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Green spaces are key aspects of urban ecology. The current study aims to estimate temporal changes in green spaces of Guwahati Metropolitan Development Authority, and also to investigate the tree community structure of three selected green patches. Change detection analysis of identified green spaces was done by comparing the normalized difference vegetation index (NDVI) maps of satellite images from 2022 with those from 1972. NDVI maps were classified into three threshold categories: no vegetation (NV), moderate vegetation (MV), and high vegetation (HV). The results show changes in the area of selected green patches as well as NV, MV, and HV regions between 1972 and 2022. The tree community structure in the three selected patches indicates a low diversity of plant species. The result of the current study prioritizes patch-wise management of urban green spaces in Guwahati city with the help of both remotely sensed and ground data. Thus, the present study can significantly contribute to plant community conservation and management of urban green spaces. [ABSTRACT FROM AUTHOR]

Climate change is a major issue in wastewater management at local and regional levels, as it affects the frequency of flooding and therefore the need to update infrastructure and design regulations. To this end, rainfall data are the main input to hydraulic models used for the design of drainage systems and, in advanced contexts, for their real-time monitoring. Field observations are of great interest and water authorities are increasing the number of existing rain gauges, but at present they are scarce and require maintenance, so their number needs to be considered with their O&M costs. Remote sensors, including both the existing satellite rain products (SRPs) and radar imagery (RI), can complete the spatial distribution of rainfall and optimise the cost of observations. While most SRPs are based on re-analysis and have a lag in availability, RI can be obtained in near real time and is becoming increasingly popular in weather forecasting applications. Unfortunately, actual rainfall forecasts from RI observations are not accurate enough for real-time monitoring of drainage systems. In this paper, the Colour Pattern Regression (CPR) algorithm is used to recalibrate the 6 h rainfall values from RI provided by the Agencia Estatal de Meteorología (AEMET) with the observed rain gauge data, using as a case study the metropolitan area of Palma (Spain). [ABSTRACT FROM AUTHOR]

Engineering works greatly influence the lateral channel migration (LCM) of meandering rivers. We aimed to characterise the spatiotemporal characteristics of LCM during the last 174 years of the almost freely meandering Upper Rába (Hungary) and to identify units with distinctive LCM histories. The studied Rába’s reach has been regulated just at some points. Due to the free meandering, its length varied between 119 and 133 km. The most intensive length increase (+291 m/y) took place between 2005 and 2008, and by the end of the process, the sinuosity had reached its historical maximum (SI2018= 1.93). The mean LCM was 3.3 m/y (1844-2018). The periods with intensive channel migration (max: 24-27 m/y) were followed by periods with low migration rates. Based on local channel morphology and LCM rates, 14 units were identified. The highest LCM rate was measured in the freely migrating units (R5: 5.8 m/y; R3: 4.4 m/y and R6 4.0 m/y). [ABSTRACT FROM AUTHOR]

Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management. [ABSTRACT FROM AUTHOR]

Radon concentrations in the tourist part of the Cango cave were measured using 25 strategically placed electret ion chambers. Airflow rates were also measured and found to be less than 1 m/s throughout the cave. An IDW interpolated radon concentration overlay was constructed using QGIS and overlayed on maps of the cave. The maximum radon concentration of 2625 Bq/m3 was measured in the Grand Hall, located in the central part of the cave following a narrow passage. The initial part of the cave near the entrance exhibited normal cave breathing characteristics, with radon concentrations of less than 300 Bq/m3. The deepest section of the cave, however, demonstrated an unexpected decrease in radon levels, temperature, and humidity. The average radon concentration in the Cango cave, measured at 1265 Bq/m3, is relatively low compared to other caves worldwide that need mitigation measures according to the International Commission on Radiological Protection (ICRP). [ABSTRACT FROM AUTHOR]

