**1. Introduction**

32 Will-be-set-by-IN-TECH

58 Remote Sensing of Biomass – Principles and Applications

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The challenging task of biomass prediction in dense and heterogeneous tropical forest requires a multi-parameter and multi-scale characterization of forest canopies. Completely different forest structures may indeed present similar above ground biomass (AGB) values. This is probably one of the reasons explaining why tropical AGB still resists accurate mapping through remote sensing techniques. There is a clear need to combine optical and radar remote sensing to benefit from their complementary responses to forest characteristics. Radar and Lidar signals are rightly considered to provide adequate measurements of forest structure because of their capability of penetrating and interacting with all the vegetation strata.

However, signal saturation at the lowest radar frequencies is observed at the midlevel of biomass range in tropical forests (Mougin et al. 1999; Imhoff, 1995). Polarimetric Interferometric (PolInsar) data could improve the inversion algorithm by injecting forest interferometric height into the inversion of P-band HV polarization signal. Within this framework, the TROPISAR mission, supported by the Centre National d'Etudes Spatiales (CNES) for the preparation of the European Space Agency (ESA) BIOMASS program is illustrative of both the importance of interdisciplinary research associating forest ecologists and physicists and the importance of combined measurements of forest properties.

Lidar data is a useful technique to characterize the vertical profile of the vegetation cover, (e.g. Zhao et al. 2009) which in combination with radar (Englhart et al. 2011) or optical (e.g. Baccini et al. 2008; Asner et al. 2011) and field plot data may allow vegetation carbon stocks to be mapped over large areas of tropical forest at different resolution scales ranging from 1 hectare to 1 km². However, small-footprint Lidar data are not yet accessible over sufficient extents and with sufficient revisiting time because its operational use for tropical studies remains expensive.

At the opposite, very-high (VHR) resolution imagery, i.e. approximately 1-m resolution, provided by recent satellite like Geoeye, Ikonos, Orbview or Quickbird as well as the forthcoming Pleiades becomes widely available at affordable costs, or even for free in certain regions of the world through Google Earth®. Compared to coarser resolution imagery with

Biomass Prediction in Tropical Forests: The Canopy Grain Approach 61

as soon as the contrast between sunlit and shadowed tree crowns becomes perceptible. This property increases with the fineness of image spatial resolution (Fig. 1) that explains why, in VHR images, the tropical forest no longer appears as a continuous homogenous layer, or 'red carpet', as it is the case on medium resolution images with pixel size greater than 5 meters (Fig. 1). Intuitively, the canopy grain depends on both the spatial distribution of trees within a scene and the shapes and dimensions of their crowns. The question is then how to derive quantitative measurements of such canopy grain texture. Following Rao and Lohse (1993), who explained that repetitiveness is the most important dimension of human perception for structural textures, our idea is to measure the degree of repetitiveness expressed in canopy grain within a forest scene. Two dimensional (2D) Fourier or wavelet transforms proved to be well adapted for this purpose (e.g. Couteron, 2002; Ouma et al., 2006) because they allow shifting canopy grain properties from the spatial domain to the frequency domain. Though of larger potential application, we focus in this paper on the 2D Fourier-based frequency spectra as a mean for relating tropical forest canopy grain to above-

The well-known Fourier transform is highly suitable for analyzing repetitiveness of canopy grain as it breaks down an intensity signal into sinusoidal components with different frequencies. We built on this principle the development of the Fourier-based Textural Ordination (FOTO) method to primarily explore the potential of digitized aerial photographs and VHR satellite images for predicting tropical forest stand structure parameters including AGB (Couteron et al., 2005; Proisy et al. 2007). We summarize,

A prerequisite of the method is to mask non-forest areas, such as clouds and their shadows, water bodies, savannas, crops and civil infrastructures areas (Fig. 2, step 1). The method then proceeds with the specification of a square window size in which 2D-Fourier spectra are computed (Fig. 2, step 2). To be clear, the window size *WS* is expressed in meters as:

in meters. *WS* may influence the FOTO results as discussed in the following sub-section. Using large *WS* also means that spatial resolution of the FOTO outputs and subsequent biomass maps will be *N* times coarser than the spatial resolution of the source image(s). Although the use of a sliding window is computationally intensive, it can attenuate the

After windowing the forest images, Fourier radial spectra (or r-spectra) are computed and give for each window, the frequency vs. amplitude of a sinusoidal signal that fits the spatial arrangement of pixels grey levels (Fig. 2, step 3) as described in the next paragraph. The rspectra may be then stacked into a common matrix in which each row corresponds to the rspectrum of a given window, whereas each column contains amplitude values. This table is then submitted to multivariate analysis techniques (ordinations/classifications). With this approach, the study can concern as many images as necessary, providing they have the same spatial resolution. The resulting table can, for instance, be submitted to a standardized principal component analysis (PCA; Fig. 2, step 4). Window scores on the 3 most prominent

WS = N.S (1)

*S* is the pixel size

hereafter, the flow of operations that yield AGB predictions from FOTO outputs.

where N is the number of pixels in the *X* or Y direction of the image and

effects of both spatial resolution degradation and study areas fringe erosion.

ground biomass (AGB).

**2.2 The FOTO method** 

**2.2.1 Workflow up to forest AGB prediction** 

pixel size greater than 4 meters, VHR imagery greatly improves thematic information on forest canopies. Indeed, the contrast between sunlit and shadowed trees crowns as visible on such images (Fig. 1) is potentially informative on the structure of the forest canopy. Furthermore, new promising methods now exist for analyzing these fine scale satellite observations (e.g. Bruniquel-Pinel & Gastellu-Etchegorry, 1998; Malhi & Roman-Cuesta, 2008; Rich et al. 2010). In addition, we believe that there is also a great potential in similarly using historical series of digitized aerial photographs that proved to be useful in the past for mapping large extents of unexplored forest (Le Touzey, 1968; Richards, 1996) for quantifying AGB changes through time. This book chapter presents the advancement of a research program undertaken by our team for estimating above ground biomass of mangrove and *terra firme* forests of Amazonia using canopy grain from VHR images (Couteron et al. 2005; Proisy et al. 2007; Barbier et al., 2010; 2011). We present in a first section, the canopy grain notion and the fundamentals of the Fourier-based Textural Ordination (FOTO) method we developed. We then introduce a dual experimental-theoretical approach implemented to understand how canopy structure modifies the reflectance signal and produces a given texture. We discuss, for example, the influence of varying sun-view acquisition conditions on canopy grain characteristics. A second section assesses the potential and limits of the canopy grain approach to predict forest stand structure and more specifically above ground biomass. Perspectives for a better understanding of canopy grain-AGB relationships conclude this work.

Fig. 1. Differences of canopy grain perception between two 300 m square subset images of different spatial resolution over a mixed savanna forest-inhabited area, French Guiana. Left: a 2.5-m SPOT5 Fusion image acquired in October 2010. Right: a 20-cm aerial photograph acquired in July 2010 (© L'Avion Jaune).
