Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jul 31;13(8):e10388.
doi: 10.1002/ece3.10388. eCollection 2023 Aug.

Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

Daniel Aja  1 , Michael Miyittah  1   2 , Donatus Bapentire Angnuureng  1
Affiliations

Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

Daniel Aja et al. Ecol Evol. .

Abstract

Cloud cover effects make it difficult to evaluate the mangrove ecosystem in tropical locations using solely optical satellite data. Therefore, it is essential to conduct a more precise evaluation using data from several sources and appropriate models in order to manage the mangrove ecosystem as effectively as feasible. In this study, the status of the mangrove ecosystem and its potential contribution to coastal resilience were evaluated using the Google Earth Engine (GEE) and the InVEST model. The GEE was used to map changes in mangrove and other land cover types for the years 2009 and 2019 by integrating both optical and radar data. The quantity allocation disagreement index (QADI) was used to assess the classification accuracy. Mangrove height and aboveground biomass density were estimated using GEE by extracting their values from radar image clipped with a digital elevation model and mangrove vector file. A universal allometric equation that relates canopy height to aboveground biomass was applied. The InVEST model was used to calculate a hazard index of every 250 m of the shoreline with and without mangrove ecosystem. Our result showed that about 16.9% and 21% of mangrove and other vegetation cover were lost between 2009 and 2019. However, water body and bare land/built-up areas increased by 7% and 45%, respectively. The overall accuracy of 2009 and 2019 classifications was 99.6% (QADI = 0.00794) and 99.1% (QADI = 0.00529), respectively. Mangrove height and aboveground biomass generally decreased from 12.7 to 6.3 m and from 105 to 88 Mg/ha on average. The vulnerability index showed that 23%, 51% and 26% of the coastal segment in the presence of mangrove fall under very low/low, moderate and high risks, respectively. Whereas in the absence of mangrove, 8%, 38%, 39% and 15% fall under low, moderate, high and very high-risk zones, respectively. This study will among other things help the stakeholders in coastal management and marine spatial planning to identify the need to focus on conservation practices.

Keywords: Ghana; Hazard index; InVEST; LULC; QADI; complex wetland.

PubMed Disclaimer

Conflict of interest statement

We have no conflicting interest to declare.

Figures

FIGURE 1
FIGURE 1
Study location map.
FIGURE 2
FIGURE 2
Flow chart of the methodological approach.
FIGURE 3
FIGURE 3
Coastal vulnerability modelling approach.
FIGURE 4
FIGURE 4
Changes in land use and cover between 2009 and 2019.
FIGURE 5
FIGURE 5
Map of land cover extent for 2009.
FIGURE 6
FIGURE 6
Map of land cover extent for 2019.
FIGURE 7
FIGURE 7
Independent accuracy assessment for 2009 land cover classification.
FIGURE 8
FIGURE 8
Independent accuracy assessment for 2019 land cover classification.
FIGURE 9
FIGURE 9
Mangrove height estimates for year 2000.
FIGURE 10
FIGURE 10
Above ground biomass estimates for year 2000.
FIGURE 11
FIGURE 11
Map of Coastal Exposure Index in the presence of mangrove ecosystem.
FIGURE 12
FIGURE 12
Map of Coastal Exposure Index in the absence of mangrove ecosystem.
See this image and copyright information in PMC

References

    1. Aheto, D. W. , Adiza, A. O. A. , & Obodai, E. A. (2011). Structural parameters and above‐ground biomass of mangrove tree species around the Kakum River estuary of Ghana. Annals of Biological Research, 2(3), 504–514.
    1. Ai, B. , Tian, Y. , Wang, P. , Gan, Y. , Luo, F. , & Shi, Q. (2022). Vulnerability analysis of coastal zone based on InVEST model in Jiaozhou Bay, China. Sustainability, 14, 6913. 10.3390/su14116913 - DOI
    1. Aja, D. , Miyittah, M. K. , & Angnuureng, D. B. (2022). Quantifying mangrove extent using a combination of optical and radar images in a wetland complex, Western region, Ghana. Sustainability, 14, 16687. 10.3390/su142416687 - DOI
    1. Al Ruheili, A. , & Boluwade, A. (2023). Towards quantifying the coastal vulnerability due to natural hazards using the InVEST coastal vulnerability model. Water, 15, 380. 10.3390/w15030380 - DOI
    1. Amani, M. , Ghorbanian, A. , Ahmadi, S. A. , Kakooei, M. , Moghimi, A. , Mirmazloumi, S. M. , Moghaddam, S. H. A. , Mahdavi, S. , Ghahremanloo, M. , Parsian, S. , Wu, Q. , & Brisco, B. (2020). Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. 10.1109/JSTARS.2020.3021052 - DOI

LinkOut - more resources