Special Issue "Land Change Modelling"

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editors

Dr. Derek T. Robinson
Website
Guest Editor
Department of Geography and Environmental Management, University ofWaterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
Interests: coupled human–natural systems; land change science; unmanned aerial vehicles; carbon cycle; erosion; agent-based modelling; landscape ecology
Dr. Jennifer Koch
Website
Guest Editor
Department of Geography and Environmental Sustainability, University of Oklahoma, 100 East Boyd Street, Norman, OK, 73069, USA
Interests: socioenvironmental systems; land systems science; integrated modeling; alternative futures analysis; transboundary basins

Special Issue Information

Dear Colleagues,

Processes driving land change have impacts on and create feedbacks between natural and human systems. The modelling approaches used to represent these processes and subsequent land change are varied and comprise different spatial and temporal resolutions and extents. We see a continued development of land change models for specific local case studies, but an increasing number of models quantify change and feedbacks at regional, national, and continental extents. While global land change data are regularly used to drive global process models (e.g., climate), there are fewer land change models operating at this scale.

This Special Issue welcomes articles that present new approaches to spatially explicit modelling of land change, highlight novel contributions to land change modelling using historical approaches, compare and contrast different modelling approaches, and make other unique contributions to land change and land systems modelling, such as frameworks, methodologies, and model coupling approaches. Those intending to submit should consider including data, model code (via common repositories such as GitHub), and appendices to facilitate replication, reuse, and expansion upon their submitted work by others.

Dr. Derek T. Robinson
Dr. Jennifer Koch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land change
  • modelling
  • model coupling and integration
  • large-scale approaches
  • land systems analysis

Published Papers (6 papers)

