A technical and scientific overview regarding satellite-derived bathymetry (SDB)—one of the most promising and relatively cheap methods of shallow water depth determination—is presented. The main goal of the article is to present information about the possibilities of the SDB method to meet the demanding standard of bathymetric measurements in coastal mapping areas up to 20 m deep, i.e., up to depth areas where the largest number of ports and access waterways are located, as obtained using the bibliometric analysis. The Web of Science (WoS) and Scopus scientific databases, as well as R studio applications Bibliometrix and Biblioshiny, were used for scientific analysis. The bibliometric analysis presents the quantitative aspects of producing and disseminating scientific and professional articles with SDB as their topic. Therefore, the purpose of this study was to give the academic community an insight into the current knowledge about the SDB method, its achievements and shortcomings. The results of the bibliometric analysis of articles dealing with SDB show that most authors use empirical statistical methods. However, in recent years, articles using automated artificial intelligence methods have prevailed, especially the machine learning method. It is concluded that SDB data can become a very important low-cost source of bathymetric data in shallow coastal areas. Satellite methods have been proven to be very effective in very shallow coastal areas (up to a depth of about 20 m), and their biggest advantage is that the depth data obtained in this way are relatively low cost, while major limitations are associated with the parameters that determine the properties of the atmosphere and water column (clear atmosphere and water column) and bottom material. Procedures for different bathymetric applications are being developed. Regardless of the significant progress of the SDB method, which was manifested in the development of sensors and processing methods, its results still do not meet the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys S-44.

Coastal vulnerability assessment due to climate change impacts, particularly for sea level rise, has become an essential part of coastal management all over the world. For the planning and implementation of adaptation measures at the household level, large-scale analysis is necessary. The main aim of this research is to investigate and propose a simple and viable assessment method that includes three key geospatial parameters: elevation, distance to coastline, and building footprint area. Two methods are proposed—one based on the Index method and another on fuzzy logic. While the former method standardizes the quantitative parameters to unit-less vulnerability sub-indices using functions (avoiding crisp classification) and summarizes them, the latter method turns quantitative parameters into linguistic variables and further implements fuzzy logic. For comparison purposes, a third method is considered: the existing Index method using crisp values for vulnerability sub-indices. All three methods were implemented, and the results show significant differences in their vulnerability assessments. A discussion on the advantages and disadvantages led to the following conclusion: although the fuzzy logic method satisfies almost all the requirements, a less complex method based on functions can be applied and still yields significant improvement.

This study presents a comprehensive framework for evaluating urban heat resilience, incorporating urban climatology models, their characteristics, and simulation programs. Utilizing the local climate zone (LCZ) classification method, this research explores how urban geomorphology influences the thermal characteristics of the area. This study integrates spatial data at different “levels of detail” (LOD), from the meso- to building scales, emphasizing the significance of detailed LOD 3 models acquired through 3D laser scanning. The results demonstrate the ability of these models to identify urban heat islands (UHIs) and to simulate urban planning scenarios, such as increasing green spaces and optimizing building density, to mitigate the UHI effect. The ST3D 3D model of the city of Split, represented using an LOD 2 object model, is utilized for meso- and local-scale analyses, while LOD 3 models derived from laser scanning provided in-depth insights at the building scale. The case studies included the Faculty of Civil Engineering, Architecture, and Geodesy building on the University of Split campus and the old town hall in the densely built city center. This framework highlights the advantages of integrating GIS and BIM technology with urban climate analyses, offering tools for data-driven decision-making and fostering sustainable, climate-resilient urban planning.

More than half of the world’s population lives in big urbanized areas. It is not rare that those areas are lacking natural green spaces. Green spaces improve different aspects of life in cities and they are becoming so important that lately more and more attention is given to the so-called green infrastructure. The first step in planning green infrastructure is acquiring information about current city greenery. In this paper, it was investigated how can airborne, spaceborne, and street-level images be used in gathering information about greenery. As spaceborne images, Sentinel-2 satellite images were used and as street-level images, Google Street View 360° photospheres have been utilized. From both sources, information about current greenery status was automatically extracted. Gathered data was aggregated on different spatial units that are suitable for decision making that aims at further developing the green spaces. These top-down and street-level images complement each other in a way that top-down images can be used to track the percentage of green area and its changing over time, while street-level images give information about greenery that is perceived by pedestrians. With proposed methods, it is possible to detect areas that should be considered for greening and also to identify areas that should have priority in that process.

The tourism sector is highly vulnerable to climate change (CC) and at the same time strongly contributes to the emissions causing global warming and other environmental problems. This reciprocal relationship between tourism and CC is most evident in coastal zones, especially those surrounding the Mediterranean Sea. These are among the most endangered zones - endangered not just by tourism but by various other activities, urbanisation and a growing population. Moreover, they are CC hotspots. Hence, being challenged by various different threats, coastal zones must be planned and managed holistically. This means that neither tourism nor any other industry can be treated as a standalone activity; they must be seen as part of the Integrated Coastal Zone Management (ICZM) framework, with the CC adaptations (CCA) embedded. In this regard, the main goal of this chapter is to explore how to mainstream CCA into coastal tourism planning activities, taking into account the ICZM principles. Hence, based on existing frameworks, we introduce an improved ‘Coastal tourist destination CCA framework mainstreamed into the ICZM’. The framework is then elaborated through the case of Makarska Riviera, a famous Croatian tourist destination, which, despite being faced with serious tourism and CC-caused problems, has neither implemented the ICZM and the tourism plans nor dealt with the CCAs. Thus, following the CCA framework phases, we elaborate on the current state-of-the-art regarding Riviera’s preparedness to address climate change and pinpoint the misgivings in implementing ICZM and the tourism planning process.