Search Results (102)

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Forests worldwide are impacted by tropical cyclones which alter their structure and ecological functions. In this study, we investigated repeat track data from ICESat-2’s (Ice, Cloud and land Elevation Satellite-2’s) land and vegetation height product (ATL08) to quantify structural changes in forests, with a focus on coastal forests in Alabama and Florida affected by Hurricane Sally (2020). We evaluated pre-hurricane ATL08 along-track canopy estimates at the ATL08 100 m segment scale and 20 m sub-segment scale and quantified structural canopy changes using exact pre- and post-repeated tracks. Results demonstrated strong agreement between ATL08’s 98th percentile canopy height (RH98) and reference airborne LiDAR-derived RH98 at both spatial scales, with improved performance at the 20 m sub-segment scale (mean bias: −1.16 m; MAE: 2.28 m; RMSE: 3.44 m; r: 0.80). Samples over evergreen forests provided reduced bias (−2 m to −0.55 m), reduced RMSE (4.02 m to 2.96 m), and improved correlation (0.77 to 0.83) than woody wetlands for canopy height acquisition. Post-hurricane analyses revealed height reductions in tall canopy (20–30 m) of 1.51 m, while smaller trees (0–10 m) increased by 0.77 m, reflecting growth. Overall, findings highlight ICESat-2’s ability to monitor canopy height changes and offer prospects for integrating ICESat-2 data for damage assessments. Full article

Oceanfront relief varies along coastlines and serves as the first barrier to wave and surge damage. However, forecasted increases in storm frequency and sea levels are anticipated to enhance coastal erosion, potentially weakening this protection. The land–sea transition is variable along the New England coast, USA, and this variability has produced a range of coastal morphologies that can vary over short distances. It is important to track the beach transition zone to better understand transformations of the system and related hazard risks. A combination of field and computer-based methods was used to evaluate the beach transition zone of southwestern Rhode Island to determine alongshore variability and dynamics. More specifically, a decadal-scale study was conducted to examine changes in morphology from 2011 to 2022, and a short-term study at South Kingstown Town Beach examined changes from November 2023 to January 2024 using time-series drone-derived elevations. Classification of over 500 cross-shore transects illustrated the dominance of sedimentary shorelines, with smaller areas of rocky outcrops and hardening. Analysis of four different years (2011, 2014, 2018, and 2022) determined that beaches with dune morphology were the most common type of transition zone (41–47% of the transects) and transects with a high bank upland were the next most frequent class (34–41%). Following Hurricane Sandy in 2012, a 6% decrease in the number of dune-classified transects was measured; however, one-third of those recovered dune morphology by 2022. The greatest beach transformations over the short-term study occurred in response to strong storms in the 2023–2024 winter season, during which lateral beach movement (erosion) exceeded 15 m in portions of South Kingstown Town Beach. Dune erosion was accompanied by overwash flooding and deposition, and the area remained low-lying and thus vulnerable to future impacts. The beach transition zone classification and insights from this research will be informative for future planning by coastal communities by determining at-risk shorelines based on underlying geology and the stability of morphological features. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Florida (FL)’s agriculture sector is undergoing rapid transformation due to biological shocks, environmental stressors, import competition, and accelerating urbanization. Citrus greening, laurel wilt, and hurricane-related damage have sharply reduced yields and acreage, while rising imports from Mexico and Brazil erode market share and depress prices. Urban development and recreational land-use expansion are accelerating land-value increases, which in turn drives farmland loss and abandonment. This policy-oriented review synthesizes these pressures and evaluates state policy responses. Our findings highlight the need for integrated strategies that improve resilience, strengthen land conservation, and enhance the long-term competitiveness of FL’s agricultural sector. Full article

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located. Full article

Extreme rainfall events often trigger landslides, debris flows, and sediment-laden floods that cause severe damage in built-up areas, yet sediment deposition is rarely quantified in hazard assessments. This study evaluates the capability of the physically based catchment model LISEMHazard to reconstruct sediment generation, transport, and deposition during Hurricane Maria (2017) in two catchments in Dominica (Coulibistrie and Grand Bay). Simulations were performed at 10 m resolution using rainfall, topography, soil, and land-use data. Model calibration and validation used mapped landslides and debris flows, field measurements of deposition height, and DEMs of Difference (DoDs). LISEMHazard reproduced the general magnitude of sediment volumes and the frequency–area distribution of medium and large landslides but showed poor ability to predict their exact locations and overestimated landslide depth and deposition height. Agreement between modeled and observed debris-flow patterns was good in major channels but weak in minor ones. Sensitivity analysis indicated that soil depth and cohesion dominate uncertainties, whereas saturated hydraulic conductivity and surface roughness exert minimal influence. Despite substantial data and model limitations, physically based modeling remains a practical approach for spatial estimation of sediment deposition needed for risk assessment, structural damage evaluation, and cleanup cost estimation. Full article

