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Land Cover and Use Change in Utah: A...

Bakken, Jennifer Lynn.

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Land Cover and Use Change in Utah: A Comparison of Field- vs. Aerial Image-Based Observations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Land Cover and Use Change in Utah: A Comparison of Field- vs. Aerial Image-Based Observations./
Author:	Bakken, Jennifer Lynn.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	68 p.
Notes:	Source: Masters Abstracts International, Volume: 80-02.
Contained By:	Masters Abstracts International80-02.
Subject:	Forestry. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10844373
ISBN:	9780438242661

Land Cover and Use Change in Utah: A Comparison of Field- vs. Aerial Image-Based Observations.
Bakken, Jennifer Lynn.

Land Cover and Use Change in Utah: A Comparison of Field- vs. Aerial Image-Based Observations. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 68 p.

Source: Masters Abstracts International, Volume: 80-02.

Thesis (M.S.)--Utah State University, 2018.

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

The Image-based Change Estimation program (ICE) was developed by the US Forest Service Forest Inventory & Analysis (FIA) program and the Geospatial Technology Applications Center in response to the 2014 Farm Bill calling for more timely and accurate estimates of land cover and use change. ICE monitors change throughout the US on a state by state basis by assessing each FIA plot using high resolution imagery from two dates in time. In the western US, FIA measures 10% of the plots each year to report on status, trends, and sustainability of our Nation's forests. However, this 10 year cycle misses disturbances because a temporal gap occurs from disturbance event to measurement. This study compares field- and image-based observations of land cover and use change to improve sampling procedures in Utah. Image-based data collected from 2011 and 2014 imagery and field-based plots measured between 2011 and 2016 are compared using three methods to compile the ICE data, termed hierarchical, majority, and point center, to determine a standardized system and better understand their relationships. Additionally, ICE change agents were compared with causes of tree mortality observed on FIA forest plots to assess how well ICE evaluates causes of change and the differences of change vs. mortality agents were explored by conducting a second review of the imagery to find trends in data discrepancies. This knowledge can help image interpreters better recognize and identify change.

ISBN: 9780438242661Subjects--Topical Terms:

895157
Forestry.

Land Cover and Use Change in Utah: A Comparison of Field- vs. Aerial Image-Based Observations.
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520 $a The Image-based Change Estimation program (ICE) was developed by the US Forest Service Forest Inventory & Analysis (FIA) program and the Geospatial Technology Applications Center in response to the 2014 Farm Bill calling for more timely and accurate estimates of land cover and use change. ICE monitors change throughout the US on a state by state basis by assessing each FIA plot using high resolution imagery from two dates in time. In the western US, FIA measures 10% of the plots each year to report on status, trends, and sustainability of our Nation's forests. However, this 10 year cycle misses disturbances because a temporal gap occurs from disturbance event to measurement. This study compares field- and image-based observations of land cover and use change to improve sampling procedures in Utah. Image-based data collected from 2011 and 2014 imagery and field-based plots measured between 2011 and 2016 are compared using three methods to compile the ICE data, termed hierarchical, majority, and point center, to determine a standardized system and better understand their relationships. Additionally, ICE change agents were compared with causes of tree mortality observed on FIA forest plots to assess how well ICE evaluates causes of change and the differences of change vs. mortality agents were explored by conducting a second review of the imagery to find trends in data discrepancies. This knowledge can help image interpreters better recognize and identify change.
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