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

Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

Advances in remote sensing applications for urban sustainability

Abstract: It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications

DESHADOWING OF HIGH SPATIAL RESOLUTION IMAGERY APPLIED TO URBAN AREA DETECTION

Different built-up structures usually lead to large regions covered by shadows, causing partial or total loss of information present in urban environments. In order to mitigate the presence of shadows while improving the urban target discrimination in multispectral images, this paper proposes an automated methodology for both detection and recovery of shadows. First, the image bands are preprocessed in order to highlight their most relevant parts. Secondly, a shadow detection procedure is performed by using morphological filtering so that a shadow mask is obtained. Finally, the reconstruction of shadow-occluded areas is accomplished by an image inpainting strategy. The experimental evaluation of our methodology was carried out in four study areas acquired from a WorldView-2 (WV-2) satellite scene over the urban area of São Paulo city. The experiments have demonstrated a high performance of the proposed shadow detection scheme, with an average overall accuracy up to 92%. Considering the results obtained by our shadow removal strategy, the pre-selected shadows were substantially recovered, as verified by visual inspections. Comparisons involving both VrNIR-BI and VgNIR-BI spectral indices computed from original and shadow-free images also attest the substantial gain in recovering anthropic targets such as streets, roofs and buildings initially damaged by shadows

Information Framework of Pervasive Real Time Monitoring System: Case of Peat Land Forest Fires and Air Quality in South Sumatera, Indonesia

The information framework aims to holistically address the problems and issues posed by unwanted peat and land fires within the context of the natural environment and socio-economic systems. Informed decisions on planning and allocation of resources can only be made by understanding the landscape. Therefore, information on fire history and air quality impacts must be collected for future analysis. This paper proposes strategic framework based on technology approach with data fusion strategy to produce the data analysis about peat land fires and air quality management in in South Sumatera. The research framework should use the knowledge, experience and data from the previous fire seasons to review, improve and refine the strategies and monitor their effectiveness for the next fire season. Communicating effectively with communities and the public and private sectors in remote and rural landscapes is important, by using smartphones and mobile applications. Tools such as one-stop information based on web applications, to obtain information such as early warning to send and receive fire alerts, could be developed and promoted so that all stakeholders can share important information with each other

Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference operations or implicitly embedded via feature interactions. Nevertheless, such difference modeling can be noisy since it suffers from non-semantic changes and lacks explicit guidance from image content or context. In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD). Firstly, alignment leverages contextual similarity to compensate for the non-semantic difference in feature space. Next, a difference module trained with semantic-wise perturbation is adopted to learn more generalized change estimators, which reversely bootstraps feature extraction and prediction. Finally, a decoupled dual-decoder structure is designed to predict semantic changes in both content-aware and content-agnostic manners. Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD and DSIFN-CD, demonstrating our proposed operations bring significant improvement and achieve competitive results under similar comparative conditions. Code is available at https://github.com/wangsp1999/CD-Research/tree/main/openAPDComment: To appear in IJCAI 202

Land cover changes mapping in cameron highlands using high resolution satellite and unmanned aerial vehicle imageries

Agriculture and tourism are two important economic activities in the hilly area of Cameron Highlands, Pahang, Malaysia. Land opening for agriculture and construction of settlements and hotels to cater for tourism activities are rapidly and continuously ongoing in this area. However, improper planning of these activities has resulted in various environmental issues such as landslide hazards. This research is undertaken to assess the land use and land cover (LULC) changes occurred in the study area for a period of 12 years (2001-2013) using high resolution optical satellite images (IKONOS and QuickBird) and unmanned aerial vehicle (UAV) images from a fixed wing Helang. An object based classification technique was used to classify the satellite images and UAV images into seven LULC classes, namely, forest, agriculture, grass, bare land, urban, water body and areas affected by landslides. The results obtained from the classification technique were verified using land use maps of 2003, 2008 and 2015 that were obtained from the Department of Town and Rural Planning. The overall accuracy and Kappa Coefficient values (values in brackets) of the LULC classification are 86.67% (0.84), 83.89% (0.81), and 93.80% (0.93) for 2001, 2007 and 2013 respectively. Post classification change detection technique was applied in this study to identify LULC changes. Results of the classification show that the forest area decreased consistently from 2001 (196.08ha) to 2007 (180.73ha) and to 2013 (160.09ha). On the other hand, the built-up area, increased during the years from 47.77ha in 2001 to 58.25ha in 2007 and to 63.43ha in 2013. In these periods, a slight increase was noticed in the agriculture and grass lands, however, water bodies did not change much. In general, bare soil areas have only minor changes. Areas affected by landslides are detected in the UAV image and it covered an area of 3.66ha. In conclusion, this study show that the optical satellites and UAV images can be processed to produce accurate classification map, therefore useful for the local authorities to identify land cover changes, furthermore to monitor land encroachment activities and to reduce landslide hazards from occurring and to mitigate its effect