Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring
Extending from central California to Alaska, bull kelp (Nereocystis luetkeana) forms seasonal kelp forests that are iconic coastal ecosystems in much of the eastern Pacific. Historical and ongoing field surveys and aerial imagery are used to provide biological data on kelp canopy cover and health, but satellite remote sensing provides the opportunity to generate consistent, long-term datasets over a large spatial scale. Robust satellite-based timeseries measurements of giant kelp (Macrocystis pyrifera) are available for much of the California coastline (e.g., Santa Barbara to Santa Cruz), but there have been no equivalent publications for bull kelp. Recent loss of bull kelp in northern California emphasized the need for more expansive long-term monitoring of canopy trends. We tested various kelp classification approaches using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to Landsat imagery, which allowed sufficient temporal and spatial data collection during bull kelp's narrow seasonal maximum, and compare with the California Department of Fish and Wildlife (CDFW) aerial survey canopy area product. We addressed five main topics that have relevance to enabling Landsat in long-term monitoring of bull kelp canopy coverage in northern California: (1) the effect of MESMA configurations applied to Landsat imagery, including software dependencies and endmember configurations, (2) comparison of Landsat to traditional surveys, (3) differences across Landsat sensors, (4) tidal influence on canopy area, and (5) trends in the decadal timeseries. We found that there was no statistical difference (p = 0.53) between MESMA platforms (IDL-based and a Python-scripted method; RMSE 1.5 km2 or 17.6% when normalized by the range in CDFW), and that a 7-endmember MESMA model provided the lowest RMSE (1.4 km2 or 16.9%). Additionally, while tidal phases can submerge or emerge kelp canopy and thus potentially affect kelp detection, we found a weak and statistically insignificant correlation between tides and performance of our remote estimate of kelp canopy area. Canopy estimations from Landsat-8 images yielded a higher NRMSE than Landsat-4 and -5 images, but the lack of matchup limits comparison. Imagery from early fall yielded the largest coverage estimates. Overall, our results show that Landsat enables broad remote measurement of bull kelp canopy coverage to supplement existing survey methods and increase continuity of timeseries for monitoring long-term trends.
- Research Organization:
- Univ. of California, Irvine, CA (United States)
- Sponsoring Organization:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- Grant/Contract Number:
- AR0000920
- OSTI ID:
- 1742045
- Alternate ID(s):
- OSTI ID: 1848310
- Journal Information:
- Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 254 Journal Issue: C; ISSN 0034-4257
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem
The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps