Special Issue "Free and Open Source Software and Tools for Environmental Applications"

Special Issue Editors

Dr. Paolo Dabove
Website
Guest Editor
Department of Environment, Land, and Infrastructure Engineering (DIATI), Politecnico di Torino, 10129 Turin, Italy
Interests: positioning; surveying and mapping
Special Issues and Collections in MDPI journals
Dr. Bianca Federici
Website
Guest Editor
Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, 16145 Genoa, Italy
Interests: remote sensing and GIS
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have been characterized by rapid developments in various fields of environmental analysis, positioning and mapping. The use of Free and Open Source Software (FOSS) has developed rapidly at both commercial and academic research levels.

This Special Issue brings together scientists, developers and advanced users in software development, analysis of environmental data, geographical data acquisition, processing and visualization, aiming to encourage cooperation and diffusion in the various fields where open source technologies are nowadays used.

With this Special Issue on "Free and Open Source software and tools for environmental applications ", we address research methods, as well as applications on the design, implementation, characterization and use of free and open-source software for geospatial and environmental analysis, positioning, mapping, photogrammetry, remote sensing and spatial information science. This includes the development of new and innovative technological concepts based on free and open source software for scientific research, as well as for education and business projects.

Prospective authors are cordially invited to contribute to this Special Issue by submitting an article containing original research. People who attend the FOSS4G-IT 2020 conference are particularly encouraged to submit a contribution in this Special Issue.

Dr. Paolo Dabove
Dr. Bianca Federici
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. ISPRS International Journal of Geo-Information 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 1400 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

  • FOSS
  • open source
  • environmental applications
  • positioning
  • mapping

Published Papers (2 papers)

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Research

Open AccessArticle
Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis
ISPRS Int. J. Geo-Inf. 2021, 10(1), 17; https://doi.org/10.3390/ijgi10010017 - 06 Jan 2021
Abstract
Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic [...] Read more.
Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity. Full article
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Open AccessEditor’s ChoiceArticle
Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data
ISPRS Int. J. Geo-Inf. 2020, 9(2), 120; https://doi.org/10.3390/ijgi9020120 - 21 Feb 2020
Cited by 2
Abstract
Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably [...] Read more.
Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes. Full article
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