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Journal articles on the topic 'Land use-change modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Anurag, Ankita Saxena, and Biswajeet Pradhan. "LAND USE/ LAND COVER CHANGE MODELLING: ISSUES AND CHALLENGES." Journal of Rural Development 37, no. 2 (2018): 413. http://dx.doi.org/10.25175/jrd/2018/v37/i2/129708.

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2

Veldkamp, A., and P. H. Verburg. "Modelling land use change and environmental impact." Journal of Environmental Management 72, no. 1-2 (2004): 1–3. http://dx.doi.org/10.1016/j.jenvman.2004.04.004.

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3

Arowolo, Aisha Olushola, and Xiangzheng Deng. "Land use/land cover change and statistical modelling of cultivated land change drivers in Nigeria." Regional Environmental Change 18, no. 1 (2017): 247–59. http://dx.doi.org/10.1007/s10113-017-1186-5.

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4

Koomen, Eric, Piet Rietveld, and Ton de Nijs. "Modelling land-use change for spatial planning support." Annals of Regional Science 42, no. 1 (2007): 1–10. http://dx.doi.org/10.1007/s00168-007-0155-1.

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5

Petit, Sandrine, and Pia Frederiksen. "Modelling land use change impacts for sustainability assessment." Ecological Indicators 11, no. 1 (2011): 1–3. http://dx.doi.org/10.1016/j.ecolind.2010.08.001.

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6

Sun, Bo, and Derek Robinson. "Comparisons of Statistical Approaches for Modelling Land-Use Change." Land 7, no. 4 (2018): 144. http://dx.doi.org/10.3390/land7040144.

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Land-use change can have local-to-global environment impacts such as loss of biodiversity and climate change as well as social-economic impacts such as social inequality. Models that are built to anaPlyze land-use change can help us understand the causes and effects of change, which can provide support and evidence to land-use planning and land-use policies to eliminate or alleviate potential negative outcomes. A variety of modelling approaches have been developed and implemented to represent land-use change, in which statistical methods are often used in the classification of land use and land cover as well as to test hypotheses about the significance of potential drivers of land-use change. The utility of statistical models is found in the ease of their implementation and application as well as their ability to provide a general representation of land-use change, given a limited amount of time, resources, and data. Despite the use of many different statistical methods for modelling land-use change, comparison among more than two statistical methods is rare and an evaluation of the performance of a combination of different statistical methods with the same dataset is lacking. The presented research fills this gap in land-use change modelling literature using four statistical methods—Markov chain, logistic regression, generalized additive models and survival analysis—to quantify their ability to represent land-use change. The selection of these methods is based on criteria: (1) the popularity of a method, (2) the difficulty level of implementation, and (3) the ability of accounting for different scenarios. The four methods were compared across three dimensions: accuracy (overall and by land-use type), sample size, and spatial independence via conventional and spatial cross-validation. Our results show that generalized additive model outperformed the other three in terms of overall accuracy and were the best for modelling most of land-use changes with both conventional and spatial cross-validation regardless of sample size. Logistic regression and survival analysis were more accurate for specific land-use types, and Markov chain was able to represent those changes that could not be modeled by other approaches due to sample size restrictions. The overall spatial cross-validation accuracies were slightly lower than the conventional cross-validation accuracies. Our results also demonstrate that not only is the choice of model by land-use type more important than sample size, but also that a hybrid land-use model comprising the best statistical modelling approaches for each land-use change outperformed the individual statistical approaches. While Markov chain was not competitive, it was useful in providing representation using other methods or in other cases where there is no predictor data.

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7

McIntyre, Neil, Caroline Ballard, Michael Bruen, et al. "Modelling the hydrological impacts of rural land use change." Hydrology Research 45, no. 6 (2013): 737–54. http://dx.doi.org/10.2166/nh.2013.145.

