Estimation of surface elevation changes underneath mangrove canopy through geospatial and geomorphological approach
Estimating surface elevation changes in mangrove forests is dynamic because it is barely visible physically and because of the canopy-covered factor that restricts aerial monitoring. It demands a technique that filters the mangrove canopy at the top of the vegetation and the complex understory struc...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/100011/1/NorhafiziMohamadPFABU2022.pdf |
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| Summary: | Estimating surface elevation changes in mangrove forests is dynamic because it is barely visible physically and because of the canopy-covered factor that restricts aerial monitoring. It demands a technique that filters the mangrove canopy at the top of the vegetation and the complex understory structures. Hence, this study estimated surface elevation changes underneath the mangrove canopy through geospatial and geomorphological approaches. The first objective of this study was to discover vegetation filtering algorithms for estimating surface elevation underneath the mangrove canopy, followed by generating an unmanned aerial vehicle-digital elevation model (UAV-DEM) underneath the mangrove canopy with vertical accuracy comparable to physical topography measurement. The other objective was to evaluate the rates of surface elevation changes using the geomorphological change detection method. The last objective was to correlate the interactions between mangrove surface elevation changes and sea level rise. This study's data processing stages included photogrammetric data processing using Structure from Motion-Multiview Stereo (SfM-MVS), filtering using the surface estimation from Nearest Elevation and Repetitive Lowering (SNERL) algorithm, and geomorphological change detection (GCD) analysis. Two epochs of UAV data collection were carried out in 2016 and 2017 at low tide conditions. UAV data processing was performed using the SfM-MVS method. Next, the SNERL algorithm was employed to extract the surface from the mangrove canopy and generate the mangrove ground as a DEM. Subsequently, GCD analysis was utilized to quantify the elevation change rates at the ground surface, which comprise erosion, accretion, and sedimentation, using the differential DEM (DoD) technique. The finding illustrated that the generated UAV-DEM using SNERL algorithms reached vertical accuracy of 0.345 m (RMSE), 0.107 m (mean), and 0.503 m (standard deviation). The other finding indicated that region of interest 5 (ROI 5) experienced the highest volumetric accretion (surface raising) at 0.566 cm3/yr. The highest erosion (surface lowering) was identified at ROI 8 at -2.469 cm3/yr. In contrast, for vertical change average rates, ROI 6 experienced the highest vertical accretion (surface raising) at 1.281 m/yr, while the highest vertical erosion (surface lowering) was spotted at ROI 3 at -0.568 m/yr. In conclusion, a geospatial approach comprising SfM-MVS, vegetation index (VI) segregation, and the SNERL filtering algorithm are efficient in generating UAV-DEM underneath the mangrove canopy at the closest level to the terrain level. The GCD map and the rates of surface elevation changes at Kilim River enabled authorities like Langkawi Development Authority (LADA) and the Department of Drainage and Irrigation (DID) to fully understand the situation and prepare a mitigation plan to avoid unbalanced surface elevation changes that could lead to long-term devastation of the mangrove ecosystem in the future. |
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