Forests exhibit much variation in light environments, and this can affect communication among animals, communication between animals and plants, photosynthesis, and plant morphogenesis. Light environments are caused by, and can be predicted from, the geometry of the light paths, the weather conditions, and the time of day. The structure of forests leads to four major light habitats when the sun is not blocked by clouds: forest shade, woodland shade, small gaps, and large gaps. These are characterized by yellow—green, blue—gray, reddish, and "white" ambient light spectra, respectively. When the sun is blocked by clouds, the spectra of these four habitats converge on that of large gaps and open areas, so the single light environment during cloudy weather will be called open/cloudy. An additional light environment (early/late) is associated with low sun angles (near dawn or dusk); it is purplish. Each light environment is well defined and was found in forests of Trinidad, Panama, Costa Rica, Australia, California, and Florida. Scattered literature references suggest similar patterns elsewhere in North America, Europe, and Java. Perceived colors of animals, flowers, and fruits depend upon the interaction between ambient light color and the reflectance color of the animal or plant parts. As a result, an animal or plant may have a different appearance in each environment, i.e., a color pattern may be relatively cryptic in some light environments while relatively conspicuous in others. This has strong implications for the joint evolution of visual signals and vision, as well as microhabitat choice. Plant growth and form may also be affected by variation in the color of forest light.

The Color of Light in Forests and Its Implications

https://archive-ouverte.unige.ch/unige:17207/ATTACHMENT01

A new simplified version of the perez diffuse irradiance model for tilted surfaces

Light and marine photosynthesis: a spectral model with geochemical and climatological implications

Clear sky shortwave solar radiation varies in response to altitude and elevation, surface gradient (slope) and orientation (aspect), as well as position relative to neighbouring surfaces While the

Modelling topographic variation in solar radiation in a GIS environment

Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.

D. K. Butt

Papers

Solar and Terrestrial Radiation