Publication: Quantifying mangrove chlorophyll from high spatial resolution imagery

Gepubliceerd op
8 september 2015

The article of Muditha Heenkenda, Karen Joyce, Stefan Maier, Sytze de Bruin: Quantifying mangrove chlorophyll from high spatial resolution imagery, has been published in ISPRS Journal of Photogrammetry and Remote Sensing, Volume 108, October 2015, Pages 234–244.


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.