Search Results (112)

A GIS-based Plant Coefficient Method (PCM), termed the Plant Coefficient Method Tool (PCMT), is presented and validated through this research. It is designed for sustainable irrigation management within arid urban environments, exemplified by Riyadh, Saudi Arabia. The study integrates remote sensing data, including Landsat 8 satellite imagery, vegetation indices (NDVI, LAI), and climatic parameters to estimate daily and seasonal plant water demand for diverse landscape species. Results demonstrate that plant-specific coefficients (${\mathrm{K}}_{\mathrm{p}\mathrm{l}}$) fluctuate seasonally, ranging from 0.1 to 1.4, with average water demand (${\mathrm{E}\mathrm{T}}_{\mathrm{p}\mathrm{l}}$) reaching up to 25 L per square meter during the summer months and decreasing to around 6 L in winter. It may be found by good management based on PCMT that average daily projected ${\mathrm{E}\mathrm{T}}_{\mathrm{p}\mathrm{l}}$ rates can be lowered to as low as 3 mm/day, resulting in a significant decrease in water needs, by around 70% to 50%, when compared to higher categories. Validation across three sites (urban trees, date palms, and turf grass), showed strong correlations (R² > 0.8) between satellite-derived vegetation indices and modeled water needs. The volumetric water demand estimates closely aligned with actual irrigation practices, albeit with some over- and under-irrigation episodes. Spatial analysis indicated that high-demand zones predominantly occur in summer, emphasizing the necessity of adaptive irrigation scheduling. Overall, the PCMT presents a scalable, accurate tool for optimizing water use, supporting sustainable landscape management aligned with Saudi Arabia’s green initiatives. Full article

Landscape metrics are one of the main tools for studying changes in the landscape and the ecological structure of the territory. However, the calculation of some metrics yields significantly different values depending on the configuration of the “Cell neighborhood” (CN) used. This makes the comparison of different analysis results often impossible. In fact, although the metrics are defined in the same way for all software, the choice of a CN with four cells, which includes only the elements on the same row or column, or eight cells, which also includes the cells on the diagonal, changes their value. QGIS’ LecoS plugin uses the value eight while GRASS’ r.li module uses the value four and these values are not modifiable by users. A previous study has shown how the value of the CN used for the calculation of landscape metrics is rarely explicit in scientific publications and its value cannot always be deduced from the indication of the software used. The difference in value for the same metric depends on the CN configuration and on the compactness of the patches, which can be expressed through the Aggregation Index (AI), of the investigated landscape. The scope of this paper is to explore the possibility of deriving an analytical relationship between the Aggregation Index and the variation in the values of some landscape metrics as the CN varies. The numerical experiments carried out in this research demonstrate that it is possible to estimate the differences in landscape metrics evaluated with a four and eight CN configuration using polynomials only for few metrics and only for some intervals of AI values. This analysis combines different Free and Open Source Software (FOSS) systems: GRASS GIS for the creation of test maps and R landscapemetrics package for the calculation of landscape metrics and the successive statistical analysis. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

Biodrying and composting are aerobic processes to treat and stabilize organic solid waste, but biodrying involves a shorter process time and does not require the addition of water. The resulting biodried material (BM) is mainly used as an energy source in cement production or in municipal solid waste incineration with energy recovery, but when obtained from agricultural or agroindustrial organic waste, it could also be used as a soil amendment, such as compost (CO). In this study, the phytotoxicity of BM compared to CO, both made from organic wastes (orange peel, mulch and grass), was evaluated on seed germination and growth (for 90 days) of lettuce (Lactuca sativa L.) seedlings on treatments prepared from mixtures of BM and soil, soil (100%) and a mixture of CO and soil. The germination index (GI%) was higher for BM extracts (200 g/L) than for CO extracts (68% vs. 53%, respectively). According to their dry weight, lettuce grew more on the CO mixture (16.5 g) than on the BM (5.4–7.4 g), but both materials far exceeded the soil values (0.15 g). The absence of phytotoxicity suggests that BM acts as a soil amendment, improving soil structure and providing nutrients to the soil. Therefore, biodrying is a quick and low-cost bioprocess to obtain a soil improver. Full article

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy. Full article

As climate change accelerates and urbanization intensifies, mitigating the Urban Heat Island (UHI) effect has become crucial for sustainable urban planning. This study evaluated the role of four key urban indicators—buildings, greenery, streets, and pedestrian paths—in reducing air temperature and improving energy efficiency within the Kartal District of Istanbul. To ensure accurate and data-driven results, multiple advanced software tools were integrated throughout the research process. QGIS, Google Earth, and OpenStreetMap were used to generate high-resolution land use/land cover (LULC) maps, while Meteoblue climate data and the Global Heat Island Map provided essential climatic parameters. The InVEST Urban Cooling Model was employed to simulate temperature reduction effects, and eQuest energy simulation software assessed the impact of building modifications on energy consumption. The study tested multiple UHI mitigation scenarios, including green roofs, increased street tree cover, grass-covered pedestrian paths, and high-albedo pavement, comparing their individual and combined effects. The results indicated that integrating all strategies achieved the most significant cooling impact, reducing air temperatures by 1.14 °C and improving energy efficiency by 61%. Among the individual interventions, green roofs provided the highest building energy savings (28% reduction), while grass-covered pedestrian paths homogenized the district-wide temperature distribution. These findings underscore the importance of combining GIS-based spatial analysis, climate modeling, and energy simulation tools to develop reliable, scalable, and effective urban heat mitigation strategies. Future urban planning should prioritize a multi-software approach to enhance sustainability, optimize energy efficiency, and improve urban resilience. Full article

