QGIS Plugin Research Articles

LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area.

In 2018, the Penn State Cancer Institute developed LionVu, a web mapping tool to educate and inform community health professionals about the cancer burden in Pennsylvania and its catchment area of 28 counties in central Pennsylvania. LionVu, redesigned in 2023, uses several open-source JavaScript libraries (i.e., Leaflet, jQuery, Chroma, Geostats, DataTables, and ApexChart) to allow public health researchers the ability to map, download, and chart 21 publicly available datasets for clinical, educational, and epidemiological audiences. County and census tract data used in choropleth maps were all downloaded from the sources website and linked to Pennsylvania and catchment area county and census tract geographies, using a QGIS plugin and Leaflet JavaScript. Two LionVu demonstrations are presented, and 10 other public health related web-GIS applications are reviewed. LionVu fills a role in the public health community by allowing clinical, educational, and epidemiological audiences the ability to visualize and utilize health data at various levels of aggregation and geographical scales (i.e., county, or census tracts). Also, LionVu is a novel application that can translate and can be used, for mapping and graphing purposes. A dialog to demonstrate the potential value of web-based GIS to a wider audience, in the public health research community, is needed.

Comprehensive Analysis of Hydrological Processes in a Programmable Environment: The Watershed Modeling Framework

Distributed hydrological modeling has increased its popularity in the community, leading to the development of multiple models with different approaches. However, the rapid growth has also opened a gap between models, interfaces, and advanced users. User interfaces help to set up and pre-process steps. Nevertheless, they also limit the implementation of more complex experiments. This work presents the Watershed Modeling Framework (WMF) as a step forward in closing the interface–usage gap. WMF is a Fortran-Python module designed to provide tools to perform hydrological analysis and modeling that conceptualizes the watershed as an object with a defined topology, properties, and functions. WMF has a built-in hydrological model, geomorphological analysis functions, and a QGIS plugin. WMF interacts with other popular Python modules, making it dynamic and expandible. In this work, we describe the structure of WMF and its capabilities. We also provide some examples of its implementation and discuss its future development.

Machine Learning Approach for Detection of Water Overgrowth in Azov Sea with Sentinel-2 Data

The Azov Sea estuaries play an important role in the reproduction of semi-anadromous fish species. Spawning efficiency is closely connected with overgrowing of those species spawning grounds; thus, the objective of the water vegetation research has vital fisheries importance. Thus, the main goal of the research was to develop a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data. The research was conducted based on field botanical and vegetation investigations in 2020–2021 in Soleniy and Chumyanniy firths. Collected field and remote sensing data were processed with the semi-automatic classification plugin for QGIS. For the classification of Azov Sea estuaries, a random forest algorithm was used. The obtained results showed that in 2020 the areas occupied by reeds reached 0.37 km2, while in 2021, they increased to 0.51 km2. There was a high level of Phragmites australis growth in the Soleniy and Chumyanniy firths. The rapid growth of Phragmites australis in the period of 2020–2021, where the area covered by the reed doubled, is primarily attributed to eutrophication. This is due to the nutrient enrichment from agricultural lands located in the northern part of the research area near Novonekrasovskiy village. Additionally, changes in water flows and hydrological conditions can also contribute to the favorable growth of the reed. This can result in a high growth rate of Phragmites australis, which can reach up to 2 m per year and can propagate both through vegetative and sexual means, leading to the formation of large and dense clusters.

Introducing tree belt designer - A QGIS plugin for designing agroforestry systems in terms of potential insolation

Agroforestry systems support all categories of ecosystem services (ESs). In providing the regulating category of ESs, these systems have become an important strategy used to attenuate drought impacts and biodiversity losses on agricultural landscapes. Within the agroforestry design process aimed at ES provision, one of the agroforestry types used is tree belts (TBs). Unfortunately, due to the inappropriate spatial arrangement of TBs, agricultural landscapes often become too shaded or too sunny. In existing agricultural models of the TB impact on light conditions, only the average values of all TB parameters are considered. Moreover, these models can only be used for whole study plots, or a given plot can be divided into several zones parallel to the TB. Therefore, we developed the QGIS plugin to design TB simulations on a spatially continuous scale for more effective estimation of their impact on potential insolation. The design process is based on the user library containing the tree and shrub species, soil data, parcel-based layout, and digital surface model. In this study, we tested our plugin and its outcomes in terms of predicting changes in potential insolation.

