This study presents an approach for low-cost mapping of tree heights at the landscape level. The proposed method integrates parameters related to landscape (slope, orientation, and topographic height), tree size (crown diameter), and competition (crown competition factor and age), and determines the mean stand tree height as a function of tree competitive capability. The model was calibrated and validated against a standard inventory dataset collected over a dryland planted forest in the eastern Mediterranean region. The validation of the model shows a high and significant level of correlation between measured and modeled datasets ( ; ), with almost negligible (less than 1？m) levels of absolute and relative errors. The validated model was implemented for mapping mean tree height on a per-pixel basis by using high-spatial-resolution satellite imagery. The resulting map was, in turn, validated against an independent dataset of ground measurements. The presented approach could help to reduce the need for fieldwork in compiling single-tree-based inventories and to apply surface-roughness properties to hydrometeorological studies and regional energy/water-balance evaluation. 1. Introduction Tree height is considered to be a useful structural variable in estimating wood volumes, biomass, carbon stocks, and productivity of forest stands. It also determines the light penetration into the forest canopy and is of importance for certain habitat studies. In addition, tree height plays an essential role in micrometeorological research and global climate modeling by determining forest aerodynamic roughness (i.e., zero-plane displacement and roughness length) and affecting the transport of energy and substances between the land surface and the atmosphere boundary layer [1]. Therefore, the computation and mapping of tree-height distribution in a widespread area becomes a key step in characterizing the land-surface physical processes. Although the relationship between vegetation structure and surface reflectance obtained from satellite observation has been a focus of a great deal of research, the evaluation of mean tree height is still one of the main challenges for remote sensing applications. The most frequently used remote sensing techniques that are relevant to evaluation of tree height are automated photogrammetry (e.g., [2]); airborne ranging radar (e.g., [3]); and laser altimetry (e.g., [4–6]). Since these methods are mainly based on airborne platforms, the data collected by them is naturally of high resolution, and usually enables observation of an individual
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