Auricularia auricula (L.) is a widely cultivated edible mushroom, and the resource utilization of its residues offers significant opportunities for sustainable waste management and nutrient recovery. This study investigated the effects of substrate composition on nutrient dynamics and microbial diversity during the aerobic composting of Auricularia auricula (L.) residues. Two treatments were established: composting of Auricularia auricula (L.) residues alone (CR) and composting supplemented with green grass (CRG) over a 49-day period. The results showed that both treatments achieved compost maturity, characterized by a slightly alkaline pH, a germination index (GI) above 80%, and an electrical conductivity below 4 mS/cm. Both composts were odorless, insect-free, and dark brown. Compared to CR, the CRG treatment exhibited higher total organic carbon (TOC) degradation, cumulative total phosphorus (TP) and potassium (TK) levels, as well as enhanced urease, cellulase, and β-glucosidase activities. In contrast, CR retained higher total nitrogen (TN), humic carbon (HEC), fulvic acid carbon (FAC), humic acid carbon (HAC), and a greater humic-to-fulvic acid (HA/FA) ratio. Microbial community analysis revealed diverse bacterial and fungal taxa, with certain species positively correlated with nutrient cycling. Notably, specific substrate compositions promoted beneficial microbial proliferation, essential for efficient composting and nutrient mineralization. These findings not only provide a scientific basis for optimizing composting strategies of mushroom residues but also offer a practical pathway to convert agricultural waste into high-quality organic fertilizers. By enhancing soil fertility, reducing reliance on synthetic fertilizers, and promoting circular bioeconomy practices, this study contributes directly to sustainable agricultural development. CR and CRG treatments, respectively, support either nutrient retention or release, allowing tailored application based on crop demand and soil condition. This study underscores the potential of Auricularia auricula (L.) residues in composting systems, contributing to waste reduction and soil fertility enhancement through tailored substrate management, and offers practical insights into optimizing composting strategies for Auricularia farming by-products. Full article

To support the ambitious bioeconomy vision outlined in Ireland’s Bioeconomy Action Plan, there is an urgent need to bring together the necessary stakeholders required to implement this vision. Farmers and other primary producers who oversee the production of sustainable biomass constitute one of the most important categories of stakeholders in the bio-based value chain. To ensure scalable, long-lasting bioeconomy collaboration, it is essential that farmers are involved in developing this bioeconomy vision. The current study provides a mixed-methods approach to co-design a green biorefinery vision with Irish farmers and other key value-chain actors. The selected value chain targeting a medium-scale grass silage biorefinery focused on the production of eco-insulation materials, with protein and biogas co-products for local markets. This was then assessed economically using an economic model, which provided a payback period of five years. To identify suitable sites for deployment of the green biorefinery in rural areas, geographical information systems (GIS) analysis was undertaken, considering various environmental, socio-economic and infrastructural variables, which identified 26 potential sites for deployment of the green biorefinery model in Ireland. This study found that early engagement with and inclusion of the farmers in a co-designed process of innovation and alternative revenue streams for them is essential. While a preferred cooperative-based business model for a grass silage biorefinery was identified in consultation with the multiple stakeholders, further research on its long-term commercial sustainability is proposed as future research. Full article

This article presents the application of novel cartographic methods of vegetation mapping with a case study of the Rif Mountains, northern Morocco. The study area is notable for varied geomorphology and diverse landscapes. The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. This approach provides a platform for processing spatiotemporal data and satellite image analysis. The objective is to determine the robustness of the “DecisionTreeClassifier” and “ExtraTreesClassifier” classification algorithms. The time series of satellite images covering northern Morocco consists of six Landsat scenes for 2023 with a bimonthly time interval. Land cover maps are produced based on the processed, classified, and analyzed images. The results demonstrated seasonal changes in vegetation and land cover types. The validation was performed using a land cover dataset from the Food and Agriculture Organization (FAO). This study contributes to environmental monitoring in North Africa using ML algorithms of satellite image processing. Using RS data combined with the powerful functionality of the GRASS GIS and FAO-derived datasets, the topographic variability, moderate-scale habitat heterogeneity, and bimonthly distribution of land cover types of northern Morocco in 2023 have been assessed for the first time. Full article

