Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana

Author:

Chan-Bagot Kim12,Herndon Kelsey E.34,Puzzi Nicolau Andréa25ORCID,Martín-Arias Vanesa34ORCID,Evans Christine34ORCID,Parache Helen6,Mosely Kene1,Narine Zola1,Zutta Brian25

Affiliation:

1. National Agricultural and Research Extension Institute (NAREI), Georgetown, Guyana

2. SERVIR Amazonia, Cali 76001, Colombia

3. Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35899, USA

4. NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35812, USA

5. Spatial Informatics Group (SIG), San Francisco, CA 94566, USA

6. NASA IMPACT, NASA Marshall Space Flight Center, Huntsville, AL 35812, USA

Abstract

Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI).

Funder

NASA and UAH

Publisher

MDPI AG

Reference77 articles.

1. Tomlinson, P. (1986). The Botany of Mangroves, Cambridge University Press.

2. Hutchings, P., and Saenger, P. (1987). Ecology of Mangroves, University of Queensland Press.

3. Ricklefs, R.E., and Latham, R.E. (1993). Global patterns of diversity in mangrove floras. Species Diversity in Ecological Communities: Historical and Geographical Perspectives, University of Chicago Press.

4. Eddy, S., Milantara, N., Sasmito, S.D., Kajita, T., and Basyuni, M. (2021). Anthropogenic drivers of mangrove loss and associated carbon emissions in South Sumatra, Indonesia. Forests, 12.

5. Kumari, P., Singh, J.K., and Pathak, B. (2020). Biotechnological Utilization of Mangrove Resources, Elsevier.

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