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The conservation of cultural heritage is a well-established fact recognized by public administrations, the scientific community, and society at large. Understanding this heritage strengthens the historical memory of communities. However, there is a type of heritage that, as it disappears or evolves over time, cannot be physically recovered: the urban fabric of historic cores. This article aims to implement a system for integrating historical urban data into a Geographic Information System (GIS) to enable the graphic recovery of urban evolution within a territory. This system facilitates the storage of alphanumeric and graphic data in a centralized database, providing a holistic view of the transformation process of a historic center’s landscape. This case study focuses on an area in the historic center of Valencia, a territory influenced by multiple cultures since the city was founded by the Romans. Each culture has altered the spatial structures within the area. Georeferenced documents from historical archives, historical cartography, and the scientific literature were utilized for this study. The results have been integrated into the current cartography of Valencia in the GIS, producing plans of successive urban stages through the overlay of historical maps and current cartography.

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

Heritage and culture tourism involve features that commemorate a valued past. Mining heritage tourism allows visitors to experience the past, guided by former mining landscapes and engaging interactively with material artifacts. This paper introduces the CoalHeritage European project, focusing on the promotion of coal mining heritage through the production and design of the European Visual Map Journal (EVMJ). The EVMJ is a user-friendly, web-based, interactive storytelling platform that supports the transfer of industrial and geoheritage from former coal mining areas. It aims to collect and disseminate heritage assets from post-mining coal areas, informing stakeholders and promoting these sites as tourist destinations. To further enhance public awareness, several ESRI StoryMaps web apps are being created to highlight specific features of each case study across Europe. The aim of this work is to introduce coal mining heritage as a new term, present the coal heritage platform and its importance for disseminating coal heritage aspects to the public, describe the methodology used for its design, and provide a brief overview of its evolving content.

A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers.

Desertification processes in arid, semi-arid, and dry sub-humid conditions have been enhanced in recent decades. The geospatial database and associated satellite data can be effectively employed for regional planning to address desertification and land degradation. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model has been used to map environmentally sensitive areas due to desertification in the Satara and Sangli districts of Maharashtra, India. This was achieved by combining Landsat-8 multispectral data, Census data, soil data, and climatic variables like temperature, rainfall, and evapotranspiration. The algorithm of MEDALUS is the geometric mean of four indicators, namely soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and socio-economic quality index (SEQI). The findings indicated that the majority of the study area comes under the potential category of desertification (60.32%) followed by fragile (27.87%) and critical (11.81%). Areas with a high propensity for desertification were found over the low to very low climatic quality and moderate to high soil quality including lower socio-economic quality. The lower socio-economic quality is mainly due to high to very high population density (>100 people/km2), low to moderate illiteracy rate (

Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

The field of archaeological excavation has seen enormous developments as a result of the quick development of digital technologies. It is critical to acknowledge the long-term benefits of new approaches. In this regard, this study intends to suggest a system that provides archaeologists with digital tools that allow them to fully and effectively document their excavations in real time while in the field, which is specifically designed for classical and Byzantine archaeology. The system not only facilitates traditional documentation practices but also integrates advanced geospatial technologies and augmented reality, enhancing the accuracy and depth of archaeological research and preservation. This dual functionality enables both the efficient recording of excavation data as experienced by excavators on-site and the detailed documentation processed by researchers post-excavation. The objective of the application is to generate a sophisticated system that will enable the excavation data and experience that the excavator gains on the site of work to be recorded in real time, as well as the documentation that is subsequently processed and accomplished by the researcher or by other researchers. The system saves detailed images and 3D models of artifacts and excavation sites. This ensures that every detail is recorded while preserving the data for future analysis and reference. Lastly, the application was tested in realistic use case scenarios and real-world settings, which increased the system’s credibility and demonstrated its capability to enhance the procedures involved in archaeological excavation and documentation.

Non-Representational Theory (NRT) emphasizes the significance of routine experience in shaping human geography. In doing so, the theory largely eschews traditional approaches that have offered area-based, longitudinal, and synoptic formalisms for geographic inquiry. Instead, NRT prioritizes the roles of individualized and often dynamic lived geographies as they unfold in the moment. To date, NRT has drawn significant inspiration from the synergies that it shares with philosophy, critical geography, and self-referential ethnography. These activities have been tremendous in advancing NRT as a concept, but the theory’s strong ties to encounter and experience invariably call for practical exposition. Alas, applications of NRT to concrete examples at scales beyond small case studies often prove challenging, which we argue artificially constrains further development of the theory. In this paper, we examine some of the thorny problems that present in applying NRT in practical terms. Specifically, we identify ten traps that NRT can fall into when moving from theory to actuality. These traps include conundrums of small geographies, circularity in representation, cognitive traps, issues of mustering and grappling with detail, access issues, limitations with empiricism, problems of subjectivity, methodological challenges, thorny issues of translation, and the unwieldy nature of process dynamics. We briefly demonstrate a novel observational instrument that can sidestep some, but not all, of these traps.

