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

Quantifying the impact of the Land Reform Programme on land use and land cover changes in Chipinge District, Zimbabwe, based on Landsat observations

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies. Johannesburg, 2016.The purpose of this research was to quantify the impact of the land reform programme on land use and land cover changes (LULCC) in Chipinge district situated in Manicaland Province of Zimbabwe. The Fast Track Land Reform Programme (FTLRP) of 2000 was selected as the major cause of LULCC in the district. This research addresses the problem of knowing and understanding if there was LULCC in the district before and after the enactment of the FTLRP in the year 2000. The research objectives of this study were as follows: to investigate the impact of the FTLRP of 2000 on land use and land cover in Chipinge district; to test the use of Landsat earth observation data in quantifying the changes on land use and cover from 1992 to 2014 in Chipinge district and to predict LULCCs in the year 2028 in Chipinge district. The methodology for detecting the impact of LULCC was based on the comparison of Landsat MSS, TM, ETM+ and OLI/ TIRS scene p168r74 images covering Chipinge district taken on diverse dates in five different years. In order to prepare the Landsat images for change detection analysis, a number of image processing operations were applied which include radiometric calibration and atmospheric correction. The images were classified using the Support Vector Machine (SVM) and evaluation was done through accuracy assessment using the confusion matrix. The prediction of LULCC in the year 2028 was modeled by the Markov Chain Analysis (MCA) and the Cellular Automata Markov Chain Analysis (CA MCA) so as to show land distribution in the future. The results show that agricultural farmland, estates and area covered by water bodies declined whilst there was an increase in built-up areas, forest land and bare land since the enactment of the FTLRP. The prediction results show that in the year 2028, there will be a decrease in the amount of land covered by water bodies, forest and agricultural farmland. There will be an increase in the amount of built-up in the year 2028 as a result of population growth. It is the recommended in this study that better remedies be put in place to increase forest cover and also the use of high resolution images in further studies. There should be exploration of the relationships between LULCC, socio-economic and demographic variables would develop more understanding of LULCC. The study also recommends the preparation of a proper land use plan to deal with a reduction in the growth of settlement which is vital in the planning and management of social and economic development programs.LG201

Urban land-use dynamics in the Niger Delta : the case of Greater Port Harcourt watershed

Cities in developing countries are urbanising at a rapid rate, resulting in substantial pressures on environmental systems. Among the main factors that lead to flooding, controlling land-use change offers the greatest scope for the management of risk. However, traditional analysis of a “from–to” change matrix is not adequate to provide information of all the land-use changes that occur in a watershed. In this study, an in-depth analysis of land-use change enabled us to quantify the bulk of the changes accumulating from swap changes in a tropical watershed. This study assessed the historical and future land-use/land-cover (LULC) dynamics in the River State region of the Niger Delta. Land-use classification and change detection analysis was conducted using multi-source (Landsat TM, ETM, polygon map, and hard copy) data of the study area for 1986, 1995, and 2003, and projected conditions in 2060. The key findings indicate that historical urbanisation was rapid; urban expansion could increase by 80% in 2060 due to planned urban development; and 95% of the conversions to urban land occurred chiefly at the expense of agricultural land. Urban land was dominated by net changes rather than swap changes, which in the future could amplify flood risk and have other severe implications for the watershed

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.

Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017. To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared

Water Resources in Lake Tana Basin: Analysis of hydrological time series data and impact of climate change with emphasis on groundwater, Upper Blue Nile Basin, Ethiopia

