Geospatial mapping and modelling of mangrove ecosystem health at Matang mangrove forest reserve
Among the multi-functional ecosystem provided by mangrove forest, the health like biotic and abiotic variables represent the most challenging components to be monitored due to their diverse characteristics and spatially distributed over mangrove area. Since the biotic and abiotic variables fac...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/98851/1/FPAS%202021%2016%20UPMIR.pdf http://psasir.upm.edu.my/id/eprint/98851/ |
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| Summary: | Among the multi-functional ecosystem provided by mangrove forest, the health
like biotic and abiotic variables represent the most challenging components to
be monitored due to their diverse characteristics and spatially distributed over
mangrove area. Since the biotic and abiotic variables factors are vital for the
mangrove ecosystem health (MEH), the information of remotely sensing data
and spatial analysis were integrated to evaluate the health condition of
mangrove. The objective of this study was (1) to examine mangrove vegetation
cover by integrating NDVI and SAVI, and (2) to model the MEH using ordinary
kriging (OK) for the entire MMFR.
Supervised classification, NDVI and SAVI were performed to determine
vegetation coverage. Since SAVI requires a suitable L-factor to be used to
distinguish the vegetation areas, four different L-factors viz. 0.1, 0.25, 0.5 and
0.75 were tested with the multiple linear regressions using the stepwise
regression method of backward elimination. The relationship of NDVI and SAVI
in detecting mangrove vegetation covers were examined with correlation-Pearson analysis. For Objective 2, eight variables from Faridah-Hanum et al.
(2019) were used as preference factors to determine the MEH. Semivariogram
model and interpolation method of OK were used to generate spatial
autocorrelation of MEH. Prediction accuracy was examined through ME, RMSE
and RMSSE. All variables were then overlay and combined via linear weight
regression (LWC) to see the overall health status. Reclassification was
conducted to standardise the health value viz. 1 (worst), 2 (poor), 3 (moderate),
4 (good) and 5 (excellent). In order to verify the health status, NDVI analysis
performed in Objective 1 were used to support the accuracy of MEH. Supervised classification was observed with good accuracy; Kuala Sepetang
(71.8%; K=0.668), Kuala Trong (83.8%; K=0.798) and Sungai Kerang (73.5%;
K=0.681). SAVI with L-factor 0.75 was found to be significant to be used for
MMFR. The vegetation indices (VIs) resulting from NDVI and SAVI demonstrate
the classification variations when compared to the initial supervised
classification. In Objective 2, all variables had an overall prediction accuracy with
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). The spatial prediction autocorrelation
delineating an area of 307.9 ha for excellent MEH, 15935.68 ha for good MEH,
5224.34 ha for moderate MEH, 17795.63 ha for bad MEH and 715.55 ha for
worst MEH. This study modelled the overall MEH through OK with selected
semivariogram model and comparison to VIs consequently promoting the
restoration affects, relevant management and facilities distribution, and therefore
improving the MEH over the entire MMFR. |
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