# 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)
For the temperate zone similar accuracies have also been achieved, although the presence of more complex forest structures, especially a higher number of tree species and a higher amount of standing dead wood leads to less accurate estimations and requires more effort in stratification and ground measurement to obtain species-specific results [105; 106; 107]. The second methodology is the individual tree approach, which has the objective of extracting single trees and modeling their properties. The procedure consists of four steps: (1) delineation of individual trees; (2) extraction of tree parameters such as height, species and crown parameters; (3) model calibration of the biophysical parameters: Diameter at Breast Height (DBH), volume and biomass with the help of reference trees measured on the ground; and finally (4) the application of models to predict DBH, volume and biomass for all delineated LiDAR trees. Based on the crown representations basic attributes reflecting tree health such as total volume, crown length, crown area and crown base height can be derived [108; 109]. The extracted individual trees also form the basis for identifying the tree species. Within the 2D or 3D representation of the individual tree point cloud and waveform features are calculated and with the help of classification techniques, tree species can be determined [90; 110]. While the differentiation between deciduous and coniferous trees can be performed with a high accuracy (>80%, up to \(\sim\)97%) the differentiation within these classes is more difficult and leads to a higher classification error [111]. Moreover, it is possible to distinguish between living trees, standing dead trees and snags [112; 113; 114] or to map dead trees on the plot or stand level [115; 116]. However, 3D LiDAR has its limitations for differentiating between trees species and dead trees unless it is not combined with multi- and hyperspectral optical data. One drawback of the single tree approach is that the LiDAR beam loses some signal strength on its way through the canopy. This results in an underestimation of individual trees in the understory [117]. To overcome this problem, methods have been developed to predict stem diameter distributions of forest stands based on the detectable trees in the upper canopy and LiDAR derived information about the vertical forest structure and density [118]. Besides the traditional parameters related to forest inventory, a multitude of FH parameters describing the ecological conditions of the forest can be estimated with LiDAR sensors. One key element to assess FH is the canopy cover which is defined as the projection of the tree crowns onto the ground divided by ground surface area [119]. This parameter can be easily translated to LiDAR data by dividing the number of returns measured above a certain height threshold by the total number of returns. Many studies have shown a strong (R\({}^{2}\) > 0.7) relationship between this LiDAR-metric and ground measurements [120]. By using hemispherical images or LAI-2000 sensor data for calibration, LAI and solar radiation can also be derived from LiDAR data with a high precision over large areas [108; 121]. The vertical forest structure is of high relevance for the description of forest heterogeneity and for biodiversity assessments [122; 123]. A widely used LiDAR metric to represent vertical canopy complexity is the coefficient of variation. Higher values correspond to more diverse multi-layer stands, while low values represent single layer stands [124]. The coefficient of variation can be applied to the point clouds, the digital crown model or individual trees. [PERSON] et al. [125] applied this principle and classified forest stands according to stand structure with LiDAR data and achieved an overall accuracy of 97%. Another approach is the partitioning of the vertical structure into different height layers in relation to ecological importance. [PERSON] et al.