## 3 Mapping of Land Cover and Physiological or Structural Variables
### Physiological Variables
#### 3.2.1 Leaf Area Structure  
Density, structure and distribution of leaves in a canopy are vital components for understanding, modelling and upscaling of ecological structure and carbon dynamics of the forest ecosystems [94,95]. LAI refers to \"the total one-sided area of leaf tissue per unit ground surface area\" [96]. The leaf area structure of canopies influences the NPP and evapotranspiration, and, hence, it is imperative for carbon biomass and flux monitoring models that are sensitive to canopy depth [97,98]. Conventional optical (passive) remote sensing provides LAI and amount of leafy biomass as a proxy parameter to canopy leaf function and leaf quality for ecological processes modelling [99]. It is a physiological index derived from spectral properties related to photosynthesis [9]. Optical measurements of LAI are carried through various sources, ranging from in situ hemispherical photographs to space-based multispectral radiometers. However, LAI measurements suffer from potential uncertainties associated with sensor saturation with high LAI levels (greater than an order of three to five) on dense canopies due to reflectance, transmittance and absorption of the signal along the full canopy vertical column [100,101]. In addition to LAI, Leaf Area Density (LAD), volumetric distribution of leaf area and three-dimensional canopy leaf structure, has emerged as a critical parameter, determined through remote sensing data to understand canopy profile (horizontal and vertical structure of canopy leaves) and canopy light environment for forest biomass estimation and carbon flux monitoring [102]. It is important to note that passive remote-sensing-based LAI measurements do not effectively incorporate vertically resolved leaf area profiles but rather suffer saturation errors. Nonetheless, active (Radar and LiDAR-based remote sensing) as well as the integration of active and passive methods perform better by incorporating the 3-D canopy structure and do not saturate over dense canopies [100,103]. In tropical forests, active remote-sensing-based estimation of LAI and/or LAD are significantly important due tosaturation and the cloud cover problem in passive remote sensing applications. [PERSON] et al. [104] determined the optimal values of laser point cloud pulse density (>15 pulses m\({}^{-2}\)) and grain size (<10 m) for a stable estimation of LAD profile using Airborne Laser Scanning (ALS) data in a dense tropical rainforest at the Ducke Forest Reserve, Brazil. Leaf area profiles derived from remote-sensing-based LiDAR mapping has been used to simulate tree size, distribution and canopy structure in the Amazon forests [103]. [PERSON] et al. [9] assessed the canopy dynamics of leaf area structure in the Amazonian forests and found that bias-corrected LAI and LAD derived from LiDAR data can detect canopy structure at the ecologically significant scale of 0.25 ha. They concluded that it is difficult to estimate LAI and LAD from the air or space-borne LiDAR scanning systems, due to landscape heterogeneity and its sensitivity to sensor type, flight dimensions and time of data acquisition [9]. At the pan-tropical scale, vertical foliage profiles derived from the Geoscience Laser Altimeter System (GLAS)/Ice, Cloud, and Land Elevation Satellite (ICESat) LiDAR data have been combined with MODIS and Landsat datasets to produce high-resolution carbon density maps of tropical forests [105]. [PERSON] et al. [106] produced the third generation of 15-day global LAI, FAPAR and Normalized difference vegetation index (NDVI) products, by applying Neural Network algorithms on GIMMS and MODIS LAI at a 0.083-degree spatial resolution since 1981 to December 2015. [PERSON] et al. [107] produced global LAI and VFP (Vertical Foliage Profile) from GLAS/ICESat LiDAR data integrated with pre-processed MODIS LAI and another GLAS-based estimate using the Geometric Optical-Radiative Transfer (GORT) model.