This study evaluates and contrasts changes in vegetation cover over three decades in two forest areas in the State of Chihuahua in northern Mexico with different management statuses: one with sustainable forest management and the other protected as a Flora and Fauna Protection Area. The hypothesis proposed that both areas would have maintained or increased their vegetation cover since 1995. Satellite images from the periods 1995–2008, 2008–2014, 2014–2022, and 1995–2022 were analyzed. The results showed that Ejido El Largo y Anexos significantly increased forest areas and reduced grasslands due to sustainable management practices, with a notable expansion of pine–oak and pine forests. In contrast, the Tutuaca Flora and Fauna Protection Area experienced a notable loss of oak and oak–pine forests, suggesting ineffectiveness in its conservation policies. However, there was less loss in Douglas Fir forests, indicating some effective protection efforts. The comparison reveals opposing dynamics: while Ejido El Largo y Anexos demonstrates success in sustainable management, the Tutuaca Flora and Fauna Protection Area faces conservation challenges. In conclusion, this study highlights the need for active management approaches to maintain ecosystem cover and functionality. [ABSTRACT FROM AUTHOR]

Over the last decade, access to global data has become increasingly critical for research, allowing insights into diverse biological, environmental and societal questions at a macro scale. Digitisation has greatly enhanced the use of herbarium data in the analysis of species distributions and ecological niche modelling. Yet, sources on modelling and mapping methodology using open-source software is greatly lacking for beginners. We have created a replicable and thorough tutorial to visualise species occurrence data and exploratory analysis that was developed by undergraduates with broad backgrounds and levels of experience. This tutorial integrates the open-source programmes QGIS, MaxEnt and R to develop distribution maps, using bryophytes as a case study, to promote the accessibility of open-source software and remote access learning. This tutorial has already set the foundation for further research into distribution modelling of rare Illinois bryophytes to better understand the potential impact of climate change.

This research focuses on developing a multipurpose riparian zone designed to effectively reduce erosion and control nutrient flow into Lake Tondano. The goal is to enhance both conservation and pollution control strategies for sustainable ecosystem management. Conducted at Lake Tondano in Minahasa, Indonesia, the research followed three main stages: data collection, analysis, and design. The data collection included a vegetation survey of riparian species, land use mapping, and measuring nitrogen and phosphorus levels in lake sediments. A total of 91 plant species from 60 genera and 42 families were documented. Based on their Importance Value Index (IVI), non-invasive status, and nutrient absorption capabilities, 15 species were selected for restoration in areas with high nutrient concentrations, with sediment levels recorded at 0.09% nitrogen and 0.06% phosphorus in impacted zones. These plant species were carefully identified and rigorously tested, originating from intact riparian zones. They will be strategically employed in areas facing significant challenges from nutrient overloading. Using QGIS analysis, a riparian zone measuring 100 m x 30 m was designed at coordinates 1.1745604 latitude and 124.8972748 longitude, targeting areas most impacted by nutrient pollution, which poses a risk of eutrophication and negatively affects aquatic ecosystems. The multipurpose riparian zone incorporates distinct wet, transition, and dry zones, employing a zigzag planting pattern to optimize pollutant filtration and nutrient uptake. This design effectively addresses critical issues of erosion and nutrient excessive enrichment, promoting ecological health and biodiversity in the region. The novel contributions of this study include identifying specific plant species capable of thriving in nutrient-rich sediments and quantifying their nutrient absorption capacities, thereby providing a scientifically grounded model for similar conservation efforts in vulnerable ecosystems and enhancing overall resilience. However, long-term impacts on water quality require further research to assess nutrient mitigation efficacy.

