# 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
### Multi-Sensor Approaches  
Various studies have shown that the implementation of multi-sensor RS approaches improves the discrimination of ST/STV over time and thus the accuracy of estimation of FH indicators [14; 134; 135; 136; 137; 190]. Depending on their sensor characteristics (spatial, radiometric, spectral, temporal or angular resolution), RS sensors can record specific ST/STV and thus discriminate between certain species, populations, communities, habitats and biomes of FES [16; 136]. Reviews, advantages and limitations using multi- and hyperspectral RS data for monitoring FH and FES diversity have been conducted [16; 136]. Therefore, in this section, we focus on innovative present and future optical and multi-sensor approaches to monitor FH. By implementing (i) Optical 3D-RS approaches, as well as (ii) the combination and/or merging of multi-RS sensors (optical, thermal, RADAR, LiDAR), additional 2/3D ST and STVs can be recorded, which considerably improve the discrimination, monitoring and understanding of FH [139]. Hyperspectral sensors are increasingly being implemented for 3D imaging spectroscopy to create hyperspectral 3D plant models [191; 192]. Hyperspectral RS datasets can be combined or merged with 3D point clouds from LiDAR [192], enabling the assessment of tree and canopy spectral traits in three dimensions. Furthermore, they allow to study the effects of plant geometry and sensor configurations, which is key for physically-based reflectance models [192]. 3D thermal tree and canopy models can contribute to the understanding of temperature distribution [193; 194]. Thermal infrared (TIR) and 3D simulations have been used to quantify and assess the spectral water stress traits of vegetation [195; 196]. Multi-sensor RS techniques such as the Airborne Oblique System with 4 thermal cameras and 4 RGB cameras have been developed with the purpose of recording 3D-temperature distributions in trees. The latest development in UAV technologies also integrates several sensors to quantify 3D architecture and spectral traits. Use of multiple platforms for a given sensor type (e.g., the forthcoming Radarsat Constellation of three platforms [197]) provides potential for more frequent data acquisition and a more dense temporal dataset, particularly with optical sensors when cloud cover is a hindrance. For example, combining data from sensors with similar radiometric and spatial characteristics such as Landsat 7 and 8, ASTER or Sentinel 2, each of which has repetition periods of multiple days or weeks, can provide a dense data set for analysis of high frequency or steep temporal gradients in FH traits. However, between-sensor calibration is required in order to match datasets. Alternatively, multi-sensor systems on single platforms are promising, as they: (a) enable the simultaneous acquisition of information related to different ST; and (b) ensure the same illumination conditions, weather conditions and flight parameters for all mounted sensors. Multi-sensor approaches on airborne platforms can include hyperspectral, multispectral, TIR, RGB, LiDAR, active RADAR and passive microwave sensors to quantify spectral trait indicators of FH [198]. For example, the Goddard LiDAR hyperspectral and thermal (G-LiHT) airborne imaging system integrates a combination of lightweight and portable multi-sensors for measuring vegetation structure, foliar spectra and surface temperatures at a high spatial resolution (1 m) to map plant species composition, plant functional types, biodiversity, biomass as well as plant growth [16; 136]. Space-borne RS of the future will increasingly focus on the developments of multi-sensor RS platforms. The HyspIRI mission (HyspIRI, [199], launch in 2020) includes two instruments, namely an imaging spectrometer (380 nm-2500 nm, 214 spectral channels) and an 8-band multispectral imager, including one band in the mid-wave-infrared region (MIR) at 4 \(\upmu\)m and seven bands in the long-wave infrared (LWIR) region between 7 and 13 \(\upmu\)m [200].