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The potential role of rural land use in mitigating flood risk and protecting water supplies continues to be of great interest to regulators and planners. The ability of hydrologists to quantify the impact of rural land use change on the water cycle is however limited and we are not able to provide consistently reliable evidence to support planning and policy decisions. This shortcoming stems mainly from lack of data, but also from lack of modelling methods and tools. Numerous research projects over the last few years have been attempting to address the underlying challenges. This paper describes these challenges, significant areas of progress and modelling innovations, and proposes priorities for further research. The paper is organised into five inter-related subtopics: (1) evidence-based modelling; (2) upscaling to maximise the use of process knowledge and physics-based models; (3) representing hydrological connectivity in models; (4) uncertainty analysis; and (5) integrated catchment modelling for ecosystem service management. It is concluded that there is room for further advances in hydrological data analysis, sensitivity and uncertainty analysis methods and modelling frameworks, but progress will also depend on continuing and strengthened commitment to long-term monitoring and inter-disciplinarity in defining and delivering land use impacts research.

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8

Brown, Calum, Ian Holman, and Mark Rounsevell. "How modelling paradigms affect simulated future land use change." Earth System Dynamics 12, no. 1 (2021): 211–31. http://dx.doi.org/10.5194/esd-12-211-2021.

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Abstract. Land use models operating at regional to global scales are almost exclusively based on the single paradigm of economic optimisation. Models based on different paradigms are known to produce very different results, but these are not always equivalent or attributable to particular assumptions. In this study, we compare two pan-European integrated land use models that utilise the same climatic and socio-economic scenarios but which adopt fundamentally different modelling paradigms. One of these is a constrained optimising economic-equilibrium model, and the other is a stochastic agent-based model. We run both models for a range of scenario combinations and compare their projections of spatially aggregate and disaggregate land use changes and ecosystem service supply levels in food, forest and associated environmental systems. We find that the models produce very different results in some scenarios, with simulated food production varying by up to half of total demand and the extent of intensive agriculture varying by up to 25 % of the EU land area. The agent-based model projects more multifunctional and heterogeneous landscapes in most scenarios, providing a wider range of ecosystem services at landscape scales, as agents make individual, time-dependent decisions that reflect economic and non-economic motivations. This tendency also results in food shortages under certain scenario conditions. The optimisation model, in contrast, maintains food supply through intensification of agricultural production in the most profitable areas, sometimes at the expense of land abandonment in large parts of Europe. We relate the principal differences observed to underlying model assumptions and hypothesise that optimisation may be appropriate in scenarios that allow for coherent political and economic control of land systems, but not in scenarios in which economic and other scenario conditions prevent the changes in prices and responses required to approach economic equilibrium. In these circumstances, agent-based modelling allows explicit consideration of behavioural processes, but in doing so it provides a highly flexible account of land system development that is harder to link to underlying assumptions. We suggest that structured comparisons of parallel and transparent but paradigmatically distinct models are an important method for better understanding the potential scope and uncertainties of future land use change, particularly given the substantive differences that currently exist in the outcomes of such models.

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9

Pinto, Alessandro De, and Gerald C. Nelson. "Modelling Deforestation and Land-Use Change: Sparse Data Environments." Journal of Agricultural Economics 58, no. 3 (2007): 502–16. http://dx.doi.org/10.1111/j.1477-9552.2007.00119.x.

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10

Verburg, Peter H., Paul P. Schot, Martin J. Dijst, and A. Veldkamp. "Land use change modelling: current practice and research priorities." GeoJournal 61, no. 4 (2004): 309–24. http://dx.doi.org/10.1007/s10708-004-4946-y.

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11

Samardžić-Petrović, Mileva, Suzana Dragićević, Branislav Bajat, and Miloš Kovačević. "Exploring the Decision Tree Method for Modelling Urban Land Use Change." GEOMATICA 69, no. 3 (2015): 313–25. http://dx.doi.org/10.5623/cig2015-305.

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Land use changes play an important role in interactions between human and physical systems, and have significant impacts on the environment at local, regional and global scales. Land use change is a complex process and so developing dynamic models to represent the process is a challenging task. Decision Trees (DT) is a Machine Learning (ML) method with the capability to extract trends and generate a representative model using historical geospatial data. While DT is used in remote sensing as an image classification method, it is not sufficiently examined in land use science. The main objective of this research study is to examine the capability of DT method to model urban land use change. Various numbers of attributes for three municipalities in the City of Belgrade, Republic of Serbia were used. Land use is represented with nine land use classes for three different time instances for the years 2003, 2007 and 2011. The kappa statistics and weighted Area Under Receiver Operating Characteristic Curve (AUC) were used to compare the model outputs with real land use datasets for year 2011. The maximum obtained values for kappa and weighted AUC indicate that DT is a useful method for modelling urban land use change. Furthermore, the derived classification tree generates information about the relationship between the considered causal factors and land use changes and allows for better understanding of the change process.