This research focuses on developing a multipurpose riparian zone designed to effectively reduce erosion and control nutrient flow into Lake Tondano. The goal is to enhance both conservation and pollution control strategies for sustainable ecosystem management. Conducted at Lake Tondano in Minahasa, Indonesia, the research followed three main stages: data collection, analysis, and design. The data collection included a vegetation survey of riparian species, land use mapping, and measuring nitrogen and phosphorus levels in lake sediments. A total of 91 plant species from 60 genera and 42 families were documented. Based on their Importance Value Index (IVI), non-invasive status, and nutrient absorption capabilities, 15 species were selected for restoration in areas with high nutrient concentrations, with sediment levels recorded at 0.09% nitrogen and 0.06% phosphorus in impacted zones. These plant species were carefully identified and rigorously tested, originating from intact riparian zones. They will be strategically employed in areas facing significant challenges from nutrient overloading. Using QGIS analysis, a riparian zone measuring 100 x 30 m was designed at coordinates 1.1745604 latitude and 124.8972748 longitude, targeting areas most impacted by nutrient pollution, which poses a risk of eutrophication and negatively affects aquatic ecosystems. The multipurpose riparian zone incorporates distinct wet, transition, and dry zones, employing a zigzag planting pattern to optimize pollutant filtration and nutrient uptake. This design effectively addresses critical issues of erosion and nutrient excessive enrichment, promoting ecological health and biodiversity in the region. The novel contributions of this study include identifying specific plant species capable of thriving in nutrient-rich sediments and quantifying their nutrient absorption capacities, thereby providing a scientifically grounded model for similar conservation efforts in vulnerable ecosystems and enhancing overall resilience. However, long-term impacts on water quality require further research to assess nutrient mitigation efficacy. [ABSTRACT FROM AUTHOR]

Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management.

Evaluation of land use change is important for understanding the relationship between nature and the future policy design in sustainable natural resources management. In this study the dam-triggered land use cover changes since the time of its commencement of construction was evaluated by using satellite remote sensing and GIS techniques. To detect changes between 2011 and 2021, the post-processing technique was applied using QGIS’s SCP tool and two raster images of 2011 and 2021 as inputs for change simulation and the output classes were edited using reference classes. The study results revealed that the overall accuracy for the 2011 classification and 2021 classification was 0.89 (89%) and 0.94 (94%) respectively. The highest conversion was recorded between agricultural land to the mixed forest (14.61%) and mixed forest to shrub land (13.40%), while the lowest conversion was observed between the built-up area to a water body (0.02%) and to agricultural land (0.1%). It was recommended to consider the correlation between forest biodiversity conservation and the sustainability of hydroelectric dam by increasing restoration of degraded forests in the GERD catchment and growing forest cover should be viewed as an issue of national energy security as early as possible.

Assessing tourism sustainability has become of paramount importance to researchers and policymakers in regard to better protecting tourism destinations, particularly with the continued increase in the number of tourists and visitors who alter the natural components of the environment and degrade ecosystems. The use of geographic data is of great importance, as it can serve as a dashboard for monitoring and controlling the sustainability of tourist areas. Due to their diversity and abundance in the city of Tangier, these tools are available and easy to use. The medina of Tangier is used as a calculation example, a method that will also be applicable to other areas of the metropolitan city and to other cities as well. Based on the annual meeting of the different component of society involved in the Medina of Tangier, the data are extracted and integrated into the proposed model through a plugin interface. Therefore, the use of geographic data is of great importance, especially in environmental monitoring and land management, due to its ease of use and availability. However, calculating composite sustainability indices involves several steps and requires a basic understanding of existing models. This article describes the development and design of a plugin package in QGIS that serves as an easy-to-use tool to automatically process and calculate economic, environmental and social indices and the overall tourism sustainability index. These plugins were built in Python and designed as plugins for the QGIS software version 3.22.

Abstract Background Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases. Methods Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann–Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations. Results In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact. Conclusions The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas.

