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TitleInvestigation of Remotely Sensed Data for Eliminating Forest Stand Attributes (extended abstract)
AuthorGemmel, F; Goodenough, D G; Kushingbor, C; Meunier, J -F; Wiart, R
SourceProceedings of the 14th Canadian Symposium on Remote Sensing, Calagary, Alberta, 6-10 May; 1991 p. 223
Alt SeriesEarth Sciences Sector, Contribution Series 20042591
AbstractThe effective integration of remote sensing data and geographic information is essential if one is to able to update a Geographic Information System (GIS) in a timely fashion. For an inventory for forest management, estimates of species, stand density, crown enclosure, age and site quality are needed. The Canada Centre for Remote Sensing (CCRS) and the British Columbia Ministry of Forests (BCMOF) are conducting experiments integrating remote sensing and geographic information on remote sensing data from LANDSAT Thematic Mapper (TM) and the airborne imager, MEIS. The acquired data are geometrically corrected, including corrections for topographic relief. GIS forest cover files, describing historical conditions over the area, are analyzed in order to develop likely interpretations for objects detected in the imagery.

Initial work has established that clustering TM and MEIS data within polygons with the same GIS class label can lead to the identification of crown closure and stand density differences within GIS polygons. Thus, in this case, remote sensing was able to provide information at finer detail than that currently included in the inventory derived from interpretation of aerial photography. Segmentation of the GIS files and the image data permit us to create objects made up of multiple segments. The historical data support the interpretation of the image segments and enable one to detect any inconsistencies in the polygon labels or boundaries. Changes in forest cover as a result of logging operations are also identified.

This presentation will focus on the use of remotely-sensed data for forest inventory, at a range of spatial and spectral resolutions, and ground information to understand the underlying physical causes of the different cluster found within GIS polygons from the image data.


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