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12

Dang, Anh Nguyet, and Akiyuki Kawasaki. "A Review of Methodological Integration in Land-Use Change Models." International Journal of Agricultural and Environmental Information Systems 7, no. 2 (2016): 1–25. http://dx.doi.org/10.4018/ijaeis.2016040101.

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Global change research communities are paying increasing attention to answering critical questions related to land-use change, questions which are at the root of many pressing socio-economic and environmental issues. In this regard, a huge number of models have been developed to support future land-use planning and environmental impact assessments of land-use change activities. Within land-use change models, methodological integration is recognized as an essential feature for a complete model, which can help to combine the strength of single modelling methods/techniques without inherent weaknesses. Despite the potential and remarkable growth of methodological integration in land-use change models, limited attention has been paid to this aspect of integration. In response to this, the authors' paper summarizes the current major land-use modelling methods/techniques, and explains the co-integration of these methods/techniques. In addition, they summarize the achievements, limitations and future trends in the use of the methodological integration approach in land-use change models.

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13

Palchoudhuri, Yajnaseni, Partha Sarathi Roy, and Vijay K. Srivastava. "A New Socio–economic Index for Modelling Land Use and Land Cover Change." Journal of Land and Rural Studies 3, no. 1 (2015): 1–28. http://dx.doi.org/10.1177/2321024914534051.

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14

Samardžić-Petrović, Mileva, Miloš Kovačević, Branislav Bajat, and Suzana Dragićević. "Machine Learning Techniques for Modelling Short Term Land-Use Change." ISPRS International Journal of Geo-Information 6, no. 12 (2017): 387. http://dx.doi.org/10.3390/ijgi6120387.

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15

Yalew, Seleshi, Marloes Mul, Ann van Griensven, et al. "Land-Use Change Modelling in the Upper Blue Nile Basin." Environments 3, no. 4 (2016): 21. http://dx.doi.org/10.3390/environments3030021.

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16

de Koning, G. H. J., P. H. Verburg, A. Veldkamp, and L. O. Fresco. "Multi-scale modelling of land use change dynamics in Ecuador." Agricultural Systems 61, no. 2 (1999): 77–93. http://dx.doi.org/10.1016/s0308-521x(99)00039-6.

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17

Hall, C. A. S., H. Tian, Y. Qi, G. Pontius, and J. Cornell. "Modelling Spatial and Temporal Patterns of Tropical Land Use Change." Journal of Biogeography 22, no. 4/5 (1995): 753. http://dx.doi.org/10.2307/2845977.

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18

Moon, Henry E. "Modelling land use change around non-urban interstate highway interchanges." Land Use Policy 5, no. 4 (1988): 394–407. http://dx.doi.org/10.1016/0264-8377(88)90074-9.

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19

Alcamo, Joseph, Rüdiger Schaldach, Jennifer Koch, Christina Kölking, David Lapola, and Jörg Priess. "Evaluation of an integrated land use change model including a scenario analysis of land use change for continental Africa." Environmental Modelling & Software 26, no. 8 (2011): 1017–27. http://dx.doi.org/10.1016/j.envsoft.2011.03.002.

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20

Xu, Ping, Fei Gao, Junchao He, Xinxin Ren, and Weijin Xi. "Modelling and optimization of land use/land cover change in a developing urban catchment." Water Science and Technology 75, no. 11 (2017): 2527–37. http://dx.doi.org/10.2166/wst.2017.121.

