The aim of this study was to develop an imputing method to map forest stand attributes (height and volume) using panchromatic aerial photographs with a spatial resolution of 30 cm and Landsat images. The method was tested on several sites in Québec, Canada, that are dominated by black spruce. The method involved four sets of procedures: (i) measuring the shadow fraction from panchromatic aerial photographs, (ii) generating regression models between the shadow fraction and forest attributes, (iii) locally mapping the forest attributes as a grid layer (30 m × 30 m), and (iv) expanding the forest attributes from the local maps to a large study area using an imputing approach. Regression models between the shadow fraction and stand attributes were calibrated with ground sample plots and a series of 73 aerial photographs. General results from linear relationships between the shadow fraction and two stand attributes showed linear shapes but had low values for the goodness of fit ( of 0.24 for height and 0.31 for volume). However, the results improved significantly ( of 0.40 for height and 0.59 for volume) when the portion of the aerial photograph used was restricted to its centre (between 6.2° and 11.3°). This restricted area of the aerial photographs produced small local maps of stand attributes from the shadow fraction. These local maps were used as the training dataset for an imputing approach, a k nearest neighbours algorithm, with the aim of mapping forest attributes over a large area.