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Special Issue "GIS and Remote Sensing advances in Land Change Science"

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

Deadline for manuscript submissions: 31 May 2018

Special Issue Editor

Guest Editor
Dr. Sotirios Koukoulas

Department of Geography, University of the Aegean, Lofos Panepistimiou, Mytilene 81100, Greece
Website | E-Mail
Interests: Geographic Information Science; remote sensing; statistics; land change science

Special Issue Information

Dear Colleagues,

In the face of the emergence of Land Change Science (LCS), Geographical Information and Remote Sensing sciences have claimed a central role in observing, quantifying, and monitoring changes in land surfaces. In this Special Issue, recent advances in Remote Sensing and GISc that are related to LCS will be presented.

Land changes studied at a variety of scales, both in space and time, will be presented in an attempt to explore the role of analytical tools and technologies in understanding changing landscapes. Priorities include novel techniques for quantifying and analyzing land change with the use of old and new remote sensors. Combining geographical data from multiple spatial, spectral and thematic scales to quantify changes and their spatial patterns are also among priorities. Issues related to spatial error distribution as well as the detection of false changes through time are of particular interest.

Papers incorporating novel and interesting techniques to study land change, as well as some interesting applications, will be considered. Well-prepared review papers are also welcomed.

Dr. Sotirios Koukoulas
Guest Editor

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. Remote Sensing 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 1600 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.


  • Land cover and Environmental changes
  • Spatio-temporal analysis/modeling
  • Old and new remote sensors (combination, fusing, comparisons)
  • Downscaling techniques
  • Spatial accuracy

Published Papers (1 paper)

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Open AccessArticle Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region
Remote Sens. 2017, 9(9), 945; doi:10.3390/rs9090945
Received: 22 August 2017 / Revised: 2 September 2017 / Accepted: 8 September 2017 / Published: 12 September 2017
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The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with
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The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images—Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m2—or 0.2% of the total catchment area—in March 2013 to 121,000 m2—or 0.3%—in May 2014, and then a decrease to 39,000 m2—or 0.1%—in January 2016. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

Journal Contact

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St. Alban-Anlage 66, 4052 Basel, Switzerland
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
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