Mapping the extent of mangrove ecosystem degradation by integrating an ecological conceptual model with satellite data

Lee, Calvin K.F., Duncan, Clare, Nicholson, Emily, Fatoyinbo, Temilola E., Lagomasino, David, Thomas, Nathan, Worthington, Thomas A., and Murray, Nicholas J. (2021) Mapping the extent of mangrove ecosystem degradation by integrating an ecological conceptual model with satellite data. Remote Sensing, 13 (11). 2047.

[img]
Preview
PDF (Published Version) - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview
View at Publisher Website: https://doi.org/10.3390/rs13112047
 
19
871


Abstract

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2 ) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.

Item ID: 70348
Item Type: Article (Research - C1)
ISSN: 2072-4292
Keywords: Degradation, Ecosystem assessment, Ecosystem conceptual model, Everglades, Mangrove, Myanmar, Satellite imagery
Copyright Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license https://creativecommons.org/licenses/by/4.0/).
Funders: Australian Research Council (ARC)
Projects and Grants: ARC DE190100101, ARC FT190100234, ARC LP170101143
Date Deposited: 19 Nov 2021 03:09
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490511 Time series and spatial modelling @ 100%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180601 Assessment and management of terrestrial ecosystems @ 100%
Downloads: Total: 871
Last 12 Months: 93
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page