Hyperspectral satellite missions, such as PRISMA of the Italian Space Agency (ASI), have opened up new research opportunities. Using PRISMA data in land cover classification has yet to be fully explored, and it is the main focus of this paper. Historically, the main purposes of remote sensing have been to identify land cover types, to detect changes, and to determine the vegetation status of forest canopies or agricultural crops. The ability to achieve these goals can be improved by increasing spectral resolution. At the same time, improved AI algorithms open up new classification possibilities. This paper compares three supervised classification techniques for agricultural crop recognition using PRISMA data: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The study was carried out over an area of 900 km2 in the province of Caserta, Italy. The PRISMA HDF5 file, pre-processed by the ASI at the reflectance level (L2d), was converted to GeoTiff using a custom Python script to facilitate its management in Qgis. The Qgis plugin AVHYAS was used for classification tests. The results show that CNN gives better results in terms of overall accuracy (0.973), K coefficient (0.968), and F1 score (0.842).

Gully erosion affects the landscape and human life in many ways, including the destruction of agricultural land and infrastructures, altering the hydraulic potential of soils, as well as water availability. Due to climate change, more areas are expected to be affected by gully erosion in the future, threatening especially low-income agricultural regions. In the past decades, quantitative methods have been proposed to simulate and predict gully erosion at different scales. However, gully erosion is still underrepresented in modern GIS-based modeling and simulation approaches. Therefore, this study aims to develop a QGIS plugin using Python to assess gully erosion dynamics. We explain the preparation of the input data, the modeling procedure based on Sidorchuk’s (Sidorchuk A (1999) Dynamic and static models of gully erosion. CATENA 37:401–414.) gully simulation model, and perform a detailed sensitivity analysis of model parameters. The plugin uses topographical data, soil characteristics and discharge information as gully model input. The plugin was tested on a gully network in KwaThunzi, KwaZulu-Natal, South Africa. The results and sensitivity analyses confirm Sidorchuck’s earlier observations that the critical runoff velocity is a main controlling parameter in gully erosion evolution, alongside with the slope stability threshold and the soil erodibility coefficient. The implemented QGIS plugin simplifies the gully model setup, the input parameter preparation as well as the post-processing and visualization of modelling results. The results are provided in different data formats to be visualized with different 3D visualization software tools. This enables a comprehensive gully assessment and the derivation of respective coping and mitigation strategies.

In this paper, we tackle the problem of the intensity attenuation at Ischia, a critical parameter in a high seismic risk area such as this volcanic island. Starting from the new revised catalogue of local earthquakes, we select a dataset of 118 macroseismic observations related to the four main historical events and analyse the characteristics of the intensity attenuation according to both the deterministic and probabilistic approaches, under the assumption of a point seismic source and isotropic decay (circular spreading). In the deterministic analysis, we derive the attenuation law through an empirical model fitting the average values of ΔI (the difference between epicentral intensity I0 and intensities observed at a site IS) versus the epicentral distances by the least-square method. In the probabilistic approach, the distribution of IS conditioned on the epicentre-site distance is given through a binomial-beta model for each class of I0. In the Bayesian framework, the model parameter p is considered as a random variable to which we assign a Beta probability distribution on the basis of our prior belief derived from investigations on the attenuation in Italy. The mode of the binomial distribution is taken as the intensity expected at that site (Iexp). The entire calculation procedure has been implemented in a python plugin for QGIS® software that, given location and I0 (or magnitude) of the earthquake to be simulated, generates a probabilistic seismic scenario according to the deterministic or probabilistic models of attenuation. This tool may be applied in seismic risk analyses at a local scale or in the seismic surveillance to produce real-time intensity shake-maps for this volcanic area.

Introducing APEXMOD - A QGIS plugin for developing coupled surface-subsurface hydrologic modeling framework of APEX, MODFLOW, and RT3D-Salt

Seaweed assemblages include a variety of structuring species providing habitats, food and shelter for organisms from different trophic levels. Monitoring intertidal seaweed traditionally involves targeting small areas to collect data on species’ biological traits, which is often labour intensive and covers only a small area of the rocky reef under study. Given the various applications for seaweeds and their compounds, there has been an increase in demand for biomass triggered by the development of new markets. Such biomass demand generates new challenges for biomass quantification and the definition of future in-take harvesting commercial quotas by regulating agencies. The use of Unoccupied Aerial Vehicles (UAVs) as a low-cost yet efficient monitoring solution, combined with new sensors such as multispectral cameras, has been proposed for mapping intertidal reefs and seaweed in particular. In this study, a new methodology was developed and validated to quantify intertidal seaweed biomass based on multispectral UAV imagery, which was made available through an easy-to-use QGIS plugin (named SWUAV_BIO) that automates such biomass estimation. This tool was applied to a case study where the standing stock of Fucus spp. beds located at Viana do Castelo rocky shore (northern Portugal) was assessed using UAV multispectral imagery, providing a reference for future UAV-based ecological studies. Although comparison with the in situ assessments showed that biomass was underestimated by 36%, the SWUAV_BIO plugin is a valuable tool, as it provides an expedited (albeit conservative) seaweed standing stock assessment that can be used to monitor seaweed populations, their changes, and assess the effect of harvesting. These data can be used for an informed and sustainable management of seaweed resources by the competent authorities.