Urbanization growth and climate change have increased the frequency and severity of floods in urban areas. One of the effective methods for reducing stormwater volume and managing urban floods is the low-impact development best management practice (LID-BMP). This study aims to mitigate flood volume and peak discharge caused by land use changes in the Darabad basin located in Tehran, Iran, using LID-BMPs. For this purpose, land use maps were extracted for a period of 23 years from 2000 to 2022 using Landsat satellite images. Then, by using a combination of geographic information system-based multi-criteria decision analysis (GIS-MCDA) method and spatial criteria, four types of LID-BMPs, including bioretention basin, green roof, grass swale, and porous pavement, were located in the study area. Next, rainfall–runoff modeling was applied to calculate the changes in the mentioned criteria due to land use changes and the application of LID-BMPs in the area using soil conservation service curve number (SCS-CN) method. The simulation results showed that the rise in built-up land use from 43.49 to 56.51 percent between the period has increased the flood volume and peak discharge of 25-year return period by approximately 60 percent. The simulation results also indicated that the combined use of the four selected types of LID-BMPs will lead to a greater decrease in stormwater volume and peak discharge. According to the results, LID-BMPs perform better in shorter return periods in a way that the average percentage of flood volume and peak discharge reduction in a 2-year return period were 36.75 and 34.96 percent, while they were 31.37 and 26.5 percent in a 100-year return period. Full article

The promotion of sustainability and resilience within urban environments is widely recognized as an essential approach to educating urban communities through innovative strategies and tools. This paper presents a process for integrating stakeholders into urban growth simulation, thereby enhancing sustainable decision-making. Currently, most urban growth models fail to incorporate the perspectives of diverse stakeholders, leading to reduced equitable participation in the decision-making process. To achieve long-term sustainability, it is imperative to include the input and viewpoints of stakeholders. This study follows a four-step approach: identifying relevant stakeholders, developing the framework, evaluating its effectiveness, and documenting lessons learned. The framework involves key steps, including initial participatory modeling, analysis of development pressures and suitability with stakeholders, and technical urban growth modeling. A unique combination of modeling tools and an innovative approach was employed, incorporating the default FUTURES (GRASS-GIS) model alongside the CA-Markov Chain, Agent-Based Modeling (ABM) (NetLogo), the Cellular-Automata-based Python model, and MOLUSCE-QGIS. This integrated approach facilitates the inclusion of stakeholder-based knowledge into conventional urban growth modeling, providing novel local lessons in science, technology, and innovation initiatives. Validation was conducted through both technical and stakeholder mechanisms, confirming the effectiveness of the proposed framework. Full article

Landslide susceptibility mapping (LSM) is a vital tool for proactive disaster mitigation. Although numerous studies utilize slope units (SUs) for LSM, the limited integration of adjacency information, including spatial autocorrelation, often reduces predictive accuracy. In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. Then, the spatial stratification heterogeneity patterns of landslide susceptibility were analyzed using GeoDetector. The results showed that the SLR model achieved an area under the curve (AUC) of 0.89, a notable improvement of 0.26 compared to the traditional logistic regression (LR) model that does not incorporate adjacency information. This indicates that incorporating adjacency information effectively enhances LSM accuracy by mitigating spatial autocorrelation. Furthermore, lithology, PGV, and distance to the epicenter were identified as the primary factors contributing to the formation of the spatial stratification heterogeneity of landslide susceptibility. Among these, the interaction between lithology and PGV exhibits the strongest nonlinear enhancement. By integrating both mapping units and their adjacency information, this study provides a novel approach to improving the predictive accuracy of LSM. Moreover, by analyzing the driving factors of spatial stratification heterogeneity in landslide susceptibility maps, the study advances the practical utility of LSM for disaster management and mitigation. Full article

Traditional grass-roofed dwellings are important components of Chinese vernacular architecture. Building a comprehensive nationwide database of traditional grass-roofed dwellings is crucial for the inherence of this cultural heritage and its traditional ecological technologies. This study proposes classifying traditional Chinese grass-roofed dwellings into three types according to recognizable appearance features. Based on the YOLOv8 deep learning framework, a recognition model is constructed to recognize and spatially locate various grass-roofed dwellings from the image dataset on a county-level. Further, by conducting spatial overlap analysis with a variety of natural and socio-environmental factors on ArcGIS, their influences on the distribution pattern of traditional grass-roofed dwellings were examined. The study findings are as follows: (1) Traditional grass-roofed dwellings are concentrated on the southeast side of the Hu Line with different distribution patterns according to their types. (2) The natural environment influences the original construction and distribution of traditional grass-roofed dwellings in terms of the growth of grass resources and the ecological adaptability of grass material. (3) The development of economy, population, and urbanization pose challenges to the retention of grass-roofed dwellings. This research provides useful references for the precise preservation of various grass-roofed dwellings and introduced a novel approach for the classification of traditional buildings. Full article

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD is organized into a spatial (raster) grid and data are available in a consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs and Grasses, there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will not be EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model. Full article

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. Full article