MINDED-FBA: An Automatic Remote Sensing Tool for the Estimation of Flooded and Burned Areas

This paper presents the MINDED-FBA, a remote-sensing-based tool for the determination of both flooded and burned areas. The tool, freely distributed as a QGIS plugin, consists of an adaptation and development of the previously published Multi Index Image Differencing methods (MINDED and MINDED-BA). The MINDED-FBA allows the integration and combination of a wider diversity of satellite sensor datasets, now including the synthetic aperture radar (SAR), in addition to optical multispectral data. The performance of the tool is evaluated for six case studies located in Portugal, Australia, Pakistan, Italy, and the USA. The case studies were chosen for representing a wide range of conditions, such as type of hazardous event (i.e., flooding or fire), scale of application (i.e., local or regional), site specificities (e.g., climatic conditions, morphology), and available satellite data (optical multispectral and SAR). The results are compared in respect to reference delineation datasets (mostly from the Copernicus EMS). The application of the MINDED-FBA tool with SAR data is particularly effective to delineate flooding, while optical multispectral data resulted in the best performances for burned areas. Nonetheless, the combination of both types of remote sensing data (data fusion approach) also provides high correlations with the available reference datasets. The MINDED-FBA tool could represent a new near-real-time solution, capable of supporting emergency response measures.

Interpolation of airborne LiDAR data for archaeology

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well established. However, interpolation, an important step in which the digital elevation model is derived from the classified point cloud, has received little attention from archaeologists. This processing step has a direct impact on the accuracy and visual performance of the digital elevation model, but remains a challenge despite numerous studies. Most studies have compared the accuracy of different interpolators (with conflicting results), but very few compare the visual performance. Also, there are no archaeology-specific studies. This article addresses this problem by providing an archaeology-specific visual performance assessment of six of the most commonly used interpolators. The data was tested at four European test sites with innovative use of the triangular assessment method. Kriging was the best interpolator in undersampled areas, and inverse distance weighting was a distant second. In other areas, triangulation with linear interpolation was marginally better than kriging. However, when availability and computational costs are also taken into account, inverse distance weighting is currently the most suitable archaeology-specific interpolator. In addition, we propose a hybrid interpolator that combines the strengths of triangulation with linear interpolation and inverse distance weighting (QGIS plug-in). All results are to be considered European-data specific.

Modelling land use and land cover changes in the Lower Neretva Region

It has been shown that simulation models are reliable tools for predicting land changes, which contributes to better understanding and management of human impact on the environment. Land use and land cover changes in the Lower Neretva Region between 1990 and 2035 have been analysed and modelled in this study. The final simulation model of future changes was created based on cellular automata and artificial neural networks, implemented in the MOLUSCE plugin for QGIS. In addition, a test simulation model for 2020 was created, which showed high accuracy. Input variables for the final simulation model included a digital elevation model (DEM), slope, distance from water bodies, distance from built-up areas, and population density by settlement in 2011 and 2021. According to the results, forests and grasslands will expand and occupy almost 45% of the area. A slight increase in built-up and agricultural areas is expected, while swamps, water bodies, and sparse vegetation areas will decrease.

Recent precipitation trends in Peninsular Spain and implications for water infrastructure design

Recent precipitation trends in Peninsular Spain and implications for water infrastructure design

Performance and validation of water surface temperature estimates from Landsat 8 of the Itaipu Reservoir, State of Paraná, Brazil.

