TY - JOUR
T1 - Quantifying mangrove chlorophyll from high spatial resolution imagery
AU - Heenkenda Mudalige, Muditha Kumari
AU - Joyce, Karen
AU - Maier, Stefan
AU - de Bruin, Sytze
PY - 2015/10
Y1 - 2015/10
N2 - 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 2m spatial resolution, as well as at decreased resampled resolutions of 5m and 10m. The root mean squared errors with validation samples were 0.82, 0.64 and 0.65g/m2 for maps at 2m, 5m and 10m 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 5m 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 5m spatial resolution.
AB - 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 2m spatial resolution, as well as at decreased resampled resolutions of 5m and 10m. The root mean squared errors with validation samples were 0.82, 0.64 and 0.65g/m2 for maps at 2m, 5m and 10m 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 5m 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 5m spatial resolution.
KW - Decision trees
KW - Image resolution
KW - Mathematical transformations
KW - Mean square error
KW - Plants (botany)
KW - Remote sensing
KW - Satellite imagery
KW - Spatial distribution
KW - Chlorophyll concentration
KW - Correlation coefficient
KW - High spatial resolution imagery
KW - Mangrove
KW - Random forests
KW - Root mean squared errors
KW - Spectral transformations
KW - Worldview-2
KW - Chlorophyll
U2 - 10.1016/j.isprsjprs.2015.08.003
DO - 10.1016/j.isprsjprs.2015.08.003
M3 - Article
VL - 108
SP - 234
EP - 244
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -