High spatial resolution image analysis

Remote sensing can be used to characterize forest ecosystems across large areas. However, the effectiveness of using remotely sensed data for large-area forest inventories depends on the relationship between the scale of the object of interest and sensor-specific characteristics such as resolution and spatial extent.

Remote sensing systems that acquire images with large spatial extents will generally have a lower resolution, and thereby capture less detail, than images acquired at a higher resolution, which usually depict forest characteristics across smaller spatial extents. For example, trees are smaller than the pixel size of medium spatial resolution remotely sensed data (10 to 30 metres), and this prohibits measurement of specific properties, such as tree locations and crown dimensions. At higher spatial resolutions, however, trees become larger than the image pixel size, allowing for direct measurement of particular properties.

High and very high resolution images (10 to 100 cm/pixel), then, mark a potential transition from mapping relatively homogeneous forest stands and interpreting their content from medium-scale photographs, to the semi-automatic computer analysis of high spatial resolution multispectral aerial or satellite images on an individual tree crown basis.

This unprecedented level of detail offers the potential extraction of a range of other types of multi-resource management information, such as snag locations, forest gap sizes and distribution, locations of highly valued trees, or riparian zone mapping, in addition to adding to the precision, accuracy and timeliness of conventional forest inventories.

Canadian Forest Service key contacts

François Gougeon, Research Scientist - Digital Remote Sensing
Mike Wulder, Research Scientist, Forest Inventory and Analysis