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The impacts of land use/cover change (LUCC) on hydrological processes and water resources are mainly reflected in changes in runoff and pollutant variations. Low impact development (LID) technology is utilized as an effective strategy to control urban stormwater runoff and pollution in the urban catchment. In this study, the impact of LUCC on runoff and pollutants in an urbanizing catchment of Guang-Ming New District in Shenzhen, China, were quantified using a dynamic rainfall-runoff model with the EPA Storm Water Management Model (SWMM). Based on the simulations and observations, the main objectives of this study were: (1) to evaluate the catchment runoff and pollutant variations with LUCC, (2) to select and optimize the appropriate layout of LID in a planning scenario for reducing the growth of runoff and pollutants under LUCC, (3) to assess the optimal planning schemes for land use/cover. The results showed that compared to 2013, the runoff volume, peak flow and pollution load of suspended solids (SS), and chemical oxygen demand increased by 35.1%, 33.6% and 248.5%, and 54.5% respectively in a traditional planning scenario. The assessment result of optimal planning of land use showed that annual rainfall control of land use for an optimal planning scenario with LID technology was 65%, and SS pollutant load reduction efficiency 65.6%.

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21

Verburg, P. H., G. H. J. de Koning, K. Kok, A. Veldkamp, and J. Bouma. "A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use." Ecological Modelling 116, no. 1 (1999): 45–61. http://dx.doi.org/10.1016/s0304-3800(98)00156-2.

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22

Fischer, Gunther. "IIASA's Land-Use Change Project Examines Land Issues in China." SIMULATION 74, no. 1 (2000): 49–51. http://dx.doi.org/10.1177/003754970007400108.

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23

Bakker, Martha M., and Anne M. van Doorn. "Farmer-specific relationships between land use change and landscape factors: Introducing agents in empirical land use modelling." Land Use Policy 26, no. 3 (2009): 809–17. http://dx.doi.org/10.1016/j.landusepol.2008.10.010.

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24

Kumar, Sathees, Nisha Radhakrishnan, and Samson Mathew. "Land use change modelling using a Markov model and remote sensing." Geomatics, Natural Hazards and Risk 5, no. 2 (2013): 145–56. http://dx.doi.org/10.1080/19475705.2013.795502.

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25

Handayani, L. D. W., M. A. Tejaningrum, and F. Damrah. "Modelling of land use change in Indramayu District, West Java Province." IOP Conference Series: Earth and Environmental Science 54 (January 2017): 012021. http://dx.doi.org/10.1088/1755-1315/54/1/012021.

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26

Qasim, Muhammad, Klaus Hubacek, Mette Termansen, and Luuk Fleskens. "Modelling land use change across elevation gradients in district Swat, Pakistan." Regional Environmental Change 13, no. 3 (2013): 567–81. http://dx.doi.org/10.1007/s10113-012-0395-1.

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27

Holzhauer, Sascha, Calum Brown, and Mark Rounsevell. "Modelling dynamic effects of multi-scale institutions on land use change." Regional Environmental Change 19, no. 3 (2018): 733–46. http://dx.doi.org/10.1007/s10113-018-1424-5.

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28

van Delden, Hedwig, Tomasz Stuczynski, Pavel Ciaian, et al. "Integrated assessment of agricultural policies with dynamic land use change modelling." Ecological Modelling 221, no. 18 (2010): 2153–66. http://dx.doi.org/10.1016/j.ecolmodel.2010.03.023.

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29

Hounkpè, Jean, Bernd Diekkrüger, Abel A. Afouda, and Luc Olivier Crepin Sintondji. "Land use change increases flood hazard: a multi-modelling approach to assess change in flood characteristics driven by socio-economic land use change scenarios." Natural Hazards 98, no. 3 (2019): 1021–50. http://dx.doi.org/10.1007/s11069-018-3557-8.

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30

Koranteng, Addo, and Tomasz Zawila-Niedzwiecki. "Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana." Folia Forestalia Polonica 57, no. 2 (2015): 96–111. http://dx.doi.org/10.1515/ffp-2015-0010.