Studies on water surface temperature (WST) from thermal infrared remote sensing are still incipient in Brazil, and for many water resources, they do not exist. Many algorithms have been developed to estimate surface temperature in satellite images. There are also many difficulties in implementing these algorithms due to their complexity, especially in free software, which restricts the satisfactory processing of these data by users of the technique. Thus, this work aimed to validate an algorithm used to estimate land surface temperature (LST) when applied to the surface of inland water bodies. Water surface temperature estimates (WSTe) were generated from Itaipu State of Paraná (PR) reservoir, Brazil, calculated from Landsat 8 - TIRS satellite images (WSTs) and water surface temperature data from 37 in situ stations (WSTi). A linear regression model of the WSTe was generated in 60% of the samples and its validation with the remaining 40%, subject to prior evaluation of some statistical indicators. The model was considered significant since the coefficient of determination (r2) was 0.90 (95% of confidence), root mean square deviation (RMSD) 0.8°C, Willmott Index (d) = 0.97, and Nash-Sutcliffe efficiency coefficient (NSE) = 0.89. The methodology used to extract WSTs from the Python QGIS plugin was relatively quick to apply, easy to understand, and had a better performance of the estimates than those presented in the literature review.

Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia

In the context of a changing climate, monitoring agricultural systems is becoming increasingly important. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This plugin was designed to manage large Earth observation (EO) data. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. This result was obtained by support vector machine (SVM). As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool.

ORGANICS: A QGIS Plugin for Simulating One-Dimensional Transport of Dissolved Substances in Surface Water

Surface water in streams and rivers is a valuable resource and pollution events, if not tackled in time, may have dramatic impacts on aquatic ecosystems. As such, in order to prepare pollution prevention plans and measures or to set-up timely remedial options, especially in the early stages of pollution incidents, simulation tools are of great help for authorities, with specific reference to environmental protection agencies and river basin authorities. In this paper, we present the development and testing of the ORGANICS plugin embedded in QGIS. The plugin is a first attempt to embed surface water solute transport modelling into GIS for the simulation of the concentration of a dissolved substance (for example an organic compound) in surface water bodies including advection dispersion and degradation. This tool is based on the analytical solution of the popular advection/dispersion equation describing the transport of contaminants in surface water. By providing as input data the concentration measured at the entry point of a watercourse (inlet boundary condition) and the average speed of the surface water, the model simulates the concentration of a substance at a certain distance from the entry point, along the profile of the watercourse. The tool is first tested on a synthetic case. Then data on the concentration of the pharmaceutical carbamazepine monitored at the inlet and outlet of a vegetated channel, in a single day, are used to validate the tool in a real environment. The ORGANICS plugin aims at popularizing the use of simple modelling tools within a GIS framework, and it provides GIS experts with the ability to perform approximate, but fast, simulations of the evolution of pollutants concentration in surface water bodies.

Mapping population distribution from open address data: application to mainland Portugal

ABSTRACT Mapping population distribution remains a common need in various fields of studies. Several approaches and methodologies have been adopted to obtain high-resolution population distribution grids. The use of addresses data to obtain gridded population distribution maps emerges as one of the more recent and accurate approaches. The increasing dissemination and availability of geo-data and more specifically address data allow us to obtain updated, granular and high spatial resolution population distribution maps. This paper describes a bottom-up open addresses data mapping-based approach of gridded population distribution with a fine spatial resolution. Through a QGIS plugin, an adaptation of the housing unit methodology was implemented to obtain 500 m × 500 and 250 m × 250 m population grids for mainland Portugal. The results showed that the use of reliable addresses databases can generate gridded population distribution maps with a high degree of adjustment to reality.