A landslide is the movement of rocks, debris, and/or soils down a slope, which often includes falls, topples, slides, flows, and spreads. Landslides, a serious natural hazard to human and human activity, often occur in the coastal and mountainous areas in the United States. Although there are some studies that have explored the landslide probability, which is typically directly modeled by inputting potential environmental variables into statistical regression models, this study designed an alternative geospatial analysis and modeling approach. We first conducted statistical diagnostic tests to examine the significance of potential driving factors including landform, land use/land cover, landscape, and climate. In eastern Tennessee, USA, we first applied the t-test and chi-squared test to select the significant factors driving landslides, including slope, clay percentage in the soil, tree canopy density, and distance to roads, having a p-value of less than 0.05. We then incorporated the four identified significant factors as covariates into logistic regression to model the relationship between these factors and landslides. The fitted logistic model, with a high area under the ROC (AUC) score of 0.94, was then applied to predict landslides and make a regional landslide susceptibility map for eastern Tennessee. The landslide’s potential impacts on eastern Tennessee were also discussed, and implications for local governments and communities for current physical infrastructure protection and new infrastructure development were summarized.

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces.

Research has shown that there has consistently been a lack of equity and accessibility to SARS-CoV-2 testing in underserved and disadvantaged areas in the United States. This study examines the distribution of Wastewater-Based Epidemiology (WBE) testing placement across the United States (US), particularly within the context of underserved communities, and explores an environmental equity approach to address the impact of WBE on future pandemics. The methods combined the Centers for Disease Control Social Vulnerability Index (CDC-SVI) data set at the county level in a geospatial analysis utilizing ArcGIS and multilinear regression analysis as independent variables to investigate disparities in WBE coverage in the US. The findings show that disparities exist between counties in the use of WBE nationwide. The results show that WBE is distributed inequitably on national and state levels. Considering the nationwide adoption of WBE and funding availability through the CDC National Wastewater Surveillance System, these findings underscore the importance of equitable WBE coverage for effective COVID-19 monitoring. These findings offer data to support that a focus on expanding WBE coverage to underserved communities ensures a proactive and inclusive strategy against future pandemics.

The Italian territory of Sardinia Island has an enormous cultural and identity heritage from the Pre-Nuragic and Nuragic periods, with archaeological evidence of more than 7000 sites. However, many other undiscovered remnants of these ancient times are believed to be present. In this context, it can be helpful to analyze data from different types of sensors on a single information technology platform, to better identify and perimeter hidden archaeological structures. The main objective of the study is to define a methodology that through the processing, analysis, and comparison of data obtained using different non-invasive survey techniques could help to identify and document archaeological sites not yet or only partially investigated. The non-invasive techniques include satellite, unmanned aerial vehicle, and geophysical surveys that have been applied at the nuraghe Nanni Arrù, one of the most important finds in recent times. The complexity of this ancient megalithic edifice and its surroundings represents an ideal use case. The surveys showed some anomalies in the areas south–east and north–east of the excavated portion of the Nanni Arrù site. The comparison between data obtained with the different survey techniques used in the study suggests that in areas where anomalies have been confirmed by multiple data types, buried structures may be present. To confirm this hypothesis, further studies are believed necessary, for example, additional geophysical surveys in the excavated part of the site.

Many single-family homes built in Germany in the first decades following the Second World War are now occupied by elderly residents. If local conditions are unfavorable, a large number of these buildings may enter the real estate market in a short period of time and put pressure on the local housing market. Planners and decision-makers therefore need detailed spatiotemporal information about these neighborhoods to effectively address and counteract such developments. We present the design and implementation of a planning support system that can generate the required information. The architecture of this newly developed software consists of a composite, multitier framework to perform the complex tasks of data importation, data processing, and visualization. Legally mandated municipal population registers provide the key data for the calculation of indicators as a base for spatiotemporal analyses and visualizations. These registers offer high data quality in terms of completeness, logical consistency, spatial, and temporal and thematic accuracy. We demonstrate the implemented method using population data from a local government in a rural area in southwestern Germany. The results show that the new tool, which relies on open software components, is capable to identify and prioritize areas with particularly high levels of problem pressure. The tool can be used not only for analyses in a local context, but also at a regional level.

Anthropogenic and environmental processes present unique challenges for preserving cultural heritage in North Africa. Large parts of this region are characterised by unfavourable arid and semi-arid conditions and rapid changes to the landscapes caused by heightened regional development (e.g., urban expansion, road building, agricultural intensification, and socio-political conflicts). As a result, we are facing a fast-paced disappearance of heritage sites in regions that are still poorly understood. Following this, the utilisation of Earth observation data through aerial photographs and satellite imagery has emerged as an unmatched tool in the exploration of endangered archaeological heritage. Drawing on this context, this paper underscores the critical significance of incorporating digital research methods, such as remote sensing, GIS, or cartographic analysis, to ensure the evaluation and (digital) preservation of the historical sites along these vulnerable areas. Furthermore, our study seeks to provide new insights into data management and dissemination, fostering open research practices within North African archaeological research.

