Despite widespread adoption of satellite-derived shorelines (SDS) for coastal-change monitoring, inference from public Earth observation (EO) archives is rarely tested against the noise floor imposed by the full measurement chain. Positional accuracy is routinely reported, but whether observed geomorphic trends exceed accumulated tidal, wave-runup, and registration uncertainty remains largely unverified. This study develops a signal-to-noise (SNR) detectability framework for microtidal, mixed-energy tropical coasts using decadal Landsat 7/8/9 and Sentinel-2 imagery, 454 beach-profile surveys, and Digital Shoreline Analysis System (DSAS) version 6.0 shoreline-change metrics across five beaches in Trinidad and Tobago. The framework evaluates geomorphic change against positional, tidal, and wave-runup noise and classifies site behaviour into robust (SNR > 1.5), marginal (0.5–1.5), and low (< 0.5) detectability regimes. Validation against 344 profile-matched observations produced a pooled root-mean-square error (RMSE) of 15.1m, decreasing to 10.5m at three well-calibrated sites. Error separated into an elevation-dependent component reducible by contour calibration and a registration-limited component that persisted across contour choices. Optimum contours grouped at two elevations, −0.2m relative to mean sea level (MSL) at Irois and Turtle and +0.5m MSL at the remaining sites, an indicative association with foreshore slope and wave forcing rather than arbitrary tuning. Only Irois was clearly signal-dominated (SNR = 2.74); the remaining four sites were marginal, and two of them (Turtle and Las Cuevas) showed directional instability, including 82.7% transect-level End Point Rate (EPR) - Linear Regression Rate (LRR) sign reversal at Turtle Beach. Runup-based SNR at the two registration-limited sites (Kilgwin and Mayaro) is an upper bound; against a combined noise floor that adds the systematic registration bias in quadrature, Kilgwin falls to low detectability (SNR = 0.45) while all other classes are unchanged. No site was simultaneously robust, well calibrated, and directionally coherent. Defensible SDS-based coastal-change inference therefore requires explicit testing that the geomorphic signal exceeds the site-specific noise floor, not positional accuracy alone.

Predicting maritime incidents requires spatially aware techniques that account for complex geographic and operational dynamics. By applying Geographic Weighted Regression (GWR), Generalized Linear Regression (GLR), Forest-Based Classification and Regression (FBCR), and Empirical Bayesian Kriging (EBK), this research models the spatial distribution of vessel incidents across the Caribbean Sea. Key influencing variables include vessel traffic density, charted zone confidence, flag state, and vessel age. Results highlight regional hotspots such as the Panama Canal and Gulf of Paria. Comparative analysis demonstrates the strengths and limitations of each technique, informing the development of adaptive, location-specific maritime risk mitigation strategies.

Maritime accidents remain a critical concern despite advancements in navigation technology and safety regulations. This paper examines maritime events over a 20-year period (2002–2021) using geostatistical analysis and GIS-based methods to identify key risk factors, trends, and high-risk zones. Analysis of the IMO’s Global Integrated Shipping Information System (GISIS) database highlights the influence of vessel type, flag state performance, accident timing, and geographical hotspots on incident frequency. Collisions, groundings, and fires/explosions are the most prevalent events, with aged vessels, open registries and outdated or incomplete hydrographic survey data posing significant risks. Poor flag state oversight, economic constraints, and human factors exacerbate maritime hazards. Although accident rates have declined, high-risk zones persist, necessitating stricter regulatory enforcement, fleet modernization, and improved accident reporting. Strengthening international collaboration and proactive risk management is crucial for ensuring safer global shipping operations, particularly in high-risk regions like the Caribbean Sea.

The Caribbean Sea, a vital maritime corridor with high vessel traffic and strategic access to the Panama Canal, faces growing navigational risks. This study develops a probabilistic risk assessment model incorporating vessel and environmental conditions, enhanced by an Artificial Neural Network to estimate incident probability. By leveraging Automatic Identification System (AIS) data and machine learning, mitigation scenarios were evaluated, confirming their effectiveness in reducing risk. The methodology advances traditional models by integrating multiple cause-related factors, offering a more comprehensive understanding of maritime safety. This research highlights the value of AIS and AI-driven approaches in enhancing risk mitigation and navigational resilience in the Caribbean Sea.

Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.