Soil moisture content (SMC) plays a significant role in land surface water and energy cycle and is essential in performing various field-related studies. It is a crucial parameter provided by passive L-band sensors on soil moisture active passive/soil moisture ocean salinity satellite missions at a resolution of ~36–40 km. To obtain inference from the SMC data and apply it to different applications, its study and analysis are required that is achievable using any geographic information systems software. Quantum Geographic Information System (QGIS) is an open-source software with a user-friendly graphical user interface (GUI) and a repository of application-specific plugins. However, no plugin provides SMC or downscales the SMC product for a required location. Q-Daily Arial Image (Q-DAI), the QGIS plugin proposed here, implements a downscaling algorithm to obtain the low-resolution SMC product from SMAP/SMOS at fine resolution using inputs from high-resolution satellite imagery. The plugin is developed by designing a GUI using Qt Creator and defining its functionality using Python. Q-DAI is tested on QGIS 3.16.16 on Windows 10, 8 GB RAM PC and QGIS 3.22 on a macOS Ventura laptop. Q-DAI can be used to obtain high-resolution SMC for any location, and in this article, sample results of Q-DAI implemented for Delhi region data have been shown. [ABSTRACT FROM AUTHOR]

Indonesia plans to reduce greenhouse gas (GHG) emissions by 29% by 2030 to address its high fossil energy use. One strategy involves PT PLN (Persero) implementing biomass cofiring technology in 52 power plants by 2025. As the 4th global rice producer, rice husk become a potential source for this strategy in Indonesia. However, sustainability of supply is a challenge, with inadequate research and only 33.52% recording at the rice mills. This study aims to quantify the rice husk availability for biomass cofiring in Indramayu Regency, the Indonesia's largest rice-producing area. Using a spatial approach, surveys, interviews, and Quantum GIS (QGIS) version 3.22.12, a visual map of rice husk availability was created. The result of the research is 95 data of rice mills (79 small, 12 medium, 4 large) which showed daily husk potentials of 0.87, 4.83, and 10.74 tons, respectively. National production data estimated theoretically an annual availability of 272,106 tons of rice husk. Spatial analysis from surveys and interviews indicated 601,669 tons/year, while distribution by milling scale suggested 588,861 tons/year. Competition for rice husk use was high in industries like roof tile, brick, and cement, with recovery fractions (α) of 13.23%, 17.50%, and 23.33% during harvest, and 3.90%, 10%, and 15% in the off-season. With information of operational days from each mill scale during the harvesting and the off-season, the mobilizable of rice husk was calculated as 77,102.17 tons/year. Policies promoting the use and management of rice husk in rice-producing areas are necessary to enhance biomass cofiring implementation. [ABSTRACT FROM AUTHOR]

The prediction of land use and land cover (LULC) patterns is essential for land use planning, natural resources management, and climate change mitigation. In this study, the artificial neural network (ANN) technique and QGIS software have been proposed to project 5- and 10-year future LULC for the Mahanadi River basin in Chhattisgarh, India. The results reveal that slope, aspect, and hillshade maps have significant effects on LULC. The projection of the future LULC for the years 2027 and 2032 reveals that the grassland and cropland areas would significantly decline and increase, respectively, in the study region. The results from trend analysis show an increasing trend for forests, permanent wetlands, cropland, urban built-up land, waterbodies, and cropland/natural vegetation, while decreasing trends for the remaining classes. The overall finding from this study suggests that remote sensing enabled with the ANN technique has the potential to project the future LULC with high accuracy. [ABSTRACT FROM AUTHOR]

