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Modelling forest structure and healt...

Levesque, Josee.

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Modelling forest structure and health using high-resolution airborne imagery: Investigation of spectral unmixing and spatial analysis of radiometric fractions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modelling forest structure and health using high-resolution airborne imagery: Investigation of spectral unmixing and spatial analysis of radiometric fractions./
Author:	Levesque, Josee.
Description:	259 p.
Notes:	Source: Dissertation Abstracts International, Volume: 62-08, Section: B, page: 3538.
Contained By:	Dissertation Abstracts International62-08B.
Subject:	Physical geography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ60963
ISBN:	9780612609631

Modelling forest structure and health using high-resolution airborne imagery: Investigation of spectral unmixing and spatial analysis of radiometric fractions.
Levesque, Josee.

Modelling forest structure and health using high-resolution airborne imagery: Investigation of spectral unmixing and spatial analysis of radiometric fractions. - 259 p.

Source: Dissertation Abstracts International, Volume: 62-08, Section: B, page: 3538.

Thesis (Ph.D.)--Carleton University (Canada), 2001.

This item must not be sold to any third party vendors.

Research was conducted in a forest adjacent to an acid mine tailings site at the abandoned KamKotia mine near Timmins, Ontario, to assess forest structural health using high spatial and spectral resolution digital camera imagery. The main goal was to develop models of forest structure and health from image spectral and spatial information. The classical approach uses raw image spectral information or basic spectral transformations and occasionally includes spatial transformations. This research introduces fractional textures and semivariance analysis of image fractions. They were compared and integrated with classical image measures in stepwise multiple regression modelling of forest structure and health variables. This analysis was conducted using canopy-scale and individual tree crown-scale samples extracted from 10 nm bandwidth spectral bands at three resolutions (0.25 m, 0.5 m, 1.0 m).

ISBN: 9780612609631Subjects--Topical Terms:

516662
Physical geography.

Modelling forest structure and health using high-resolution airborne imagery: Investigation of spectral unmixing and spatial analysis of radiometric fractions.
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100 1 $a Levesque, Josee. $3 3174998
245 1 0 $a Modelling forest structure and health using high-resolution airborne imagery: Investigation of spectral unmixing and spatial analysis of radiometric fractions.
300 $a 259 p.
500 $a Source: Dissertation Abstracts International, Volume: 62-08, Section: B, page: 3538.
500 $a Adviser: Douglas King.
502 $a Thesis (Ph.D.)--Carleton University (Canada), 2001.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Research was conducted in a forest adjacent to an acid mine tailings site at the abandoned KamKotia mine near Timmins, Ontario, to assess forest structural health using high spatial and spectral resolution digital camera imagery. The main goal was to develop models of forest structure and health from image spectral and spatial information. The classical approach uses raw image spectral information or basic spectral transformations and occasionally includes spatial transformations. This research introduces fractional textures and semivariance analysis of image fractions. They were compared and integrated with classical image measures in stepwise multiple regression modelling of forest structure and health variables. This analysis was conducted using canopy-scale and individual tree crown-scale samples extracted from 10 nm bandwidth spectral bands at three resolutions (0.25 m, 0.5 m, 1.0 m).
520 $a Image brightness (DN) and image fraction variables (IF) were found to be complementary. Of the best models for each forest measure at the canopy scale, 48% incorporated DN variables and 52% incorporated IF variables. More IF variables were present in the best models at the tree-crown scale (33% DN vs. 67% IF variables). IF variables alone, or in combination with DN variables, provided better models than DN variables alone in 14 of 18 cases at the canopy scale. Thus, modelling forest structure and health with image fractions has proven to be highly beneficial. More significant, however, are the spatial transformations (texture, semivariogram range) of DN and IF variables. They were the most significant variables in models at both the canopy (96% of all variables in models) and tree-crown scales (88% of all variables in models). Semivariogram variables were the most often entered into models at the canopy scale and texture variables were the most entered at the tree-crown scale. Spatial information in image fractions and image brightness has therefore proven to be more significant than spectral information in these analyses. Of the spatial resolutions evaluated, 0.5 m consistently produced similar or better models than those using the 0.25 or 1.0 m resolutions. Of the two sampling scales, the tree-crown scale analysis improved significantly the prediction of individual tree crown closure. However, all other variables were best predicted at the canopy scale as variations in gap-size distribution, tree-crown size and stem density, which respond to long-term stress, were captured in these samples. This research provides significant insight into the information contained in the image spectral/spatial data and provides a means to understand the relationships between high-resolution image data and forest structure and health.
590 $a School code: 0040.
650 4 $a Physical geography. $3 516662
650 4 $a Forestry. $3 895157
650 4 $a Remote sensing. $3 535394
650 4 $a Ecology. $3 516476
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710 2 $a Carleton University (Canada). $3 1018407
773 0 $t Dissertation Abstracts International $g 62-08B.
790 $a 0040
791 $a Ph.D.
792 $a 2001
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ60963