Light detection and ranging (lidar) digital elevation models are widely used in modeling coastal marsh systems as well as many other natural resource management applications. However, topographic elev..
Light detection and ranging (lidar) digital elevation models are widely used in modeling coastal marsh systems as well as many other natural resource management applications. However, topographic elevation errors in these models mischaracterizes the current state of the system which propagates inaccuracies when estimating future changes. For the Pascagoula, MS, region, the bias in the salt marsh lidar DEM is mitigated by combining multispectral satellite imagery from Sentinel-2A with field measurements of topographic elevation and biomass density using the Random Forest machine learning technique. This mitigation adjustment technique can be replicated in other coastal regions with similar vegetation profiles. As the world becomes increasingly vulnerable to the effects of climate change and sea level rise, it is important to accurately characterize the current state of the system so that we may effectively monitor the impacts of future changes such as marsh restoration and migration, natural and nature based protective infrastructure, and land use planning policies.