Educational institutions in Poland often struggle with various problems, such as the lack of an adequate number of rooms or the poor technical condition of buildings. This is due to many factors, such as the age of the buildings, demographic trends, migration, political, social, historical, and cultural conditions and, above all, financial conditions. In order to address these problems, the Krakow University of Technology undertook the implementation of the "Green Classroom" scientific project as part of the "Science for Society" programme of the Ministry of Education and Science. The Green Classroom is a mobile, free-standing educational facility consisting of a geometric arrangement of four basic modules. Integrating this type of facility into existing infrastructure, especially in urban areas, requires the availability of suitable land, taking into account hydro-meteorological and wastewater conditions. This study presents a method using geographic information system (GIS) tools to select school areas where it is possible to locate "Green Classrooms", taking into account sustainable land retention. Based on typical rainfall for the city of Krakow, stormwater runoff was calculated taking into account the adopted "Green Classroom" module. An additional sealed surface (a "Green Classroom" system) increases the rainwater runoff by approximately 1 m3. In order to balance the rainwater runoff, it is recommended to install a rainwater collection tank with a capacity of 1 m3 next to the "Green Classroom" module. In order to relieve the storm sewer system, especially in highly sealed urban areas, it is recommended to use aboveground or underground stormwater tanks. The size of the tanks should depend on the impervious surfaces and their number on the site conditions. Nomograms for the city of Krakow have been developed to estimate the size of tanks. [ABSTRACT FROM AUTHOR]

This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study's objectives. While existing platforms like Esri's software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement. [ABSTRACT FROM AUTHOR]

Background: Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases. Methods: Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann–Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations. Results: In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact. Conclusions: The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas. [ABSTRACT FROM AUTHOR]

The article explores the relations among space, caste and gender in Dalit massacres by locating the female survivors of the Kilvenmani massacre (1968) using feminist geocriticism and digital cartography. The introduction explores feminist scholarship in the field of GIS in order to situate the present study within the broader scholarship. This is followed by a section on the background and existing research on the Kilvenmani massacre. The next section summarizes the hybrid/mixed methodology which is a combination of feminist geocriticism (locating the female survivors in the place of violence) and digital cartography (physically locating the survivors in geographical maps and analyzing the relations). The section also details the materials that are considered in this article to identify the female survivors. The materials consist of both fictional (novels, films) and non-fictional (documentary, newspaper reports) texts that are closely read to understand the Dalit female experiences of the massacre. The next section gives the mapping of the female survivors using QGIS software along with the analysis of the data and the results to foreground the relation among caste, space, and gender in Dalit massacres. Both case studies, I (on the Marichjhapi massacre) and II (on the Kilvenmani massacre), are part of a larger study that aims to create a comprehensive spatial archive on the female survivors of selected Dalit massacres in independent India. [ABSTRACT FROM AUTHOR]

Accurate land use-land cover mapping is essential to policymakers for future planning. This study aims to assess the land use-land cover dynamics and estimate its future projection in the Jagatsinghpur district of Odisha state from India. In recent years, cloud-based platforms like Google Earth Engine and domains like machine learning have attracted considerable attention from researchers. In this study, five machine learning algorithms, such as Classification and Regression Tree, Naive Bayes, Support Vector Machine, Gradient Tree Boost and Random Forest are experimented on the multitemporal Sentinel-1 C-band dataset from Google Earth Engine. The results are evaluated based on metrics like overall accuracy and Kappa statistics. The performance metrics indicate that Random Forest with 60 trees outperforms others. Next, the land use-land cover maps of the study area are generated with Random Forest classifier for the years 2017 and 2021. The results are compared to ESRI land cover maps and ESA world cover maps. The 2017 and 2021 maps are exported to QGIS, and these maps are used to generate a simulation map for 2021. The simulated land use-land cover map for 2021 indicates promising results with an overall Kappa value of 0.97 and a percentage of correctness of 98.21%. The simulated map is validated against a factual map. Finally, future projections of land-use changes are forecasted for the years 2030 and 2050 using QGIS-MOLUSCE. The predicted maps project a significant rise in agricultural and built-up areas. These findings will assist policymakers in future planning. [ABSTRACT FROM AUTHOR]

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables. [ABSTRACT FROM AUTHOR]

