Remote sensing of mangrove wetlands: Relating canopy spectra to site-specific data
No abstract available.
Florida↗
Geology topics
John R. Jensen
Publications and source records attributed to John R. Jensen.
Modeling mangrove canopy reflectance using a light interaction model and an optimization technique
At 20 sites, incorporating mixtures of black, red, and white mangroves, canopy reflectance spectra were derived from high resolution spectral data taken from a helicopter platform. Canopy characteristics were predicted from the canopy reflectance spectra by using measured and estimated data as inputs into a light-canopy interaction model within a optimization routine. Pertinent to average conditions typifying the area and time of the study, the light-canopy interaction model accomplished two goals. Using the model as a predictor, a sensitivity analysis suggested that little error in modelling the near nadir view canopy reflectance (R cv ) would result from assuming an average soil reflectance of about 0.1, at leaf area index (LAI) values above 2, at near infrared (NIR) leaf reflectances higher than about 0.45. and at sun elevation angles >40 o . Moderate errors could result from assuming a spherical leaf angel distribution (LAD), and relatively high errors could result from errors in estimating visible leaf reflectances (and NIR leaf reflectances <0.45) and percent skylight. Differences between canopy hemispherical reflectance (R c ) and R cv were dominated by percent skylight variation, while differences between R c and R cv were moderate to slight at a sun elevation above 20 o to 30 o , a near spherical LAD, a soil reflectance near 0.1, a LAI up to 4, and a NIR leaf reflectance less than 0.7. Simulated canopy reflectance spectra were close predictors of obtained spectra, with R 2 values >0.97. Mean predicted LAI values were 2.6±0.86 (mean ±1 standard deviation) and were highly related to LAI values derived from field measurements. Seventy-eight percent of the modelled LAI variance was predicted by a normalized difference vegetation index transform of the field canopy spectra data. Predicted LAD values had a near spherical mean value, while the mean difference between input (estimated from laboratory measurements) and predicted leaf reflectances was nearly zero.
Florida↗
The measurement of mangrove characteristics in southwest Florida using spot multispectral data
An intensive in situ sampling program near Marco Island, Florida during 19–23 October 1988 collected information on mangrove type, maximum canopy height, and percent canopy closure. These data were correlated with selected vegetation index information derived from analysis of SPOT multispectral (XS) data obtained on 21 October 1988. The Normalized Difference (ND) vegetation index information was the most highly correlated index with percent canopy closure (r=0.91). Percent canopy closure information can be used as a surrogate for mangrove density which is of great value when predicting which parts of the mangrove ecosystem are at greatest risk after an oil spill occurs. Such information is very valuable when constructing oil spill Environmental Sensitivity Index (ESI) Maps for tropical regions of the world.
Florida↗
Remote sensing of water quality
Water property data were collected within 3 cooling water reservoirs (active to inactive, large [1,068 ha] to small [68.6 ha]. oligotrophic to eutrophic) at 31 locations. A description of the water characteristics was obtained including algal pigments, total suspended particles, dissolved and particulate organic matter, and total particle absorption spectra. Field data included generated water volume reflectance spectra and secchi depth. Specialized regression techniques were used to derive specific absorption coefficients from measured water property concentrations and total particle absorption spectra. Subsequently, using first order approximations relating water volume reflectance to water intrinsic properties, generated absorption coefficients were combined with an optimization procedure that minimized the difference between the observed and predicted water volume reflectances resulting in specific backscatter coefficient estimates.
South Carolina↗