Habitat Mapping of Ma-le’l Dunes Coupling with UAV and NAIP Imagery

Abstract

The Ma-le’l Dunes are located at the upper end of the North Spit of Humboldt Bay, California and are home to a range of plant and animal species. The goal of this study was to determine which classification method was the most accurate in identifying dune features when performed on a large, diverse area. The data sources used for this study were an orthomosaic image (2017) with 14 cm spatial resolution and NAIP images (2012, 2014, and 2016) with 1 m spatial resolution. A DJI Mavic Pro Unmanned Aerial Vehicle (UAV) was used to fly a 31 acre plot of the Ma-le’l Dunes at a height of about 80 m. The images from this flight were used to create an orthomosaic image in AgisoftPhotoScan. The dune feature classes were compared with two images using supervised, unsupervised, and feature extraction classification methods and accuracy assessments were performed using 100 ground control points. The classified feature classes were beach grass, shore pine, sand, other vegetation, and water. Overall, the NAIP classified maps showed a higher accuracy for all classification methods than UAV classified maps, with 86% overall accuracy for the supervised classification. A feature extraction method showed a low accuracy for both NAIP (46%) and UAV ortho classified images (30%). Of the classified methods for the orthomosaic image, the unsupervised classification showed a high accuracy (44%). The Ma-le’l dune habitats are more heterogeneous and some classes were overlapping (i.e., beach grass and sand) due to high microtopographic variation of the dune, resulting in lower accuracy for the feature extraction method. Monitoring dune habitats and geomorphic changes over time with UAV images is important for implementing suitable management practices for species conservation and mitigating coastal vulnerabilities.