Over the last few decades, flooding has resulted in many problems that significantly impact countries in the Caribbean. This has been especially challenging in urban areas where widespread damage has occurred. In addition, given that over 50% of the world's population lives in urban areas, these locations are deemed to be vulnerable to climate-related disaster events that would further exacerbate the challenges in the region. These urban spaces in the Caribbean have limited access to real-time flood monitoring data for formulating and supporting policies for disaster practitioners to coordinate timely preparedness and mitigation efforts. While flooding is complex, with a series of negative impacts on social and economic sectors, it is essential to provide a basis to support decision-making information on vulnerability and resilience through early warning systems (EWS). However, the main obstacle in creating early warnings in the Caribbean is the suitability and availability of data for real-time flood prediction. While many methods of flood models have been applied in the traditional ways, the methods are complex and not readily adaptable to the context of the local community spaces, especially with limited available data. The Caribbean urban landscape is dynamically changing, which requires careful monitoring for predicting flooding. In a disaster, informing the community is critical for managing flooding risk. The adoption of GeoAI and IoT prepares the next frontier of models for usage in the Caribbean to close the gaps of modeling in data scarcity in urban areas. Consequently, there is a research gap from the perspective of short-term forecasting for sudden rainfall events in urban spaces in the Caribbean. Given the early warning system culture in the Caribbean has an environment of real-time data of scarce resources, it is necessary to forge an approach for real-time forecasting flooding impact in urban spaces. The paper provides a preliminary analysis of the feasibility of machine learning and IoT use in supporting EWS in Caribbean urban spaces.

Abstract Many countries have seen a significant increase in the number of high-rise and multi-story buildings with strata units with the shrinking availability of horizontal land space in urban areas. Most existing cadastres record tenure and associated information in a two-dimensional format, and therefore will need to be upgraded to 3D cadastres to facilitate the recording of title information for these strata units. The geospatial aspects of implementing a 3D cadastre require cost-effective, rapid, accessible, non-labor intensive, and accurate means by which complete data may be collected to model strata units. Existing data sources such as Building Information Models (BIMs) have been used to delineate these units. However, BIMs are not easily accessible in developing countries. There is no single spatial data source that can easily fulfill all the criteria of a 3D data source for mapping urban strata boundaries. However, leveraging multi-perspective and multi-sensor data can theoretically serve this purpose. In this paper, we report on research conducted to demonstrate the feasibility of using low cost and accessible cell phone data, integrated with higher accuracy terrestrial laser scan data, and drone aerial imagery to produce a multi-perspective 3D data source. It was found that the integration of these data sets as a 3D data source, resulted in absolute accuracies within centimetres and decimetres in the horizontal plane and within millimetres in the vertical plane. Overall, the integration of these three sources of data may be appropriate in meeting the needs for a low-cost source of 3D data.

Vertical separation models are valuable for coastal zone management and protection against the effects of climate change. To date, the development of such models has been undertaken in areas where long-term sea level measurements exist and there are resources for extensive offshore bathymetric and Global Navigation Satellite Systems surveys. Many small island developing states and other resource constrained territories host vulnerable coastal zones and would benefit from such models, however, financial constraints and data sparsity make it difficult. This article describes the establishment of a vertical separation model using an amalgamation of long- and short-term sea level measurements with hydrodynamic modeling. With existing vertical separations at only two coastal points for comparison, the model was designed to include a tidal prediction element which allowed for validation against sparse independently observed sea levels. Considering that unmodeled influences on sea levels in the study area can exceed 0.2 m at times, the method was tested against independently observed sea levels and can be considered successful with variances in the range of 1.3-4.5% of the average tidal range for the study area. This research provides the means of addressing a significant need in developing territories where long-term sea level records are unavailable and resource deficiencies exist.

In the immediate aftermath of a hurricane, rapid and reliable assessment of building damage is critical. The timely delivery of such information is essential for emergency responders to identify those areas that are severely impacted so that they can act accordingly. This step is crucial for saving lives and reducing economic losses. This paper demonstrates the potential of Remote Sensing for rapid building damage detection using an automated approach in small island states in the Caribbean. Object-Based and Pixel based methods were compared with visually identified reference information from high resolution imagery for the 2004 Hurricane Ivan impact on Grenada. The efficacy of the Object-Based approach is demonstrated using image segmentation and classification in eCognition Developer Software. This approach utilises not only the spectral content but also the context, morphological and textural properties of image objects. In relation to the reference data, the object-based method achieved over 85% classification accuracy among a three damages grade classification scheme in two separate scenarios with different study area extents.

