Search Results (21)

Manta ray tourism has become a multi-million-dollar industry proposed as a conservation tool in recent decades; however, its impacts remain unclear. We use drones and Markov models to quantify the effects of diver presence on a juvenile population of the recently described Atlantic manta ray (Mobula yarae) off the coast of Florida. We contrast diver effects on behavioral states (avoidance, feeding, and neutral), examine the responses of individual manta rays, and estimate the energetic costs of diver presence. Diver presence significantly influenced manta ray behavior. Manta rays spent 37% of their time avoiding divers, with neutral and feeding manta rays having an increased probability of transitioning to avoidance states in the presence of divers. We found a significant difference in the proportion of time individual manta rays spent in avoidance, with some individuals being highly avoidant (up to 70%), while others were less affected by diver presence (<20% avoidance). While wingbeat frequency did not change in the presence of divers, manta rays with divers spent significantly more time with their cephalic fins unfurled. Our findings suggest that tourism could negatively impact this small, vulnerable population, making it unsuitable for development. We recommend similar behavioral and kinematic assessments to guide sustainable wildlife tourism management. Full article

The safeguarding and enhancement of historic buildings and artifacts in Italy’s inner areas are essential to protect their outstanding cultural value. However, these territories often face complex orographic and environmental conditions that make traditional surveying and documentation challenging. To address these issues, this study proposes a framework for the digitalization and virtual dissemination of architectural heritage aimed at supporting safe and sustainable tourism. The proposed approach integrates unmanned aerial vehicle (UAV) photogrammetry with laser scanning to produce three-dimensional models of historic structures. These digital models are then semantically enriched and simplified for use within a web-based virtual reality (VR) platform, enabling interactive learning experiences for increase cultural heritage accessibility. The framework is validated through the case study of the Roccapreturo Tower in Acciano (AQ), located in the inner areas of the Abruzzo region, a landscape characterized by high morphological complexity. Results demonstrate the effectiveness of drone photogrammetry in capturing detailed and accurate representations of cultural heritage assets while ensuring operational efficiency and accessibility. The resulting VR models promote heritage safeguarding and sustainable tourism, confirming the potential of UAV-based technologies in the digital transformation of cultural heritage. Full article

Drones have rapidly emerged as transformative tools in landscape research, enabling high-resolution spatial data acquisition, real-time environmental monitoring, and advanced modelling that surpass the limitations of traditional methodologies. This scoping review systematically explores and synthesises the technological applications of drones within the context of landscape studies, addressing a significant gap in the integration of Uncrewed Aerial Systems (UASs) into environmental and spatial planning disciplines. The study investigates the typologies of drone platforms—including fixed-wing, rotary-wing, and hybrid systems—alongside a detailed examination of sensor technologies such as RGB, LiDAR, multispectral, and hyperspectral imaging. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a comprehensive literature search was conducted across Scopus, Web of Science, and Google Scholar, utilising predefined inclusion and exclusion criteria. The findings reveal that drone technologies are predominantly applied in mapping and modelling, vegetation and biodiversity analysis, water resource management, urban planning, cultural heritage documentation, and sustainable tourism development. Notably, vegetation analysis and water management have shown a remarkable surge in application over the past five years, highlighting global shifts towards sustainability-focused landscape interventions. These applications are critically evaluated in terms of spatial efficiency, operational flexibility, and interdisciplinary relevance. This review concludes that integrating drones with Geographic Information Systems (GISs), artificial intelligence (AI), and remote sensing frameworks substantially enhances analytical capacity, supports climate-resilient landscape planning, and offers novel pathways for multi-scalar environmental research and practice. Full article

(1) Background: Cultural heritage plays a vital role in shaping collective identity and supporting tourism, yet it faces increasing threats from natural and human-induced disasters. As a response, digital technologies—especially drone-based monitoring systems—are being explored for disaster prevention. This study examines whether a Genetic Algorithm can effectively optimize the placement of drone stations for the economic protection of cultural heritage. (2) Method: A simulation was conducted in a 2500 km² virtual space divided into 25 km² grid units, each assigned a random land price. Drone stations have an operational radius of 40 km. GA optimization uses a fitness function based on the ratio of cultural artifacts covered to installation cost. To prevent premature convergence, multi-point crossover and roulette wheel selection are employed. Key GA parameters were fine-tuned through repeated simulations. (3) Results: The optimal parameter set—population size of 300, mutation rate of 0.2, mutation strength of ±5 km, and crossover ratio of 0.3—balances exploration and convergence. The results show convergence toward low-cost, high-coverage locations without premature stagnation. Visualization clearly illustrates the optimization process. (4) Conclusions: GA proves effective for economically optimizing drone station placement. Though virtual, this method offers practical implications for real-world cultural heritage protection strategies. Full article

