land change modeling


  • [citation needed] Cyberinfrastructure developments may also increase the ability of land change modeling to meet computational demands of various modeling approaches given
    increasing data volumes and certain expected model interactions.

  • One improvement for land change modeling can be made through better data and integration with available data and models.

  • Here, structural economic or agent-based approaches are useful, but specific patterns and trends in land change as with many ecological systems may not be as useful.

  • [5] Approaches Machine learning and statistical models[edit] A machine-learning approach uses land-cover data from the past to try to assess how land will change in the future,
    and works best with large datasets.

  • Combining various structural-based data-collecting methods can improve the availability of microdata and the diversity of people that see the findings and outcomes of land
    change modeling projects.

  • [3] Land change models are significant in their ability to help guide the land systems to positive societal and environmental outcomes at a time when attention to changes
    across land systems is increasing.

  • [2] Integrating positive and normative approaches[edit] Land Change Modeling should also better integrate positive and normative approaches to explanation and prediction based
    on evidence-based accounts of land systems.

  • [3] For the science community, land change models are important in their ability to test theories and concepts of land change and its connections to human-environment relationships,
    as well as explore how these dynamics will change future land systems without real-world observation.

  • The multitudes of land change models that have been developed are significant in their ability to address land system change and useful in various science and practitioner

  • When one needs to grasp the early stages of problem identification, and thus needs to understand the scientific patterns and trend of land change, machine learning and cellular
    approaches are useful.

  • To avoid model uncertainty and interpret model outputs more accurately, a model diagnosis is used to understand more about the connections between land change models and the
    actual land system of the spatial extent.

  • Improvement in sensitivity analysis are needed to gain a better understand of the variation in model output in response to model elements like input data, model parameters,
    initial conditions, boundary conditions, and model structure.

  • Process like telecoupling, indirect land use change, and adaption to climate change at multiple scales requires better representation by cross-scale dynamics.

  • [5] Furthermore, land change model design are a product of both decision-making and physical processes.

  • Fine data can give a better conceptual understanding of underlying constructs of the model and capture additional dimensions of land use.

  • Land change modeling is a key component of land change science, which uses LCMs to assess long-term outcomes for land cover and climate.

  • [3][4] A plethora of science and practitioner communities have been able to advance the amount and quality of data in land change modeling in the past few decades.

  • For example, model and software infrastructure development can help avoid duplication of initiatives by land change modeling community members, co-learn about land change
    modeling, and integrate models to evaluate impacts of land change.

  • Universities, non-profit agencies, and volunteers are needed to collect information on events like this to make positive outcomes and improvements in land change modeling
    and land change modeling applications.

  • [13] Additional improvements that have been discussed within the field include characterizing the difference between allocation errors and quantity errors, which can be done
    through three map comparisons, as well as including both observed and predicted change in the analysis of land change models.

  • [5][6] Another uncertainty within land change models are data and parameter uncertainties within physical principles (i.e., surface typology), which leads to uncertainties
    in being able to understand and predict physical processes.

  • [7] Cellular models[edit] A cellular land change model uses maps of suitability for various types of land use, and compares areas that are immediately adjacent to one another
    to project changes into the future.

  • [14] Implementation opportunities Scientists use LCMs to build and test theories in land change modeling for a variety of human and environmental dynamics.

  • Improvement in pattern validation can help land change modelers make comparisons between model outputs parameterized for some historic case, like maps, and observations for
    that case.

  • [17] A number of modern challenges in land change modeling can potentially be addressed through contemporary advances in cyberinfrastructure such as crowd-source, “mining”
    for distributed data, and improving high-performance computing.

  • For instance, one uncertainty within land change models is a result from temporal non-stationarity that exists in land change processes, so the further into the future the
    model is applied, the more uncertain it is.

  • [10] Hybrid approaches[edit] Many models do not limit themselves to one of the approaches above – they may combine several in order to develop a fully comprehensive and accurate

  • Land change models are valuable in development policy, helping guide more appropriate decisions for resource management and the natural environment at a variety of scales
    ranging from a small piece of land to the entire spatial extent.

  • It is also important to have better information on land change actors and their beliefs, preferences, and behaviors to improve the predictive ability of models and evaluate
    the consequences of alternative policies.

  • The purpose for model evaluation is not to develop a singular metric or method to maximize a “correct” outcome, but to develop tools to evaluate and learn from model outputs
    to produce better models for their specific applications [11] Methods[edit] There are two types of validation in land change modeling: process validation and pattern validation.

  • [18] Model evaluation[edit] An additional way to improve land change modeling is through improvement of model evaluation approaches.

  • Even the best single summary metrics often leave out important information, and reporting metrics like FoM along with the maps and values that are used to generate them can
    communicate necessary information that would otherwise be obfuscated.

  • For example, improving the development of processors, data storage, network bandwidth, and coupling land change and environmental process models at high resolution.

  • [15] Land change modeling has a variety of implementation opportunities in many science and practice disciplines, such as in decision-making, policy, and in real-world application
    in public and private domains.

  • Process validation is most commonly used in agent-based modeling whereby the modeler is using the behaviors and decisions to inform the process determining land change in
    the model.


Works Cited

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and Modeling Approaches”. EconPapers. Archived from the original on 2017-05-15. Retrieved 2017-05-06.
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b Pontius Jr R, Castella J, de Nijs T, Duan Z, Fotsing E, Goldstein N, Kasper K, Koomen E, D Lippett C, McConnell W, Mohd Sood A (2018). “Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons”. In Behnisch
M, Meinel G (eds.). Trends in Spatial Analysis and Modelling. Geotechnologies and the Environment. Vol. 19. Springer International Publishing. pp. 143–164. doi:10.1007/978-3-319-52522-8_8. ISBN 978-3-319-52520-4.
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SK, Szabó S (June 2019). “Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata – Markov simulation model”. Ecological Indicators. 101: 933–942. doi:10.1016/j.ecolind.2019.01.057. ISSN 1470-160X.
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up to:a b Pontius Jr RG, Boersma W, Castella J, Clarke K, de Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen E (2007-08-16). “Comparing the input, output, and validation maps for several models of land change”. The Annals of Regional
Science. 42 (1): 11–37. doi:10.1007/s00168-007-0138-2. ISSN 0570-1864. S2CID 30440357.
14. ^ Pontius Jr, Robert Gilmore; Si, Kangping (2014-01-06). “The total operating characteristic to measure diagnostic ability for multiple thresholds”. International
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500 Fifth Street, N.W. Washington, DC 20001: The National Academy of Sciences. p. 13. ISBN 978-0-309-28833-0.
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Academic Press. p. 1. ISBN 978-0-309-28833-0.
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