In this article we explore the text of the over 16,000 historical markers erected in the state since 1936, using GIS and corpus linguistics to determine the where, how, what, and when of how Texas memorializes its racial and ethnic groups. Unsurprisingly, our results indicate that the story of Texas is implicitly a narrative of white people. More interestingly, the term “African (Americans)” begins to be commemorated especially after the 1990s, but only in stories of community, religion, school, and children, as Texas historical markers do not to dwell on narratives of slavery, the civil rights movement, and lynchings. “Indians” and “Mexicans” in the 1930s and 1960s exemplify the most egregious case of derogatory semantics we found in the markers. As concerns racial and ethnic groups, in general they tend to be memorialized where they were historically present, whether or not such groups are still there. The analysis also reveals the increasing concentration of the markers in urban areas.

The inefficiency of transporting goods contributes to reduced economic growth and environmental sustainability in a country. Autonomous trucks (ATs) are emerging as a solution, but the imbalance in the weight moved and ton-miles produced by long-haul and short-haul trucking creates a challenge in targeting initial deployments. This study offers a unique solution by presenting a robust method that combines data mining and geographic information systems (GISs) to identify the optimal routes for ATs based on a top-down approach to maximize business benefits. Demonstrated in a U.S. case study, this method revealed that despite accounting for only 16% of the weight moved, long-haul trucking produced 56% of the ton-miles, implying a high potential for ATs in this segment. The method identified eight key freight zones in five U.S. states that accounted for 27% of the long-haul weight and suggested optimal routes for initial AT deployment. Interstate 45 emerged as a pivotal route in the shortest paths among these freight zones. This suggests that stakeholders should seek to prioritize funding for infrastructure upgrades and maintenance along that route and the other routes identified. The findings will potentially benefit a broad range of stakeholders. Companies can strategically focus resources to achieve maximum market share, regulators can streamline policymaking to facilitate AT adoption while ensuring public safety, and transportation agencies can better plan infrastructure upgrades and maintenance. Users globally can apply the methodological framework as a reliable tool for decision-making about where to initially deploy ATs.

Previous research showed that living closer to bus stops could be a factor in promoting a healthy and active lifestyle. However, most of the studies relied on self-reported measures of distance, which might be affected by several confounders. In this study, self-reported distances among study participants were compared to actual ones, computed by the use of GIS (Geographic Information System) technology and routing algorithms. We tested whether distance to the bus stop is associated with health and socioeconomic conditions of the respondents, using data among 2398 older people (75–90 years) in three cities in Sweden. We found that several variables including older age, female gender, living alone, and worse health status are associated with an over-estimation of bus stop distance. People who use public transport daily or several times a week and are satisfied with the walking environment in the neighbourhood tend to underestimate bus stop distances. Evidence based on self-reported measures only should be treated cautiously. Considering the limitations still present in open-data-based routing algorithms, the best indication is to combine the subjective with the objective measure of distance. Having the possibility to combine the two measures appears as a sound strategy to overcome the limitations associated with each single measure.

Soil erosion has been causing an imbalance in nature and the environment. It is mainly caused naturally but is also due to human interventions leading to desertification and possible contamination. Therefore, engineering, geography, and cartography have been allies in applying erosion models to predict, address, and remediate the impacts. Therefore, the Revised Universal Soil Loss Equation (RUSLE) and Soil and Water Assessment Tool (SWAT) linked to Geographic Information Systems (GISs) could boost decision making as tools to mitigate issues. This study applies the RUSLE and SWAT models from a geotechnical point of view to analyze a sub-watershed at Idanha-a-Nova (Portugal) over 4 years, showing a predominant erosion risk class with losses lower than 5 t.ha−1.year−1 (60 to 86%), characterized as very low risk. The modeling permitted the development of soils erosion susceptibility charts, in addition to material availability and the suitability for construction areas, exposing a replicable methodology that could contribute to minimizing environmental impacts while encouraging a more intelligent use of the land towards a greener exploration.

As a critical component within power systems, Gas Insulated Switchgear (GIS) is notably susceptible to insulation degradation due to submillimeter metal particles, compromising its operational safety and stability. Moreover, impact vibrations induced by circuit breaker operations can dislodge particles that typically adhere to the interior walls, causing them to become airborne and thereby intensifying their movement and discharge activities. To investigate this phenomenon, this study establishes a testing platform where alternating current (AC) voltage is superimposed with impact vibration. A camera system, interfaced with an upper computer, is used to capture the real-time motion behavior of the particles. This study focuses on characterizing the initial movement of submillimeter metal particles of varying sizes under the influence of impact vibration by analyzing critical voltages, such as the initial lift-off voltage, stable jumping voltage, and extinction voltage. Furthermore, the effects of introducing large, centimeter-scale spherical metal and rubber particles on these initial characteristics are examined. The findings provide crucial insights into the initial behavior of submillimeter metal particles in GIS, particularly in relation to circuit breaker operations.

