Special Issue "Free and Open Source Tools for Geospatial Analysis and Mapping"

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

Deadline for manuscript submissions: closed (30 October 2019).

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 geospatial analysis 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, 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 Tools for Geospatial Analysis and Mapping", 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.

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 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.

Published Papers (11 papers)

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Open AccessArticle
ACYOTB Plugin: Tool for Accurate Orthorectification in Open-Source Environments
ISPRS Int. J. Geo-Inf. 2020, 9(1), 11; https://doi.org/10.3390/ijgi9010011 - 20 Dec 2019
Abstract
High-resolution satellite images must undergo a geometric rectification process in order to be used for metrical purposes. This operation, called orthorectification, is necessary because of deformations mainly due to camera distortions and acquisition geometry. To correctly orthorectify an image, it is necessary to [...] Read more.
High-resolution satellite images must undergo a geometric rectification process in order to be used for metrical purposes. This operation, called orthorectification, is necessary because of deformations mainly due to camera distortions and acquisition geometry. To correctly orthorectify an image, it is necessary to accurately reconstruct the photogrammetric-acquisition characteristics and the image position with respect to a reference system connected to the ground. This operation, called orientation, can be done using various mathematical models such as rigorous, rational polynomial function (RPF), and rational polynomial coefficient, or, according to some authors, rapid positioning coefficient (RPC) models. Orientation and orthorectification are usually performed within specific commercial software, but in QGIS, these complex operations can be performed using the open libraries of the Orfeo Tool Box (OTB). Unfortunately, instructions given by OTB developers lead to scarce results. In fact, the procedure proposed in OTB does not allow for the full exploitation of the potential of RPC models, on which OTB itself is based. As OTB is open-source software, a plugin was developed to overcome these limitations and exploit its full potential. In fact, OTB interfaces are unfortunately essential, and some necessary functions are missing. Therefore, a new QGIS plugin was developed in order to run the entire process in the most photogrammetrically and statistically correct way, and, at the same time, to simplify the relative procedures. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations
ISPRS Int. J. Geo-Inf. 2019, 8(12), 586; https://doi.org/10.3390/ijgi8120586 - 13 Dec 2019
Abstract
Landscape metrics constitute one of the main tools for the study of the changes of the landscape and of the ecological structure of a region. The most popular software for landscape metrics evaluation is FRAGSTATS, which is free to use but does not [...] Read more.
Landscape metrics constitute one of the main tools for the study of the changes of the landscape and of the ecological structure of a region. The most popular software for landscape metrics evaluation is FRAGSTATS, which is free to use but does not have free or open source software (FOSS). Therefore, FOSS implementations, such as QGIS’s LecoS plugin and GRASS’ r.li modules suite, were developed. While metrics are defined in the same way, the “cell neighborhood” parameter, specifying the configuration of the moving window used for the analysis, is managed differently: FRAGSTATS can use values of 4 or 8 (8 is default), LecoS uses 8 and r.li 4. Tests were performed to evaluate the landscape metrics variability depending on the “cell neighborhood” values: some metrics, such as “edge density” and “landscape shape index”, do not change, other, for example “patch number”, “patch density”, and “mean patch area”, vary up to 100% for real maps and 500% for maps built to highlight this variation. A review of the scientific literature was carried out to check how often the value of the “cell neighborhood” parameter is explicitly declared. A method based on the “aggregation index” is proposed to estimate the effect of the uncertainty on the “cell neighborhood” parameter on landscape metrics for different maps. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
Analysis of Bird Flyways in 3D
ISPRS Int. J. Geo-Inf. 2019, 8(12), 535; https://doi.org/10.3390/ijgi8120535 - 27 Nov 2019
Abstract
Bird migration is a long studied phenomenon that involves animals moving back and forth from wintering sites and to reproductive grounds. Several studies have focused on identifying the timing, physiology and evolution of migration, but a spatial approach to understand the migratory routes [...] Read more.
Bird migration is a long studied phenomenon that involves animals moving back and forth from wintering sites and to reproductive grounds. Several studies have focused on identifying the timing, physiology and evolution of migration, but a spatial approach to understand the migratory routes is still an open challenge. Geographic Information Systems (GIS) can provide the tools to explore such a complicated issue. Birds usually move from the wintering sites to spring breeding grounds in multiple flights, stopping at intermediate sites to rest and refuel, being unable to cover the distance in a single travel. The choice of stopover sites by birds depends not only on their ecological features but also on their position and visibility along main migratory flyways. In this work, we calculated the possible migratory routes that minimize the distance covered or the elevation gaps for birds crossing the Southern Alps, simulating the flight within a network connecting potential stopover sites and other relevant point of passage, using the shortest path computation. Subsequently, we performed a visibility analysis along the identified flyways to understand which stopover sites, belonging to the Natura2000 network, were visible for a bird in an area with complex morphology. Data available from ringing stations confirm the selection or avoidance of some stopover sites based on their en route visibility. The knowledge of bird flyways and stopover sites has implications for conservation as well as for planning, especially for wind farms or other infrastructures. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping
ISPRS Int. J. Geo-Inf. 2019, 8(11), 513; https://doi.org/10.3390/ijgi8110513 - 12 Nov 2019
Cited by 2
Abstract
The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific [...] Read more.
The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale
ISPRS Int. J. Geo-Inf. 2019, 8(6), 269; https://doi.org/10.3390/ijgi8060269 - 08 Jun 2019
Cited by 11
Abstract
Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional ‘global’ regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates [...] Read more.
Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional ‘global’ regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates an ensemble of local linear models at any number of locations using ‘borrowed’ nearby data. This provides a surface of location-specific parameter estimates for each relationship in the model that is allowed to vary spatially, as well as a single bandwidth parameter that provides intuition about the geographic scale of the processes. A recent extension to this framework allows each relationship to vary according to a distinct spatial scale parameter, and is therefore known as multiscale (M)GWR. This paper introduces mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity. It provides novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements to efficiency in estimation routines. We provide two case studies using mgwr, in addition to reviewing core concepts of local models. We present this in a literate programming style, providing an overview of the primary software functionality and demonstrations of suggested usage alongside the discussion of primary concepts and demonstration of the improvements made in mgwr. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
A QGIS Tool for Automatically Identifying Asbestos Roofing
ISPRS Int. J. Geo-Inf. 2019, 8(3), 131; https://doi.org/10.3390/ijgi8030131 - 06 Mar 2019
Cited by 2
Abstract
Exposure to asbestos fibers implies a long-term risk for human health; therefore, the development of information systems that are able to detect the extent and status of asbestos over a certain territory has become a priority. This work presents a tool (based on [...] Read more.
Exposure to asbestos fibers implies a long-term risk for human health; therefore, the development of information systems that are able to detect the extent and status of asbestos over a certain territory has become a priority. This work presents a tool (based on the geographic information system open source software, QGIS) that is conceived for automatically identifying buildings with asbestos roofing. The area under investigation is the metropolitan area around Prato (Italy). The performance analysis of this system was carried out by classifying images that were acquired by the WorldView-3 sensor. These images are available at a low cost when compared with those obtained by means of aerial surveys, and they provide adequate resolution levels for roofing classification. The tool, a QGIS plugin, has shown fairly good performance in identifying asbestos roofing, with some false negatives and some false positives when applying a per-pixel classification. A performance improvement is obtainable when considering the percentage of asbestos pixels that are contained in each roof of the analyzed image. This value is also available with the plugin. In the future, this tool should make it possible to monitor the asbestos roof removal process over time in the area of interest, in accordance with other image data that give evidence of such removals. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
The CM SAF R Toolbox—A Tool for the Easy Usage of Satellite-Based Climate Data in NetCDF Format
ISPRS Int. J. Geo-Inf. 2019, 8(3), 109; https://doi.org/10.3390/ijgi8030109 - 28 Feb 2019
Cited by 2
Abstract
The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) provides satellite-based climate data records of essential climate variables of the energy budget and water cycle. The data records are generally distributed in NetCDF format. To simplify the preparation, analysis, and visualization of [...] Read more.
The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) provides satellite-based climate data records of essential climate variables of the energy budget and water cycle. The data records are generally distributed in NetCDF format. To simplify the preparation, analysis, and visualization of the data, CM SAF provides the so-called CM SAF R Toolbox. This is a collection of R-based tools, which are optimized for spatial data with longitude, latitude, and time dimension. For analysis and manipulation of spatial NetCDF-formatted data, the functionality of the cmsaf R-package is implemented. This R-package provides more than 60 operators. The visualization of the data, its properties, and corresponding statistics can be done with an interactive plotting tool with a graphical user interface, which is part of the CM SAF R Toolbox. The handling, functionality, and visual appearance are demonstrated here based on the analysis of sunshine duration in Europe for the year 2018. Sunshine duration in Scandinavia and Central Europe was extraordinary in 2018 compared to the long-term average. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
A Field Data Acquisition Method and Tools for Hazard Evaluation of Earthquake-Induced Landslides with Open Source Mobile GIS
ISPRS Int. J. Geo-Inf. 2019, 8(2), 91; https://doi.org/10.3390/ijgi8020091 - 15 Feb 2019
Abstract
The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake Induced slope FAiLures) method was applied to the survey of post-earthquake landslides in central Italy for seismic microzonation purposes. In order to optimize time and resources, while also reducing errors, the paper-based method of [...] Read more.