The interests, responsibilities and opportunities of states to provide infrastructure and resource management are not limited to their land territory but extend to marine areas as well. So far, although the theoretical structure of a Marine Administration System (MAS) is based on the management needs of the various countries, the marine terms have not been clearly defined. In order to define an MAS that meets the spatial marine requirements, the specific characteristics of the marine environment have to be identified and integrated in a management system. Most publications that address the Marine Cadastre (MC) concept acknowledge the three-dimensional (3D) character of marine spaces and support the need for MC to function as a multipurpose instrument. The Land Administration Domain Model (LADM) conceptual standard ISO 19152 has been referenced in scholarly and professional works to have explicit relevance to 3D cadastres in exposed land and built environments. However, to date, very little has been done in any of those works to explicitly and comprehensively apply LADM to specific jurisdictional MAS or MC, although the standard purports to be applicable to those areas. Since so far the most comprehensive MC modeling approach is the S-121 Maritime Limits and Boundaries (MLB) Standard, which refers to LADM, this paper proposes several modifications including, among others, the introduction of class marine resources into the model, the integration of data on legal spaces and physical features through external classes, as well as the division of law and administrative sources. Within this context, this paper distinctly presents both appropriate modifications and applications of the IHO S-121 standard to the particular marine and maritime administrative needs of both Greece and the Republic of Trinidad and Tobago.

SUMMARY The use of Unmanned Aerial Vehicles (UAVs) in civilian applications has increased greatly over the last few years. Especially for small area coverage, such a system has the advantages of being more flexible, rapid, efficient, and weath er independent when compared to standard airborne aerial surveys. Of high interest is their application in the acquisition of aerial imagery for post-disaster assessment where accessibility to current and accurate spatial information is critical for the effective response to a crisis by the relevant agencies. This use of UAVs calls for near-real time processin g of the images to create orthophoto mosaics. However, many commercial systems are incapable of performing automatic image matching on UAV imagery due to the high variability present in the image scenes. This paper presents a method for the automatic generation of o rthophoto mosaics using Scale Invariant Feature Transform (SIFT) approach for the automatic keypoint detection and matching problem. The proposed workflow makes use of efficient and robust algorithms to achieve a method that will meet the needs of the near real-ti me requirements. The results of this paper demonstrate a suitable ap proach to the automated processing of UAV images and further promote the applicability of this technology to the acquisition of geospatial data for natural hazard and disaster man agement.

SUMMARY Precise monitoring of changes in the mean sea level of the oceans is critical for understanding not just the climate but also the socioeconomic con sequences of any rise in sea level. Though the impacts of sea level change are of internationa l concern, they are especially significant for small island states, such as those in the Caribbean . Small Island States are especially at risk from the effects of climate change and sea level ri se largely because of their environmental and economic dependence on coastal zones. Exacerbating this vulnerability is the fact that the Caribbean region is plagued by a lack of dependable in-situ tide gauges and spatially effective systems for monitoring sea level. This paper aims to fill the gap in available mean s ea level data in the Caribbean region using satellite altimetry. Satellite altimetry provides a method that resolves the sparse, unreliable sea level monitoring system in the Caribbean. The techn ique of satellite altimetry is examined in its use to effectively monitor and compute mean sea level (MSL) and subsequently derive sea level rise (SLR) rates for the Caribbean.

The global impacts of sea level change is of major interest internationally, especially for small island states, like those in the Caribbean which are amongst the regions that are most at risk from the effects of climate change and sea level rise. This is largely due to their environmental and economic dependence on coastal zones. Previous studies have been conducted in an attempt to investigate and monitor sea level rise in the Caribbean. Unfortunately, these studies were incomplete and deficient owing to limitations in the tide gauge data reliability and a lack of data coverage for the Caribbean region. This paper evaluates the method of satellite altimetry data to determine sea level change in the Caribbean region through a comparative analysis to eight tide gauge stations over a ten-year period. The sea level anomalies derived from the satellite altimetry technique agree with the tide gauge data with a mean RMS (Root Mean Square) of 0.058 m. The sea level change rates are on average ± 0.45 mm/yr within the tide gauge results, confirming the viability of satellite altimetry as a technique to determine sea level variations for the Caribbean region.

