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Land Change Assessment Using Remote Sensing and Geographic Information Science

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 4366

Special Issue Editors

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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

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 Land.

Prof. Dr. Robert Gilmore Pontius, Jr.
Prof. Dr. John Rogan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.


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

Published Papers (1 paper)

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18 pages, 1994 KiB  
The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping
by Zhen Liu and Robert Gilmore Pontius Jr
Remote Sens. 2021, 13(19), 3922; - 30 Sep 2021
Cited by 10 | Viewed by 3492
The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. [...] Read more.
The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers apply stratified random sampling to collect reference data because stratified random sampling is more efficient than simple random sampling for many applications. Our manuscript derives a new methodology that uses stratified random sampling to generate the TOC. An application to flood mapping illustrates how the TOC compares the abilities of three indices to diagnose water. The TOC shows visually and quantitatively each index’s diagnostic ability relative to baselines. Results show that the Modified Normalized Difference Water Index has the greatest diagnostic ability, while the Normalized Difference Vegetation Index has diagnostic ability greater than the Normalized Difference Water Index at the threshold where the Diagnosed Presence equals the Abundance of water. Some researchers consider only one accuracy metric at only one threshold, whereas the TOC allows visualization of several metrics at all thresholds. The TOC gives more information and clearer interpretation compared to the popular Relative Operating Characteristic. Our software generates the TOC from a census, simple random sample, or stratified random sample. The TOC Curve Generator is free as an executable file at a website that our manuscript gives. Full article
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