIR1 and NIR2 bands that profoundly explain the variation in built-up areas on WorldView-2 image (WV-2). The result showed that NBEI improves the extraction of built-up areas with high accuracy (area under the receiver operating characteristic curve, AUROC = ~0.82) compared to the existing indices such as Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78 ), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material specific, and would be necessary for urban area mapping.Environmental SciencesD. Phil. (Environmental Sciences