# 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
### Close-Range RS Approaches--Wireless Sensor Networks (WSN)
(2017) and [PERSON] et al. (2018) used WSNs to demonstrate the interplay between hydraulic traits, growth performance and the stomata regulation capacity in three shrub species in a tropical montane scrubland of Brazil under contrasting water availability. The results showed that forest plant species employ different strategies in the regulation of hydraulic and stomatal conductivity during drought stress and thus substantiate the need for setting up WSN for different forest tree species and communities (Stein  
of the trees (first reflection) and the terrain (last reflection). With the more recently developed full waveform systems, the whole pathway of the LiDAR beam through the canopy can be detected and recorded [88]. Moreover, additional echo attributes such as amplitude and intensity of the return signal can be provided, which can support the classification process. As a result, the characterization of the canopy structure is much more detailed with full waveform data [89], so that important FH indicators such as forest height and understory cover can be estimated with a lower bias and higher consistency [90]. For FHM applications, LiDAR systems are generally used that have a beam footprint of less than one-meter diameter on the ground. These so-called small footprint systems are preferred, because they provide a good link between the LiDAR beam and the structural vegetation attributes that subtly change as a consequence of stress or damage, sometimes within individual trees. By comparison, large footprint systems have beam diameters with up to several tens of meters on the ground; e.g., the GLAS instrument mounted on the Ice, Cloud and Land Elevation Satellite ICESat Platform with a footprint of 38 m [91]. Such systems can be used to model broad forest structural attributes but can only detect significant and widespread changes in forest structural condition or health [92]. The most important environmental application of LiDAR is the precise mapping of terrain and surface/canopy elevations. Such digital terrain models (DTMs) or digital surface models (DSMs) can be useful in monitoring FH indicators such as changes in tree height resulting from damage or deviations in growth due to stress [93; 94; 95]. Recent studies show that it is even possible to detect objects located on the ground surface such as coarse woody debris [96; 97]. Coarse woody debris is an important indicator of FH, because it provides habitat to a multitude of endangered plant and animal species and plays an important role in the forest carbon cycle [98; 99]. Because of its characteristics, LiDAR is well suited for measuring forest biophysical parameters, such as tree dimensions and canopy properties. Two main approaches have been developed over recent years. The area-based approach is a straightforward methodology where the height distribution of the LiDAR beam reflections is analyzed for a given area [100]. In a first step, different \"LiDAR metrics\", such as the maximum height or fractional cover are calculated for each area. In a second step, these metrics are set in relation to conventional ground measurements of FH indicators such as Above Ground Biomass (AGB) or stand density for model calibration. In a final step, the models are used to predict the selected FH indicators for large areas using square grid cells. Such analysis is generally conducted using a priori stratification of stand types and tree species. In the years that followed, this methodology was proven to be able to determine key biophysical forest variables on a larger scale. So far, this method has been shown to deliver a precision of 4%-8% for height, 6%-12% for mean stem diameter, 9%-12% for basal area, 17%-22% for stem density and 11%-14% for volume estimations of boreal forests [100; 101; 102; 103]. Because of the high accuracy of estimation of important FH parameters the area-based approach was further developed and adopted to operational forest inventories in boreal forests of Scandinavia [104].