# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 2 Trends in Close-Range RS Approaches for Assessing FH
### Radar
two phase components declines if changes in the forest have occurred. Lastly, the RADAR signal is also subject to constructive and destructive interference due to multiple scattering elements within a resolution cell, producing an essentially random cell to cell backscatter variation (speckle) [162] that can be reduced using multiple looks or window-based filtering [163; 164]. SAR RS for estimation and mapping of forest structure parameters is well developed and includes several previous literature reviews [165; 166; 167; 168]. However, literature on assessment and monitoring of FH related attributes is more limited for applications such as changes in forest structure, leaf quantity, distribution or size, or canopy moisture content from deforestation, canopy degradation (i.e., reduced biomass or LAI) or mortality caused by insect infestation, disease, drought, flooding, or other environmental factors. Studies in Table 7 are drawn from both groups of literature to provide illustrative examples of the major FH applications applicable to SAR: deforestation, degradation, fire, and inundation. Detection of deforestation has been conducted with backscatter data, often using classification techniques; examples from earlier studies to recent more advanced studies are given in Table 7. In contrast to classification, biophysical modelling of forest structure parameters is a major application that has seen intensive research. Many studies have been conducted in tropical areas where persistent cloud cover requires RADAR data, or in northern boreal areas where low sun angle effects can reduce the quality of optical model estimates. The most common parameter modelled is above ground biomass (AGB). In a similar manner to visible-near infrared reflectance-based indices, the SAR backscatter-AGB relationship is curvilinear and many studies have found that it typically saturates in the range of 100-150 t/ha (from the reviews of [165; 167; 169]), whereas others have found lower thresholds of saturation [170; 171]. Thus, biomass and biomass loss are difficult to model and estimate using backscatter or backscatter-based indices in dense forests. Studies in forests below the saturation threshold have been successful, however. Examples in Table 7 include explicit estimation of AGB loss as well as studies that only estimated biomass for a single date (i.e., where the context was not forest degradation), but that have potential for temporal or spatial application to estimate biomass loss. Other examples are given for leaf area index (LAI), canopy height and forest density that have potential for canopy degradation mapping. With the BIOMASS mission of ESA planning a polarimetric and interferometric P-band satellite sensor for 2020 [172], the combined use of these three SAR features (polarimetry, InSAR, long wavelength) will offer better means for temporal monitoring of forest structural condition and degradation. The other two FH applications of RADAR presented in Table 7 are fire impacts and forest inundation. Both have been addressed using backscatter and polarimetric analysis, mostly in classification to detect areas impacted by either disturbance type. It is clear that forest removal, disturbance and degradation analysis and monitoring using RADAR depends on structural forest change that is manifested as a consequence of a given source of stress or degradation. A direct analysis of backscatter response in specific polarizations and wavelengths, or combinations of these, has been the most common approach and has shown some success. However, more recently, improved potential has been shown using multi temporal data, InSAR, polarimetric and decomposition techniques to detect and estimate changes in signal polarization, phase, and volume, surface and double bounce scattering contributions that are related to changes in canopy structure. It is in these areas that FH change research using RADAR data needs continued efforts.