This paper presents a comprehensive study of the size and conservation status of the only Italian population of Centaurea pumilio (Asteraceae) and the threats to its survival. The population is located on a short stretch of sandy shoreline along the Ionian coast of Puglia, near Torre S. Giovanni (Ugento, Lecce). It was estimated in the 1990s to number about 500 plants, but in recent years a significant reduction, bringing the population to fewer than 100 individuals, has been observed. This study involved a census of the individuals (differentiating young plants from adult and reproductive ones) conducted with a precision GPS tool, phytosociological analysis and high-definition orthophoto image acquisition using a drone. Concerning the latter, to evaluate anthropic pressure from tourism, data were acquired in spring 2023 and autumn 2024 and compared using GIS geoprocessing, showing a significant increase in the area occupied by footpaths. GIS analysis also revealed that the survival of C. pumilio is strongly linked to the intensity of the walking routes, which have fragmented the population into small and isolated clusters. On the basis of all the collected data, the current conservation status of the species in Italy was assessed as critically endangered. Finally, our study provides a series of suggestions to improve the conservation status of the species and strategies to reduce the risk of extinction in Italy. Full article

In this study, we analyzed regional spatial features and optimized tourism routes based on point cloud data provided by unmanned aerial vehicles (UAVs) with the goal of developing the Xiaosongyuan Red Tourism Scenic Area in Kunming, Yunnan Province, China. We first proposed a novel method for UAV point cloud data coverage based on an irregular regional segmentation technique along with an optimized search path designed to minimize travel time within the specified area. Three DJI Phantom drones were employed to collect data over the designated region, and an improved progressive triangular irregular network densification filtering algorithm was used to extract ground points from the UAV-acquired point cloud data. DJI Terra software was used for image stitching to generate a comprehensive map of spatial features in the target area. Using this three-dimensional map of spatial features, we explored tourist routes in complex environments and applied an improved particle swarm optimization algorithm to identify optimal tourist routes characterized by safety, smoothness, and feasibility. The findings provide valuable technical support for enhancing tourism planning and management in scenic areas while maintaining a balance with conservation efforts. Full article

Monitoring coastal marine wildlife is crucial for biodiversity conservation, environmental management, and sustainable utilization of tourism-related natural assets. Conducting in situ censuses and population studies in extensive and remote marine habitats often faces logistical constraints, necessitating the adoption of advanced technologies to enhance the efficiency and accuracy of monitoring efforts. This study investigates the utilization of aerial imagery and deep learning methodologies for the automated detection, classification, and enumeration of marine-coastal species. A comprehensive dataset of high-resolution images, captured by drones and aircrafts over southern elephant seal (Mirounga leonina) and South American sea lion (Otaria flavescens) colonies in the Valdés Peninsula, Patagonia, Argentina, was curated and annotated. Using this annotated dataset, a deep learning framework was developed and trained to identify and classify individual animals. The resulting model may help produce automated, accurate population metrics that support the analysis of ecological dynamics. The resulting model achieved F1 scores of between 0.7 and 0.9, depending on the type of individual. Among its contributions, this methodology provided essential insights into the impacts of emergent threats, such as the outbreak of the highly pathogenic avian influenza virus H5N1 during the 2023 austral spring season, which caused significant mortality in these species. Full article

Seaplane operations connect remote areas, promote tourism, and provide unique transportation solutions. After many years of preparations, commercial seaplane operations on a network of 100 water airports and 200 waterways in Greece are about to commence. The network can serve the needs of 1.6 million permanent residents of the Greek islands, the inhabitants of the mainland, and over 35 million annual tourists. This paper aims to conduct a PESTLE (Political, Economic, Social, Technological, Legal, and Environmental) analysis to identify the factors that have delayed operations and those that will affect the success of future operations. As such, 26 factors are examined. It was found that the Greek debt crisis and the COVID-19 pandemic were impediments to operations. The potential of using electric seaplanes is discussed. Recent developments in using drone inspection capabilities for aviation safety are examined. Management strategies for the Etesian winds and other environmental issues are presented. Overall, seaplane operations have enormous potential, while the Greek economic recovery provides favorable conditions for completing the project. The critical issue determining success is executing a multi-faceted business model to ensure seaplane operations’ financial viability. The network can act in synergy with other modes of transportation to help achieve social cohesion, improve tourism services, and foster economic development. Full article

A myriad of types of artificial intelligence (AI) systems—namely AI-powered site search, augmented reality, biometric data recognition, booking systems, chatbots, drones, kiosks/self-service screens, machine translation, QR codes, robots, virtual reality, and voice assistants—are being used by companies in the tourism and hospitality industry. How are consumers reacting to these profound changes? This study aims to address this issue by identifying the types of AI systems that are used by tourists, the purposes they are used for in the present, and how likely they are to be used in the future. This study also aims to identify the types of emotions (positive vs. negative) that tourists associate with the use of AI systems, as well as the advantages and disadvantages they attribute to them. Considering the exploratory nature of the research, data were collected through an online survey shared on social media, which was available from September to December 2023. Results show that most respondents have already used several AI systems, assign more advantages than disadvantages to their use, and that the emotions they associate with their use are significantly positive. Moreover, compared to the small number of respondents (13.7%) who associate negative emotions with the use of AI systems, respondents who claim to feel positive emotions when using AI systems also evaluate them more positively in terms of their usefulness for tourism and hospitality. They identify more advantages, use a greater diversity of AI systems, and admit that they would use a more diverse range of AI systems in tourism contexts in the future. Full article

