Characterizing Low-Lying Coastal Upland Forests to Predict Future Landward Marsh Expansion

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Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in-situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from Light detection and ranging (Lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS-derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the Plant Area Index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar-derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated sea-level rise conditions.


We conducted TLS scanning between July and September 2022 (during leaf-on conditions). For each plot, we performed single-scan positions using a RIEGL VZ-400i (RIEGL Laser Measurement Systems GmbH, Horn, Austria). The VZ-400i operates in the near infrared wavelength (1550 nanometers) with range distances up to 350 meters. The scanner was mounted on a tripod 1.3 meters above the ground and equipped with a tilt mount in order to obtain one upright and one titled scan (90° from the vertical). To create a single point cloud from the two scans, reflector targets were placed around the scanner prior to scanning. The reflectors remain in the exact same positions for both scans, and are used to compute a transformation matrix in the RIEGL RiSCAN Pro software which facilitates the co-registration and merging of the two-point clouds. Alignment of the scans was also further refined using the multi-station adjustment (MSA) module in RiSCAN Pro (Wilkes et al. 2017, Decuyper et al. 2018). The purpose of using the single scan positions from the TLS was to provide an objective representation of forest stand structure at fixed distances from the forest edge and was more time efficient compared to scanning multiple positions within a plot (Calders et al. 2014, Seidel et al. 2016, Meeussen et al. 2020).