# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 1 Introduction  
There is a growing awareness of the multitude of ecosystem services that forests provide and their importance for the wellbeing of humans and conservation of the environment. Forest ecosystems (FES)are well known as hosts of biodiversity, carbon and oxygen pools, sources of timber, water purification systems and providers of livelihoods, and yet an increasing number of anthropogenic threats endanger FES. While direct stressors are a result of forest utilization or land use changes, indirect stress is caused by drivers such as climate change, air pollution and globalization, which in turn may bring about an additional impact from invasive species [1]. Due to the crucial importance of forests, it is of high relevance to better understand how different stressors affect forest conditions and how these mechanisms interact with the type, structure and history of the forest. It is only then that forest management and conservation strategies can be adapted to maintain and improve forest health (FH) and to cope with new and upcoming threats. For both, it is important to develop monitoring techniques, which provide information on the past and present state of FH. Such information forms the basis for research on the interactions between forest conditions and stressors. This is particularly relevant and urgent for FES where growth and regeneration can take centuries. Thus, today's management decisions will determine the shape of our forests for future generations. To monitor and understand the complexity of FH, its entities, drivers, functional mechanisms and impacts, it is imperative to develop effective standardized large scale long-term monitoring systems. FH monitoring (FHM) has a long history and is carried out by private forest owners, forest administrations and other public entities at local, national and international levels. There are numerous in-situ monitoring programs at the local and regional scales that increasingly utilize standardized indicators of FH. Among the largest monitoring programs are the FOREST EUROPE program of the European Union and the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (EU/ICP Forests) which started in 1990 [2], the FHM program of the United States [3] and the Chinese national program on ecological functions [4]. While in some countries, FHM belongs to independent monitoring programs, other countries include the assessment of FH in their national forest inventories [5]. Table 1 provides an overview on current long-term FHM monitoring programs that use in-situ methods. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Level/Scale** & **Responsible Body** & **Description** & **Resources/Links** \\ \hline \multirow{8}{*}{**Germany**} & \multirow{8}{*}{Johann Heinrich von Thünen-Institute} & **Forest condition monitoring (FCM). Level-I-Monitoring. Frequency: Annual. ** & **Forest condition monitoring (FCM). FH as such cannot be observed or monitored directly but needs to be assessed using indicators which are then combined to create a holistic picture of the forest condition. Different initiatives and programs have developed FH indicators. For example, FHM conducted by the US Forest Service collects variables on lichen communities, forest soils, tree crown conditions, vegetation diversity and structure, downed woody materials and ozone damage ([PERSON] et al., 2017). The FOREST EUROPE program defines 7 criteria to describe the status of European forests, where criterion 3 analyses the \"Maintenance of Forest Ecosystem Health and Vitality\" ([PERSON] et al., 2016). Both programs use sample based in-situ assessments, where the set of indicators is either directly observed (e.g., amount of deadwood, species composition) or quantified by expert judgement (e.g., defoliation and discoloration classes). The quality and consistency of such assessments depend on the expertise of the field teams, which can limit comparability between assessments.