A Quick QGIS-Based Procedure to Preliminarily Define Time-Independent Rockfall Risk: The Case Study of Sorba Valley, Italy

Rockfalls are widespread, rapid, and high-energy landslide phenomena that could potentially affect large portions of populated lands. The preliminary identification of the most rockfall-prone zones is a challenging task, especially in times of extreme and unpredictable climate change. Even slight environmental modifications can produce significant consequences in terms of exposure, hazard, and risk. Therefore, a timely risk assessment is paramount for territorial administrators to plan and prioritize adequate countermeasures. Risk assessment is crucial to guaranteeing the safety of human lives, the integrity of structures and infrastructures, the preservation of historic and environmental heritage, and the safeguard of economic activities. Hence, new and rapid evaluation methods for rockfall hazard, vulnerability, and risk are needed to identify the most critical areas where more indepth analyses aimed at the design of protective works should be carried out. This study proposes a quick, innovative, and completely GIS-based procedure to preliminarily assess rockfall time-independent hazard and risk in large areas. Propagation analysis is performed by integrating powerful QGIS plugin QPROTO, which can estimate rockfall energy within the invasion area in a simplified way, with the slope units polygons of the Italian territory for the definition of the input parameters. The quantification of risk was obtained by the application of the multidisciplinary IMIRILAND methodology, again within a free and open QGIS environment. Lastly, to test the capabilities of the method, the procedure was applied to a case study of the Sorba Valley (Piemonte, Italy), a tourist region in the northwestern Italian Alps. The findings offer an important contribution to the field of land-planning activities and risk-management strategies.

Generate_SWMM_inp: An Open-Source QGIS Plugin to Import and Export Model Input Files for SWMM

SWMM is an open-source model and software developed by the US EPA for the simulation of rainfall-runoff and routing in water bodies, sewer systems and wastewater infrastructures. It has been applied in numerous practical works and research projects. For a new SWMM model, objects such as nodes, links and catchments can either be drawn via SWMM’s graphical user interface (GUI) or specified manually in a plain text file in “.inp” format (“input file”). Since the required data regarding sewer geometries and river systems are usually available as spatial data in a GIS environment, there is a need for user-friendly interfaces for the model setup. SWMM contains neither an import function for geodata nor processing tools as provided in geographic information systems (GIS) such as the open-source software QGIS. Existing approaches were script-based or required commercial all-in-one products. We developed a free and open-source QGIS plugin to generate SWMM models from geodata and to import existing SWMM input files into QGIS. An application example is presented to demonstrate the basic features and usage of the plugin.

Open Geospatial System for LUCAS In Situ Data Harmonization and Distribution

The use of in situ references in Earth observation monitoring is a fundamental need. LUCAS (Land Use and Coverage Area frame Survey) is an activity that has performed repeated in situ surveys over Europe every three years since 2006. The dataset is unique in many aspects; however it is currently not available through a standardized interface, machine-to-machine. Moreover, the evolution of the surveys limits the performance of change analysis using the dataset. Our objective was to develop an open-source system to fill these gaps. This paper presents a developed system solution for the LUCAS in situ data harmonization and distribution. We have designed a multi-layer client-server system that may be integrated into end-to-end workflows. It provides data through an OGC (Open Geospatial Consortium) compliant interface. Moreover, a geospatial user may integrate the data through a Python API (Application Programming Interface) to ease the use in workflows with spatial, temporal, attribute, and thematic filters. Furthermore, we have implemented a QGIS plugin to retrieve the spatial and temporal subsets of the data interactively. In addition, the Python API includes methods for managing thematic information. The system provides enhanced functionality which is demonstrated in two use cases.

SEILAPLAN

Cable-based technologies have been the backbone of forest management and harvesting on steep slopes for decades. The design of a cable road is a complex task. It essentially comprises the identification of the start and end points of a cable road, as well as the intermediate supports. With the aim of simplifying this design process, we developed a semi-automated cable road design tool (QGIS plugin SEILAPLAN) that is easy and intuitive to use. SEILAPLAN is based on mechanical assumptions for the structural analysis that are »close-to-reality«, contains an algorithm that checks all possible intermediate support combinations and automatically identifies the optimal solution, and integrates tools and geodata within a GIS application. We present its main components and present an example of application. The integration into a GIS program, the implemented cable mechanics, and the associated information for the construction of a cable road were highly appreciated by the users.

