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Land
Special Issues
Land Change Assessment Using Remote Sensing and Geographic Information Science
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Special Issue "Land Change Assessment Using Remote Sensing and Geographic Information Science"

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

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 5151

Special Issue Editors

Dr. Robert Gilmore Pontius Jr.
E-Mail Website
Guest Editor
School of Geography, Clark University, Worcester, MA 01610, USA
Interests: Geographic Information Science (GIS); land change science; modelling statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. John Rogan

E-Mail Website
Guest Editor
Graduate School of Geography, Department of Biology, Clark University, 950 Main Street, Worcester, MA 01610, USA
Interests: biophysical remote sensing; change detection; time series analysis; disturbance ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote Sensing has increased our ability to produce maps concerning land cover, while Geographic Information Science has facilitated our ability to characterize differences among maps from various time points. However, some practices have become popular despite their flaws. Typical blunders include the adoption of inappropriate metrics, the misinterpretation of appropriate metrics, and the presentation of data that are insufficiently accurate to address the research question. Meanwhile, novel metrics and sufficient data exist to assess land change for a variety of research questions. This special issue aims to present articles that apply appropriate methods to measure temporal changes in landscapes so the profession can distinguish between what the methods and data demonstrate from what we must strive to learn. The special issue welcomes articles concerning novel methods or case studies to characterize past changes or to project future changes.

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Robert Gilmore Pontius Jr
Prof. Dr. John Rogan
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 submissions that pass pre-check are 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 2200 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

  • category
  • GIS
  • error
  • land change
  • metric
  • model
  • remote sensing

Published Papers (2 papers)

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Article
Landscape Changes in the Southern Coalfields of West Virginia: Multi-Level Intensity Analysis and Surface Mining Transitions in the Headwaters of the Coal River from 1976 to 2016
by
Vincenzo Cribari
,
Michael P. Strager
,
Aaron E. Maxwell
and
Charles Yuill
Land 2021, 10(7), 748; https://doi.org/10.3390/land10070748 - 17 Jul 2021
Cited by 7 | Viewed by 1946
Abstract
This study analyzes land-cover transitions in the headwaters of the Big Coal River in the Central Appalachian Region of the US, from 1976 to 2016, where surface mining was found as the major driver of landscape change. The land-change analysis combined Multi-Level Intensity [...] Read more.
This study analyzes land-cover transitions in the headwaters of the Big Coal River in the Central Appalachian Region of the US, from 1976 to 2016, where surface mining was found as the major driver of landscape change. The land-change analysis combined Multi-Level Intensity Analysis for two-time intervals (1976–1996, 1996–2016) with Difference Components, to differentiate suspected misclassification errors from actual changes. Two land cover classifications were obtained with segmentation analysis and machine learning algorithms from historical high-resolution aerial images and ancillary data. Intensity Analysis allowed for the inspection of transitions across five land cover (LC) classes and measure the degree of non-stationarity of land change patterns. Results found surface mining-related classes and their transitions, including the effects of reclamation processes on areas mined before the enactment of the Surface Mining Control and Reclamation Act (SMCRA, 1977). Results included changes in settlement distribution, low vegetation, water bodies, and forest class transitions. The findings can be applied to infer similar land-change processes in the more extensive Appalachian region where Mountain Top Removal (MTR) operations are widespread. The overall method can be used to address similar problems and inform landscape managers with detailed data to support land use alternatives and conservation in regions that experienced intense changes and are characterized by anthropogenic disturbances and novel ecosystems. Full article
(This article belongs to the Special Issue Land Change Assessment Using Remote Sensing and Geographic Information Science)
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Article
Prototyping a Methodology for Long-Term (1680–2100) Historical-to-Future Landscape Modeling for the Conterminous United States
by
Jordan Dornbierer
,
Steve Wika
,
Charles Robison
,
Gregory Rouze
and
Terry Sohl
Land 2021, 10(5), 536; https://doi.org/10.3390/land10050536 - 19 May 2021
Cited by 3 | Viewed by 2242
Abstract
Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios [...] Read more.
Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios of future landscape change are required to facilitate planning and mitigation efforts. A methodology for modeling long-term historical and future landscape change was applied in the Delaware River Basin of the United States. A parcel-based modeling framework was used to reconstruct historical landscapes back to 1680, parameterized with a variety of spatial and nonspatial historical datasets. Similarly, scenarios of future landscape change were modeled for multiple scenarios out to 2100. Results demonstrate the ability to represent historical land cover proportions and general patterns at broad spatial scales and model multiple potential future landscape trajectories. The resulting land cover collection provides consistent data from 1680 through 2100, at a 30-m spatial resolution, 10-year intervals, and high thematic resolution. The data are consistent with the spatial and thematic characteristics of widely used national-scale land cover datasets, facilitating use within existing land management and research workflows. The methodology demonstrated in the Delaware River Basin is extensible and scalable, with potential applications at national scales for the United States. Full article
(This article belongs to the Special Issue Land Change Assessment Using Remote Sensing and Geographic Information Science)
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