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Quantifying mangrove chlorophyll from high spatial resolution imagery

Heenkenda, Muditha K., Joyce, Karen E., Maier, Stefan W. and de Bruin, Sytze (2015). Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing,108(October):234-244.

Document type: Journal Article
Citation counts: Altmetric Score Altmetric Score is 11
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IRMA ID 84376995xPUB261
Title Quantifying mangrove chlorophyll from high spatial resolution imagery
Author Heenkenda, Muditha K.
Joyce, Karen E.
Maier, Stefan W.
de Bruin, Sytze
Journal Name ISPRS Journal of Photogrammetry and Remote Sensing
Publication Date 2015
Volume Number 108
Issue Number October
ISSN 0924-2716   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-84941100549
Start Page 234
End Page 244
Total Pages 11
Place of Publication Netherlands
Publisher Elsevier BV
HERDC Category C1 - Journal Article (DIISR)
Abstract Lower than expected chlorophyll concentration of a plant can directly limit photosynthetic activity, and resultant primary production. Low chlorophyll concentration may also indicate plant physiological stress. Compared to other terrestrial vegetation, mangrove chlorophyll variations are poorly understood. This study quantifies the spatial distribution of mangrove canopy chlorophyll variation using remotely sensed data and field samples over the Rapid Creek mangrove forest in Darwin, Australia. Mangrove leaf samples were collected and analyzed for chlorophyll content in the laboratory. Once the leaf area index (LAI) of sampled trees was estimated using the digital cover photography method, the canopy chlorophyll contents were calculated. Then, the nonlinear random forests regression algorithm was used to describe the relationship between canopy chlorophyll content and remotely sensed data (WorldView-2 satellite image bands and their spectral transformations), and to estimate the spatial distribution of canopy chlorophyll variation. The imagery was evaluated at full 2 m spatial resolution, as well as at decreased resampled resolutions of 5 m and 10 m. The root mean squared errors with validation samples were 0.82, 0.64 and 0.65 g/m2 for maps at 2 m, 5 m and 10 m spatial resolution respectively. The correlation coefficient was analyzed for the relationship between measured and predicted chlorophyll values. The highest correlation: 0.71 was observed at 5 m spatial resolution (R2 = 0.5). We therefore concluded that estimating mangrove chlorophyll content from remotely sensed data is possible using red, red-edge, NIR1 and NIR2 bands and their spectral transformations as predictors at 5 m spatial resolution.
Keywords Mangrove
Satellite imagery
Remote sensing
Random forests
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Created: Tue, 26 Jul 2016, 13:00:14 CST