QGIS Plugin Research Articles

SHP Buddy: a QGIS plugin for generating shapefiles to support remote sensing in plant breeding and agronomic experiments

BackgroundShapefiles are a geospatial vector data format used to indicate geographic features in geographic information systems (GIS) software. Shapefiles are used in high-throughput phenotyping plant breeding and agronomic studies to identify plots from aerial imagery and extract remote sensing data. However, the process of manually creating shapefiles is tedious and error prone. Current options that assist in shapefile generation suffer from issues such as installation processes that require a degree of programming knowledge or inefficient methods for incorporating plot-level information from field books. In this study, we have developed a program called ‘SHP Buddy’, a QGIS plugin that provides accessible and intuitive functions that quickly generate shapefiles for common experimental layouts used in agricultural research.ResultsSHP Buddy is a free and open source QGIS plugin that is easily downloaded directly from the QGIS plugin repository. It provides options for generating serpentine replicated and unreplicated experimental layouts. Further, SHP Buddy is the first of its type to provide an intuitive method for removing non-experimental plots, such as non-experimental “fill” plots at the end of experiments or plots in irrigation wheel tracks. Plot information is easily incorporated by uploading a field book CSV file that contains a column of matching plot numbers. Lastly, plot dimensions can be modified to produce more precise regions of interest.ConclusionsSHP Buddy substantially reduces the time and increases the accuracy of shapefile generation. This results in reliable shapefiles that improve record keeping and the quality of high-throughput phenotyping data extracted. By working natively in QGIS, SHP Buddy provides an efficient solution to shapefile generation while maintaining a low learning curve.

An autonomous GIS agent framework for geospatial data retrieval

ABSTRACT Powered by the emerging large language models (LLMs), autonomous geographic information system (GIS) agents can perform spatial analyses and cartographic tasks. However, a research gap exists in enabling these agents to autonomously discover and retrieve the necessary data for spatial analysis. This study proposes an autonomous GIS agent framework capable of retrieving required geospatial data by generating, executing, and debugging programs. The framework, with an LLM-driven decision core, selects data sources from a predefined list and fetches data using source-specific handbooks that document metadata and data retrieval details. Designed in a plug-and-play style, the framework allows human users or automated data crawlers to add new sources by creating additional handbooks. A prototype agent based on the framework is developed and released as a QGIS plugin and a Python program. Experiment results demonstrate its capability of retrieving data from various sources, including OpenStreetMap, administrative boundaries and demographic data from the U.S. Census Bureau, satellite basemaps from ESRI World Imagery, global digital elevation model (DEM) from OpenTopography.org, weather data from a commercial provider, and the COVID-19 case data from the NYTimes GitHub. This study is among the first attempts to develop an autonomous GIS agent for geospatial data retrieval.

TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models

TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models

Development of QGIS plugin for flood inundation mapping: applying Otsu’s thresholding technique

Development of QGIS plugin for flood inundation mapping: applying Otsu’s thresholding technique

DAI Algorithm: a QGIS plugin for Daily Aerial Image interpolation

DAI Algorithm: a QGIS plugin for Daily Aerial Image interpolation

Enhanced Forest Inventories: A QGIS Plugin to incorporate R processing tools in forest management

Enhanced Forest Inventories: A QGIS Plugin to incorporate R processing tools in forest management

POI VizNet: New QGIS Tool to construct Visibility Networks in Cities

Abstract. Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various 2-dimentianal undirected graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of Point Of Interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travel – street intersections and POIs, origins, and destinations of travel, by connecting these potential observer’s decision locations in an open space between buildings. In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS

An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS

Remote detection of asbestos-cement roofs: Evaluating a QGIS plugin in a low- and middle-income country

Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for generating new knowledge from observations. In the realm of geographic information systems (GIS), machine learning techniques have become essential for spatial analysis tasks. Satellite image classification methods offer valuable decision-making support, particularly in land-use planning and identifying asbestos cement roofs, which pose significant health risks. In Colombia, where asbestos has been used for decades, the detection and management of installed asbestos is critical. This study evaluates the effectiveness of the RoofClassify plugin, a machine learning-based GIS tool, in detecting asbestos cement roofs in Sibaté, Colombia. By employing high-resolution satellite imagery, the study assesses the plugin's accuracy and performance. Results indicate that RoofClassify demonstrates promising capabilities in detecting asbestos cement roofs, achieving an overall accuracy score of 69.73%. This shows potential for identifying areas with the presence of asbestos and informing decision-makers. However, false positives remain a challenge, necessitating further on-site verification. The study underscores the importance of cautious interpretation of classification results and the need for tailored approaches to address specific contextual factors. Overall, RoofClassify presents a valuable tool for identifying asbestos cement roofs, aiding in asbestos management strategies.