Historic buildings hold significant cultural value and their repair and protection require diverse approaches. With the advent of 3D digitalization, drones have gained significance in heritage studies. This research focuses on applying digital methods for restoring architectural heritage. It utilizes non-contact measurement technology, specifically unmanned aerial vehicles (UAVs), for data collection, creating 3D point cloud models using heritage building information modeling (HBIM), and employing virtual reality (VR) for architectural heritage restoration. Employing the “close + surround” oblique photography technique combined with image matching, computer vision, and other technologies, a detailed and comprehensive 3D model of the real scene can be constructed. It provides crucial data support for subsequent protection research and transformation efforts. Using the case of the Santo Stefano Church in Volterra, Italy, an idealized reconstructed 3D model database was established after data collection to preserve essential resources such as the original spatial data and relationships of architectural sites. Through the analysis of relevant historical data and the implementation of VR, the idealized and original appearance of the case was authentically restored. As a result, in the virtual simulation space, the building’s style was realistically displayed with an immersive experience. This approach not only safeguards cultural heritage but also enhances the city’s image and promotes tourism resources, catering to the diverse needs of tourists. Full article

This research investigates the utilisation of the scan-to-HBIM-to-XR process and unmanned aerial vehicle (UAV) photogrammetry to improve the depiction of archaeological ruins, specifically focusing on the Claudius Anio Novus aqueduct in Tor Fiscale Park, Rome. UAV photogrammetry is vital in capturing detailed aerial imagery of the aqueduct and its surroundings. Drones with high-resolution cameras acquire precise and accurate data from multiple perspectives. Subsequently, the acquired data are processed to generate orthophotos, drawings and historic building information modelling (HBIM) of the aqueduct, contributing to the future development of a digital twin. Virtual and augmented reality (VR-AR) technology is then employed to create an immersive experience for users. By leveraging XR, individuals can virtually explore and interact with the aqueduct, providing realistic and captivating visualisation of the archaeological site. The successful application of the scan-to-HBIM-to-XR process and UAV photogrammetry demonstrates their potential to enhance the representation of building archaeology. This approach contributes to the conservation of cultural heritage, enables educational and tourism opportunities and fosters novel research avenues for the comprehension and experience of ancient structures. Full article

Over the past decades, climate change has accelerated the deterioration of heritage sites and archaeological resources in Arctic and subarctic landscapes. At the same time, increased tourism and growing numbers of site visitors contribute to the degradation and manipulation of archaeological sites. This situation has created an urgent need for new, quick, and non-invasive tools and methodologies that can help cultural heritage managers detect, monitor, and mitigate vulnerable sites. In this context, remote sensing and the applications of UAVs could play an important role. Here, we used a drone equipped with an RGB camera and a single multispectral/thermal camera to test different possible archeological applications at two well-known archaeological sites in the UNESCO World Heritage area of Kujataa in south Greenland. The data collected were used to test the potential of using the cameras for mapping (1) ruins and structures, (2) the impact of human activity, and (3) soil moisture variability. Our results showed that a combination of RGB and digital surface models offers very useful information to identify and map ruins and structures at the study sites. Furthermore, a combination of RGB and NDVI maps seems to be the best method to monitor wear and tear on the vegetation caused by visitors. Finally, we tried to estimate the surface soil moisture content based on temperature rise and the Temperature Vegetation Dryness Index (TVDI), but did not achieve any meaningful connection between TVDI and on-site soil moisture measurements. Ultimately, our results pointed to a limited archaeological applicability of the TVDI method in Arctic contexts. Full article

In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras. Full article

The study of banana herbs and fruits is rarely conducted using multidisciplinary approaches. However, a multidisciplinary approach could be useful for gaining information on many aspects, including remote sensing, biodiversity and biogeography, owing to the uniqueness of bananas. The present article reviews a study on banana herbs from an Indonesian perspective. It starts by obtaining information related to firmness, peel’s colour change, water content and sugar content corresponding to Brix and Starch values. It then proceeds to find the relation between remote sensing (RS) technologies of all these biophysical characteristics and genomics, transcriptomic and metabolomics. Besides this, geospatial sciences, such as geographic information systems (GIS), may help visualise biogeographical factors that help analyse a land’s suitability for growing bananas. Furthermore, the plant canopy, health and plant disease, and the herbs’ water content, analysed through satellite images and aerial photos of drones, helps describe the banana distribution in Indonesia, at both the local and regional levels. Similar techniques may be applied to explore and analyse the characteristics of the fruit. In the end, the integration of these methods can foster advanced studies on bananas, even making it possible for its scope to extend to industries, food technology, post-harvest, and eco-tourism. Full article

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses. Full article