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The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick’s texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pleiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ∼ 1.1 m is expected on crown diameter, ∼ 0.9 m on tree spacing, ∼ 3 m on height and ∼ 0.06 m on diameter at breast height.

The potential of very high spatial resolution Pléiades image texture for forest structure quantification and mapping was assessed on maritime pine stands in south-western France. A preliminary step showed that multi-linear regressions allowed a reliable prediction of forest variables (such as crown diameter or tree height) from a set of features automatically selected among a huge number of texture features with various spatial parameterizations. The image classification was performed using the Random Forests (RF) ensemble classifier in order to discriminate five forest structure classes using a hierarchical approach. The RF-variable importance is used for texture feature selection. The results highlight the contribution of process automation and the need for a joint use of both Pléiades image resolutions (panchromatic and multispectral) to derive the best performing texture features., Cette étude montre le potentiel de l'information texturale des images à très haute résolution spatiale Pléiades pour la quantification et la cartographie de la structure forestière des peuplements de pin maritime du sud-ouest de la France (massif forestier landais). Une première étape montre qu'il est possible d'estimer, par régressions linéaires multiples, les variables de structure forestière (comme le diamètre des couronnes ou la hauteur des arbres) à partir d'un ensemble d'attributs de texture automatiquement sélectionnés parmi un grand nombre de paramétrages possibles. La classification de l'image est ensuite effectuée en utilisant l'algorithme des forêts aléatoires (RF) pour discriminer cinq classes de structure forestière avec une approche hiérarchique. L'importance de variable des RF est utilisée pour la sélection des attributs de texture. Les résultats montrent l'intérêt de l'automatisation du processus et de l'utilisation conjointe des deux résolutions des images Pléiades (mode panchromatique et mode multispectral) pour dériver les attributs de texture les plus performants pour détecter de fines variations de structure forestière.