The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time. Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D geometric information of the environment. The objective of this work is to use point clouds acquired by Mobile Laser Scanning (MLS) to segment the main elements of road environment (road surface, ditches, guardrails, fences, embankments, and borders) through the use of PointNet. Previously, the point cloud was automatically divided into sections in order for semantic segmentation to be scalable to different case studies, regardless of their shape or length. An overall accuracy of 92.5% has been obtained, but with large variations between classes. Elements with a greater number of points have been segmented more effectively than the other elements. In comparison with other point-by-point extraction and ANN-based classification techniques, the same success rates have been obtained for road surfaces and fences, and better results have been obtained for guardrails. Semantic segmentation with PointNet is suitable when segmenting the scene as a whole, however, if certain classes have more interest, there are other alternatives that do not need a high training cost.

Mangrove ecotourism is gaining immense popularity in the Gulf Cooperation Council (GCC) countries as a neoliberal conservation tool, and it has contributed significantly to the growth of the tourism sector in the region over the past two decades. However, there is no comprehensive review on the full extent of mangrove ecotourism activities and the contribution to mangrove conservation/restoration and economic growth in the region. A systematic literature review approach was used to examine the evolution of mangrove ecotourism in the GCC countries from 2010 to 2023. A total of 55 articles were retrieved from the Google and Google Scholar search engines, and the Scopus and Web of Science databases were incorporated. We synthesized the results and provided perspectives on the following: (1) the geographical and temporal distribution of studies in relation to mangrove extent, (2) key sites, attractions, and values for mangrove ecotourism activities, (3) the positive and negative impacts of mangrove ecotourism, and (4) existing mangrove conservation and restoration initiatives for the growth of mangrove ecotourism in the GCC countries. The findings underscore the significance of mangrove ecotourism in supporting economic development, protecting coastal ecosystems, and sustaining local livelihoods in the GCC countries. However, this study highlights the crucial need for sustainable coastal environmental management through integrated land use planning and zoning to address the negative impacts of anthropogenic pressures on mangrove ecosystems and ecotourism attractions. The use of remote sensing tools is invaluable in the monitoring of mangrove ecosystems and associated ecotourism impacts for informing evidence-based conservation and restoration management approaches. Thus, harnessing mangrove ecotourism opportunities can help the GCC countries with balancing economic growth, coastal environmental sustainability, and community well-being.