This study investigates landslide occurrence in Taiwan, a region highly susceptible to landslides due to steep mountains and frequent typhoons (TYPs). The primary objective is to understand how both geomorphological factors and TYP characteristics contribute to landslide occurrence, which is essential for improving hazard prediction and risk management. The research analyzed landslide events that occurred during the TYP seasons of 2019 and 2023. The methodology involved using satellite-derived landslide inventories from SPOT imagery for events larger than 0.1 hectares, tropical cyclone track and intensity data from IBTrACS v4 (classified by Saffir–Simpson Hurricane Scale), and detailed topographic variables (elevation, slope, aspect, Stream Power Index) extracted from a 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM). Land use and land cover classifications were based on Landsat imagery. To establish a timeline, landslides were matched with TYPs within a ±3-day window, and proximity was analyzed using buffer zones ranging from 50 to 500 km around storm centers. Key findings revealed that landslide susceptibility results from a complex interplay of meteorological, topographic, and land cover factors. The critical controls identified include elevations above 2000 m, slope angles between 30 and 45 degrees, southeast- and south-facing aspects, and low Stream Power Index values typical of headwater and upper slope locations. Landslides were most frequent during Category 3 TYPs and were concentrated 300 to 350 km from storm centers, where optimal rainfall conditions for slope failures exist. Interestingly, despite the stronger storms in 2023, the number of landslides was higher in 2019. This emphasizes the importance of interannual variability and terrain preparedness. These findings support sustainable disaster risk reduction and climate-resilient development, aligning with Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action). Furthermore, they provide a foundation for improving hazard assessment and risk mitigation in Taiwan and similar mountainous, TYP-prone regions. Full article

Hurricane Maria crossed Puerto Rico with winds as high as 250 km/h, resulting in widespread damages and loss of weather station data, thus limiting direct weather measurements of wind variability. Here, we identified more than 155 million trees to estimate the distribution of wind speed over 9000 km² of land from island-wide LiDAR point clouds collected before and after the hurricane. The point clouds were classified and rasterized into the canopy height model to perform individual tree identification and perform change detection analysis. Individual trees’ stem diameter at breast height were estimated using a function between delineated crown and extracted canopy height, validated using the records from Puerto Rico’s Forest Inventory 2003. The results indicate that approximately 35.7% of trees broke at the stem (below the canopy center) and 28.5% above the canopy center. Furthermore, we back-calculated the critical wind speed, or the minimum speed to cause breakage, at individual tree level this was performed by applying a mechanical model using the estimated diameter at breast height, the extrapolated breakage height, and pre-Hurricane Maria canopy height. Individual trees were then aggregated at 115 km² cells to summarize the critical wind speed distribution of each cell, based on the percentage of stem breakage. A vertical wind profile analysis was then applied to derive the hurricane wind distribution using the mean hourly wind speed 10 m above the canopy center. The estimated wind speed ranges from 250 km/h in the southeast at the landfall to 100 km/h in the southwest parts of the islands. Comparison of the modeled wind speed with the wind gust readings at the few remaining NOAA stations support the use of tree breakages to model the distribution of hurricane wind speed when ground readings are sparse. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article

Natural disasters often result in significant damage to infrastructure, generating vast amounts of debris in towns and water bodies. Timely post-disaster damage assessment is critical for enabling swift cleanup and recovery efforts. This study presents a combination of methods to efficiently estimate and analyze debris on land and on water. Specifically, analyses were conducted at Claytor Lake and Damascus, Virginia where flooding occurred as a result of Hurricane Helene on 27 September 2024. We use the Phoenix U15 motor glider equipped with the GoPro Hero 9 camera to collect aerial imagery. Orthomosaic images and 3D maps are generated using OpenDroneMap (ODM) software, version 3.5.6, providing a detailed view of the affected areas. For lake debris estimation, we employ a hybrid approach integrating machine learning-based tools and traditional techniques. Lake regions are isolated using segmentation methods, and the debris area is estimated through a combination of color thresholding and edge detection. The debris is classified based on the thickness and a volume range of debris is presented based on the data provided by the Virginia Department of Environmental Quality (VDEQ). In Damascus, debris estimation is achieved by comparing pre-disaster LiDAR data (2016) with post-disaster 3D ODM data. Furthermore, we conduct flood modeling using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) to simulate disaster impacts, estimate the flood water depth, and support urban planning efforts. The proposed methodology demonstrates the ability to deliver accurate debris estimates in a time-sensitive manner, providing valuable insights for disaster management and environmental recovery initiatives. Full article

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments. Full article

Although numerous observational and theoretical studies have examined the mean and turbulent structure of the tropical cyclone boundary layer (TCBL) over the open ocean, there have been comparatively fewer studies that have examined the kinematic and thermal structure of the TCBL across the land–ocean interface. This study examines the impact of different continental environments on the thermodynamic evolution of the TCBL during the landfall transition using high-resolution, full-physics numerical simulations. During landfall, the changes in the wind field within the TCBL due to the development of the internal boundary layer (IBL), combined with the formation of a surface cold pool, generates a pronounced thermal asymmetry in the boundary layer. As a result, the maximum thermodynamic boundary layer height occurs in the rear-right quadrant of the storm relative to its motion. In addition, azimuthal and vertical advection by the mean flow lead to enhanced turbulent kinetic energy (TKE) in front of the vortex (enhancing dissipative heating immediately onshore) and onshore precipitation to the left of the storm track (stabilizing the environment). The strength and depth of thermal asymmetry in the boundary layer depend on the contrast in temperature and moisture between the continental and storm environments. Dry air intrusion enhances cold pool formation and stabilizes the onshore boundary layer, reducing mechanical mixing and accelerating the decay of the vortex. The temperature contrast between the continental and storm environments establishes a coastal baroclinic zone, producing stronger baroclinicity and inflow on the left of the track and weaker baroclinicity on the right. The resulting gradient imbalance in the front-right quadrant triggers radial outflow through a gradient adjustment process that redistributes momentum and mass to restore dynamical balance. Therefore, the surface thermodynamic conditions over land play a critical role in shaping the evolution of the TCBL during landfall, with the strongest asymmetries in thermodynamic boundary layer height emerging when there are large thermal contrasts between the hurricane and the continental environment. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article