Optical remote sensing measurements reflect the interaction between radiation and forest canopies and are influenced by the canopy’s structural properties. For this reason remote sensing data have been extensively used to map land cover and forest structural variables such as forest density and timber volume (Franklin 1986, Cohen 1992, Puhr 2000). Remote sensing also has the potential for providing spatial and temporal information on biomass availability because forest characteristics such as crown size and forest density are correlated with biomass (Shugart 2000, Franklin 1986). New techniques and the availability of remotely sensed data with higher radiometric and spatial characteristics such as MODIS, now make it possible to assess biomass availability at the regional and local scale (Baccini _et al._ 2004).

In this study, we derive biomass estimates for the year 2004 by applying a regression tree algorithm to identify the relationship between the output variable, biomass, and two predictors: remotely sensed data and elevation. Tree-based models have been previously used to predict both categorical (Friedl 1997) and continuous variables (Michaelsen 1994).<sup>9</sup> The basic theory behind such models is reported in Breiman (1984).<sup>10</sup> For the work reported here, the specific methodology is referred to as a regression tree, because the model is predicting a continuousvalued response variable.

The regression tree is a supervised algorithm that uses a set of examples to identify the relationship between the regressor (biomass) and the predictors. In order to identify examples to train the algorithm, 1990 Landsat data similar to the ones used in the Department of Forestry, Government of Malawi, report (GOM 1993) were first analyzed. Regions with vegetation formations were selected from the Landsat 1990 scenes and then compared with the scenes for the year 2000 to verify that no changes occurred within the area selected. If changes occurred the sample was discarded. A last check was then performed using MODIS 500 meters data for the

> 9 A variety of techniques are used to classify vegetation and forest structure from satellite data. Unsupervised clustering algorithms and parametric supervised algorithms such as the maximum likelihood algorithms are the most commonly used methods. Vegetation structures such as forest canopy density and above ground forest biomass are commonly analyzed using linear statistical models based on ordinary least squares. These methods present limitations when the dependent and predictor variables are not linearly related, or when the predictors include high-dimensional input data. Further, multi-collinearity among the regressor variables can result in misleading inferences. Alternative methods such as neural networks and tree based-models overcome these limitations, and have been shown to be efficient in dealing with complex non-linear relationships (Friedl 1997, Gopal 1998).

10 Tree-based models (1) make no assumptions regarding the distributional properties of the input data, (2) are able to capture nonlinear relationships between the response and predictor variables, (3) and provide easily understandable outputs.

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