Tensile forces and deflections on skylines of cable yarders: comparison of measurements with close-to-catenary predictions

ABSTRACT Cable-based technologies are the backbone of timber harvesting on steep slopes. To simplify the cable road design process, the QGIS plugin Seilaplan was recently developed. Seilaplan is tailored for Central European cable yarder technology, with standing skylines. To analyze and predict the load path and occurring forces, the close-to-catenary Zweifel approach is implemented in Seilaplan. The aim of this study was to validate the catenary analyses (deflection and skyline tensile force) under realistic, heavy load conditions for multi-span cable roads. The main finding is that sufficient accuracy, for practical applications under real loading configurations and cable road settings, can be achieved by applying the Zweifel approach. This holds for both the predicted static skyline tensile force, for which a deviation of −3.5 % to +12.7 % was measured (root mean square error [RMSE] = 7 kN), and for the deflection, which deviated from measured values by −0.73 m to +0.9 m (RMSE = 0.5 m). A slight limitation of the implemented Zweifel approach is the missing aspect of the mainline loading, in particular for steep spans.

Mapping Susceptibility With Open-Source Tools: A New Plugin for QGIS

In this study, a new tool for quantitative, data-driven susceptibility zoning (SZ) is presented. The SZ plugin has been implemented as a QGIS plugin to maximize its operational use within the geoscientific community. QGIS is in fact a commonly used open-source geographic information system. We have scripted the plugin in Python, and developed it as a collection of functions that allow one to pre-process the input data, calculate the susceptibility, and then estimate the quality of the classification results. The susceptibility zoning can be carried out via a number of classifiers including weight of evidence, frequency ratio, logistic regression, random forest, support vector machine, and decision tree. The plugin allows one to use any kind of mapping units, to fit the model, to test it via a k-fold cross-validation, and to visualize the relative receiving operating characteristic (ROC) curves. Moreover, a new classification method of the susceptibility index (SI) has been implemented in the SZ plugin. A typical workflow of the SZ plugin is described, and its application for landslide susceptibility zoning in Northeast India is reported. The data of the predisposing factors used are open, and the analysis has been carried out using a logistic regression and weight of evidence models. The corresponding area under the curve of the relative ROC curves reflects an optimal model prediction capacity. The user-friendly graphical interface of the plugin has allowed us to perform the analysis efficiently in few steps.

Volumetric Obscurance as a New Tool to Better Visualize Relief from Digital Elevation Models

The use of digital elevation models (DEMs) has become much more widespread in recent years, thanks to technological developments that facilitate their creation and availability. To exploit these data, a set of processing techniques has been developed to reveal the characteristic structures of the relief. This paper presents a new method based on the volumetric approach, and two derivatives. These methods are evaluated on three DEMs at different resolutions and scales: a freely accessible DEM from JAXA DEM covering part of North-East Tanzania, a DEM corresponding to rock art in Siberia, and a DEM of an archaeological Bronze Age funeral structure. Our results show that with the volumetric approach, concave and convex areas are clearly visible, with contrast marking slope breaks, while the overall relief is attenuated. Furthermore, the use of volume reduces the impact of noise, which can occur when processing is based on sky visibility (e.g., sky-view factor or positive openness) or second derivatives. Finally, the volumetric approach allows the implementation of a vertical exaggeration factor, the result of which will enhance the particular characteristics of the landscape. The present study comes with a standalone executable program for Windows, a QGIS plugin, and the scripts written in Python, including GPU compute capability (via CUDA) for faster processing.

FSLAM: A QGIS plugin for fast regional susceptibility assessment of rainfall-induced landslides

FSLAM: A QGIS plugin for fast regional susceptibility assessment of rainfall-induced landslides