# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 5 Conclusions  
Even though there is an increasing number of data sets available to the public from in-situ forest inventories, experimental studies, and RS, there are only a handful of programs in operation that integrate these sources into FHM programs. This paper reviewed the approaches of how close-range, air and space-borne RS techniques can be used in combination with different platforms, sensor types, and modelling approaches to assess indicators of FH. Both RS as well as in-situ approaches have advantages and limitations and should therefore be combined to provide a holistic view of forest conditions. In-situ forest monitoring approaches record indicators of FH that are species-specific and in some cases use additional environmental ecosystem factors that describe climate and soil characteristics. This kind of monitoring is accurate but limited to sample points. Furthermore, in-situ forest inventory monitoring does not record indicators of FH according to the ST/STV approach, making it difficult to link in-situ observations and RS approaches. Here, new avenues for linking these approaches need to be explored and applied. Compared to RS approaches, through long-term monitoring, field-based monitoring approaches can draw conclusions about various processes and stress scenarios in FES. However, assessment, monitoring and the capability to draw conclusions about short-term processes and stress scenarios are often limited. Why can RS measure ST/STV as indicators of stress, disturbance or resource limitations of FES and therefore serve as a valuable means to assess FH? * Forest plant traits are anatomical, morphological, biochemical, physiological, structural or phenological features and characteristics that are influenced by taxonomic and phylogenetic characteristics of forest species ([257; 258]). * Stress, disturbances or resource limitations in FES can manifest in the molecular, genetic, epigenetic, biochemical, biophysical or morphological-structural changes of traits and affect trait variations [17; 259; 260; 261; 262; 263; 264; 265; 266; 267; 268; 269; 270; 260; 261] which can lead to irreversible changes in taxonomic, structural and functional diversity in FES. * RS is a physically-based system that can only directly or indirectly record the Spectral Traits (ST) and spectral trait variations (STV)\" [16] in FES [136; 262]. Airborne- and space-borne RS approaches can monitor indicators of various processes and stress scenarios taking place in FES via the ST/STV approach in an extensive, continuous, and repetitive manner. Furthermore, insights on both short-term and long-term processes and stress scenarios in FES can be gained. Hence, further knowledge is required about the phylogenetic reactions and adaptive mechanisms of forest species on various stress factors. This paper expands on existing close-range RS measurements to observe FH indicators and illustrates their advantages and disadvantages. In a similar way to air-and space-borne RS approaches, close-range RS measurements also record indicators of FH based on the ST/STV approach. By linking in-situ, close-range and air- and space-borne RS approaches, key relations can be observed between the genetic and phylogenetic characteristics and species-specific reactions to stress and their direct relationship with the spectral response recorded by RS sensors. Moreover, close-range RS approachesoffer crucial calibration and validation information for air-and space-borne RS data with a high temporal resolution. Furthermore, there is a lack of a standardized framework to link various information sources for recording and assessing indicators of FH. The concept of ST and STV presented in [16; 136] provides a framework to develop a standardized monitoring approach for FH indicators. Ultimately, different requirements will be needed to link the different approaches, sensors and platforms of RS with in-situ forest inventories and experimental studies to better describe, explain, predict and understand FH.