Enhancing the production of energy from renewable energy sources (RES) has been a consistently important issue for many years, both in Poland and other countries around the world. Selecting sites for devices that convert renewable energy into electricity requires various spatial data, especially during the initial design stage when optimal investment locations are identified. The article presents a new method for assessing the usefulness of publicly accessible Polish geodetic databases—the Topographic Objects Database (Polish name: BDOT10k) and the Digital Elevation Model (DEM, Polish name: NMT)–in the process of renewable energy infrastructure siting. This study is the first to jointly assess these two databases from the user’s perspective rather than the creator’s or administrator’s viewpoint. User requirements for spatial data were defined through research factors identified in a literature review. The methodology developed includes checking the availability of Polish geodetic databases and evaluating the quality of spatial data. Analyses were performed in a GIS environment for eighteen research areas in Poland. A suitability coefficient was developed to determine the usefulness of the databases studied. The obtained value of the suitability coefficient in each area was above 50% of its maximum value, which was taken as a threshold value proving the suitability of the analyzed databases for the purpose specified in the study. The databases are fully useful for a group of province and poviat capital cities—there the suitability coefficient value exceeds 80% of its maximum value. The studies confirmed the validity of using publicly accessible BDOT10k and DEM geodetic databases in GIS analyses for the search for sites for solar, wind, and small hydroelectric power plants.

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data.

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R2 value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R2 of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts.

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century.

The design of urban systems that allow growth while also maximising ecosystem services is identified as an important priority for creating a Good Anthropocene. An ecosystem service (ES)-based approach to landscape interventions maximises the provision of ESs, and in doing so, repairs and reinforces threatened ecological planetary boundaries. As an urbanising planet, cities are critical frontiers of human interaction with these planetary boundaries, and therefore a critical arena for ES-based intervention. Globally, the predominant pattern of urbanisation is dedensification, an outwardly expanding trend where cities are growing in physical extent at a higher rate than their population growth. We therefore require spatially explicit tools capable of reconciling dedensification and Good Anthropocene visions. We propose a methodology that integrates agroecology and urbanisation and is focussed specifically on the supply of targeted regulating ESs. This ‘Agroecology for the City’ differs from conventional urban agriculture discourse and its preoccupation with food security. Our research interest is agroecological farm systems’ (AFSs) capacity to provide critical life support services in a spatially effective manner to urban systems. Our recent research introduced a new GIS-based model (ESMAX) and a spatial agroecology approach that identified AFS configurations at a 1 ha scale which maximised the supply of three regulating ESs, as well as multifunctional performance across all three ESs combined. In the present research, we apply this process at a larger scale, with 1 ha and 4 ha AFS parcels being integrated with a real-world 200 ha peri-urban residential development. The AFS parcels and built-up areas are configured differently to maximise the supply of ESs identified as critical by the local community. We found that arrangements with AFS parcels interspersed evenly with built-up areas provided the best multifunctionality across the four ESs tested. This supports pathways for a Good Anthropocene that work with the global urbanising reality of dedensification and underpin the need for a hybrid science of rural/urban systems.

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.

Protected areas are essential for the conservation of biodiversity. However, the rapid expansion of urbanization and the intensification of human activities have significantly disrupted environmental integrity, leading to a continuous deterioration in both the quantity and quality of large ecological patches. This has further diminished the connectivity among ecological patches, leading to significant consequences for regional biodiversity conservation. Taking Poyang Lake as a case study, which serves as a crucial wintering habitat for migratory birds along the East Asia–Australasia flyway, this research employs ArcMap technology. It considers various factors including land use type, slope, and elevation to evaluate habitat quality and degradation through the application of the InVEST model. Additionally, the study utilizes the minimum cumulative resistance (MCR) model alongside circuit theory to delineate ecological corridors within the area and to establish a comprehensive ecological network system. The research results in this paper are as follows. (i) During the period from 2000 to 2020, there was an overall decline in habitat quality within the study area, indicating a clear trend of habitat degradation. However, it is worth noting that there was an increase in habitat quality in certain local areas within the protected area. (ii) The ecological resistance values in the core area of the migratory bird reserve in Poyang Lake are generally low. However, the ecological resistance values of the habitats have shown a consistent increase from 2000 to 2020. Additionally, there has been a significant decrease in the density of ecological corridors during this time period. (iii) Over the period from 2000 to 2020, both the number and connectivity of ecological corridors decreased and their integrity and functionality degraded. Consequently, this weakened role of the ecological network has had implications for maintaining regional biodiversity and ecosystem service functions. The findings indicate two conclusions. (i) Ecological connectivity is essential for the conservation of migratory bird habitats. Strengthening control measures aimed at expanding ecological corridors can effectively safeguard flagship and umbrella species, thereby promoting biodiversity conservation. (ii) The establishment of ecological corridors can help reconcile conflicts between conservation efforts and development objectives. This reconciliation carries significant theoretical implications for fostering a harmonious coexistence between humans and birds in Poyang Lake’s migratory bird sanctuary.