SUMMARY Coastal areas in the Caribbean have a high population density which is anticipated to increase in the future. For many of the islands, the coastal zone represents a high economic activity, as tourism based economies are predominant in the Caribbean. Historically and in recent times, coastal hazards such as hurricanes, tropical storms, tsunamis and landslides have had negative impacts on both coastal communities and national economies in the region. Additionally, the threat of coastal inundation as a result of sea level rise is becoming more of an issue for many of the islands. Coastal zone management, monitoring and defence mechanisms are being implemented to mitigate and adapt to these threats, and these initiatives can benefit from a seamless coastal spatial model that may be a crucial part of the decision support. A major obstacle that presents itself is the difficulty in modelling and integrating datasets across the land-sea interface, where a consistent vertical datum does not exist. In this regard, the development of a separation model that simulates a vertical reference surface is applicable. While several projects of this nature have been carried out in countries such as the United Kingdom, the United States of America, Canada and Australia there is a growing need for this type of research in the Caribbean. This paper discusses the significance of developing a methodology that will facilitate transformations among vertical reference frames to support the modelling of data across the land-sea interface in the Caribbean. An examination and assessment of the existing methodologies developed by the other nations will be made so as to ascertain their applicability in the Caribbean. A methodology for use in the Caribbean is also proposed.

SUMMARY Traditional methods of Sea Level Rise (SLR) determination in the Caribbean have been plagued with problems of currency and availability of data to establish reliable regional models. Since most of the operational tide gauges used are in the northern Caribbean and the southern Florida coastline, the studies were focused on areas in the northern Caribbean, creating a gap in the data for the southern Caribbean island states. There also existed a potential problem of data reliability in that data from the tide gauges were not necessarily reduced to any level previously established from sea level observations. This paper examines presents a review of the state of SLR determination in the Caribbean and outlines satellite altimetry as an alternative method of sea level observation, where sea level change based on satellite altimetry is measured with respect to the Earth’s centre of mass, and thus is not distorted by land motions. This, along with a network of GPS monitor stations and tide gauges and incorporating the creation of localised geoidal models for the sites are proposed as a methods to bridge the current data gap to develop regional SLR models with respect to a geocentric reference frame.

Diamondback moth, Plutella xylostella, larvae were infected with a primary pathogen, Bacillus thuringiensis kurstaki (Btk) in single strain and mixed infections. Mixed infections comprised Btk and a non-pathogenic isolate, either Bacillus thuringiensis tenebrionis (Btt) or Bacillus cereus (Bc). All strains reproduced in larval cadavers, but there was evidence of competition between different isolates within hosts. Non-pathogenic isolates (Btt, Bc) had growth rates that were faster than Btk in vivo, whereas Btk outcompeted Btt in vitro. Passage through insects increased the in vitro competitive ability of Btk against Btt.

The spatial and temporal variability in the fish component of the diet of Antarctic fur seals (Arctocephalus gazella (Peters, 1875)) in the Atlantic sector of the Southern Ocean was examined using diet data from 10 sites in the region including a 13-year time series from South Georgia. The fish species composition in the diet at each site showed a strong relationship with the local marine habitat / topography. The absence of formerly harvested fish species indicates a lack of recovery of stocks of Notothenia rossii Richardson, 1844 at South Georgia and Champsocephalus gunnari Lönnberg, 1905 at the South Orkney Islands. At South Georgia, Protomyctophum choriodon Hulley, 1981, Lepidonotothen larseni (Lönnberg, 1905), and C. gunnari were the most important species in the diet between 1991 and 2004. Variability in the occurrence of C. gunnari was driven mainly by annual scale processes, particularly those that influence the availability of Antarctic krill (Euphausia superba (Dana, 1852)). The occurrence of the pelagic P. choriodon was primarily influenced by shorter-term water mass changes within the foraging range of the seals. The fish composition in the diet reflects differences in marine habitat / topography, as well as variability, at a range of time scales that reflect environmental variability and harvesting.

SUMMARY The Caribbean historically experiences a variety of natural disasters including hurricanes, earthquakes and volcanic eruptions, droughts among other things. Climate change is reported to potentially exacerbate many of these extreme eve nts in the region, and add persistent sea level rise as another threat to Caribbean coastal c ommunities. GIS-based sea level rise predictive inundation models have been, and are bei ng, used to assess potential physical and socioeconomic impacts on coastal communities in the Caribbean and other geographic areas. The results of these models are expected to form pa rt of the information base used to develop appropriate adaptation and mitigation strategies. T he veracity of the models’ results, and the usefulness of the models, are questioned because mo re often than not the models are constructed with less than ideal data, especially i n developing regions such as the Caribbean where there is often a paucity of long term dependa ble spatial data, including tidal data to determine mean sea level and, as well, coastal defo rmation data among other things. Within the context of all the foregoing, this paper presen ts three case studies where GIS-based sea level rise inundation models are produced relevant to selected Caribbean communities. It was found that the models have utility in raising aware ness, and support for the development of appropriate adaptation and mitigation strategies.

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