Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Urbanization has become a global trend under the combined influence of population growth, socioeconomic development, and globalization. Even though recent urban planning in London has been more deliberate, the relationships between climate change and urban growth in the context of economic geography are still somewhat unclear. This study relies on rainfall prediction with the aid of the Statistical DownScaling Model (SDSM), which provides the statistical foundation for future flooding potential within the urban space of London while considering major socioeconomic policies related to land use management. These SDSM findings, along with current land use policies, were included as other factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in London for 2030 and 2050. Two scenarios with the inclusion and exclusion of flood impact factor, respectively, were applied to evaluate the impact of climate change on urban growth. Findings indicated: (1) mean monthly projected precipitation derived by SDSM is expected to increase for the year 2030 in London, which will affect the flooding potential and hence the area of open space; (2) urban and open space are expected to increase > 16 and 20km 2 (in percentage of 1.51 and 1.92 compared to 2012) in 2030 and 2050, respectively, while agriculture is expected to decrease significantly due to urbanization and climate change; (3) the inclusion of potential flood impact induced from the future precipitation variability drives the development toward more open space and less urban area.The research is supported by the Global Innovation Initiative (British Council Grant No. Gll206), funded by the British Council and the Department for Business, Innovation and Skills

Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change

Using scenario modelling for adapting to urbanization and water scarcity: towards a sustainable city in semi-arid areas

Sustainable development on a global scale has been hindered by urbanization and water scarcity, but the greatest threat is from decision-makers ignoring these challenges, particularly in developing countries. In addition, urbanization is spreading at an alarming rate across the globe, affecting the environment and society in profound ways. This study reviews previous studies that examined future scenarios of urban areas under the challenges of rapid population growth, urban sprawl and water scarcity, in order to improve supported decision-making (SDM). Scholars expected that the rapid development of the urbanization scenario would cause resource sustainability to continually be threatened as a result of excessive use of natural resources. In contrast, a sustainable development scenario is an ambitious plan that relies on optimal land use, which views land as a limited and non-renewable resource. In consequence, estimating these threats together could be crucial for planning sustainable strategies for the long term. In light of this review, the SDM tool could be improved by combining the cellular automata model, water evolution and planning model coupled with geographic information systems, remote sensing and criteria analytic hierarchical process modelling. Urban planners could optimize, simulate and visualize the dynamic processes of land-use change and urban water, using them to overcome critical conditions

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050

Patterns of historical and future urban expansion in Nepal

Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal's Tarai region-a populous area experiencing land-use change due to urbanization and other factors-we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989-2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal's fertile Tarai region is of serious concern for food-insecure countries like Nepal

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.5) scenarios, and predicted future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. The study area had significant coastal LULC change between 2000 and 2020. The tidal flats, whose change was driven mainly by sea level, registered a total net gain of 57.93 km2 . We also observed the significant loss of developed land whose change was influenced by tidal flat with a total loss of − 75.58 km2. The tidal flat will experience a stunning net gain of 80.55 km2 between 2020 and 2060, making developed land the most negatively impacted land in the study area. The study led to the conclusion that the uncontrolled conversion of saltmarshes, mixed-forest, and mangroves into agriculture and infrastructures were the main factors affecting the coastal systems, including the faster coastal erosion and accretion observed during a 20-year period. The study also concluded that a low coastal elevation of − 1 m and a slope of less than 2◦have contributed to coastal change. Unprecedented changes will unavoidably pose a danger to coastal ecological services, socioeconomic growth, and food security. Timely efforts should be made by establishing sustainable mitigation methods to avoid the future impact.info:eu-repo/semantics/publishedVersio

Geospatial approach using socio-economic and projected climate change information formodelling urban growth

Urban growth and climate change are two interwoven phenomena that are becoming global environmental issues. Using Niger Delta of Nigeria as a case study, this research investigated the historical and future patterns of urban growth using geospatialbased modelling approach. Specific objectives were to: (i) examine the climate change pattern and predict its impact on urban growth modelling; (ii) investigate the historical pattern of urban growth; (iii) embrace some selected parameters from United Nations Sustainable Development Goals (UN SDGs) and examine their impacts on future urban growth prediction; (iv) verify whether planning has controlled urban land use sprawl in the study area; and (v) propose standard operating procedure for urban sprawl in the area. A MAGICC model, developed by the Inter-Governmental Panel on Climate Change (IPCC), was used to predict future precipitation under RCP 4.5 and RCP 8.5 emission scenarios, which was utilized to evaluate the impact of climate change on the study area from 2016 to 2100. Observed precipitation records from 1972 to 2015 were analysed, and 2012 was selected as a water year, based on depth and frequency of rainfall. A relationship model derived using logistic regression from the observed precipitation and river width from Landsat imageries of 2012 was used to project the monthly river width variations over the projected climate change, considering the two emission scenarios. The areas that are prone to flooding were determined based on the projected precipitation anomalies and a suitability map was developed to accommodate the impact of climate change in the projection of future urban growth. Urban landscape changes between 1985 and 2015 were also analysed, which revealed a rapid urban growth in the region. A Cellular Automata/Markov Chain (CA-Markov) model was used to project the year 2030 land cover of the region considering two scenarios; normal projection without any constraint, and using some designed constraints (forest reserves, population and economy) based on some selected UN SDGs criteria and climate change. On validation, overall simulation accuracies of 89.25% and 91.22% were achieved based on scenarios one and two, respectively. The projection using the first scenario resulted to net loss and gains of - 7.37%, 11.84% and 50.88%, while that of second scenario produced net loss and gains of -4.72%, 7.43% and 48.37% in forest, farmland and built-up area between 2015 and 2030, respectively. The difference between the two scenarios showed that the UN SDGs have great influence on the urban growth prediction and strict adherence to the selected UN SDGs criteria can reduce tropical deforestation, and at the same time serve as resilience to climate change in the region

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

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

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results were applied. These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future