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Development of Forest Degradation Indicators From Long-Term Trajectories of Multispectral Satellite Images, and Their Projections into the Future Under Climate Change, in Ontario, Canada.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Development of Forest Degradation Indicators From Long-Term Trajectories of Multispectral Satellite Images, and Their Projections into the Future Under Climate Change, in Ontario, Canada./
Author:	Hoque, Md. Mozammel.
Description:	1 online resource (248 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Remote sensing. -
Online resource:	click for full text (PQDT)
ISBN:	9798380584340

Development of Forest Degradation Indicators From Long-Term Trajectories of Multispectral Satellite Images, and Their Projections into the Future Under Climate Change, in Ontario, Canada.
Hoque, Md. Mozammel.

Development of Forest Degradation Indicators From Long-Term Trajectories of Multispectral Satellite Images, and Their Projections into the Future Under Climate Change, in Ontario, Canada. - 1 online resource (248 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--Trent University (Canada), 2024.

Includes bibliographical references

Ontario forests are affected by natural and anthropogenic disturbances leading to forest degradation, which significantly impact local ecosystems, health, safety, and economy. This thesis develops a methodology for the continuous assessment, mapping, and monitoring of present and historic (1972-2020) forest disturbances, and future forest degradation trends and projections, using remote sensing data, ground measurements, and predictive models in an Ontario forested area.After testing four supervised classification algorithms, support vector machine was found to be the most robust, consistent, and effective for land cover classification. Seven vegetation indices derived from Landsat and MODIS platforms were used to derive forest degradation indicators (FDIs), which were combined into one composite forest degradation indicator (CFDI) for each year, using the principal component analysis image fusion approach. The CFDI was the most informative indicator. The computed FDIs from available large multispectral image stacks were statistically related to historical climate variables. These relationships were used to project future FDIs related to climate variables derived from General Circulation Models through multiple linear regression models. Spatially-explicit maps of relevant climatic variables and of long-term historical forest degradation were developed from the LandTrendr trajectory analysis. Climate variables P, MA1, MA2, and CFDI were strongly correlated, allowing for the development of a model with a high coefficient of determination, R2 (0.93), and low RMSE (0.28) to predict future values.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380584340Subjects--Topical Terms:

557272
Remote sensing.
Subjects--Index Terms:

Forest degradation indicatorsIndex Terms--Genre/Form:

554714
Electronic books.

Development of Forest Degradation Indicators From Long-Term Trajectories of Multispectral Satellite Images, and Their Projections into the Future Under Climate Change, in Ontario, Canada.
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