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An Unsupervised Classification-Based...

University of Lethbridge (Canada)., Geography and Environment.

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An Unsupervised Classification-Based Time Series Change Detection Approach for Mapping Forest Disturbance.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Unsupervised Classification-Based Time Series Change Detection Approach for Mapping Forest Disturbance./
作者:	Parshakov, Ilia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	208 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
Contained By:	Dissertations Abstracts International82-09B.
標題:	Remote sensing. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28319588
ISBN:	9798582553496

An Unsupervised Classification-Based Time Series Change Detection Approach for Mapping Forest Disturbance.
Parshakov, Ilia.

An Unsupervised Classification-Based Time Series Change Detection Approach for Mapping Forest Disturbance. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 208 p.

Source: Dissertations Abstracts International, Volume: 82-09, Section: B.

Thesis (Ph.D.)--University of Lethbridge (Canada), 2021.

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

Unsupervised Classification to Change (UC-Change) is a new remote sensing approach for mapping areas affected by logging and wildfires. It addresses the main limitations of existing image time-series change detection techniques, such as limited multi-sensor capabilities, use of purely spectral-based forest recovery metrics, and poor detection of salvage harvesting. UC-Change detects disturbances and tracks forest recovery by analyzing changes in the spatial distribution of spectral classes over time. The algorithm detected approximately 85% and 70% of reference cutblock and fire scar pixels at a ±2-year temporal agreement, respectively, consistently outperforming existing algorithms across different biogeoclimatic zones of British Columbia, Canada. The results indicate an upper estimate of 7.5 million ha of forest cleared between 1984 and 2014, which is above estimates based on existing maps and databases (6.3 - 6.7 million ha). Also presented is a new framework for using open-access data for validation of change detection results.

ISBN: 9798582553496Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Change detection

An Unsupervised Classification-Based Time Series Change Detection Approach for Mapping Forest Disturbance.
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500 $a Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
500 $a Advisor: Peddle, Derek R.
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506 $a This item must not be sold to any third party vendors.
520 $a Unsupervised Classification to Change (UC-Change) is a new remote sensing approach for mapping areas affected by logging and wildfires. It addresses the main limitations of existing image time-series change detection techniques, such as limited multi-sensor capabilities, use of purely spectral-based forest recovery metrics, and poor detection of salvage harvesting. UC-Change detects disturbances and tracks forest recovery by analyzing changes in the spatial distribution of spectral classes over time. The algorithm detected approximately 85% and 70% of reference cutblock and fire scar pixels at a ±2-year temporal agreement, respectively, consistently outperforming existing algorithms across different biogeoclimatic zones of British Columbia, Canada. The results indicate an upper estimate of 7.5 million ha of forest cleared between 1984 and 2014, which is above estimates based on existing maps and databases (6.3 - 6.7 million ha). Also presented is a new framework for using open-access data for validation of change detection results.
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