Special Issue "Land Change Modelling"

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

Deadline for manuscript submissions: 12 December 2019.

Special Issue Editors

Guest Editor
Dr. Derek T. Robinson Website E-Mail
Department of Geography and Environmental Management, University ofWaterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
Phone: 519-888-4567
Interests: coupled human–natural systems; land change science; unmanned aerial vehicles; carbon cycle; erosion; agent-based modelling; landscape ecology
Guest Editor
Dr. Jennifer Koch Website E-Mail
Department of Geography and Environmental Sustainability, University of Oklahoma, 100 East Boyd Street, Norman, OK, 73069, USA
Phone: 541-908-3914
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 1000 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 (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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 1
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)
Show Figures

Figure 1

Back to TopTop