Over the years, vegetation in cities has been affecting the climate in the valley. A method called Normalized Difference Vegetation Index (NDVI) Assessment combined with Remote Sensing through satellite images is an innovative way to track the changes in vegetation cover in these areas. This study sought to examine alterations in vegetation regions within the Koronadal valley by employing the Normalized Difference Vegetation Index (NDVI) for analysis. The research design involved quantitative descriptive research, utilizing numerical data from NDVI analysis as the foundation for predicting vegetation cover trends over the next decade through linear regression. It also utilized satellite imagery from platforms like Landsat 7, 8, and 9. The results revealed a substantial increase in vegetation cover from (183.41 ± 46.53) km² to (230.73 ± 16.82) km² within Koronadal City between 2003 and 2023. Statistical analysis indicates a significant positive trend in vegetation area expansion over the specified timeframe with a regression (R) value of 0.97. Through NDVI analysis using the QGIS software, the visual diagrams show the increase in vegetation cover, illustrating a consistent rise over decades, emphasizing the government's demonstrated environmental conservation methods. The linear regression indicates a sustained increase in vegetation cover in Koronadal over the next decade. The rise in vegetation cover underscores the successful environmental preservation efforts and highlights the city's growing environmental resilience, emphasizing the need for sustainable land management practices in addressing environmental challenges. [ABSTRACT FROM AUTHOR]

Assessing tourism sustainability has become of paramount importance to researchers and policymakers in regard to better protecting tourism destinations, particularly with the continued increase in the number of tourists and visitors who alter the natural components of the environment and degrade ecosystems. The use of geographic data is of great importance, as it can serve as a dashboard for monitoring and controlling the sustainability of tourist areas. Due to their diversity and abundance in the city of Tangier, these tools are available and easy to use. The medina of Tangier is used as a calculation example, a method that will also be applicable to other areas of the metropolitan city and to other cities as well. Based on the annual meeting of the different component of society involved in the Medina of Tangier, the data are extracted and integrated into the proposed model through a plugin interface. Therefore, the use of geographic data is of great importance, especially in environmental monitoring and land management, due to its ease of use and availability. However, calculating composite sustainability indices involves several steps and requires a basic understanding of existing models. This article describes the development and design of a plugin package in QGIS that serves as an easy-to-use tool to automatically process and calculate economic, environmental and social indices and the overall tourism sustainability index. These plugins were built in Python and designed as plugins for the QGIS software version 3.22. [ABSTRACT FROM AUTHOR]

In many West African countries, ongoing rapid urbanization urges reliable and proactive land use plans for sustainability purposes, which hinges on a complete assessment of land suitability. GIS-based multi-criteria decision analysis (GIS-MCDA) is one of the most widely applied techniques in land-use suitability analysis. It is pivotal that planners and analysts in the developing world have adequate support in conducting such analysis. To lower the financial and technological barriers, a new free and open-source software (FOSS) for GIS-MCDA is developed, called PyLUSATQ. It is designed as a QGIS plugin following a tight-coupling integration strategy, where analytic tools for GIS-based suitability analysis and MCDA are interconnected and seamlessly integrated into QGIS's processing framework. With this implementation, users can create customized models with the PyLUSATQ tools using QGIS's Graphical Modeler to automate the workflow for suitability analysis. PyLUSATQ is the first of its kind amongst all plugins published on the QGIS Python Plugin Repository, offering a range of tools for GIS-MCDA within the context of land use planning. To demonstrate its practical application, we created two 2050 future land-use scenarios in the Ashanti Region of Ghana. The first scenario depicts a "business-as-usual" model, whereas the second shows an "alternative" scenario if a higher development density was adopted. Comparisons of such scenarios provide evidential support for making informed decisions on land-use policies. Additionally, the methodology introduced here is easily replicable for developing new QGIS plugins based on third-party Python libraries. [ABSTRACT FROM AUTHOR]

