# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 1 Introduction
Thus, for various important FH indicators such as tree crown density, defoliation, \"naturalness\" or regeneration potential, there are still no standardized metrics nor direct measuring procedures available. However, these in-situ FH assessments are regularly conducted and have proven to be useful to detect changes in FH conditions over longer periods. Remote sensing (RS) techniques are regularly used for forest monitoring. However, in the context of operational national and international FHM programs, RS currently only plays a minor role. To provide but one example: in the 364-page report for 46 European countries, the term \"remote sensing\" occurs only once where it is stated that RS and geographic information systems are new technologies that will be used by some countries ([PERSON] et al., 2016). [PERSON] et al. (2016) concluded that a major advantage of implementing remote-sensing techniques is the possibility of repeatedly acquiring standardized information over large areas at low costs with high frequency. However, RS techniques are only helpful to improve the objectivity of FHM when they can be reliably linked to FH indicators. Furthermore, a combined terrestrial and remote-sensing monitoring system not only serves to monitor changes in forest conditions, but also to relate the changes to the drivers ([PERSON] et al., 2016). In the first part of this paper series, [PERSON] et al. (2016) provided a working definition of FH and reviewed FH indicators, as well as their importance and relevance for the development of FHM programs. They introduced the concept of Spectral Traits (ST) and spectral trait variations (STV) as a \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Level/Scale** & **Responsible Body** & **Description** & **Resources/Links** \\ \hline \multirow{6}{*}{**Country level: Canada**} & \multirow{3}{*}{Canadian Forest Service (CFS)} & **National FHM Network.** & \multirow{3}{*}{([PERSON] et al., 2016)} \\  & & Frequency: 5-years. & \\  & & Established 1994 based on earlier Acid Rain Monitoring Network. Plot-based Bi-annual to 5-year repetition depending on the variables. & \\ \cline{2-4}  & & **National Forest Inventory.** & \\ \cline{2-4}  & \multirow{3}{*}{National Forest Inventory (NFI), Canada} & **National Forest Inventory.** & \multirow{3}{*}{([PERSON] et al., 2016)} \\  & & Frequency: 10-years. & \\ \cline{3-4}  & & Mixed plot and RS based. Includes inventory parameters + assessment of insect, disease, fire and other disturbance damage. & \\ \hline \multirow{6}{*}{**European level**} & \multirow{3}{*}{United Nations Economic Commission for Europe (UNECE)} & **ICP.** & \multirow{3}{*}{([PERSON] et al., 2016)} \\  & & Frequency: Annual. & \\ \cline{1-1} \cline{3-4}  & & International Co-operative Programme on the & \\ \cline{1-1} \cline{3-4}  & & Assessment and Monitoring of Air Pollution & \\ \cline{1-1} \cline{3-4}  & & Effects on Forest. Developed to standardise the & \\ \cline{1-1} \cline{3-4}  & & recording of different FH indicators on three & \\ \hline \multirow{6}{*}{**Global level**} & \multirow{3}{*}{Food and Agriculture Organization of the United Nations (FAO)} & **Forest Resources Assessment.** & \multirow{3}{*}{([PERSON] et al., 2016)} \\ \cline{1-1} \cline{3-4}  & & Frequency: 5-years. & \\ \cline{1-1} \cline{3-4}  & & FH recorded by the FAO as part of the Forest & \\ \cline{1-1} \cline{3-4}  & & Food and Agriculture Organization of the & \\ \cline{1-1} \cline{3-4}  & & FH recorded by the FAO as part of the Forest & \\ \cline{1-1} \cline{3-4}  & & Resources Assessment (FRA). Individual & \\ \cline{1-1} \cline{3-4}  & & countries report their findings to the FAO, which & \\ \cline{1-1} \cline{3-4}  & & then complies a report. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. framework for FHM. In this second paper, we review existing remote-sensing systems and evaluate their potential for monitoring specific FH indicators. This paper addresses the following questions:  
1.