# 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)  
Wireless sensor networks (WSN) consist of a potentially large set of sensors that communicate with each other based on wireless communication channels and standardized protocols. They can collect data on various environmental variables which can be used to describe complex ecosystem forest processes continually in a non-invasive, cost-effective, automated and real-time manner [53; 79; 80; 81]. The design of the network, e.g., the spatial separation of the network nodes (sensor nodes), depends on the spatial variability of the environmental variables and ranges from centimetres to a maximum of about 1 km depending on the wireless communication technology used. In the context of FHM, WSNs are implemented for the detection and verification of forest fires in real time [53; 55]. [PERSON] et al. [60] used WSNs to demonstrate the effects of the 2015 El-Nino extreme drought on the sap flow of trees in eastern Amazonia. [PERSON] et al. [58] used WSNs to record important processes of soil-plant-atmosphere interactions in tropical montane cloud forests in Brazil. They investigated how key forest ecosystem processes such as transpiration, carbon uptake and storage, and water stripping from clouds are affected by climatic variation and the temporal and \begin{table}
\begin{tabular}{p{71.1 pt} p{130.1 pt} p{130.1 pt}} \hline \hline
**Application** & **Example Studies** & **Main Findings** \\ \hline \multirow{4}{*}{Phenocam networks} & \multirow{4}{*}{[51]} & US National Ecological Observatory Network (NEON) and the European Union’s Integrated Carbon Observation System (ICOS). \\ \cline{3-3}  & & Fully-automated digital time-lapse cameras (phenocams) and other cameras can be easily mounted on towers. They are crucial sensors for recording, quantifying, monitoring and understanding of phenological traits and the interactions of ST/STV relations to stress, disturbances and resource limitations in forest ecosystems. \\ \hline \multirow{4}{*}{Individual towers} & \multirow{4}{*}{[52]} & Fully automated spectral data recording system for phyto-pigments (chlorophyll, carotenoids, anthocyanins) under different view and sun angles. Used to assess diurnal and seasonal variations of plant physiological processes under different illumination and weather conditions. High spatial resolution allows measurement of spectral response of individual tree crowns. Systematic recording of ST/STV can be linked to eddy covariance gas exchange measurements. \\ \hline \multirow{4}{*}{Flux tower networks} & \multirow{4}{*}{[49; 50]} & Linking flux towers in an international network (FLUXNET, [76]). Flux towers generally include integrated sampling of ecosystem parameters such as carbon dioxide, water vapour and energy fluxes, as they cycle through the atmosphere, vegetation and soil. FLUX towers are often coupled with the sensor technologies such as spectrometers or soil sensors. \\ \hline \multirow{4}{*}{Spectral networks} & \multirow{4}{*}{[77]} & Spectral network (SpecNet, [78]). Multi-scale spectral RS from satellites, aircraft, UAVs, mobile tram systems, portable spectrometers over same area as flux measurements. The goals of SpecNet are: (1) Monitoring surface–atmosphere fluxes of water, carbon and vapor; (2) Understanding and assessing the impacts of disturbance and dynamic events (e.g., fire, extreme weather events, climate, land-use change). \\ \cline{1-1} \cline{2-2}  & & SpecNet, [78]). Multi-scale spectral RS from satellites, aircraft, UAVs, mobile tram systems, portable spectrometers over same area as flux measurements. The goals of SpecNet are: (1) Monitoring surface–atmosphere fluxes of water, carbon and vapor; (2) Understanding and assessing the impacts of disturbance and dynamic events (e.g., fire, extreme weather events, climate, land-use change). \\ \hline \hline \end{tabular}
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Table 4: Example studies illustrating the use of towers with different RS technology (mobile, permanently installed) in common applications. spatial forest structure. Moreover, [PERSON] et al.