The International Union for Conservation of Nature (IUCN)’s Red List of Threatened Species is regarded as the primary source of global extinction risk for species. Despite this importance, the IUCN has assessed less than 20% of the world′s estimated 400,000 flowering plants due to issues like insufficient data or a lack of experts. Thousands of conservation status assessments were generated recently in response to the call to action by the revised Target 2 of the Global Strategy for Plant Conservation (GSPC), “An assessment of the conservation status of all known plant species, as far as possible, to guide conservation action.” Accelerated extinction risk assessments are needed to identify species that are most at risk of extinction, which can then be thoroughly evaluated by the Red List assessment procedure. The land area in the Pacific Islands is significantly smaller than in other parts of the world. Plant species restricted to these islands may be identified as “At Risk” due to the limited land area of these islands, despite being widespread locally. Distribution and abundance data of single-island endemic plant taxa of Kosrae, Federated States of Micronesia, are analyzed and combined with digital elevation model files from this island, ArcGIS, and herbarium data, to calculate the percentage of land area that is available within a species′ elevational occurrence, a metric we have coined the Extent of Inhabitable Elevation of Island Occurrence (EIEIO). This metric can be used as a streamlined system to rapidly identify species that need plant conservation attention and allows for a swifter response to Target 2 of the GSPC.

The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.

The impact of sporting events on local economies and their spatial distribution is a topic of active policy debate. This study adds to the discussion by examining granular cellphone location data to assess the spillover effects of Major League Baseball (MLB) games in a major US city. Focusing on the 2019 season, we explore granular geospatial patterns in mobility and consumer spending on game days versus non-game days in the Saint Louis region. Through density-based clustering and hotspot analysis, we uncover distinct spatiotemporal signatures and variations in visitor affluence across different teams. This study uses features like game day characteristics, location data (latitude and longitude), business types, and spending data. A significant finding is that specific spatial clusters of economic activity are formed around the stadium, particularly on game days, with multiple clusters identified. These clusters reveal a marked increase in spending at businesses such as restaurants, bars, and liquor stores, with revenue surges of up to 38% in certain areas. We identified a significant change in spending patterns in the local economy during games, with results varying greatly across teams. Notably, the XGBoost model performs best, achieving a test R2 of 0.80. The framework presented enhances the literature at the intersection of urban economics, sports analytics, and spatial modeling while providing data-driven actionable insights for businesses and policymakers.

Brick warehouses represent interdisciplinary heritage sites developed by social, cultural, and economic impacts. This study aimed to connect warehouse details and GIS maps in augmented reality (AR) based on the former Camphor Refinery Workshop Warehouse. AR was applied as an innovation interface to communicate the differences between construction details, providing a feasible on-site solution for articulating historical brick engineering technology. A complex warehouse cluster was georeferenced by the AR models of brick details. The map was assisted by a smartphone-based comparison of the details of adjacent warehouses. Sixty AR models of warehouse details exemplified the active and sustainable preservation of the historical artifacts. The side-by-side allocation of warehouse details in AR facilitated cross-comparisons of construction differences. We found that a second reconstructed result integrated AR and reality in a novel manner based on the use of a smartphone AR. GIS and AR facilitated a management effort using webpages and cloud access from a remote site. The vocabulary of building details can be enriched and better presented in AR.

There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff—valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially safe areas—they are not threatened by floods and are therefore often occupied by buildings. The importance of addressing flash floods in land use planning is essential for sustainable development and disaster risk reduction. The objective of this research was to assess the level of the hazard and to evaluate its presence in land use planning activities. This manuscript fills a research gap, as to date flash flood threats have not been analyzed for individual buildings located in catchments of dry valleys in temperate climates. More than 12,000 first-order catchments were analyzed. The study covered an upland area located in East Poland, which is characterized by high population density and dispersed rural settlement. Within the 10 municipalities, buildings located on potential episodic runoff lines were identified. Qualitative assessment was applied to ascertain the susceptibility of catchments to flash floods. Such criteria as slopes, size, shape of the catchment area, and land cover, among others, were used. Between 10 and 20% of the buildings were located on episodic runoff lines, and about 900 sub-catchments were highly or very highly susceptible to flash floods. The way to reduce the negative effects of these phenomena is to undertake proper land use planning based on knowledge of geohazards, including flash floods. However, an analysis of available planning documents shows that phenomena of this type are not completely taken into account in spatial management processes.

Despite the ecological importance of giant clams (Tridacninae), their effective management and conservation is challenging due to their widespread distribution and labour-intensive monitoring methods. In this study, we present an alternative approach to detecting and mapping clam density at Pioneer Bay on Goolboddi (Orpheus) Island on the Great Barrier Reef using drone data with a combination of deep learning tools and a geographic information system (GIS). We trained and evaluated 11 models using YOLOv5 (You Only Look Once, version 5) with varying numbers of input image tiles and augmentations (mean average precision—mAP: 63–83%). We incorporated the Slicing Aided Hyper Inference (SAHI) library to detect clams across orthomosaics, eliminating duplicate counts of clams straddling multiple tiles, and further, applied our models in three other geographic locations on the Great Barrier Reef, demonstrating transferability. Finally, by linking detections with their original geographic coordinates, we illustrate the workflow required to quantify animal densities, mapping up to seven clams per square meter in Pioneer Bay. Our workflow brings together several otherwise disparate steps to create an end-to-end approach for detecting and mapping animals with aerial drones. This provides ecologists and conservationists with actionable and clear quantitative and visual insights from drone mapping data.

