Geostatistical analysis of mangrove ecosystem health: mapping and modelling of sampling uncertainty using Kriging

• Main Authors: Parman, Rhyma Purnamasayangsukasih, Kamarudin, Norizah, Ibrahim, Faridah Hanum, Nuruddin, Ahmad Ainuddin, Omar, Hamdan, Abdul Wahab, Zulfa
• 格式: Article
• 出版: Multidisciplinary Digital Publishing Institute 2022
• 在线阅读: http://psasir.upm.edu.my/id/eprint/101628/; https://www.mdpi.com/1999-4907/13/8/1185

This study assessed the health of the mangrove ecosystem and mapped the spatial variation in selected variables sampled across the Matang Mangrove Forest Reserve (MMFR) by using a geostatistical technique. A total of 556 samples were collected from 56 sampling points representing mangrove biotic and abiotic variables. All variables were used to generate the semivariogram model. The predicted variables over the entire MMFR have an overall prediction accuracy of 85.16% (AGB), 90.78% (crab abundance), 97.3% (soil C), 99.91% (soil N), 89.23% (number of phytoplankton species), 95.62% (number of diatom species), 99.36% (DO), and 87.33% (turbidity). Via linear weight combination, the prediction map shows that mangrove ecosystem health in Kuala Trong throughout the Sungai Kerang is excellent (5: MQI > 1.5). Some landward areas of Kuala Trong were predicted to have moderate health (3: −0.5 ≤ MQI ≤ 0.5), while Kuala Sepetang was predicted to have the bad ecosystem health (2: −1.5 ≤ MQI ≤ −0.5), with active timber harvesting operations and anthropogenic activities in the landward areas. The results of this method can be utilised to carry out the preferred restoration, through appropriate management and facilities distribution, for improving the ecosystem health of mangroves.