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

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved a kappa value of 86.6% with a figure of merit (FoM) of 12.18% when compared to the empirical land use/cover map in 2015. The results showed that the occupation of built-up areas increased from 29.13% in 2015 to 38.68% (EPS), 36.18% (CPS), and 47.94% (SS) in 2035. In this context, current urbanization would bring unprecedented stress on agricultural resources and forest ecosystems, which could be attenuated by implementing protection policies along with decelerating urban expansion. The findings provide valuable information for urban planners to achieve sustainable development goals

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

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study

Projecting land use changes using parcel-level data : model development and application to Hunterdon County, New Jersey

This dissertation is to develop a parcel-based spatial land use change prediction model by coupling various machine learning and interpretation algorithms such as cellular automata (CA) and decision tree (DT). CA is a collection of cells that evolves through a number of discrete time steps according to a set of transition rules based on the state of each cell and the characteristics of its neighboring cells. DT is a data mining and machine learning tool that extracts the patterns of decision process from observed cell behaviors and their affecting factors. In this dissertation, CA is used to predict the future land use status of cadastral parcels based on a set of transition rules derived from a set of identified land use change driving factors using DT. Although CA and DT have been applied separately in various land use change models in the literature, no studies attempted to integrate them. This DT-based CA model developed in this dissertation represents the first kind of such integration in land use change modeling. The coupled model would be able to handle a large set of driving factors and also avoid subjective bias when deriving the transition rules. The coupled model uses the cadastral parcel as a unit of analysis, which has practical policy implications because the responses of land use changes to various policy usually take place at the parcel level. Since parcel varies by their sizes and shapes, its use as a unit of analysis does make it difficult to apply CA, which initially designed to handle regular grid cells. This dissertation improves the treatment of the irregular cell in CA-based land use change models in literature by defining a cell\u27s neighborhood as a fixed distance buffer along the parcel boundary. The DT-based CA model was developed and validated in Hunterdon County, New Jersey. The data on historical land uses and various land use change driving factors for Hunterdon County were collected and processed using a Geographic Information System (GIS). Specifically, the county land uses in 1986, I995 and 2002 were overlaid with a parcel map to create parcel-based land use maps. The single land use in each parcel is based on a classification scheme developed thorough literature review and empirical testing in the study area. The possible land use status considered for each parcel is agriculture, barren land, forest, urban, water or wetlands following the land use/land cover classification by the New Jersey Department of Environment Protection. The identified driving factors for the future status of the parcel includes the present land use type, the number of soil restrictions to urban development, and the size of the parcel, the amount of wetlands within the parcel, the distribution of land uses in the neighborhood of the parcel, the distances to the nearest streams, urban centers and major roads. A set of transition rules illustrating the land use change processes during the period 1986-1995 were developed using a TD software J48 Classifier. The derived transition rules were applied to the 1995 land use data in a CA model Agent Analyst/RePast (Recursive Porous Agent Simulation Toolkit) to predict the spatial land use pattern in 2004, which were then validated by the actual land use map in 2002. The DT-based CA model had an overall accuracy of 84.46 percent in terms of the number of parcels and of 80.92 percent in terms of the total acreage in predicting land use changes. The model shows much higher capacity in predicting the quantitative changes than the locational changes in land use. The validated model was applied to simulate the 2011 land use patterns in Hunterdon County based on its actual land uses in 2002 under both business as usual and policy scenarios. The simulation results shows that successfully implementing current land use policies such as down zoning, open space and farmland preservation would prevent the total of 7,053 acres (741 acres of wetlands, 3,034 acres of agricultural lands, 250 acres of barren land, and 3,028 acres of forest) from future urban development in Hunterdon County during the period 2002-2011. The neighborhood of a parcel was defined by a 475-foot buffer along the parcel boundary in the study. The results of sensitivity analyses using two additional neighborhoods (237- and 712-foot buffers) indicate the insignificant impacts of the neighborhood size on the model outputs in this application

Direct and indirect loss of natural habitat due to built-up area expansion:A model-based analysis for the city of Wuhan, China