Ethiopia is a source region of the Nile River and famous for its water resources potential. The available annual average water per person per year is estimated to be 1575 m3. The Lake Tana accounts for 50% of the national fresh water. It has a total catchment area of about 15,321 km2 and hosts more than three million people. The climate is characterized by a high seasonality of rainfall with a rainy season between June and September. However, the scientific understanding of the hydrologic response to intensive agriculture, the interconnection of groundwater and surface water, and future perspectives of the water availability under global climate change is limited. Therefore, the main aim of this study is to improve our understanding of past, present, and future hydrologic conditions in the Lake Tana basin. To this end, long-term time series analysis and hydrological modeling using SWAT (Soil and Water Assessment Tools) and a coupled surface water and groundwater model (SWAT-MODFLOW) were applied. Time series analysis and modelling results revealed that the hydrology of the basin was changed significantly during the last half century and is expected to change during the 21st century mainly due to land use change and climate change. Although projections of annual rainfall did not show a significant change, surface runoff increased, whereas base flow decreased during the past and for mid- and long-term periods in the 21st century. Results from the coupled model revealed a high connectivity of groundwater and surface water systems. Agricultural crops influence the hydrologic components differently. Groundwater recharge was relatively high on agricultural land covered by cereal crops, whereas surface runoff was significantly enhanced on cultivated land covered by leguminous crops like peas. Overall, the results of this dissertation reveal that hydrology of the Lake Tana Basin has changed considerably during the last half century and more changes are to be expected in the future. Consequently, the results of this dissertation can contribute to develop future water management plans in the region and beyond

Impact assessment of global change on wetland-catchment interactions in a tropical East African catchment

Water is the key to sustainable development, especially in sub-Saharan Africa (SSA), where a large part of the population lives on subsistence farming. Reliable knowledge of available water resources therefore is an indispensable component of sustainable water resource management. An important tool for the management of water resources is hydrological modeling, which, depending on the model type, is capable to quantify water quantities spatially explicit and to predict water availability under changing conditions. The bottleneck for these simulations often is the lack of data availability, especially in sub-Saharan Africa. In recent decades, however, satellite data sources have been developed for hydrological modelling on different scales. The aim of this work is to develop a modeling framework for a meso-scale catchment area in Tanzania based on locally collected data and freely available satellite data sets. This model system should serve to better understand the hydrological processes in the catchment area with an emphasis on wetland-catchment interactions. At the same time the model should be able to estimate the availability of water resources under changing environmental conditions for the catchment area. The Soil and Water Assessment Tool (SWAT) was applied to the Kilombero Catchment area in Tanzania, which, like many other East African catchments, is characterized by a general data shortage. Due to the lack of current discharge data, the model was calibrated for the period 1958-1965 (R² = 0.86, NSE = 0.85, KGE = 0.93) and validated from 1966-1970 (R² = 0.80, NSE = 0.80, KGE = 0.89) with the sequential uncertainty fitting algorithm (SUFI-2) at a daily resolution. The model results show the water-related dependency of the floodplain in the center of the catchment area on the base flow of the surrounding highland forests and savannas, especially in the dry season. In addition, this study investigates the influence of climate change on water resources in the catchment area. To account for these changes, regional climate models of the Coordinated Regional Downscaling Experiment (CORDEX) Africa project were applied to investigate changes in climate patterns up to 2060 according to the RCP4.5 (representative concentration paths) and RCP8.5 scenarios. The SWAT model was used to investigate the impacts of climate change on water resources under different scenarios and model combinations. The climate models show a clear temperature increase, especially in the hot dry season, which further reinforces the pronounced differences between the dry and rainy seasons. This, together with changing precipitation patterns, leads to an intensification of hydrological extremes in the catchment area, e.g., more pronounced flooding in the rainy season and decreasing low flows in the dry season. Overall, the annual average values of water yield and surface runoff within the simulations increase by up to 61.6% and 67.8%, respectively, by 2060 compared to the historical simulations. The changes of the hydrological processes show a heterogeneous spatial-temporal pattern within the catchment area. In many parts of sub-Saharan Africa and also in the study area, natural systems are being converted into agricultural land in order to feed the growing population. Therefore, this study additionally examines historical land use and land cover patterns as well as potential future land use and land cover patterns and their impacts on water resources in the catchment area. The Land Change Modeler (LCM) is used for the analysis and projection of land use patterns until 2030 and the SWAT model is then utilized to simulate the water balance under changing conditions. The results show that the low flow in the land use/land cover scenarios decreases by 6-8%, while the high flow in the combined land use/land cover scenarios increases by up to 84% considering also the climate scenarios. The impacts of climate change are therefore more pronounced than the impacts of changing land use/land cover patterns, but also contain higher uncertainties and show different patterns in the climate model combinations applied in this study. Within this study, a methodological approach was developed to quantify the impacts of land use/land cover patterns and climate change for data-scarce regions. The results and the methodology from this study thus contribute to the sustainable management of the investigated catchment area, as they show the effects of environmental changes on hydrological extremes (low flows and high flows) and additionally identify particularly sensitive subcatchments that are of essential importance for the preservation of the social-ecological system