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.

Countries face challenges of excess, scarcity, pollution, and uneven water distribution. This study highlights the benefits of advances in groundwater engineering that improve the understanding of utilizing local geological characteristics due to their crucial role in resisting drought in southern Lebanon. The type of drought in the region was determined using the Standardized Precipitation Index (SPI), Standardized Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Soil Moisture Anomaly Index (SM). The dry aquifer and its characteristics were analyzed using mathematical equations and established hydrogeological principles, including Darcy’s law. Additionally, a morphometric assessment of the Litani River was performed to evaluate its suitability for artificial recharge, where the optimal placement of the water barrier and recharge tunnels was determined using Spearman’s rank correlation coefficient. This analysis involved excluding certain parameters based on the Shapiro–Wilk test for normality. Accordingly, using the Geographic Information System (GIS), we modeled and simulated the potential water table. The results showed the importance and validity of linking groundwater engineering and morphometric characteristics in combating the drought of groundwater layers. The Eocene layer showed a clearer trend for the possibility of being artificially recharged from the Litani River than any other layer. The results showed that the proposed method can enhance artificial recharge, raise the groundwater level to four levels, and transform it into a large, saturated thickness. On the other hand, it was noted that the groundwater levels near the surface will cover most of the area of the studied region and could potentially store more than one billion cubic meters of water, mitigating the effects of climate change for decades.

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.

Global urbanization has made urban governance a crucial aspect of sustainable urban development. While geo-information technologies have emerged as indispensable tools for effective urban governance, a comprehensive analysis of their application in this context remains lacking. This study seeks to review and assess the pivotal role of geo-information technologies in the field of urban governance. A total of 219 related studies were used for bibliometric analysis and key content analysis. Planning Support Systems (PSSs), Participatory Geographic Information Systems (PGISs), Building Information Modeling (BIM), and City Information Modeling (CIM) are identified as the main information technologies progressively employed across diverse stages of urban planning and construction over recent decades. These advancements have propelled the digital and intelligent management of urban areas, yielding significant benefits such as enhanced visualization, informed decision-making, and increased opportunities for citizen participation. However, a noticeable disparity between supply and demand during the application process arises from a lack of transdisciplinary cooperation. This study sheds light on the existing literature and offers policy implications and recommendations for more effective utilization of geo-information technologies in future spatial governance.

The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and natural resources are limited. In this study, we developed a methodology to assess these fragilities using an environmental vulnerability index (EVI) that combines physical and environmental indicators related to the natural sensitivity of ecosystems and their exposure to anthropogenic factors. The developed EVI was applied to the headwater region of the São Francisco River Basin (SFRB), Brazil. The proposed index was based on the AHP multicriteria analysis and was adapted to include four variables representative of the study area: Land Use Adequacy, Burned Area, Erosion Susceptibility, and quantitative water balance. The EVI analysis highlighted that the presence of easily erodible soils, associated with sloping areas and land use above their capacity, generate the most vulnerable areas in the headwaters of the SFRB. The highest EVI values are primarily linked to regions with shallow, easily erodible soils like Leptosols and Cambisols, found in steep areas predominantly used for pasture. In the SFBR, the greatest vulnerability was observed within a 5 km buffer around conservation units, covering approximately 32.4% of the total area. The results of this study indicate where resources should be applied for environmental preservation in the basin under study, directing the allocation of efforts to areas with lower resilience to maintain ecosystem services.

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping.

In the last decades, tourism in urban areas has been constantly increasing. The need for short-term accommodations has been coupled with the emergence of internet-based services, which makes it easier to match demand (i.e., tourists) and supply (i.e., housing). As a new mass tourist destination, Bologna, Italy, has been experiencing tensions between tourists and long-, mid-, or short-term renters. The possibility of easy profits for lessees has led to an increase in such housing, which can be rented out either for touristic reasons or not. This paper aims to unveil the contribution of short-term rental accommodations in distorting the real estate market and conditioning social and economic inequalities. To do this, multiple linear regression analyses (MLR) were performed between accommodation density, real estate market information, and indicators about social, economic, and demographic vulnerability and fragility. Analyses were based on official open data and datasets from a major web-based hospitality exchange platform, i.e., Airbnb, able to provide information on registered accommodations, e.g., type, characteristics (e.g., number of bedrooms and average rating), and location. Outputs of the analyses reveal the role of Airbnb in both rental market and social, economic, and demographic vulnerability and fragility and, hence, can be a solid tool for public policies, both housing- and tourism-related.

This paper presents a methodology for using geospatial tools to enact efficient tourism planning and management in streets and other public spaces in historic centres. The study uses 3D laser scanning, GIS, and spatial data processing techniques to analyse the visitor carrying capacity of streets near cultural attractions. The methodology was tested on Miguelete Street in València (Spain), next to the Cathedral. The results show that these digital tools are efficient and accurate for the spatial analysis of visitor carrying capacity studies.

