Lidar-derived elevation data for Kipnuk, Southwest Alaska, collected August 18, 2021

Contribution of longshore sand exchanges to mesoscale barrier-island behavior: Insights from the Virginia Barrier Islands, U.S. East Coast

The Sea Level Affecting Marshes Model (SLAMM) was applied at six major estuaries along Florida’s Gulf Coast (Pensacola Bay, St. Andrews/Choctawhatchee Bays, Apalachicola Bay, Southern Big Bend, Tampa Bay and Charlotte Harbor) to provide quantitative and spatial information on how coastal ecosystems may change with sea level rise (SLR) and to identify how this information can be used to inform adaption planning. High resolution LiDAR-derived elevation data was utilized under three SLR scenarios: 0.7 m, 1 m and 2 m through the year 2100 and uncertainty analyses were conducted on selected input parameters at three sites. Results indicate that the extent, spatial orientation and relative composition of coastal ecosystems at the study areas may substantially change with SLR. Under the 1 m SLR scenario, total predicted impacts for all study areas indicate that coastal forest (-69,308 ha; -18%), undeveloped dry land (-28,444 ha; -2%) and tidal flat (-25,556 ha; -47%) will likely face the greatest loss in cover by the year 2100. The largest potential gains in cover were predicted for saltmarsh (+32,922 ha; +88%), transitional saltmarsh (+23,645 ha; na) and mangrove forest (+12,583 ha; +40%). The Charlotte Harbor and Tampa Bay study areas were predicted to experience the greatest net loss in coastal wetlands The uncertainty analyses revealed low to moderate changes in results when some numerical SLAMM input parameters were varied highlighting the value of collecting long-term sedimentation, accretion and erosion data to improve SLAMM precision. The changes predicted by SLAMM will affect exposure of adjacent human communities to coastal hazards and ecosystem functions potentially resulting in impacts to property values, infrastructure investment and insurance rates. The results and process presented here can be used as a guide for communities vulnerable to SLR to identify and prioritize adaptation strategies that slow and/or accommodate the changes underway.

Lidar-derived elevation data for Wrangell Island, Southeast Alaska, collected November 28-29, 2023

Abstract. The increasing demand for accurate 3D urban models is driven by rapid urbanization and the need for advanced geospatial tools in smart cities and digital twin applications. However, integrating Digital Terrain Models (DTMs) with 3D city models remains a persistent challenge due to misalignment between building footprints and terrain surfaces. Existing methodologies suffer from elevation discrepancies, inefficient interpolation techniques, and computational limitations, leading to inaccuracies in urban simulations, particularly for flood risk assessment and infrastructure planning. This study proposes a dual-iteration morphometric algorithm to enhance DTM-3D building integration, ensuring precise alignment of urban structures with real-world topography. The methodology involves node-based terrain adjustments, vertex and planar interpolation, and an iterative refinement process including land lot and building footprints geometry that improves elevation conformity. The algorithm was applied to a case study in Section 13, Petaling Jaya, Malaysia, using LiDAR-derived elevation data for validation. The results demonstrate a reduction in Root Mean Square Error (0.387 to 0.112) and a 75.8% decrease in Mean Absolute Error (0.219 to 0.053) compared to conventional DTM models, indicating significantly improved terrain adherence. Hydrodynamic simulations further reveal that the refined DTM reduces flood overestimation errors by 205.49%, capturing localized elevation variations more accurately. These improvements facilitate more reliable flood modelling, infrastructure planning, and urban resilience strategies. This research advances geospatial modelling for smart cities and digital twins, offering a scalable, high-accuracy framework for urban planning, disaster risk management, and climate adaptation. The proposed algorithm enhances urban simulation fidelity, ensuring more precise decision-making in sustainable city development.