Canadian Forest Service Publications

Stability of surface LIDAR height estimates on a point and polygon basis. 2000. Wulder, M.A.; Magnussen, S.; Harding, D.; Boudewyn, P.A.; Seemann, D. Pages 433-438 in Remote Sensing and Spatial Data Integration: Measuring, Monitoring and Modelling., Proceedings: 22nd Symposium of the Canadian Remote Sensing Society. August 20-25, 2000, Victoria, British Columbia. Canadian Remote Sensing Society, Ottawa.

Year: 2000

Issued by: Pacific Forestry Centre

Catalog ID: 5516

Language: English

Availability: PDF (download)

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LIDAR has been demonstrated as a tool for remotely sensing information on the vertical structure of forests. The Scanning LIDAR Imager of Canopies by Echo Recovery (SLICER) records data on canopy height, vertical structure, and ground elevation. Based upon the sensor configuration for this study, the vertical resolution of the SLICER is approximately 1m, with a horizontal resolution of approximately 9m, with five adjacent footprints resulting in an approximate 45m wide swath. Information on the height of trees within forest stands is an important attribute in forest inventories. The ability to remotely sense height information for forest inventory purposes may allow for procedures such as up-date, audit, calibration, and validation.

Prior to applying remote estimates of height in an inventory context the consistency of the estimates at locations and over areas is assessed. Locations which have more than one LIDAR observations from differing flight over-passes allow for an assessment of the stability of point height estimates. To assess the stability of area estimates, the height estimates from multiple flight lines through individual forest inventory polygons are compared.

For the boreal forest conditions present in our central Saskatchewan study area the following conclusions are made. On a point stability basis, LIDAR observations are found to vary little when separation distances between points are small. On a polygon basis, considering both between and within line standard deviations, the within polygon variability in LIDAR heights is well captured by collecting data over any portion of a polygon.