Urbanization has been responsible for the loss of cropland worldwide, especially in China. To guarantee national food security, China has implemented a series of policies to protect cropland. One of these policies requires that one-hectare cropland should be reclaimed when urban expansion occupies one-hectare cropland. Since most cropland reclamation leads to a conversion of natural habitat, such as wetland and grassland, urban expansion may lead to (indirect) natural habitat loss in addition to direct loss from conversion of into urban area. While several studies assessed the direct habitat loss resulted from built-up area expansion, few studies investigated the indirect losses caused by cropland displacement. In this paper, a model-based approach is applied to explore both direct and indirect impacts of built-up area expansion on natural habitat loss for the city of Wuhan, China, between 2010 and 2020 using different scenarios. Our scenarios differ in the implementation of strict cropland protection policies and ecosystem conservation strategies. Results show that the indirect loss of natural habitat due to cropland displacement under strict cropland protection policies far outweighs the direct loss due to built-up area expansion alone. Moreover, we found that ecosystem conservation strategies mainly influence the type of natural habitat that is affected, while the total amount of natural habitat loss remains relatively constant

Ecological risk assessment based on land cover change: A case of Zanzibar-Tanzania, 2003-2027

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesLand use under improper land management is a major challenge in sub-Saharan Africa, and this has drastically affected ecological security. Addressing environmental impacts related to this major challenge requires faster and more efficient planning strategies that are based on measured information on land-use patterns. This study was employed to access the ecological risk index of Zanzibar using land cover change. We first employed Random Forest classifier to classify three Landsat images of Zanzibar for the year 2003, 2009 and 2018. And then the land change modeler was employed to simulate the land cover for Zanzibar City up to 2027 from land-use maps of 2009 and 2018 under business-as-usual and other two alternative scenarios (conservation and extreme scenario). Next, the ecological risk index of Zanzibar for each land cover was assessed based on the theories of landscape ecology and ecological risk model. The results show that the built-up areas and farmland of Zanzibar island have been increased constantly, while the natural grassland and forest cover were shrinking. The forest, agricultural and grassland have been highly fragmented into several small patches relative to the decrease in their patch areas. On the other hand, the ecological risk index of Zanzibar island has appeared to increase at a constant rate and if the current trend continues this index will increase by up to 8.9% in 2027. In comparing the three future scenarios the results show that the ERI for the conservation scenario will increase by only 4.6% which is at least 1.6% less compared to 6.2% of the business as usual, while the extreme scenario will provide a high increase of ERI of up to 8.9%. This study will help authorities to understand ecological processes and land use dynamics of various land cover classes, along with preventing unmanaged growth and haphazard development of informal housing and infrastructure

Spatial modeling of plant distributions: coupling remote sensing with GIS-based models

Spatial species distributions and the relationship between species and environmental factors have been studied for several years. Climate change and habitat fragmentation can be considered as the factors effective in biodiversity changes. Therefore prediction of species range shifts under climate change and other physical processes is a crucial challenge for the management of natural resources. The major objective of this thesis was to integrate MigClim, SDM and CA-Markov chain models so as to assess the effects of future landscape fragmentation and climate change scenarios on the geographic distributions of three open-land plant species in 21st century. For all target plants, simulations were performed for four dispersal events (full dispersal, no dispersal, regular dispersal (short-distance dispersal), and regular dispersal along with long-distance dispersal), two landscape (static and dynamic change) and two climate change (RCP4.5 and RCP8.5) scenarios (chapter 5). In this investigation, it was shown that the predicted distribution areas for all the three species under RCP8.5 scenario will largely increase in the coming decades. Also, a significant difference appears to be between the simulations of realistic dispersal limitations and those considering full or no dispersal for projected future distributions during the 21st century. Besides, the results obtained by the limited projections of future plant distributions via realistic dispersal restrictions showed to be generally closer to no-dispersal than to full-dispersal scenario when compared with real dispersal scenarios. Overall, the results of this study indicate that dispersal limitations can have an important impact on the outcome of future projections of species distributions under climate change scenarios. Also our findings clearly showed that change in landscape fragmentation is more effective than the climate change impacts on species distributions in our study area

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