Estimating mangrove biophysical variables using Worldview-2 satellite data: Rapid Creek, Northern Territory, Australia
Heenkenda, Muditha K., Maier, Stefan W., and Joyce, Karen E. (2016) Estimating mangrove biophysical variables using Worldview-2 satellite data: Rapid Creek, Northern Territory, Australia. Journal of Imaging, 2 (3). 24. pp. 1-19.
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Abstract
Mangroves are one of the most productive coastal communities in the world. Although we acknowledge the significance of ecosystems, mangroves are under natural and anthropogenic pressures at various scales. Therefore, understanding biophysical variations of mangrove forests is important. An extensive field survey is impossible within mangroves. WorldView-2 multi-spectral images having a 2-m spatial resolution were used to quantify above ground biomass (AGB) and leaf area index (LAI) in the Rapid Creek mangroves, Darwin, Australia. Field measurements, vegetation indices derived from WorldView-2 images and a partial least squares regression algorithm were incorporated to produce LAI and AGB maps. LAI maps with 2-m and 5-m spatial resolutions showed root mean square errors (RMSEs) of 0.75 and 0.78, respectively, compared to validation samples. Correlation coefficients between field samples and predicted maps were 0.7 and 0.8, respectively. RMSEs obtained for AGB maps were 2.2 kg/m² and 2.0 kg/m² for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would suggest implementing the transects method for field sampling and establishing end points of these transects with a highly accurate positioning system. The study demonstrated the possibility of assessing biophysical variations of mangroves using remotely-sensed data.
| Item ID: | 45676 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 2313-433X |
| Keywords: | mangrove; above ground biomass; leaf area index; WorldView-2; partial least squares regression |
| Additional Information: | This is an open access article distributed under the Creative Commons Attribution License (CC BY 4.0). |
| Date Deposited: | 12 Sep 2016 02:24 |
| FoR Codes: | 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing @ 60% 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 40% |
| SEO Codes: | 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 60% 97 EXPANDING KNOWLEDGE > 970105 Expanding Knowledge in the Environmental Sciences @ 40% |
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