ELULC-10, a 10 m European land use and land cover map using Sentinel and landsat data in Google Earth Engine

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.Peer ReviewedPostprint (published version

Potential impacts of climate change and land-use change on hydrological drought in the Western Cape (South Africa)

The Western Cape (South Africa) recently witnessed the most severe drought on record. The meteorological drought, which was characterised by below-normal rainfall for three consecutive years (2015 – 2017), cascaded to agricultural and then hydrological drought, resulting in devastating socio-economic consequences. While some studies indicate that climate change may increase the severity and frequency of droughts in the Western Cape in the future, there is a lack of information on how to mitigate the effects of future climate change on hydrological drought. This dissertation therefore investigated the extent to which land-use changes could be applied to reduce climate change impacts on future hydrological drought in this region. For the study, the revised Soil Water Assessment Tool (SWAT+) was calibrated and evaluated over four river basins in the Western Cape, and the climate simulation dataset from the COordinated Regional Downscaling EXperiment (CORDEX) was bias-corrected. Using the bias-corrected climate data as a forcing, the SWAT+ was used to project the impacts of future climate change on water yield and hydrological drought in the four basins and to quantify the sensitivity of the projection to four feasible land-use change scenarios in these basins. The relevant land-use scenarios are the expansion of mixed forests (FrLand), the restoration of grassland (GrLand), the restoration of shrubland (SrLand), and the expansion of cropland (CrLand). The model evaluation shows good agreement between the simulated and observed monthly streamflow at hydrological stations, and the bias correction of the CORDEX datasets improved the quality of the SWAT+ hydrological simulations in the four basins. The climate change projection depicts an increase in temperature and potential evapotranspiration but a decrease in precipitation and all the hydrological variables. Drying is projected across the Western Cape, and the magnitude of such drying increases with higher global warming levels (GWLs). The land-use changes alter the impacts of climate change by influencing the hydrological balance. While FrLand mitigates the impacts of climate change on the frequency of hydrological drought by increasing streamflow, soil water and percolation, CrLand mitigates the impacts by increasing surface runoff. However, the magnitudes of these land-use change impacts are very small compared to the climate change impacts. Hence, the results suggest that land-use changes may not be an efficient strategy for mitigating the climate change impacts on hydrological drought over the region. The findings obtained from this 2 research provide relevant information towards mitigating the severity of future droughts and improving water security in Western Cape River Basins

Effects of land use and land cover changes on water quality of the upper Umngeni River, KwaZulu-Natal Province, South Africa.

