The Implementation of the Mangrove Quality Index: A Way to Overcome Overestimation and Classification Concerns in Detecting Mangrove Forest Cover
Abstract
The increasing applications of Geographic Information Systems (GIS) and Remote Sensing (RS) for mapping, predicting, and monitoring are practical for sustainable mangrove ecosystem management. This study evaluated various geospatial techniques for detecting healthy mangroves on the eastern coast of Sri Lanka, including single spectral indices, supervised/unsupervised classification, and developed methods using Landsat data. The use of medium-resolution satellite data and the uniqueness of the mangrove ecosystem are generally involved in discriminating healthy mangroves from non-mangrove areas. This study focused on detecting degraded narrow patches of mangroves on the Eastern coast of Sri Lanka using Landsat 8 remote sensing data and five vegetation indices. The accuracy of the results was assessed using randomly generated points. The study used ArcGIS Desktop software for processing, analyzing, and integrating spatial data to meet the research objectives. The mangroves were detected using Landsat 8 OLI satellite images from 2018 and 2021. The results showed high overestimation/underestimation and misclassification of mangroves, thus applying Mangrove Quality Index (MQI). Findings of MQI provide insights into overall mangrove health and identify three degradation classes of mangroves on the Eastern coast of Sri Lanka. The application of MQI in well-developed and degraded mangrove ecosystems merits further investigations, which provide reliable information for conservation priorities.
References
H. Tran, T. Tran, and M. Kervyn, “Dynamics of land cover/land use changes in the Mekong Delta, 1973–2011: A remote sensing analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam,” Remote Sens., vol. 7, no. 3, pp. 2899–2925, 2015.
T. G. Jones et al., “Madagascar’s mangroves: Quantifying nation-wide and ecosystem specific dynamics, and detailed contemporary mapping of distinct ecosystems,” Remote Sens., vol. 8, no. 2, p. 106, 2016.
S. X. Fei, C. H. Shan, and G. Z. Hua, “Remote sensing of mangrove wetlands identification,” Procedia Environ. Sci., vol. 10, pp. 2287–2293, 2011.
M. D. Kanakaratne, W. K. T. Perera, and B. U. S. Fernando, “An attempt at determining the mangrove coverage in Puttalam lagoon, Dutch bay and Portugal bay, Sri Lanka, using remote sensing techniques,” 1983.
A. C. Shapiro, C. C. Trettin, H. Küchly, S. Alavinapanah, and S. Bandeira, “The mangroves of the Zambezi Delta: Increase in extent observed via satellite from 1994 to 2013,” Remote Sens., vol. 7, no. 12, pp. 16504–16518, 2015.
L. T. H. Pham and L. Brabyn, “Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms,” ISPRS J. Photogramm. Remote Sens., vol. 128, pp. 86–97, 2017.
B. Chen et al., “A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform,” ISPRS J. Photogramm. Remote Sens., vol. 131, pp. 104–120, 2017.
F. Dahdouh‐Guebas, E. Van Hiel, J. C. Chan, L. P. Jayatissa, and N. Koedam, “Qualitative distinction of congeneric and introgressive mangrove species in mixed patchy forest assemblages using high spatial resolution remotely sensed imagery (IKONOS),” Syst. Biodivers., vol. 2, no. 2, pp. 113–119, 2004.
T. Wang, H. Zhang, H. Lin, and C. Fang, “Textural–spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery,” Remote Sens., vol. 8, no. 1, p. 24, 2015.
C. Kuenzer, A. Bluemel, S. Gebhardt, and T. Vo Quoc, “S. Dech. 2011.",” Remote Sens. Mangrove Ecosyst. A Rev. Remote Sens., vol. 3, pp. 878–928.
H. Zhang et al., “Potential of combining optical and dual polarimetric SAR data for improving mangrove species discrimination using rotation forest,” Remote Sens., vol. 10, no. 3, p. 467, 2018.
M. F. Cougo et al., “Radarsat-2 backscattering for the modeling of biophysical parameters of regenerating mangrove forests,” Remote Sens., vol. 7, no. 12, pp. 17097–17112, 2015.
A. Olagoke et al., “Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data,” Trees, vol. 30, pp. 935–947, 2016.
E. P. Green, C. D. Clark, P. J. Mumby, A. J. Edwards, and A. C. Ellis, “Remote sensing techniques for mangrove mapping,” Int. J. Remote Sens., vol. 19, no. 5, pp. 935–956, 1998.
W. Li, H. El-Askary, M. A. Qurban, J. Li, K. P. ManiKandan, and T. Piechota, “Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast,” Ecol. Indic., vol. 102, pp. 734–745, 2019.
X. Zhang, P. M. Treitz, D. Chen, C. Quan, L. Shi, and X. Li, “Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure,” Int. J. Appl. earth Obs. Geoinf., vol. 62, pp. 201–214, 2017.
V. Pathmanandakumar, “Mangrove forest cover change detection along the coastline of Trincomalee District, Sri Lanka using GIS and remote sensing techniques,” J. Mar. Sci. Res. Oceanogr., vol. 2, no. 1, 2019.
K. B. Ranawana, “Mangroves of Sri Lanka,” Publ. Seacology-sudeesa Mangrove Mus, vol. 1, pp. 25–28, 2017.
C. J. Tucker, “Red and photographic infrared linear combinations for monitoring vegetation,” Remote Sens. Environ., vol. 8, no. 2, pp. 127–150, 1979.
S. Barati, B. Rayegani, M. Saati, A. Sharifi, and M. Nasri, “Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas,” Egypt. J. Remote Sens. Sp. Sci., vol. 14, no. 1, pp. 49–56, 2011.
F. Foussenia, H. H. Guoa, Z. X. Haia, J. L. Seburanga, S. A.-S. Mande, and A. Koffi, “Urban area vegetation changing assessment over the last 20 years based on NDVI,” Energy Procedia, vol. 11, pp. 2449–2454, 2011.
G. S. Birth and G. R. McVey, “Measuring the color of growing turf with a reflectance spectrophotometer 1,” Agron. J., vol. 60, no. 6, pp. 640–643, 1968.
K. Gupta et al., “An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery,” MethodsX, vol. 5, pp. 1129–1139, 2018.
Q. Xia, C.-Z. Qin, H. Li, C. Huang, F.-Z. Su, and M.-M. Jia, “Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data,” Ecol. Indic., vol. 113, p. 106196, 2020.
F.-H. Ibrahim et al., “How to develop a comprehensive Mangrove Quality Index?,” MethodsX, vol. 6, pp. 1591–1599, 2019.
M. Gunathilaka, “Environmental Communication for Mangrove Restoration and Conservation in a Fishing Village, Sri Lanka,” Int. J. Reseach Innov. Soc. Sci. (IJRISS), IV, pp. 22–27, 2020.
S. Mathanraj and M. I. M. Kaleel, “Hreats of mangrove flora and the management actions; a case study in Kaluwanchikudy area.,” 2015.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright of the published article belongs to the authors and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC BY SA) International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See the Effect of Open Access).
