Three-dimensional mapping of light transmittance and foliage distribution using lidar

The horizontal and vertical distributions of light transmittance were evaluated as a function of foliage distribution using lidar (light detection and ranging) observations for a sugar maple (Acer saccharum) stand in the Turkey Lakes Watershed. Along the vertical profile of vegetation, horizontal slices of probability of light transmittance were derived from an Optech ALTM 1225 instrument's return pulses (two discrete, 15-cm diameter returns) using indicator kriging. These predictions were compared with (i) below canopy (1-cm spatial resolution) transect measurements of the fraction of photosynthetically active radiation (FPAR) and (ii) measurements of tree height. A first-order trend was initially removed from the lidar returns. The vertical distribution of vegetation height was then sliced into nine percentiles and indicator variograms were fitted to them. Variogram parameters were found to vary as a function of foliage height above ground. In this paper, we show that the relationship between ground measurements of FPAR and kriged estimates of vegetation cover becomes stronger and tighter at coarser spatial resolutions. Three-dimensional maps of foliage distribution were computed as stacks of the percentile probability surfaces. These probability surfaces showed correspondence with individual tree-based observations and provided a much more detailed characterization of quasi-continuous foliage distribution. These results suggest that discrete-return lidar provides a promising technology to capture variations of foliage characteristics in forests to support the development of functional linkages between biophysical and ecological studies

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale*

To further develop the methods to remotely sense the biochemical content of plant canopies, we report the results of an experiment to estimate the concentrations of three biochemical variables of corn, i.e., nitrogen (N), crude fat (EE) and crude fiber (CF) concentrations, by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed, and a set of estimation models were established using curve-fitting analyses. Coefficient of determination (R 2), root mean square error (RMSE) and relative error of prediction (REP) of estimation models were calculated for the model quality evaluations, and the possible optimum estimation models of three biochemical variables were proposed, with R 2 being 0.891, 0.698 and 0.480 for the estimation models of N, EE and CF concentrations, respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation, and that the first derivative reflectances at 759 nm, 1954 nm and 2370 nm were most suitable to develop the estimation models of N, EE and CF concentrations, respectively. In addition, the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained, especially for nitrogen (r=0.948)