Flooding is Pakistan's most common natural hazard, and it is exacerbated by increased rainfall and urbanization. Khyber Pakhtunkhwa (KPK), Pakistan flood-prone zones were determined by superimposing six flood parameters in an ArcGIS environment: elevation, slope, rainfall accumulation, land cover, soil geometry, and gap/buffer from water channel. Cellular automata based on artificial neural network (CA-ANN) along QGIS plugin module of Land Use Change Simulations (MOLUSCE) was used for predicting year 2050 land use, with a kappa value of 0.83. The results indicated that of the 75775 km2 land area covered by this research region, 3.37% (2553.62 km2) falls in extremely high risk, 18.44% (13972.91 km2) falls in high risk, 11.26% (8532.27 km2) falls in moderate risk, 0.51% (386.45 km2) falls in low risk, and just 66.42% (50329.76 km2) falls in very low risk areas. In KPK, like in any other place, a multi-criteria flood risk-vulnerability assessment is consequently necessary for preparation and post-hazard planning. Without a doubt, the outcomes reported here are crucial for flood risk assessments and hazard management decision-making.
 Key words: natural disasters; floods; remote sensing; geographic information system, multi-criteria evaluation; weighted overlay. 

Abstract. The paper introduces a software called Bulldozer, designed to extract a Digital Terrain Model (DTM) from a Digital Surface Model (DSM) obtained from various sensors. The software is based on a modified version of the multi-scale Drap Cloth principle to process noisy DSMs of any size, employing a tiling strategy and a stability margin to ensure consistent results. A parameter called max object size is introduced to differentiate objects from the ground during the drap cloth process. Gravity steps and ground distance sampling resolution are adjusted based on the input DSM. No-data and noisy values present in the DSM are detected and converted into no-data values to improve the quality of the Cloth simulation. The paper describes a memory-aware parallel execution strategy using both the multiprocessing and the shared memory Python modules. A benchmark dataset has been created to analyze the results and compare them with alternative approaches and reference datasets. Bulldozer offers an extensive Python API. It is open-source and available on PyPi and GitHub. Additionally, a QGIS plugin has been developed.

Dissolved organic carbon (DOC) and particulate organic carbon (POC) play a fundamental role in biogeochemical cycles of freshwater ecosystems. However, the lack of readily available distributed models for carbon export has limited the effective management of organic carbon fluxes from soils, through river networks and to receiving marine waters. We develop a spatially semi-distributed mass balance modeling approach to estimate organic carbon flux at a sub-basin and basin scales, using commonly available data, to allow stakeholders to explore the impacts of alternative river basin management scenarios and climate change on riverine DOC and POC dynamics. Data requirements, related to hydrological, land-use, soil and precipitation characteristics are easily retrievable from international and national databases, making it appropriate for data-scarce basins. The model is built as an open-source plugin for QGIS and can be easily integrated with other basin scale decision support models on nutrient and sediment export.We tested the model in Piave river basin, in northeast Italy. Results show that the model reproduces spatial and temporal changes in DOC and POC fluxes in relation to changes in precipitation, basin morphology and land use across different sub-basins. For example, the highest DOC export were associated with both urban and forest land use classes and during months of elevated precipitation. We used the model to evaluate alternative land use scenarios and the impact of climate on basin level carbon export to Mediterranean.

Abstract. One sector that feels the effects of global warming and climate change on all levels is agriculture. In order to prepare for possible yield loss, as well as market, storage, and import planning challenges brought on by climate change, businesses can utilise agricultural decision support applications. Within the scope of this study, a crop yield prediction module has been developed that can provide in and end of season estimation of crop yields to be obtained from the determined regions. The Python programming language was used in the creation of the module as a QGIS plugin. The area for which crop yield predictions are to be made is covered by retrieving MODIS SR, MODIS LST, and Daymet data from the Google Earth Engine data catalogue. Histograms obtained from remotely sensed images are used as input data to two deep learning methods (CNN-LSTM and HistCNN). As a result, the HistCNN model outperformed CNN-LSTM for in season soybean yield prediction, with an R2 of 0.72, while the CNN-LSTM model outperformed it for in end of season soybean yield prediction, with an R2 of 0.67.

This paper presents the development of a QGIS plugin to support evaluating the planimetric positional quality for point and linear features based on the metrics established by Brazilian legislation. For this purpose, we used the QGIS environment Graphical Modeler, which consists of an interface to concatenate a series of processes into a single algorithm. The set of tools, called QPEC, allows for performing the statistical tests from the automatic identification of the sample size and discrepancies. In order to demonstrate the implemented functionalities, a case study was carried out. In this illustrative example, the vector files from the Cartographic and Cadastral System of the Municipality of Salvador - BA (SICAD) were the reference data, and their homologous OpenStreetMap (OSM) features were the analysed database. The results obtained are presented in the attributes table. In addition, the spatial distribution of the discrepancies is visualised through the visual variable colour value in a quartile classification. The creation of this toolset corroborates the feasibility of developing more visual, automated and complete interfaces to support users of geospatial data in analysing the quality of the information available, especially when it involves free applications with open-source code.

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.

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.

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.

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.

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.

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.

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

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.

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.