Q-DAI: Design and Implementation of a QGIS Plugin for Disaggregation of Soil Moisture Content at 30 m Spatial Resolution

Q-DAI: Design and Implementation of a QGIS Plugin for Disaggregation of Soil Moisture Content at 30 m Spatial Resolution

QGIS‐based support tools to simplify the complex design challenges of subsurface drainage systems

AbstractWe developed a comprehensive set of tools to simplify the design and layout of subsurface drainage systems. These tools, which utilize publicly available LiDAR (light detection and ranging) datasets, may also be used for other applications. The tool streamline operations include harmonizing geographic information system (GIS) layers into a single State Plane or Universal Transverse Mercator Coordinate System, clipping and thinning LiDAR data within a defined boundary, generating drainage networks with contour lines and laylines for surface flow visualization, identifying depressions and creating gridlines with user‐specified angles and spacings. To showcase their use, an illustrative example using a diverse dataset from the University of Illinois’ South Farm is provided. These user‐friendly tools, optimized for compatibility with all versions of QGIS3 (quantum geographic information system 3), are freely accessible for download or installation from the official QGIS plugin repository. A user manual with step‐by‐step instructions is available online on the Illinois Drainage Guide website.

Rock-fall runout simulation using a QGIS plugin along north–west coast of Malta (Mediterranean Sea)

Coastal instability in the form of rock fall is widespread along the northern coasts of Malta and is strictly connected to structural controls, such as lithology and tectonics. The local geological and geomorphological setting combined with extreme weather events, whose spatial and temporal occurrence is enhanced by ongoing climate and land use change, determines rock-fall hazard along the north–west coast of Malta, a hotspot for Mediterranean tourism. The present research portrays the results of a pilot study aimed at analyzing rock-fall runout probability along this stretch of coast. Cliffs and downslope terrains were the object of detailed field surveys. This included the detection of persistent joints in the source areas and the identification of slope-forming materials like debris and boulders present along the downslope terrains. The outcomes of the field surveys and GIS-based processing of topographic information were used for identification of the input parameters for rock-fall runout simulation. The latter was performed using a QGIS based plugin (QPROTO) that allows to determine expected runout extents and assess the rock-fall susceptibility. The result is the characterization and mapping of rock-fall runout probability zones along six investigated sites. The outputs of the research and the replicability of the method can be of interest for authorities aiming at defining risk management actions and undertaking mitigation measures compliant with sustainable development of coastal areas.

Advancing water productivity monitoring: WaPLUGIN for the analysis and validation of FAO WaPOR data in QGIS

Abstract. The FAO WaPOR (Water Productivity Open Access Portal) is a significant initiative developed by the Food and Agriculture Organization of the United Nations (FAO). It provides extensive datasets derived from remote sensing, specifically aimed at monitoring water productivity in agriculture. The primary goal of WaPOR is to enhance water use efficiency and promote sustainable agricultural practices globally. Given the importance of this datasets and with the objective to streamline access to the datasets for users, we have developed WaPLUGIN. WaPLUGIN is a QGIS plugin designed to help users easily retrieve WaPOR datasets directly into QGIS environment and enables the computation of various performance metrics that are useful to assess efficiency of the irrigation schemes. This plugin is a useful tool which helps users and stakeholders to focus on the interpretation of the datasets and make subsequent decisions. The plugin also helps to download and process the WaPOR data.

Introducing MapSWAT: An open source QGIS plugin integrated with google earth engine for efficiently generating ready-to-use SWAT+ input maps.

