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

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Dr. Paolo Dabove
E-Mail 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
E-Mail 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 (7 papers)

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Research

Article
Effects of Climate and Land Use/Land Cover Changes on Water Yield Services in the Dongjiang Lake Basin
ISPRS Int. J. Geo-Inf. 2021, 10(7), 466; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070466 - 07 Jul 2021
Viewed by 522
Abstract
Spatial and quantitative assessments of water yield services in watershed ecosystems are necessary for water resource management and improved water ecological protection. In this study, we used the InVEST model to estimate regional water yield in the Dongjiang Lake Basin in China. Moreover, [...] Read more.
Spatial and quantitative assessments of water yield services in watershed ecosystems are necessary for water resource management and improved water ecological protection. In this study, we used the InVEST model to estimate regional water yield in the Dongjiang Lake Basin in China. Moreover, we designed six scenarios to explore the impacts of climate and land use/land cover (LULC) changes on regional water yield and quantitatively determined the dominant mechanisms of water yield services. The results are expected to provide an important theoretical reference for future spatial planning and improvements of ecological service functions at the water source site. We found that (1) under the time series analysis, the water yield changes of the Dongjiang Lake Basin showed an initial decrease followed by an increase. Spatially, water yield also decreased from the lake area to the surrounding region. (2) Climate change exerted a more significant impact on water yield changes, contributing more than 98.26% to the water yield variability in the basin. In contrast, LULC had a much smaller influence, contributing only 1.74 %. (3) The spatial distribution pattern of water yield services in the watershed was more vulnerable to LULC changes. In particular, the expansion of built-up land is expected to increase the depth of regional water yield and alter its distribution, but it also increases the risk of waterlogging. Therefore, future development in the basin must consider the protection of ecological spaces and maintain the stability of the regional water yield function. Full article
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Article
Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background
ISPRS Int. J. Geo-Inf. 2021, 10(6), 402; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060402 - 11 Jun 2021
Viewed by 533
Abstract
In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the [...] Read more.
In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method. Full article
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Article
An Open Source GIS Application for Spatial Assessment of Health Care Quality Indicators
ISPRS Int. J. Geo-Inf. 2021, 10(4), 264; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040264 - 14 Apr 2021
Viewed by 734
Abstract
Prevention quality indicators (PQIs) constitute a set of measures that can be combined with hospital inpatient data to identify the quality of care for ambulatory care sensitive conditions (ACSC). Geographical information system (GIS) web mapping and applications contribute to a better representation of [...] Read more.
Prevention quality indicators (PQIs) constitute a set of measures that can be combined with hospital inpatient data to identify the quality of care for ambulatory care sensitive conditions (ACSC). Geographical information system (GIS) web mapping and applications contribute to a better representation of PQI spatial distribution. Unlike many countries in the world, in Portugal, this type of application remains underdeveloped. The main objective of this work was to facilitate the assessment of geographical patterns and trends of health data in Portugal. Therefore, two innovative open source applications were developed. Leaflet Javascript Library, PostGIS, and GeoServer were used to create a web map application prototype. Python language was used to develop the GIS application. The geospatial assessment of geographical patterns of health data in Portugal can be obtained through a GIS application and a web map application. Both tools proposed allowed for an easy and intuitive assessment of geographical patterns and time trends of PQI values in Portugal, alongside other relevant health data, i.e., the location of health care facilities, which, in turn, showed some association between the location of facilities and quality of health care. However, in the future, more research is still required to map other relevant data, for more in-depth analyses. Full article
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Article
Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard
ISPRS Int. J. Geo-Inf. 2021, 10(4), 236; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040236 - 07 Apr 2021
Viewed by 835
Abstract
In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as [...] Read more.
In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales. Full article
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Article
End Point Rate Tool for QGIS (EPR4Q): Validation Using DSAS and AMBUR
ISPRS Int. J. Geo-Inf. 2021, 10(3), 162; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030162 - 12 Mar 2021
Cited by 1 | Viewed by 879
Abstract
This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS graphical modeler for calculating the shoreline change with the end point rate method. The EPR4Q tries to fill the gaps in user-friendly and free [...] Read more.
This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS graphical modeler for calculating the shoreline change with the end point rate method. The EPR4Q tries to fill the gaps in user-friendly and free open-source tools for shoreline analysis in a geographic information system environment since the most used software—Digital Shoreline Analysis System (DSAS)—although being a free extension, it is created for commercial software. Additionally, the best free, open-source option to calculate EPR is called Analyzing Moving Boundaries Using R (AMBUR); since it is a robust and powerful tool, the complexity can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR with different types of shorelines found in nature, extracted from the US Geological Survey Open-File. The obtained results of each tool were compared with Pearson’s correlation coefficient. The validation results indicate that the EPR4Q tool acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, proving that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide. Full article
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Article
Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis
ISPRS Int. J. Geo-Inf. 2021, 10(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010017 - 06 Jan 2021
Viewed by 687
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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Article
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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020120 - 21 Feb 2020
Cited by 6 | Viewed by 1534
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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