Multi-Temporal Trend Analysis of Coastal Vegetation Using Metrics Derived from Hyperspectral and LiDAR Data

Abstract

Monitoring and modeling of coastal vegetation and wetland systems are considered major challenges, especially when considering environmental response to hazards, disturbances, and management activities. Remote sensing applications can provide alternatives and complementary approaches to the often costly and laborious field-based collection methods traditionally used for coastal ecosystem monitoring. New and improved sensors and data analysis techniques have become available, making remote sensing applications attractive for evaluation and potential use in monitoring coastal vegetation properties and ecosystem conditions and change. This study involves the extraction of vegetation metrics from airborne LiDAR (Light Detection and Ranging) and hyperspectral imagery (HSI) to quantify coastal dune vegetation characteristics and assesses landscape-level trends from those derived metrics. HSI- and LiDAR-derived elevation (digital elevation model) and vegetation metrics (canopy height model, leaf area index, and normalized difference vegetation index) were used in conjunction with per-pixel linear regression and hot spot analyses to evaluate hurricane-induced spatial and temporal changes in elevation and vegetation properties. These assessments showed areas with greatest decreases in vegetation metric values were associated with direct tropical storm energies and processes (i.e., overwashing events eroding beach and dune features), while those with the greatest increases in vegetation metric values were in areas where overwashed sediments were distributed. This study narrows existing gaps in dune vegetation data by advancing new methodologies to classify, quantify, and estimate critical coastal vegetation metrics. The tools and methods developed in this study will ultimately improve future estimates and predictions of nearshore dynamics and impacts from disturbance events.