Spectroradiometry as a Tool in Vegetation Analysis
Contact Persons
Keywords
Assimilation of RS Data, Model Inversion, Empirical Approaches, Support
Abstract
Much attention has been given to the application of remote sensing techniques in vegetation analysis from the time when such data first became available. A detailed knowledge about biochemical composition and structure of the vegetation cover is mandatory to take appropriate action in agricultural and forestry management. Vegetation cover characteristics are needed for precision farming purposes or ecological studies to optimize profitability, sustainability and protection of the environment.
Since measurements of canopy characteristics of agricultural crops using conventional field sampling methods are time-consuming and expensive, the use of remotely sensed hyperspectral data bears a high potential for extraction of biochemical and biophysical parameters. Hyperspectral data not only accounts for the spatial variability of the surface but also expands the spectral domain. It allows to relate measured spectral radiance to the chemical composition of vegetation as well as to the biophysical characteristics.
In the domain of hyperspectral vegetation analysis, SpectroLab's aims are as follows:
- Modeling approaches of canopy characteristics retrieval
- Assimilation of RS data (forward modeling)
- Model inversion
- Radiative Transfer modeling within forest canopies (SPREAD)
- Empirical approaches of canopy characteristics retrieval
- Hyperspectral vegetation analysis support