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. pp. 234-244.

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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/m^2 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 (R^2 = 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.

Item ID: 40432
Item Type: Article (Refereed Research - C1)
Keywords: mangrove, chlorophyll, WorldView-2, satellite imagery, remote sensing, random forests
ISSN: 1872-8235
Date Deposited: 09 Sep 2015 00:25
FoR Codes: 09 ENGINEERING > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing @ 70%
05 ENVIRONMENTAL SCIENCES > 0501 Ecological Applications > 050199 Ecological Applications not elsewhere classified @ 15%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050206 Environmental Monitoring @ 15%
SEO Codes: 96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960802 Coastal and Estuarine Flora, Fauna and Biodiversity @ 50%
96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments @ 50%
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