# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 4 Physical vs. Empirical Models  
The estimation of ST and trait variations of forest stands from RS data generally relies on two different approaches: (1) empirical modelling and (2) physical modelling. A third group of approaches usually combines the two methodologies. Empirical models build on a statistical relationship between one or more biotic traits (dependent variable) and one or more spectral (independent) variables that are known to physically respond to the given biotic trait. In most cases, spectral reflectance in the visible, near infrared, or vegetation indices, e.g., the Normalized Difference Vegetation Index \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Example Studies** & **Main Findings** \\ \hline Estimation of ST/STV of FH & [14; 16; 138; 197] & Implementation of multi-sensor RS in modelling approaches improves the discrimination, quantification and accuracy when estimating ST/STV. \\ \hline Forest diversity indicators (taxonomic, structural and functional diversity) & [197] & Multiple platforms for a given sensor type, e.g., the forthcoming Radarsat Constellation of three platforms provides potential for more frequent data acquisition. \\ \hline
3D structural ST/STV of trees and canopies & [191; 192] & Multi-sensor systems on single platforms are promising, as they: (1) Enable data related to different ST to be recorded at the same time; (2) Ensure the same illumination conditions, weather conditions and flight parameters. \\ \hline Canopy temperature distribution & [193; 194] & Sensor characteristics (spatial, radiometric, spectral, temporal or angular or directional resolution) determine the discrimination and classification capabilities of forest diversity indicators. \\ \hline FH indicators on the plot and field scale & [212; 213; 214; 215; 216; 217] & 3D-imaging spectroscopy is crucial to create hyperspectral 3D plant models. \\ \hline  & [192] & Multi-hyperspectral RS datasets combined or merged with 3D point clouds from LiDAR. \\ \hline Canopy temperature distribution & [193; 194] & 3D thermal tree models and 3D thermal canopy models are available for a better understanding of tree and canopy temperature distribution. \\ \hline FH indicators on the plot and field scale & [212; 213; 214; 215; 216; 217] & UAVs can carry different sensor types and thus contribute to a more comprehensive, rapid, cost-effective, comparable and repetitive recording of FH indicators at very high resolution. LiDAR, thermal infrared, multispectral, RGB, hyperspectral, real time video. \\ \hline  & [218; 219] & RS data can be merged to integrate the advantages of high spatial, spectral and temporal resolution within one kind of sensor. Pan-sharpening processes to combine a spatially higher resolution pan-chromatic band with synchronously recorded lower resolution multi-spectral bands. \\ \hline Fusion of multi-sensor RS Data & [219] & Fusion of a large number of spectral bands with high spatial resolution data can be achieved using multi-resolution methods (e.g., Wavelets) and high-frequency injection methods. \\ \hline  & [220; 221] & Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM,) that combines the spatial accuracy of Landsat data with the temporal resolution of MODIS RS data. \\ \hline  & [219] & Data fusions approaches that merge spatial, spectral as well as temporally high resolution RS sensors into one data set. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Example studies illustrating the use of the multi-sensor approach in common FH applications. (NDVI), serve as input for such models. The spectral information can further be complemented by spatial information, e.g., image texture [222; 223; 224], semivariogram [225] and radiometric fraction analysis [196]. The statistical relation is interpreted and applied by means of linear or non-linear models created through regression and related techniques or through classification.