Abstract
Relationships between biophysical parameters and radiometric data have been tested and evaluated by several professionals using empirical and/or physical approaches. Remote sensing data collected from airborne or orbital platforms are, of course, influenced by different factors, such as illumination/observation geometry (data collection geometry), atmospheric effects, etc., rather than by target spectral properties. Besides that, the target topographic positioning actually defines the amount of incident energy, as well as the amount of energy that is reflected toward the sensor. The sum of both data collection geometry and topographic positioning defines the so-called “local illumination”. The objective of this paper was to evaluate the influence of local illumination on empirical relationships between a biophysical variable (plant area index, PAI) and two vegetation indices calculated from Resourcesat/Linear Imaging Self-Scanner sensor (LISS-3) orbital data. Local illumination was expressed by the cosine factor (Fcos) and calculated from topographic and solar position data at three different dates. The study area was based on a typical Brazilian southeastern forest fragment located in the Augusto Ruschi municipal preservation park dispersed on roughhouse topography. PAI was estimated by hemispherical photographs taken under the forest canopy from sample points arbitrarily dispersed on the forest fragment. Results confirmed a stronger relationship between vegetation indices and local illumination conditions.
Highlights
Remote sensing technology has been applied to vegetation studies, including biophysical parameters estimation, such as above ground biomass (AGB), water and chlorophyll leaf content, leaf area index (LAI), etc. [1]
The objective of this paper is to evaluate the influence of local illumination on the empirical relationships between Plant Area index (PAI) estimated from a tropical rain forest fragment and vegetation indices calculated from Resourcesat/Linear Imaging Self-Scanner sensor (LISS-3) data
Note that PAI and Normalized Difference Vegetation Index (NDVI) values decreased from the end of the wet season (February) toward the end of the dry season (October)
Summary
Remote sensing technology has been applied to vegetation studies, including biophysical parameters estimation, such as above ground biomass (AGB), water and chlorophyll leaf content, leaf area index (LAI), etc. [1]. Independent of the adopted approach (empirical or physical), radiometric orbital data are frequently converted to surface reflectance before the modeling definition or the vegetation index calculation. It is influenced by several aspects, including illumination/observation geometry, since vegetation presents anisotropic behavior [3]. The influence of data acquisition geometry on the surface reflectance from vegetation cover has been reported by several authors, such as [4,5,6,7,8], but its effects become more complex when the vegetation cover under study is spread out on severe topographic conditions. There is an additional influence on the surface reflectance that will be treated here as “local illumination”
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
