applications of satellite remote sensing to develop forest inventory for strategic-level planning
abstract
forest inventory is the fundamental base information for most decision making
processes in today’s forest management planning. recently in ontario,
with increasing industrial involvement, new environmental and multiple use
issues, and rapidly developing technology, the requirement and opportunity for
investigation into new inventory methods has increased. the method developed
in this thesis focuses on the inventory requirements for large scale, strategic-level
forest management for the boreal forest region. with recent improvement in
satellite sensors and computer tools, the process of acquiring the imagery and
analyzing the information has become significantly cheaper and faster. a multisource
approach is used in this project to improve upon current forest
classification attempts using satellite imagery. by merging the superior
multispectral properties of landsat 7 etm+ (30 m multispectral) with the spatially
detailed irs-1 d panchromatic (5 m) imagery, an attempt is made to derive a
species-level classification scheme. image data merging techniques are
explored and the utilization of image segmentation procedures is evaluated.
principle component substitution is used to integrate the imagery, and a nearest
neighbour algorithm is used in an object-based classification system. areabased
accuracy assessment is used to test the success of the methods with
reference derived from interpreted aerial photography. accuracy assessments
show satisfactory agreement between the thematic product and reference data,
with overall accuracies reaching 72%. pure species groups such as black
spruce, jack pine and trembling aspen exhibited producer’s accuracies of 90%,
83%, and 87%, respectively, with user’s accuracies as high as 73%, 75%, and
61% respectively.
collections
- retrospective theses [1604]