## 1 Introduction  
Forests provide a broad range of ecosystem services, e.g., carbon sequestration, water regulation, fuelwood and timber production [1; 2]. Information on forest structure, such as canopy cover, tree height or canopy volume, is needed to support the sustainable management of these resources [3]. Despite the South African Government's legal requirement of reporting on the state of national forests on a three-year basis [4], there are no regularly updated national forest maps or spatial datasets describing woody structural diversity, e.g., canopy cover, tree height. To date in South Africa, the only quantitative spatial data on forests are available in the form of occasional global products, such as the 1 km global tree height map [5], the 30 m global tree cover maps [6; 7], or the 30 m Sub-Saharan African tree height map [8]. However, there is uncertainty regarding the accuracies of these products at regional scales, particularly in open forest ecosystems such as savannas, due to limited calibration outside boreal and tropical forest or for tree height below 5 m [8; 9; 10]. Remote sensing (RS) technologies produce valuable data for mapping forest structural parameters at a variety of spatial scales. They are particularly useful for providing dense and frequent coverage over large areas, which thus enables the monitoring of forest structural parameters cost-effectively [11; 12]. A number of studies have explored the capabilities of RS data for retrieving structural information of plant canopies, including those derived from LiDAR [13; 14], Synthetic Aperture Radar (SAR) [15; 16; 17], or optical sensors or a combination of these [18; 19; 20]. Research using optical data mostly exploits multispectral observations from nadir-pointing instruments and/or single view imagery [21; 22]. However, most of the Earth's continental surfaces, due to their three-dimensional characteristics, are strongly anisotropic, i.e., for a given illumination (sun) angle the reflectance changes with the viewing direction [23; 24]. As a consequence, vegetation structural and biochemical variables largely control the surface reflectance anisotropy [23; 25; 26; 27; 28], which is described by the bidirectional reflectance distribution function (BRDF) [29]. The BRDF of a surface target is a function describing the ratio of the spectral radiance reflected in a given direction to the irradiance received by this target according to a specific illumination geometry; hence it measures reflectance changes with the illumination and observation geometry [23]. In practice it is not possible to measure the BRDF directly, and it is estimated by the bi-directional reflectance factor (BRF) [30; 31]. Reflectance anisotropy has often been ignored or considered to be a source of noise for mapping forests [32; 33]. Mono-angular, generally nadir-pointing, instruments can collect multi-angular data by accumulating data over repeated overflights or orbits (e.g., 16 days in the case of MODIS) [34; 35]. However, this approach is inherently hindered by the daily evolution of the surface as well as atmospheric changes [35]. Optical multi-spectral and multi-directional (MSMD) instruments may offer promising opportunities [36; 37; 38; 34]. MSMD instruments acquire quasi-simultaneous surface reflectance measurements in multiple spectral bands at various observation angles [39]. Examples include the POLarization and Directionality of the Earth's Reflectance's (POLDER) instrument or the Multi-Angle Imaging SpectroRadiometer (MISR) [28]. The MISR instrument is hosted on the NASA's Earth Observation System (EOS) Terra platform, launched on 18 December 1999 [37]. At present, MISR is the only multi-angular earth observation satellite instrument sampling the anisotropy of the reflected solar radiation at sub-km spatial resolution. It uses nine cameras pointing at various angles fore and aft of the platform, each acquiring data in four spectral bands: three in the visible and one in the near-infrared range [26].