Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data
Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques us...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Online Access: | http://eprints.utm.my/102720/1/NorhafiziMohamad2022_SurfaceElevationChangesEstimationUnderneath.pdf |
| _version_ | 1796867319091167232 |
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| author | Mohamad, Norhafizi Ahmad, Anuar Abdul Khanan, Mohd. Faisal Md. Din, Ami Hassan |
| author_facet | Mohamad, Norhafizi Ahmad, Anuar Abdul Khanan, Mohd. Faisal Md. Din, Ami Hassan |
| author_sort | Mohamad, Norhafizi |
| collection | ePrints |
| description | Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques using two epochs of unmanned aerial vehicle (UAV) data carried out during 2016 and 2017. A novel filtering algorithm named Surface estimation from Nearest Elevation and Repetitive Lowering (SNERL) is used to estimate the elevation height underneath the mangrove canopy. Consequently, DoD technique is used to quantify the elevation change rates at the ground surface, which comprise erosion, accretion, and sedimentation. The significant findings showed that region of interest (ROI) 5 experienced the highest volumetric accretion (surface raising) at 0.566 cm3 . The most increased erosion (surface lowering) was identified at ROI 8 at −2.469 cm3 . In contrast, for vertical change average rates, ROI 6 experienced the highest vertical accretion (surface raising) at 1.281 m. In comparison, the most increased vertical erosion (surface lowering) was spotted at ROI 3 at −0.568 m. The change detection map and the rates of surface elevation changes at Kilim River enabled authorities to understand the situation thoroughly and indicate the future situation, including its interaction with sea-level rise impacts. |
| first_indexed | 2024-03-05T21:25:27Z |
| format | Article |
| id | utm.eprints-102720 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:25:27Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-1027202023-09-18T04:19:56Z http://eprints.utm.my/102720/ Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data Mohamad, Norhafizi Ahmad, Anuar Abdul Khanan, Mohd. Faisal Md. Din, Ami Hassan G70.39-70.6 Remote sensing Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques using two epochs of unmanned aerial vehicle (UAV) data carried out during 2016 and 2017. A novel filtering algorithm named Surface estimation from Nearest Elevation and Repetitive Lowering (SNERL) is used to estimate the elevation height underneath the mangrove canopy. Consequently, DoD technique is used to quantify the elevation change rates at the ground surface, which comprise erosion, accretion, and sedimentation. The significant findings showed that region of interest (ROI) 5 experienced the highest volumetric accretion (surface raising) at 0.566 cm3 . The most increased erosion (surface lowering) was identified at ROI 8 at −2.469 cm3 . In contrast, for vertical change average rates, ROI 6 experienced the highest vertical accretion (surface raising) at 1.281 m. In comparison, the most increased vertical erosion (surface lowering) was spotted at ROI 3 at −0.568 m. The change detection map and the rates of surface elevation changes at Kilim River enabled authorities to understand the situation thoroughly and indicate the future situation, including its interaction with sea-level rise impacts. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/102720/1/NorhafiziMohamad2022_SurfaceElevationChangesEstimationUnderneath.pdf Mohamad, Norhafizi and Ahmad, Anuar and Abdul Khanan, Mohd. Faisal and Md. Din, Ami Hassan (2022) Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data. ISPRS International Journal of Geo-Information, 11 (1). pp. 1-31. ISSN 2220-9964 http://dx.doi.org/10.3390/ijgi11010032 DOI: 10.3390/ijgi11010032 |
| spellingShingle | G70.39-70.6 Remote sensing Mohamad, Norhafizi Ahmad, Anuar Abdul Khanan, Mohd. Faisal Md. Din, Ami Hassan Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data |
| title | Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data |
| title_full | Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data |
| title_fullStr | Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data |
| title_full_unstemmed | Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data |
| title_short | Surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav-derived dsm data |
| title_sort | surface elevation changes estimation underneath mangrove canopy using snerl filtering algorithm and dod technique on uav derived dsm data |
| topic | G70.39-70.6 Remote sensing |
| url | http://eprints.utm.my/102720/1/NorhafiziMohamad2022_SurfaceElevationChangesEstimationUnderneath.pdf |
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