The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake Induced slope FAiLures) method was applied to the survey of post-earthquake landslides in central Italy for seismic microzonation purposes. In order to optimize time and resources, while also reducing errors, the paper-based method of survey data sheets was translated into digital formats using such instruments as Tablet PCs, GPS and open source software (QGIS). To the base mapping consisting of Technical Regional Map (Carta Tecnica Regionale—CTRs) at the scale of 1:10,000, layers were added with such sensitive information as the Inventory of Landslide Phenomena in Italy (Inventario dei Fenomeni Franosi in Italia—IFFI), for example. A database was designed and implemented in the SQLite/SpatiaLite Relational DataBase Management System (RDBMS) to store data related to such elements as landslides, rock masses, discontinuities and covers (as provided by PARSIFAL). To facilitate capture of the datum on the ground, data entry forms were created with Qt Designer. In addition to this, the employment of some QGIS plug-ins, developed for digital surveying and enabling of quick annotations on the map and the import of images from external cameras, was found to be of considerable use. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
An Analysis of the Evolution, Completeness and Spatial Patterns of OpenStreetMap Building Data in China
ISPRS Int. J. Geo-Inf. 2019, 8(1), 35; https://doi.org/10.3390/ijgi8010035 - 16 Jan 2019
Cited by 3
Abstract
OpenStreetMap (OSM) is a free map that can be created, edited, and updated by volunteers globally. The quality of OSM datasets is therefore of great concern. Extensive studies have focused on assessing the completeness (a quality measure) of OSM datasets in various countries, [...] Read more.
OpenStreetMap (OSM) is a free map that can be created, edited, and updated by volunteers globally. The quality of OSM datasets is therefore of great concern. Extensive studies have focused on assessing the completeness (a quality measure) of OSM datasets in various countries, but very few have been paid attention to investigating the OSM building dataset in China. This study aims to present an analysis of the evolution, completeness and spatial patterns of OSM building data in China across the years 2012 to 2017. This is done using two quality indicators, OSM building count and OSM building density, although a corresponding reference dataset for the whole country is not freely available. Development of OSM building counts from 2012 to 2017 is analyzed in terms of provincial- and prefecture-level divisions. Factors that may affect the development of OSM building data in China are also analyzed. A 1 × 1 km2 regular grid is overlapped onto urban areas of each prefecture-level division, and the OSM building density of each grid cell is calculated. Spatial distributions of high-density grid cells for prefecture-level divisions are analyzed. Results show that: (1) the OSM building count increases by almost 20 times from 2012 to 2017, and in most cases, economic (gross domestic product) and OSM road length are two factors that may influence the development of OSM building data in China; (2) most grid cells in urban areas do not have any building data, but two typical patterns (dispersion and aggregation) of high-density grid cells are found among prefecture-level divisions. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle
Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R
ISPRS Int. J. Geo-Inf. 2018, 7(8), 293; https://doi.org/10.3390/ijgi7080293 - 24 Jul 2018
Cited by 4
Abstract
In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph [...] Read more.
In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessFeature PaperTechnical Note
HidroMap: A New Tool for Irrigation Monitoring and Management Using Free Satellite Imagery
ISPRS Int. J. Geo-Inf. 2018, 7(6), 220; https://doi.org/10.3390/ijgi7060220 - 15 Jun 2018
Cited by 5
Abstract
Proper control and planning of water resource use, especially in those catchments with large surface, climatic variability and intensive irrigation activity, is essential for a sustainable water management. Decision support systems based on useful tools involving main stakeholders and hydrological planning offices of [...] Read more.
Proper control and planning of water resource use, especially in those catchments with large surface, climatic variability and intensive irrigation activity, is essential for a sustainable water management. Decision support systems based on useful tools involving main stakeholders and hydrological planning offices of the river basins play a key role. The free availability of Earth observation products with high temporal resolution, such as the European Sentinel-2B, has allowed us to combine remote sensing with cadastral and agronomic data. This paper introduces HidroMap to the scientific community, an open source tool as a geographic information system (GIS) organized in two different modules, desktop-GIS and web-GIS, with complementary functions and based on PostgreSQL/PostGIS database. Through an effective methodology HidroMap allows monitoring irrigation activity, managing unregulated irrigation, and optimizing available fluvial surveillance resources using satellite imagery. This is possible thanks to the automatic download, processing and storage of satellite products within field data provided by the River Surveillance Agency (RSA) and the Hydrological Planning Office (HPO). The tool was successfully validated in Duero Hydrographic Basin along the 2017 summer irrigation period. In conclusion, HidroMap comprised an important support tool for water management tasks and decision making tackled by Duero Hydrographic Confederation which can be adapted to any additional need and transferred to other river basin organizations. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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