Introducing MapSWAT: An open source QGIS plugin integrated with google earth engine for efficiently generating ready-to-use SWAT+ input maps.

CONTEXTS.py (CS.py): A supervised contextual post-classification method to access multiple dimensions of complex geospatial objects

The qualitative dimensions of visible features in space can be captured by connecting spatial configurations arranged in a variety of different ways to diverse conceptual spaces. By conceptual spaces, we intend mental concepts describing specific spatial configurations present in a geographical area, defined by the contextual relationships among their constitutive elements.This paper presents a new supervised post-classification method allowing the extraction of semantically complex spatial objects from a single image of the Earth as, for instance, diverse conceptual spaces referring to multiple dimensions of land use (temporal, cultural, social, etc.).Computationally, our method is operationalised by CONTEXTS.py (CS.py), a plugin written in Python for QGIS. CS.py relies on training areas, defined by the user at diverse scales, to identify and extract in the input image conceptual spaces whose spatial contexts have the same spatial features present in the training areas.Applied to a case study on the island of Sicily, where millennial land use dynamics have resulted in a mosaic landscape, CS.py could detect from an orthophoto diverse conceptual spaces of land use in an area ordinarily classified as one land cover, thus expanding the capabilities of geospatial analysis to reach additional qualitative dimensions of information from image data.•CS.py simplifies a supervised contextual post-classification routine in an easy-to-use, practical and accessible QGIS plugin;•CS.py joins a family of tools for supervised object-based classification (e.g. OTB, GRASS), providing, additionally, the possibility to include contextual information as spatial criteria to train the classification routine.•CS.py has broad applications in different disciplines investigating landscape from quantitative and qualitative perspectives, allowing both, as in multiple environments.

MONITORING AND ASSESSMENT OF TENURE INSTRUMENTS FOR LAND ADMINISTRATION AND MANAGEMENT

Abstract. The Unified Map and Data Sharing or UMD-Sharing Project are tools and protocols for the preparation of a unified map for the various tenure instrument. It aims to provide NGAs and LGUs with guidelines on how to effectively utilize several tenure data from various NGA and LGU sources. With varying scales of data sources, in both spatial and textual dimensions, the project aims to explore these dimensions to create the Unified Map’s spatial data model. In general, map is regarded as a basic tool in land administration and management (LAM) and a unified map’s solution of harmonizing these disparate tenure instruments could mitigate conflicts. To implement the Unified Map and to show how data sharing is handled among the different organizations, including the LGUs, the project developed a QGIS plugin interface that can utilize a remote database repository.

AIRS: A QGIS plugin for time series forecasting using deep learning models

AIRS: A QGIS plugin for time series forecasting using deep learning models

Planning shared mobility hubs in European cities: A methodological framework using MCDA and GIS applied to Barcelona

Planning shared mobility hubs in European cities: A methodological framework using MCDA and GIS applied to Barcelona

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

A management unit-based land cover change analysis was examined in Kahayan Tengah Forest Management Unit (FMU) to understand past, present, and future land cover to assist forest management planning in Kahayan Tengah FMU. This study aims to model land cover change in 2011 and 2016, predict 2021, and simulate land cover in 2026 in Kahayan Tengah FMU. Modeling land cover prediction and simulation using MOLUSCE from the QGIS plugin. The results revealed that agricultural land experienced significant increase in total area during 2011–2016. Modeling potential land cover transitions in 2011 and 2016 with the Artificial Neural Network method showed a Kappa coefficient of 0.701 in the good category, and simulation of land cover in 2021 with the Cellular Automata method showed a Kappa coefficient of 0.672 in the good category. By 2026, the agricultural land will continue to increase while forest land tends to remain stable in its total area. This study managed to predict land cover in 2021 and simulated 2026 with good accuracy. Thus, this data and information can support forest management planning in Kahayan Tengah FMU. Keywords: forest management unit, Kahayan Tengah, land cover change, land cover prediction, MOLUSCE

Introducing QAnnAGNPS - A QGIS plugin to facilitate the use of AnnAGNPS (Annualized Agricultural Nonpoint source model)

Introducing QAnnAGNPS - A QGIS plugin to facilitate the use of AnnAGNPS (Annualized Agricultural Nonpoint source model)