Evaluating Coastal Wetland Mapping Accuracy with High-Resolution Multi-spectral Imagery and LiDAR Remote Sensing Data

Abstract

The coastal area of New Hanover County in North Carolina encompasses diverse wetland habitats influenced by unique coastal and tidal dynamics, with researchers examining the impacts of landscape changes, sea-level rise, and climate fluctuations on wetland health and biodiversity. This study integrates multispectral imagery data, LiDAR, and additional sources to enhance classification accuracy. The study also addresses binary classification for wetland and non-wetland classification and a multi-classification for different wetland classes, leveraging on the Random Forest algorithm which significantly improved the overall accuracy of wetland mapping. The Random Forest model’s performance in different scenarios was evaluated, with Scenario 1 achieving an overall accuracy of nearly 93.9%, Scenario 2 achieving an overall accuracy of 93.5%, Scenario 3 achieving an overall accuracy of 94.1%, and Scenario 4 achieving an overall accuracy of 88.2%. These results underscore the model’s effectiveness in accurately classifying coastal wetland areas under diverse remote sensing scenarios, highlighting its potential for practical applications in wetland mapping and ecological research.