Wildfires represent a significant threat to protected areas around the world. This threat has become even more pronounced in recent years due to climate change. These fires can destroy, among other things, natural habitats, cause a loss of biodiversity, and affect air quality. In addition, they can have a significant economic impact on countries that depend on tourism and protected area activities. In this context, Geographic Information Systems (GIS) can provide an important solution for fire management in protected areas. In this case study, the Basilicata region (Southern Italy) was taken as an example where data on protected areas and fire events are freely accessible and constantly updated. By exploiting several interoperable GIS tools, it was possible to perform a complex geospatial analysis that provided important information for sustainable and resilient land use planning. In particular, areas with the highest frequency of fires within or close to protected areas emerged, which can therefore be attended to at different planning levels. In addition, in general, the work has made it possible to implement simple methodologies of relational analysis between fires and protected areas that can be easily employed in administrative settings so as to make the territory more resilient to the expected increase in fires due to climatic changes.

The NH 58 area in India has been experiencing an increase in landslide occurrences, posing significant threats to local communities, infrastructure, and the environment. The growing need to identify areas prone to landslides for effective disaster risk management, land use planning, and infrastructure development has led to the increased adoption of advanced geospatial technologies and statistical methods. In this context, this research article presents an in-depth analysis aimed at developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing, GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation, geology, geomorphology, land use land cover (LULC), road distance, and slope angle. Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine correlations between causal factors and landslide occurrences. The logistic regression model was then employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which were subsequently classified into relative susceptibility zones using a statistical class break technique. The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial information for land use planning and management to reduce landslide risk in the NH 58 area. The study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional disaster risk management. This research offers a comprehensive analysis of landslide-influencing factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating landslide risks. The map also serves as a critical reference for future research and contributes to the broader understanding of landslide susceptibility in the region.

Groundwater availability in the Zakho Basin faces significant challenges due to political issues, border stream control, climate change, urbanization, land use changes, and poor administration, leading to declining groundwater quantity and quality. To address these issues, this study utilized the Analytic Hierarchy Process (AHP) and geospatial techniques to identify potential groundwater sites in Zakho. The study assigned weights normalized through the AHP eigenvector and created a final index using the weighted overlay method and specific criteria such as slope, flow accumulation, drainage density, lineament density, geology, well data, rainfall, and soil type. Validation through the receiver operating characteristic (ROC) curve (AUC = 0.849) and coefficient of determination (R2 = 0.81) demonstrated the model’s accuracy. The results showed that 17% of the area had the highest potential as a reliable groundwater source, 46% represented high-to-moderate potential zones, and 37% had low potential. Flat areas between rivers and high mountains displayed the greatest potential for groundwater development. Identifying these potential sites can aid farmers, regional planners, and local governments in making precise decisions about installing hand pumps and tube wells for a regular water supply. Additionally, the findings contribute to the development of a sustainable groundwater management plan, focusing on improving water usage and protecting water-related ecosystems in the region. Identification of the optimum influencing factors, arrangement of the factors in a hierarchy, and creation of a GWPI map will allow further planning for groundwater preservation and sustainability. This project can be conducted in other areas facing droughts.

The UN Convention on the Rights of the Child, Article 12, states that young people should be able to participate in issues that affect them. In this study, the indigenous Orang Rimba community examines the natural resources of their area through mapping so that the community can be more aware of environmental changes and at the same time their culture can be preserved. This research employs participatory resource mapping (PRM) to gather information about how young Orang Rimba view the forest in relation to customs. The study includes workshops on using GPS and GIS as well as resource mapping activities in the forest ecosystem. Through the participatory resource mapping, the study successfully maps 12 sacred places, 6 animal sites, and 14 medicinal plants in a short survey. The young Orang Rimba were also capable of addressing current environmental issues, including deforestation events, and simultaneously protecting the forest through local cultural practices. The study recommends involving indigenous communities in natural resource protection and awareness through mapping activities from a young age.

This work aims to map the optimal routes based on time and distance, via e-scooters and walking, to visit 54 historical heritage sites in Faro. Implementing these routes promotes environmental sustainability by reducing CO2 emissions and encouraging healthier, greener tourism. The route optimization was conducted in ArcGIS, utilizing the Network Analyst extension and vector data obtained from OpenStreetMap. The results showed that there are routes that can be completed in one or more days, depending on visitors’ availability, physical capacity, or their chosen method of transportation. The optimal route to visit the 54 historical heritage sites forms a closed circuit spanning 17.35 km. If visits are split into two routes, one covering 31 monuments in the old city and the other 24 monuments in the exterior area of the urban center, the optimal closed-circuit routes measure 6.16 km and 11.31 km, respectively. This study is expected to enhance tourism promoted by the Faro municipality and make it more environmentally friendly.