Doctor of Philosophy in Hydrology University of KwaZulu-Natal. Pietermaritzburg, 2017.Changes of land use and land cover are important drivers of the quality of water reaching a waterbody. These changes affect the catchment and modify the chemical composition of the atmosphere, and thus altering the cycle of nutrients and the flux of energy. With current developments in Geographic Information Systems (GIS) techniques, hydrological modelling and statistical analyses, one or a combination of many methods can be used to assess the relationships between land use and land cover (LULC) classes and water quality variables. However, all these approaches are reliant on the collection of field measurements, LULC data and water sampling. Typically funding for such long-term information is not generally available in Africa. A three-year study involving analysis of historical data, field work and desktop investigations was conducted in the upper reaches of the uMngeni Catchment (1653 km2), South Africa, to assess the spatial and temporal variation of land use and land cover and its influence on the flux of water, nutrients (nitrogen and phosphorus) and Escherichia coli (E. coli) in the catchment. This involved the analysis of historical land use and land cover information (1994, 2000, 2008 and 2011), analysis and processing of historical datasets of E. coli, electrical conductivity, ammonium, nitrate, soluble reactive phosphorus (SRP), total phosphorus (TP), total suspended solids (TSS), temperature and turbidity. A water quality index based on a long-term data base of water quality emanating from existing monitoring programmes was assessed. In addition, stations were established for river sampling (14) and collection of bulk atmospheric deposition (3) of ammonium, nitrates, SRP and TP, in the Midmar Dam catchment (927 km2). These were consolidated with the application and testing of the Hydrological Predictions for the Environment (HYPE) model in the catchment, in simulating streamflow, transport and dynamic of inorganic nitrogen and total phosphorus, resulting from LULC changes. Results showed that the natural vegetation declined by 17% between 1994 and 2011, coinciding with an increase in cultivated, urban/built-up and degraded lands by 6%, 4.5% and 3%, respectively. This resulted in high variability in the concentrations of water quality parameters, but Midmar and Albert Falls Dams retain over 20% of nutrients and sediment and approximately 85% of E. coli. It was concluded that these dramatic changes in LULC directly affect the chemical composition of water in the catchment. However, these linkages are complex, site-specific and vary from one sub-catchment to another and decision-making regarding water resources management in the catchment must recognise this. The level of E. coli in water is a major issue for human contact during recreational activities in the entire study area. Higher concentrations of E. coli, ammonium, nitrates, SRP and TP were attributed to the poor or lack of sanitation facilities in the informal settlements, dysfunctional sewage systems, effluent discharged from wastewater works, expansion of agricultural activities, as well as a runoff from livestock farming and urban areas. Moreover, water quality in the catchment ranged between “marginal” and “fair”, predominantly “marginal” in 90% of the sites and completely poorer in the Mthinzima Stream, an important tributary of Midmar Dam. A declining monitoring frequency and resultant poorly reporting of water quality in the catchment, led to a recommendation for the establishment of automatic or event-based samplers, which should provide the optimum information on nutrient loadings to the waterbodies. Bulk atmospheric deposition and river inflows into the Midmar Dam studies were conducted under severe drought conditions. Higher concentrations of NH4, NO3 and TP in precipitation samples than those of rivers were found because of the high retention of nutrients in the landscape. In terms of loading, the bulk atmospheric deposition provided significant quantities of NH4, while TP, SRP and nitrates were predominantly from river flows. Specific loads of DIN (nitrate + ammonium) and TP in the catchment were slightly higher that the previously reported values for the catchment and are comparable to the other human-disturbed catchments of the world. HYPE model has successfully simulated streamflow (1961-1999), DIN and TP (1989-1999). For simulations of streamflow NSE values = 0.7 in four out of the nine sites (at a monthly time-step) and NSE > 0 in eight out of nine sites (at a daily time-step). Major floods and drought events were represented very well in the model, with a general over-simulation of baseflow events. The water balance was captured well at calibration sites with over-simulation of streamflow on the Lions River (PBIAS=28%) and their under-simulation in outlet sub-catchments (PBIAS < 0). This is ascribed to the simplification of some processes in the model i.e. evapotranspiration, water release, water abstraction and inter-basin transfer. There has been good fit between the simulations and observations of TP and streamflow with a lagging of the observed values. However, mismatches were noted for DIN. Evaluation of seasonal distribution of DIN suggested that denitrification, crop uptake of DIN and dilution were intensive during the period of rainfall and high temperatures in the catchment, while TP was highly mobilised during rainfall events, due to its strong binding with the soil. The information from this study highlighted the current state of LULC changes, the sub-catchments with the potentiality to export high levels of DIN and TP, the complexity of the relationship between LULC-water quality, the gaps in existing data collection programmes, the catchment responses to LULC changes and the usefulness of hydrological models which may apply beyond the upper reaches of the uMngeni Catchment