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Abstract Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automata-Markov) are utilized to predict for land use land cover (LULC) change for 2020 and 2030. The processes used include: (i) a data pre-processing (geometric corrections, radiometric corrections, subset creation and image enhancement) of epoch Landsat images acquired in 1990, 2000, and Disaster Monitoring Constellation (DMC) 2010; (ii) classification of multispectral imagery (iii) Change detection mapping (iv) using Cellular Automata-Markov to generate land use change in the next 20 years. The results illustrate that in years 2020 to 2030 in the foreseeable future, there will an upsurge in built up areas, while a decline in agricultural land use is envisaged. Agricultural land use would still be the dominant land use type. Forests would be drastically reduced from close to 50% in 1990 to just fewer than 10% in 2030. Land use decision making must be very circumspect, especially in an era where Ghana has opted to take advantage of REDD+. Studies such as this provide vital pieces of information which may be used to monitor, direct and influence land use change to a more beneficial and sustainable manner

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31

Blyth, Eleanor M., Vivek K. Arora, Douglas B. Clark, et al. "Advances in Land Surface Modelling." Current Climate Change Reports 7, no. 2 (2021): 45–71. http://dx.doi.org/10.1007/s40641-021-00171-5.

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AbstractLand surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

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32

Hashem, Nadeem, and Perumal Balakrishnan. "Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar." Annals of GIS 21, no. 3 (2014): 233–47. http://dx.doi.org/10.1080/19475683.2014.992369.

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33

Mahamane, Mansour, Pedro Zorrilla-Miras, Peter Verweij, et al. "Understanding Land Use, Land Cover and Woodland-Based Ecosystem Services Change, Mabalane, Mozambique." Energy and Environment Research 7, no. 1 (2017): 1. http://dx.doi.org/10.5539/eer.v7n1p1.

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Charcoal production constitutes a key ecosystem service in Mozambique, with an estimated market value of US$400 million a year. Due to the central role the charcoal industry plays in local livelihoods, availability of suitable wood for charcoal production has decreased because of changes in land use and land cover (LULC). This paper applied a probabilistic modelling approach combining Bayesian Belief Networks (BBNs), Geographic Information Systems, Remote Sensing data, field data, and expertise from different stakeholders to understand how changes in LULC affect woodland-based ecosystem services (ES) in the Mabalane landscape, southern Mozambique. Three scenarios of policy interventions were tested: Large private; Small holder and Balanced. A BBNs was used to explore the influence of these scenarios from 2014 to 2035 on the resulting LULC. This research facilitated stakeholder engagement and improved the understanding of the interaction between LULC changes and woodland-based ES. The results highlighted the importance and spatial distribution of woodland-based ES to the local communities and that availability of suitable wood for ES will decrease under the first scenario.

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34

Dzieszko, Piotr. "LAND-COVER MODELLING USING CORINE LAND COVER DATA AND MULTI-LAYER PERCEPTRON." Quaestiones Geographicae 33, no. 1 (2014): 5–22. http://dx.doi.org/10.2478/quageo-2014-0004.

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Abstract Last decades of research have revealed the environmental impacts of Land-Use/Cover Change (LUCC) throughout the globe. Human activities’ impact is becoming more and more pronounced on the natural environment. The key activity in the LUCC projects has been to simulate the syntheses of knowledge of LUCC processes, and in particular to advance understanding of the causes of land-cover change. Still, there is a need of developing case studies regional models to understand LUCC change patterns. The aim of this work is to reveal and describe the main changes in LUCC patterns occurring in Poznań Lakeland Mesoregion according to CORINE Land Cover database. Change analysis was the basis for the identification of the main drivers in land cover changes in the study area. The dominant transitions that can be grouped and modelled separately were identified. Each submodel was combined with all submodels in the final change prediction process. Driver variables were used to model the historical change process. Transitions were modelled using multi-layer perceptron (MLP) method. Using the historical rates of change and the transition potential model scenario for year 2006 was predicted. Corine Land Cover 2006 database was used for model validation.

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35

Murray-Rust, Dave, Derek T. Robinson, Eleonore Guillem, Eleni Karali, and Mark Rounsevell. "An open framework for agent based modelling of agricultural land use change." Environmental Modelling & Software 61 (November 2014): 19–38. http://dx.doi.org/10.1016/j.envsoft.2014.06.027.

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36

Fohrer, N., D. Möller, and N. Steiner. "An interdisciplinary modelling approach to evaluate the effects of land use change." Physics and Chemistry of the Earth, Parts A/B/C 27, no. 9-10 (2002): 655–62. http://dx.doi.org/10.1016/s1474-7065(02)00050-5.

