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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
Website1 | Website2 | E-Mail
Interests: geographic information science; remote sensing; spatial statistics; land change science, environmental monitoring and modeling

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.

Keywords

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

Published Papers (4 papers)

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Research

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Open AccessArticle Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images
Remote Sens. 2017, 9(11), 1112; doi:10.3390/rs9111112
Received: 18 August 2017 / Revised: 19 October 2017 / Accepted: 23 October 2017 / Published: 31 October 2017
PDF Full-text (6753 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming.
[...] Read more.
Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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Open AccessArticle Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development
Remote Sens. 2017, 9(11), 1098; doi:10.3390/rs9111098
Received: 1 September 2017 / Revised: 17 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
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Abstract
Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30
[...] Read more.
Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This paper describes the mapping approach used for generating this land cover dataset for Canada from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensor observations. The innovative part of the mapping approach is the local optimization of the land cover classifier, which has resulted in increased spatial consistency and accuracy. Training and classifying with locally confined reference samples over a large number of partially overlapping areas (i.e., moving windows) ensures the optimization of the classifier to a local land cover distribution, and decreases the negative effect of signature extension. A weighted combination of labels, which is determined by the classifier in overlapping windows, defines the final label for each pixel. Since the approach requires extensive computation, it has been developed and deployed using the Government of Canada’s High-Performance Computing Center (HPC). An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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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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Abstract
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
[...] Read more.
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)
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Other

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Open AccessTechnical Note Usability Study to Assess the IGBP Land Cover Classification for Singapore
Remote Sens. 2017, 9(10), 1075; doi:10.3390/rs9101075
Received: 7 September 2017 / Revised: 11 October 2017 / Accepted: 11 October 2017 / Published: 22 October 2017
PDF Full-text (2333 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing
[...] Read more.
Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing them with Google Earth images from different parts of Singapore, asking survey-takers to classify these images according to their understanding by the IGBP definitions provided. We also conducted interviews with experts from major governmental agencies working with satellite imagery, which highlighted the need for a detailed land classification for Singapore. In addition to the qualitative analysis of the IGBP land classification scheme, we carried out a validation of the MCD12Q1-1 remote sensing product against SPOT-5 imagery for our study area. The user study revealed that survey-takers were able to correctly classify urban areas, as well as densely forested areas. Misclassifications between Cropland and Mixed Forest classes were highest and were attributed by users to the broad terminology of the IGBP of the two land cover class definitions. For the accuracy assessment, we obtained validation points using weighted and unweighted stratified sampling. The overall classification accuracy for all 17 IGBP land classes is 62%. Upon selecting only the four most occurring IGBP land classes in Singapore, the classification accuracy improved to 71%. Validation of the MCD12Q1-1 against ground truth for Singapore revealed less-common land classes that may be of importance in a global context but are sources of error when the same product is applied at a smaller scale. Combining the user study with the accuracy assessment gives a comprehensive overview of the challenges associated with using global-level land cover data to derive localized land cover information specifically for smaller land masses like Singapore. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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