This study investigates the utilization of 3D laser scanning, GPS, BIM and GIS technologies for safeguarding historical wooden structures. As a case study, it focuses on two historic Chinese wooden buildings, the Guanyin Pavilion and Tangwang Palace, renowned for their exceptional historical significance and intricate timber architecture. The Guanyin Pavilion was subjected to high-precision modeling and tilt monitoring, which found that certain columns had a maximum tilt angle of 0.7892°. Additionally, a general tilt tendency was seen across various structural elements. The measurements from the point cloud data exhibited an error range of ±10 mm. However, the relative errors in smaller components approached 2.5%, which necessitated regular verification through human measurements. Using GIS and BIM technology in Tangwang Palace facilitated the meticulous documentation and dynamic supervision of architectural components, encompassing historical development and present state. The maintenance records revealed persistent problems with the roof tiles and wooden beams, exposing structural vulnerabilities. During various maintenance intervals, it was observed that the roof tiles were frequently loose and separated, indicating potential flaws in the design or materials used. The application of these modern technologies enhances the effectiveness and precision of conservation efforts and provides a robust scientific foundation for the continuous monitoring and study of cultural heritage.

This research aims to evaluate the operational effectiveness of current flood protection infrastructure and measures in a flood-prone area using geo-referenced information systems and SWOT analysis. To achieve this, all existing flood protection measures and works in the case study basin, namely Strymonas River basin in Greece, were mapped and recorded. These data, along with water-related spatial information, were stored in a geo-referenced database created within an open-source GIS environment. Additionally, the system was populated with the basin’s recorded historic floods, derived from the European Union’s Floods Directive implementation process. The outputs of the research, which include a spatial comparison of flood protection measures and works with flood event occurrences as well as analyses of the figures, density, and locations of flood protection works, were evaluated as an integrated system and further processed using SWOT analysis. The latter was informed by questionnaire results, and the identified strengths and weaknesses of the flood protection infrastructure were used to explore potential opportunities and threats, which could respectively reinforce or jeopardize the basin’s capacity to effectively respond to future floods. The research framework can be applied to any river basin and could provide important assets in flood protection planning at a basin scale.

Re-considering the interactions between society and natural resources is fundamental in pursuing sustainability and adaptation to climate change in cities. The representation in urban planning instruments reveals an inadequate consideration of valley hillslopes as interface systems or as ‘places’ with possible multiple roles and meanings for populations. Beyond landslide and flooding considerations, valley hillslopes are scarcely identified as distinct entities from the valley and the plateau, investigated as sites of diverse possible uses, and analysed in terms of which variables related to the relief’s forms influence their use. Confronting urban planning instruments that reduce slopes to building spaces, this contribution advocates for a specific representation of valley hillslopes, highlighting the environment’s variety within which the system interacts, the diversity of uses, and the interrelationships between form and land cover. By combining GIS mapping and statistical analysis, this research proposes a multi-scalar approach based on identifying valley hillslopes delineated through minimum units (geochore), integrated with land cover clustering and an analysis of the potential relations between land cover and six explanatory variables. The research points out the singularity and complexity of valley hillslopes, which should be incorporated into urban planning policy as potential cultural, ecological, or recreational resources for populations.

Landslides represent a major problem in human activities, especially in the proximity of cities or infrastructure. In this paper, we present the peculiar case of Pietracamela, Italy. Pietracamela is a small village located in the Central Apennines, a few kilometers north of the Gran Sasso Mountain. The peculiarity of the case study is the fact that the village is simultaneously affected by two different types of slope instabilities. The southwest sector, representing the historical part of the village, has been affected by large rockfalls generated from the “Capo le Vene” cliff located in the south of the village. The northeastern sector of the village represents the most recent urbanized area and is involved in a deep-seated landslide that, in the last decades, has damaged buildings and infrastructure. In this context, we have used two different types of remote sensing techniques to study the two phenomena. The rockfall area has been surveyed through the use of an Unmanned Aerial Vehicle (UAV) that allowed the definition of main joint sets and the volume of blocks associated with the most recent (2011) rockfall event. Three main joint sets have been highlighted, which are responsible for the failure of the “Capo le Vene” cliff. The volume of blocks that failed during the last rock failure in 2011 ranged from a few to 1500 m3. The deep-seated landslide has been studied by analyzing borehole data and 20 years of InSAR data from ERS1/2, ENVISAT, COSMO-SkyMed, and SENTINEL-1. It has been highlighted by InSAR analysis that the northeast sector of the village shows a perfectly linear displacement trend that generates movements up to about 230 mm (about 1 cm/year).

Located in the south of the Iberian Peninsula, the Doñana salt marshes occupy around half of Doñana National Park and are currently considered among the most important wetlands worldwide due to the importance of their ecosystem. In this research work, using a novel patented procedure, the effects of climate change on the study area between 2009 and 2020 were evaluated. For this reason, DEMs were downloaded from the 30-meter Shuttle Radar Topography Mission (SRTM). Furthermore, to check the depth of the flooded area, 792 satellite images (L5 TM, L7 ETM+, and L8 OLI) with a resolution of 30 m were analyzed. The results show how the combined use of geomatic techniques, such as radar, optical, and geographic information system (GIS) data, along with regression models and iterative processes, plays a key role in the prediction and analysis of the flooded area volume in the Doñana salt marshes. Another significant contribution of this work is the development of a new remote sensing index. In conclusion, given that the study area depends on its aquifers’ status, it would be advisable to implement policies aimed at eradicating illegal aquifer extraction, as well as recovery plans to avoid the complete clogging of this salt marsh.