ORCID
0000-0001-8337-7441 (Hill), 0000-0002-9399-4264 (Zimmerman)
Document Type
Article
Publication Date
2024
DOI
10.3390/rs16234351
Publication Title
Remote Sensing
Volume
16
Issue
23
Pages
4351 (1-18)
Abstract
This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km² of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which each pixel was classified as seagrass. Seagrass patches encompassing at least nine pixels (~200 m²) were almost always detected by our classification algorithm. Using an OF threshold equal to or greater than >60% provided a high level of confidence in seagrass presence while effectively reducing the impact of small misclassifications, often of individual pixels, that appeared sporadically in individual images. The image-to-image uncertainty in seagrass retrieval from the satellite images was 0.1 km² or 2.3%, reflecting the robustness of our classification method and allowing confidence in the accuracy of the seagrass area estimate. The satellite-retrieved leaf area index (LAI) was consistent with previous in situ measurements, leading to the estimate that 2700 tons of carbon per year are produced by the Santa Rosa Sound seagrass ecosystem, equivalent to a drawdown of approximately 10,070 tons of CO₂. This satellite-based approach offers a cost-effective, semi-automated, and scalable method of assessing the distribution and abundance of submerged aquatic vegetation that provides numerous ecosystem services.
Rights
© 2024 The Authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Data Availability
Article states: "Data for image processing were derived from SuperDove satellites from the PlanetScope constellation, accessed via NASA's Commercial SmallSat Data Acquisition (CSDA) Program."
Original Publication Citation
Hill, V. J., Zimmerman, R. C., Byron, D. A., & Heck Jr, K. L. (2024). Mapping seagrass distribution and abundance: Comparing areal cover and biomass estimates between space-based and airborne imagery. Remote Sensing, 16(23), 1-18, Article 4351. https://doi.org/10.3390/rs16234351
Repository Citation
Hill, Victoria J.; Zimmerman, Richard C.; Byron, Dorothy A.; and Heck, Kenneth L. Jr., "Mapping Seagrass Distribution and Abundance: Comparing Areal Cover and Biomass Estimates Between Space-Based and Airborne Imagery" (2024). OES Faculty Publications. 527.
https://digitalcommons.odu.edu/oeas_fac_pubs/527
Included in
Oceanography and Atmospheric Sciences and Meteorology Commons, Plant Sciences Commons, Theory and Algorithms Commons