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37

Karimi, M., M. S. Mesgari, M. A. Sharifi, and P. Pilehforooshha. "Developing a methodology for modelling land use change in space and time." Journal of Spatial Science 62, no. 2 (2017): 261–80. http://dx.doi.org/10.1080/14498596.2017.1283253.

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38

Bronstert, Axel. "Rainfall-runoff modelling for assessing impacts of climate and land-use change." Hydrological Processes 18, no. 3 (2004): 567–70. http://dx.doi.org/10.1002/hyp.5500.

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39

Geary, Matthew, Alan H. Fielding, Philip J. K. McGowan, and Stuart J. Marsden. "Scenario-Led Habitat Modelling of Land Use Change Impacts on Key Species." PLOS ONE 10, no. 11 (2015): e0142477. http://dx.doi.org/10.1371/journal.pone.0142477.

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40

Batlle-Aguilar, J., A. Brovelli, A. Porporato, and D. A. Barry. "Modelling soil carbon and nitrogen cycles during land use change. A review." Agronomy for Sustainable Development 31, no. 2 (2010): 251–74. http://dx.doi.org/10.1051/agro/2010007.

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41

Guse, Björn, Matthias Pfannerstill, and Nicola Fohrer. "Dynamic Modelling of Land Use Change Impacts on Nitrate Loads in Rivers." Environmental Processes 2, no. 4 (2015): 575–92. http://dx.doi.org/10.1007/s40710-015-0099-x.

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42

Claessens, L., J. M. Schoorl, P. H. Verburg, L. Geraedts, and A. Veldkamp. "Modelling interactions and feedback mechanisms between land use change and landscape processes." Agriculture, Ecosystems & Environment 129, no. 1-3 (2009): 157–70. http://dx.doi.org/10.1016/j.agee.2008.08.008.

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43

Zang, Shuying, and Xi Huang. "An aggregated multivariate regression land-use model and its application to land-use change processes in the Daqing region (northeast China)." Ecological Modelling 193, no. 3-4 (2006): 503–16. http://dx.doi.org/10.1016/j.ecolmodel.2005.08.026.

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44

Siddiqui, Saima, Mirza Wajid Ali Safi, Naveed Ur Rehman, and Aqil Tariq. "Impact of Climate Change on Land use/Land cover of Chakwal District." International Journal of Economic and Environmental Geology 11, no. 2 (2020): 65–68. http://dx.doi.org/10.46660/ijeeg.vol11.iss2.2020.449.

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Alterations in land use and land cover, either natural or anthropogenic can disturb the balance of fragile ecosystems. Climate change plays a unique role in governing the structure and state of land features and alters the structure of ecosystem as well as its services required by earth. Human health and environment are matter of concern due to changes induced by human in its natural environment (Jat et al., 2008). Human has an urge to remain near nature, for that they shift from dense urban areas to less dense areas (Western, 2001). So is the case of new housing societies where the land mafias intimate the people about new settlements (Zaman and Baloch, 2011), which are made by cutting the forests, removing trees and disturbing the ecosystem. For proper planning and management of natural resources, it is necessary to study the land cover and its associated changes (Asselman and Middelkoop, 1995). Modelling of changes within land cover to identify environmental trends on the local, national or regional level, have been realized in the scientific community (Nath et al., 2020). GIS/RS provides continuous change dynamics (Berlanga-Robles and Ruiz-Luna, 2011) of land cover/land use, i.e. by satellite monitoring (Ruiz-Ruano et al., 2016). The understanding of land cover changes is necessary for decision making (Lu et al., 2004) in the natural resource management (Seif et al., 2012). This study was carried out to identify the impact of changes in climate upon land use and land cover of Chakwal district from 1995 to 2020. Geospatial techniques were applied to estimate the differences in land features, using different time interval satellite datasets (Table 1). Six major classes of land features including, agriculture, bare land, built-up, forest, shrubs/grass and water were selected for this study, with respect to time.

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45

Moulds, S., W. Buytaert, and A. Mijic. "An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)." Geoscientific Model Development Discussions 8, no. 4 (2015): 3359–402. http://dx.doi.org/10.5194/gmdd-8-3359-2015.

