Affiliation:
1. Department Environmental Studies, University of California, 1156 High Street, Santa Cruz, CA 95064, USA
Abstract
Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.
Funder
Earth Future’s Frontier Fellows fellowship program
UCSC Center-Scale Seed Funding Initiative
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Reference67 articles.
1. Using GIS and remote sensing for ecological mapping and monitoring;Miller;Integr. GIS Remote Sens.,2007
2. Schowengerdt, R. (2006). Remote Sensing: Models and Methods for Image Processing, Elsevier.
3. UAVs as remote sensing platforms in plant ecology: Review of applications and challenges;Sun;J. Plant Ecol.,2021
4. Free and open-access satellite data are key to biodiversity conservation;Turner;Biol. Conserv.,2015
5. Questad, E.J., Antill, M., Liu, N., Stavros, E.N., Townsend, P.A., Bonfield, S.N., and Schimel, D. (2022). A Camera-Based Method for Collecting Rapid Vegetation Data to Support Remote-Sensing Studies of Shrubland Biodiversity. Remote Sens., 14.