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J. Imaging 2016, 2(3), 24; doi:10.3390/jimaging2030024

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

1
Research Institute for the Environment and Livelihoods, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0909, Australia
2
Maitec, P.O. Box U19, Charles Darwin University, NT 0815, Australia
3
College of Science, Technology and Engineering, James Cook University, P.O. Box 6811, Cairns, QLD 4870, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Francisco Rovira-Más
Received: 2 June 2016 / Revised: 2 September 2016 / Accepted: 5 September 2016 / Published: 8 September 2016
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
View Full-Text   |   Download PDF [4160 KB, uploaded 13 September 2016]   |  

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. View Full-Text
Keywords: mangrove; above ground biomass; leaf area index; WorldView-2; partial least squares regression mangrove; above ground biomass; leaf area index; WorldView-2; partial least squares regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Heenkenda, M.K.; Maier, S.W.; Joyce, K.E. Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia. J. Imaging 2016, 2, 24.

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