Special Issue "Geographic Data Analysis and Modeling in Remote Sensing"

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Dr. Joanne N. Halls
E-Mail Website
Guest Editor
Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S College Rd., Wilmington, NC 28403, USA
Interests: geographic information science; spatial statistics; coastal ecosystems; applications of remote sensing and UAS; population dynamics; ecosystem health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chuanrong Zhang
E-Mail Website
Guest Editor
Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd., Unit 4148, Storrs, CT 06269, USA
Interests: geographical information science and systems; cyberinfrastructure; land use and land cover; spatial data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weidong Li
E-Mail Website
Guest Editor
Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Road, U-4148, Storrs, CT 06269, USA
Interests: geostatistics; geographical information science; environmental informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic data analysis and modeling are becoming more important than ever to extract meaningful information or specific objects of interest from all types of remote sensing imagery. Geographic data analysis and modeling tools enable users to extract meaningful information, compute spatial metrics and statistics, or identify objects such as buildings, roads, coastlines, lakes, rivers, trees, power lines, and other features. Geographic data analysis and modeling techniques also assist with image preparation, database integration and data fusion.

The aim of this Special Issue is to assemble papers that explore spatial data analysis and modeling methods and approaches for remote sensing imagery processing, classification, analysis and inference across multiple disciplines, including, but not limited to, atmospheric and environmental sciences, ecology, earth sciences, health, energy, agriculture, hydrology, population, and socio-economic studies. The topics covered by this Special Issue include, but are not limited to:

  • The latest developments of image classification methods
  • Machine learning or deep learning for spatial data analysis
  • Development of new spatial analysis algorithms for deriving information from imagery such as spatial patterns and landscape modelling
  • Application of geographic data analysis and modeling for change detection including spatial statistical significance of change and methods of measuring spatial and attribute accuracy
  • New tools and approaches for geographic data acquisition, integration or fusion such as innovative cyberinfrastructure, data mining, machine learning/deep learning techniques for data acquisition and fusion from multi-source remote sensing
  • Methods for deriving spatial objects/features including assessing object accuracy

This Special Issue seeks submissions on any topic that 1) applies geographic data analysis and modeling to data derived from remote sensing or 2) applies geographic data analysis and modeling in the processing of remote sensing imagery.

The Special Issue " Geographic Data Analysis and Modeling in Remote Sensing " is jointly organized between “Remote Sensing” and “Geomatics” journals. Contributors are required to check the website below and follow the specific instructions for authors: https://0-www-mdpi-com.brum.beds.ac.uk/journal/remotesensing/instructions
https://0-www-mdpi-com.brum.beds.ac.uk/journal/geomatics/instructions

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Joanne Halls
Prof. Dr. Chuanrong Zhang
Prof. Dr. Weidong Li
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. Geomatics is an international peer-reviewed open access quarterly 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

  • Remote sensing
  • Imagery classification
  • Spatial modelling
  • Spatial analysis
  • Spatial statistics
  • Spatial data processing
  • GIS
  • Spatial patterns extraction/modelling

Published Papers (1 paper)

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Research

Article
Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution
Geomatics 2021, 1(4), 464-495; https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1040027 - 29 Nov 2021
Viewed by 812
Abstract
This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize [...] Read more.
This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed. Full article
(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Comparing VIIR-, NBR, and NDVI-based Post-fire Regeneration Assessments in Okanagan, British Columbia
Authors: Philip Lynch, Tarmo K. Remmel ([email protected])
Affiliation: Department of Geography, York University, Toronto, Ontario, Canada
Abstract: Evaluation of regeneration for spatially expansive wildfires is well-suited to satellite image assessments. Spectral indices are widely used to assess the presence, phenology, and overall health of vegetation. However, compared to traditional indices leveraging quantities of reflected visible and near-infrared light, shortwave- and thermal-infrared vegetation indices have been shown to have a greater correlation with biophysical properties of vegetation. This study investigates how much more variability can be quantified while tracking post-fire regeneration from a shortwave- and thermal-infrared-based index, the infrared vegetation index (VIIR) compared to traditional indices leveraging shorter wavelength bands, the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR). We evaluate a time-series of Landsat 7 ETM+ images through a 17-year period, filling the scan line correction (SLC) errors using a co-kriging interpolation approach to improve data and visual integrity where necessary. Spatial correlations among methods are assessed along with a residual analysis. Index variability, clustering, and association with topography and soil conditions are all presented to identify those indices that are most sensitive to vegetation states during the regeneration period

Title: Explainable Multi-Criteria Fusion for Environmental Status Assessment from Remote sensing
Authors: Gloria Bordogna
Affiliation: CNR IREA, via Bassini 15, 20133 Milano, Italy
Abstract: The paper proposes a human interpretable multi criteria data fusion approach modelling the attitude towards decisions. It is exemplified in territorial decision making to map the status of the environment in order to identify critical situations and anomalies due to phenomena such as wildfires and floods. The fusion function is based on Ordered Weighted Averaging (OWA) operators, the behaviour of which is here characterized by degrees of pessimism and democracy. The paper proposes to specify the fusion function, i.e., the OWA operator, through a linguistic expression that corresponds to given degrees of pessimism/optimism and democracy/monarchy. Alternatively, when the decision maker is uncertain to make a choice, the OWA operator is learnt from available ground truth data and then its degrees of pessimism and democracy are computed to interpret the decision attitude of the learnt fusion: the explanation will indicate if the fused map is more prone to omission or commission errors, and is generated by considering only a few or most inputs. By knowing this information the decision maker is more awareness of the risks when using the map

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