The management of trees in urban areas requires accurate maps, which are difficult to build in the dense patchwork of numerous material properties. Remote sensing is a useful technique that measures the response of all vegetation occurrences, including trees, when high spatial resolution is available. The continuous narrow spectral bands of hyperspectral images enable the detection of the oxygen and water content, which ensures a perfect correction of the atmospheric effect. When calibrated, sunlight reflectance images can be used to map surface chemical compositions by the detection of diagnostic and sharp absorption features. In the visible and near-infrared, the vegetation is detected by chlorophyll-a absorption features that are characteristic of the pigment content. The reflectance intensities due to the texture of leaves occur between 450 and 920 nm while the water content imprint is detectable beyond 920 nm. The sharp spectral feature intensities of the main associated pigments, not only chlorophylls, are well quantified by indices measuring a normalized difference of reflectance in a spectral interval between two bounding wavelengths. A regression line calculated on all bands within that interval ensures a low sensitivity of the indices to the smaller variations in reflectance intensity. Such unbiased indices may be combined, using successive index thresholds deduced from a training spectral library, to divide the spectra into subsets, minimizing the confusion between the numerous vegetation types with almost identical compositions. Therefore, for each subset of the spectra, a classic spectral angle mapping SAM method can be used on the corresponding sub-selection of the spectral library to measure angles at full spectral resolution and map tree types with great accuracy, grouped according to their spectral similarity. In this study, chemical and physical information is carefully separated. The tree crown physical properties are studied by comparing the local juxtaposition of pixel sets to a characteristic texture identifiable by image segmentation into objects. Instead of looking for objects in the reflectance image or any statistical compression of its information, a 25 channel co-image, built from 11 information layers of chemical sharp spectral feature indices and 14 information layers of SAM indices matching a spectral library of reference vegetation groups, was used. Tree canopies also present wide internal variations due to i a complex mixture with a background in the case of sparse foliage, or ii pigment content adaptation to light exposure intensity from one side to another. Both effects are minimized by using the mean spectrum of each object, assuming that less significant spectra, being at plus or minus one or two standard deviations from an object mean spectrum, would be less affected by anomalous pixel data. Thus, two overlapping hierarchic layers at the pixel scale and the object scale are available to describe the main chemistry or pigment content that identifies the vegetation types. The final classification is given by the upper layer at the object scale but in such an organization, the pixel scale layers can be used to analyse the data further and reorder them to obtain other parameters potentially useful for management purposes.
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