The further promotion of cycling is a key component for each city to reach its sustainability goals. To make decisions to improve comfort or safety for cyclists, the amount of motorized traffic should be taken into account. Therefore, traffic data play a crucial role not only in the construction of roads but also in cycling planning. This data source provides insights essential for road infrastructure development and optimizing various modes of transportation, such as bike paths. However, processing municipal traffic data becomes a challenge when stationary traffic-counting stations lack geo-referencing in relational databases. In this case, the locations of traffic counters are solely displayed on a PDF-based site map without inherent geo-referencing, and the geo-coordinates are not stored in any relational database. The absence of geo-references poses a significant hurdle for traffic-planning experts in decision-making processes. Hence, this study aims to address this issue by finding a suitable approach to extract the geo-coordinates from the site maps. Several potential solutions are discussed and compared in terms of time dimension, usability, extensibility, error treatment and the accuracy of results. Leveraging the open-source tool QGIS, geo-coordinates may be successfully extracted from the PDF-based site maps, resulting in the creation of a GeoTIFF file incorporating coordinates and the rotated site map. Geo-coordinates can then be derived from the GeoTIFF files using x and y coordinates, computed through the rotation matrix formula. Over 1400 measurement locations may be extracted based on the preferred approach, facilitating more informed decision-making in traffic planning. [ABSTRACT FROM AUTHOR]

A avaliação da acurácia de mapeamentos da cobertura da terra é essencial para os usos científico, prático e político dos mapas. No Brasil, o projeto MapBiomas vem mapeando anualmente a cobertura da terra em todo o território via classificação automática de imagens Landsat de média resolução espacial (30 m) desde 1985. Cada nova versão do algoritmo de classificação gera uma nova coleção de mapas que são sujeitos a uma avaliação da acurácia em nível nacional. Entretanto, é cada vez mais frequente o uso do MapBiomas para estudos regionais, municipais ou locais para os quais a avaliação da acurácia em nível nacional da não é adequada. Aqui avaliamos a acurácia e a evolução da acurácia das principais categorias de cobertura do MapBiomas para o estado de São Paulo (SP), o mais urbanizado do país e objeto de muitos estudos e políticas públicas relacionadas à cobertura da terra. Analisamos as coleções 3.1, 4.1, 5.0, 6.0 e 7.0 para o ano de 2017, o mais recente com coincidência de classes em todas as coleções, considerando as classes: Formação Florestal, Floresta Plantada, Pastagem, Cana de Açúcar, Infraestrutura Urbana e Rio, Lago e Oceano. A acurácia global teve seu menor valor na coleção 4.1 e maior na 7.0. As acurácias do produtor (AP) e do usuário melhoraram da coleção 3.0 para a 7.0, com exceção das AP para Floresta Plantada, que praticamente permaneceu inalterada, e de Infraestrutura Urbana que vem mostrando tendência de piora ao longo das coleções, atingindo o seu menor valor na 7.0

Oebobo District has the highest density level among all sub-districts in Kupang City. The impact of population growth and circulation is that the mobility of people and goods often occurs quickly, thus hampering efficient observation. This research highlights the need for accurate recognition to develop recommended effective and optimal space management and utilization. The focus of this research is population movement patterns by considering spatial structure. Quantitative descriptive research method used over the space syntax approach on QGIS and DepthMapX softwares. Quantification is carried out on the connectivity value to measure the level of connection in the road network and the integration value to reflect the integration between spaces. With the highest connectivity and integration scores, Oebobo District has the potential to become the city center or main activity center in Kupang City.

In this study, we conduct for providing information on the status of vegetation space distribution in the Duung wetland protected area and to help manage the wetland protected area. To understand the spatial distribution of vegetation in Duung Wetland, used the results of surveys in 2019 and 2023. As a result of the study, the number of vegetation types increased by 4 from 20 to 24. Four communities were newly investigated, including the Utricularia tenuicaulis community, Pueraria montana var. lobata-Elymus tsukushiensis community, Spiraea prunifolia for. simpliciflora community, and Miscanthus sinensis var. purpurascens community. In accordance with the environment, the range of aquatic plant communities such as Trapa japonica community and Nymphaea tetragona var. angusta community increased, and the succession zone of cultivated land expanded dry grassland. The survey results can be used as basic data for systematic management of the Duung wetland protected area.