How to develop a comprehensive Mangrove Quality Index?

Faridah-Hanum, I.; Yusoff, Fatimah Md.; Fitrianto, Anwar; Nuruddin, Ahmad Ainuddin; Gandaseca, Seca; Samdin, Zaiton; Kamarudin, Nurzatulshima; Nurhidayu, Siti; Kassim, Mohamad Roslan; Hakeem, Khalid Rehman; Ibrahim, Shamsuddin; Ismail, Ahmad; Ghani, Awang Noor Abdul; Parman, Rhyma Purnamasayangsukasih; Razak, Siti Balqis Abd.; Ibrahim, Siti Aminah; Hilaluddin, Fareha; Ramli, Fatin Najwa; Zaidin, Nik Harun Al-Rashid Nik

doi:10.1016/j.mex.2019.06.014

Cited by 8 publications

(1 citation statement)

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“…Following Ibrahim et al [25], mangrove quality was assessed for five distinct factorial categories consisting of 43 variables, namely; (i) biotic variables, basal area, mangrove tree height, tree volume, crab abundance, above-ground biomass; (ii) soil variables; pH, sulfur, calcium, phosphorus, potassium, nitrogen, carbon, magnesium; (iii) aquatic ecosystem variables; abundance of phytoplankton, diatoms, dinoflagellates, copepods, jellyfish, no of phytoplankton species, diatoms, dinoflagellates, copepods, jellyfish; (iv) hydrology variables; pH, turbidity, electric conductivity, total dissolved solids, temperature, dissolved oxygen, total suspended solids, chemical oxygen demand, biochemical oxygen demand, salinity, ammonia, phosphate, nitrate, fecal coliform; and (v) socio-economic variables; fishing hours/days and weight of fish catches, monthly income, age, education. Where the results of the calculation range from 1 standing for the worst condition, while up to 5 indicates excellent.…”

Section: Advancements In Remote Sensing Of Mangrove Ecosystemsmentioning

confidence: 99%

The Implementation of the Mangrove Quality Index: A Way to Overcome Overestimation and Classification Concerns in Detecting Mangrove Forest Cover

Gunathilaka,

Harhana

2023

Int. J. Environ. Eng. Educ.

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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.

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Section: Advancements In Remote Sensing Of Mangrove Ecosystemsmentioning

confidence: 99%