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Research

Open AccessEditor’s ChoiceArticle
Forest Area Change in the Shifting Landscape Mosaic of the Continental United States from 2001 to 2016
Land 2020, 9(11), 417; https://doi.org/10.3390/land9110417 - 29 Oct 2020
Abstract
The landscape context (i.e., anthropogenic setting) of forest change partly determines the social-ecological outcomes of the change. Furthermore, forest change occurs within, is constrained by, and contributes to a dynamic landscape context. We illustrate how information about local landscape context can be incorporated [...] Read more.
The landscape context (i.e., anthropogenic setting) of forest change partly determines the social-ecological outcomes of the change. Furthermore, forest change occurs within, is constrained by, and contributes to a dynamic landscape context. We illustrate how information about local landscape context can be incorporated into regional assessments of forest area change. We examined the status and change of forest area in the continental United States from 2001 to 2016, quantifying landscape context by using a landscape mosaic classification that describes the dominance and interface (i.e., juxtaposition) of developed and agriculture land in relation to forest and other land. The mosaic class changed for five percent of total land area and three percent of total forest area. The least stable classes were those comprising the developed interface. Forest loss rates were highest in developed-dominated landscapes, but the forest area in those landscapes increased by 18 percent as the expansion of developed landscapes assimilated more forest area than was lost from earlier developed landscapes. Conversely, forest loss rates were lowest in agriculture-dominated landscapes where there was a net loss of five percent of forest area, even as the area of those landscapes also increased. Exposure of all land to nearby forest removal, fire, and stress was highest in natural-dominated landscapes, while exposure to nearby increases in developed and agriculture land was highest in developed- and agriculture-dominated landscapes. We discuss applications of our approach for mapping, monitoring, and modeling landscape and land use change. Full article
(This article belongs to the Special Issue Land Change Modelling)
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Open AccessArticle
A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling
Land 2020, 9(11), 407; https://doi.org/10.3390/land9110407 - 25 Oct 2020
Abstract
Land change models commonly model the expected quantity of change as a Markov chain. Markov transition probabilities can be estimated by tabulating the relative frequency of change for all transitions between two dates. To estimate the appropriate transition probability matrix for any future [...] Read more.
Land change models commonly model the expected quantity of change as a Markov chain. Markov transition probabilities can be estimated by tabulating the relative frequency of change for all transitions between two dates. To estimate the appropriate transition probability matrix for any future date requires the determination of an annualized matrix through eigendecomposition followed by matrix powering. However, the technique yields multiple solutions, commonly with imaginary parts and negative transitions, and possibly with no non-negative real stochastic matrix solution. In addition, the computational burden of the procedure makes it infeasible for practical use with large problems. This paper describes a Regression-Based Markov (RBM) approximation technique based on quadratic regression of individual transitions that is shown to always yield stochastic matrices, with very low error characteristics. Using land cover data for the 48 conterminous US states, median errors in probability for the five states with the highest rates of transition were found to be less than 0.00001 and the maximum error of 0.006 was of the same order of magnitude experienced by the commonly used compromise of forcing small negative transitions estimated by eigendecomposition to 0. Additionally, the technique can solve land change modeling problems of any size with extremely high computational efficiency. Full article
(This article belongs to the Special Issue Land Change Modelling)
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Open AccessArticle
Quantifying the Effect of Land Use Change Model Coupling
Land 2020, 9(2), 52; https://doi.org/10.3390/land9020052 - 12 Feb 2020
Abstract
Land-use change (LUC) is a complex process that is difficult to project. Model collaboration, an aggregate term for model harmonization, comparison and/or coupling, intends to combine the strengths of different models to improve LUC projections. Several model collaborations have been performed, but to [...] Read more.
Land-use change (LUC) is a complex process that is difficult to project. Model collaboration, an aggregate term for model harmonization, comparison and/or coupling, intends to combine the strengths of different models to improve LUC projections. Several model collaborations have been performed, but to the authors’ knowledge, the effect of coupling has not been evaluated quantitatively. Therefore, for a case study of Brazil, we harmonized and coupled the partial equilibrium model GLOBIOM-Brazil and the demand-driven spatially explicit model PLUC, and then compared the coupled-model projections with those by GLOBIOM-Brazil individually. The largest differences between projections occurred in Mato Grosso and Pará, frontiers of agricultural expansion. In addition, we validated both projections for Mato Grosso using land-use maps from remote sensing images. The coupled model clearly outperformed GLOBIOM-Brazil. Reductions in the root mean squared error (RMSE) for LUC dynamics ranged from 31% to 80% and for total land use, from 10% to 57%. Only for pasture, the coupled model performed worse in total land use (RMSE 9% higher). Reasons for a better performance of the coupled model were considered to be, inter alia, the initial map, more spatially explicit information about drivers, and the path-dependence effect in the allocation through the cellular-automata approach of PLUC. Full article
(This article belongs to the Special Issue Land Change Modelling)
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Open AccessArticle
Simulation of an Urban-Rural Spatial Structure on the Basis of Green Infrastructure Assessment: The Case of Harbin, China
Land 2019, 8(12), 196; https://doi.org/10.3390/land8120196 - 15 Dec 2019
Cited by 1
Abstract
Due to their long-term dual structures and rapid urbanization, cities and villages in developing countries are undergoing the challenges of urban-rural integration and ecological security. This study aims to determine the pattern of urban-rural spatial structures under the circumstances of ecological security in [...] Read more.
Due to their long-term dual structures and rapid urbanization, cities and villages in developing countries are undergoing the challenges of urban-rural integration and ecological security. This study aims to determine the pattern of urban-rural spatial structures under the circumstances of ecological security in the future to promote the integrated, coordinated, green, and sustainable development of urban-rural spaces. Using a quantitative evaluation method, the logistic-CA model, the LCP (least cost path) model, and a classification of ecological importance, this study constructed an integrated simulation model based on a green infrastructure assessment and applied the model to simulate and predict the urban-rural spatial structure of the Harbin city territory (Harbin) in 2035. The results indicate that the urban-rural hierarchical scale structure of Harbin comprises a central city, sub-central city, central town, major town, common town, central village, and general village. The urban-rural traffic network structure forms a pattern of “radiation + grid”, with Harbin city at the center of the structure. The urban-rural land use zoning structure consists of eco-spaces, agricultural spaces, and construction spaces. It can be concluded that in 2035, the urban-rural spatial structure of Harbin will show an increasing development tendency, where single-center, medium, and small cities in will Harbin develop, and traffic systems above the county level will also improve. Full article
(This article belongs to the Special Issue Land Change Modelling)
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Open AccessArticle
Projecting Urbanization and Landscape Change at Large Scale Using the FUTURES Model
Land 2019, 8(10), 144; https://doi.org/10.3390/land8100144 - 24 Sep 2019
Abstract
Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated [...] Read more.
Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated urban development across the South Atlantic States (SAS), a region experiencing rapid population growth and urbanization, using FUTURES—an open source land change model that uses demand for development, local development suitability factors, and a stochastic patch growing algorithm for projecting alternative futures of urban form and landscape change. New advances to the FUTURES modelling framework allow for high resolution projections over large spatial extents by leveraging parallel computing. We simulated the adoption of different urban growth strategies that encourage settlement densification in the SAS as alternatives to the region’s increasing sprawl. Evaluation of projected patterns indicate a 15% increase in urban lands by 2050 given a status quo development scenario compared to a 14.8% increase for the Infill strategy. Status quo development resulted in a 3.72% loss of total forests, 2.97% loss of highly suitable agricultural land, and 3.69% loss of ecologically significant lands. An alternative Infill scenario resulted in similar losses of total forest (3.62%) and ecologically significant lands (3.63%) yet consumed less agricultural lands (1.23% loss). Moreover, infill development patterns differed qualitatively from the status quo and resulted in less fragmentation of the landscape. Full article
(This article belongs to the Special Issue Land Change Modelling)
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Open AccessArticle
Comparison of Statistical Approaches for Modelling Land-Use Change
Land 2018, 7(4), 144; https://doi.org/10.3390/land7040144 - 24 Nov 2018
Cited by 4
Abstract
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 analyze land-use change can help us understand the causes and effects of change, which [...] Read more.
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 analyze 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 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 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 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 the generalized additive model outperformed the other three models in terms of overall accuracy and was the best for modelling most 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. Spatial cross-validation accuracies were slightly lower than the conventional cross-validation accuracies. Our results 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 can outperform 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. Full article
(This article belongs to the Special Issue Land Change Modelling)
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