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Abstract. Land use change has important consequences for biodiversity and the sustainability of ecosystem services, as well as for global environmental change. Spatially explicit land use change models improve our understanding of the processes driving change and make predictions about the quantity and location of future and past change. Here we present the lulccR package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of different models; (3) different aspects of the modelling procedure must be performed in different environments because existing applications usually only perform the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the widely used CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a dataset included with the package. It is envisaged that lulccR will enable future model development and comparison within an open environment.

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Yang, Xin, Xin-Qi Zheng, and Rui Chen. "A land use change model: Integrating landscape pattern indexes and Markov-CA." Ecological Modelling 283 (July 2014): 1–7. http://dx.doi.org/10.1016/j.ecolmodel.2014.03.011.

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Harfuch, Leila, André Meloni Nassar, Wilson Milani Zambianco, and Angelo Costa Gurgel. "Modelling Beef and Dairy Sectors' Productivities and their Effects on Land Use Change in Brazil." Revista de Economia e Sociologia Rural 54, no. 2 (2016): 281–304. http://dx.doi.org/10.1590/1234.56781806-947900540205.

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Abstract: This paper aims to develop new methodology for the Brazilian beef and dairy sectors incorporating different levels of productivities in the Brazilian Land Use Model (BLUM), analyzing land use dynamics. Several datasets combinations were used and supply and demand equations were re-estimated. Historical database developed in this paper shows that the livestock sector increased productivity levels per hectare (in both beef and dairy sectors), being an important land releaser for other agricultural uses. Even in frontier regions, the occupation process was followed by productivity increase. When technologies were implemented in BLUM, results show that there were significant differences on land use in 2030, reducing land for pasture compared to BLUM previous version. In this sense, the study concludes that: using average productivity levels on modeling can overestimate pastureland; migration between technologies (lower to higher levels) will continue in the future; and, finally, market and agents' behavior changes might be incorporated in land use economic models, so they can reproduce empirical evidences.

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Ralha, Célia G., Carolina G. Abreu, Cássio G. C. Coelho, Alexandre Zaghetto, Bruno Macchiavello, and Ricardo B. Machado. "A multi-agent model system for land-use change simulation." Environmental Modelling & Software 42 (April 2013): 30–46. http://dx.doi.org/10.1016/j.envsoft.2012.12.003.

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Tompkins, Adrian Mark, Luca Caporaso, Riccardo Biondi, and Jean Pierre Bell. "A Generalized Deforestation and Land-Use Change Scenario Generator for Use in Climate Modelling Studies." PLOS ONE 10, no. 9 (2015): e0136154. http://dx.doi.org/10.1371/journal.pone.0136154.

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Ikegwuoha, Darlington Chineye, Harold Louw Weepener, and Megersa Olumana Dinka. "Future land use change simulations for the Lepelle River Basin using Cellular Automata Markov model with Land Change Modeller-generated transition areas." F1000Research 10 (August 12, 2021): 796. http://dx.doi.org/10.12688/f1000research.55186.1.

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Background: Land cover/land cover (LULC) change is one of the major contributors to global environmental and climate variations. The ability to predict future LULC is crucial for environmental engineers, civil engineers, urban designers, and natural resources managers for planning activities. Methods: TerrSet Geospatial Monitoring and Modelling System and ArcGIS Pro 2.8 were used to process LULC data for the region of the Lepelle River Basin (LRB) of South Africa. Driver variables such as population density, slope, elevation as well as the Euclidean distances of cities, roads, highways, railroads, parks and restricted areas, towns to the LRB in combination with LULC data were analysed using the Land Change Modeller (LCM) and Cellular-Automata Markov (CAM) model. Results: The results reveal an array of losses (-) and gains (+) for certain LULC classes in the LRB by the year 2040: natural vegetation (+8.5%), plantations (+3.5%), water bodies (-31.6%), bare ground (-8.8%), cultivated land (-29.3%), built-up areas (+10.6%) and mines (+14.4%). Conclusions: The results point to the conversion of land uses from natural to anthropogenic by 2040. These changes also highlight how the potential losses associated with resources such as water that will negatively impact society and ecosystem functioning in the LRB by exacerbating water scarcity driven by climate change. This modelling study provides a decision support system for the establishment of sustainable land resource utilization policies in the LRB.

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