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Reducing uncertainties associated wi...

Cook, Bruce Douglas.

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Reducing uncertainties associated with remotely sensed estimates of forest growth and carbon exchange in the Great Lakes Region.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Reducing uncertainties associated with remotely sensed estimates of forest growth and carbon exchange in the Great Lakes Region./
Author:	Cook, Bruce Douglas.
Description:	140 p.
Notes:	Adviser: Paul V. Bolstad.
Contained By:	Dissertation Abstracts International69-03B.
Subject:	Agriculture, Forestry and Wildlife. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3303938
ISBN:	9780549512400

Reducing uncertainties associated with remotely sensed estimates of forest growth and carbon exchange in the Great Lakes Region.
Cook, Bruce Douglas.

Reducing uncertainties associated with remotely sensed estimates of forest growth and carbon exchange in the Great Lakes Region. - 140 p.

Adviser: Paul V. Bolstad.

Thesis (Ph.D.)--University of Minnesota, 2008.

NASA satellites Terra and Aqua orbit the Earth every 100 minutes and collect data that is used to compute an 8 day time series of gross photosynthesis and annual plant production for each square kilometer of the earth's surface. This is a remarkable technological and scientific achievement that permits continuous monitoring of plant production and quantification of CO2 fixed by the terrestrial biosphere. It also allows natural resource scientists and practitioners to identify global trends associated with land cover/use and climate change. Satellite-derived estimates of photosynthesis and plant production from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) generally agree with independent measurements from validation sites across the globe, but local biases and spatial uncertainties exist at the regional scale. This dissertation evaluates three sources of uncertainty associated with MODIS algorithms in the Great Lakes Region, and evaluates LiDAR (Light Detection and Ranging) remote sensing as a method for improving model inputs. Chapter 1 examines the robustness of model parameters and errors resulting from canopy disturbances, which were assessed by inversion of flux tower observations during a severe outbreak of forest tent caterpillars. Chapter 2 examines model logic errors in wetland ecosystems, focusing on surface water table fluctuations as a potential constraint to photosynthesis that is not accounted for in the MODIS algorithm. Chapter 3 examines errors associated with pixel size and poor state data, using fine spatial resolution LiDAR and multispectral satellite data to derive estimates plant production across a heterogeneous landscape in northern Wisconsin. Together, these papers indicate that light- and carbon-use efficiency models driven by remote sensing and surface meteorology data are capable of providing accurate estimates of plant production within stands and across landscapes of the Great Lakes Region. It is demonstrated that model results can be improved using spatial inputs derived from a combination of LiDAR data and multispectral imagery, and uncertainties can be reduced by accounting for variations in leaf area and cloudiness. Land cover generalizations and failure to account for wetlands and wetland processes were less important for predicting plant production in this predominantly forested region.

ISBN: 9780549512400Subjects--Topical Terms:

783690
Agriculture, Forestry and Wildlife.

Reducing uncertainties associated with remotely sensed estimates of forest growth and carbon exchange in the Great Lakes Region.
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020 $a 9780549512400
035 $a (UMI)AAI3303938
035 $a AAI3303938
040 $a UMI $c UMI
100 1 $a Cook, Bruce Douglas. $3 1264080
245 1 0 $a Reducing uncertainties associated with remotely sensed estimates of forest growth and carbon exchange in the Great Lakes Region.
300 $a 140 p.
500 $a Adviser: Paul V. Bolstad.
500 $a Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1534.
502 $a Thesis (Ph.D.)--University of Minnesota, 2008.
520 $a NASA satellites Terra and Aqua orbit the Earth every 100 minutes and collect data that is used to compute an 8 day time series of gross photosynthesis and annual plant production for each square kilometer of the earth's surface. This is a remarkable technological and scientific achievement that permits continuous monitoring of plant production and quantification of CO2 fixed by the terrestrial biosphere. It also allows natural resource scientists and practitioners to identify global trends associated with land cover/use and climate change. Satellite-derived estimates of photosynthesis and plant production from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) generally agree with independent measurements from validation sites across the globe, but local biases and spatial uncertainties exist at the regional scale. This dissertation evaluates three sources of uncertainty associated with MODIS algorithms in the Great Lakes Region, and evaluates LiDAR (Light Detection and Ranging) remote sensing as a method for improving model inputs. Chapter 1 examines the robustness of model parameters and errors resulting from canopy disturbances, which were assessed by inversion of flux tower observations during a severe outbreak of forest tent caterpillars. Chapter 2 examines model logic errors in wetland ecosystems, focusing on surface water table fluctuations as a potential constraint to photosynthesis that is not accounted for in the MODIS algorithm. Chapter 3 examines errors associated with pixel size and poor state data, using fine spatial resolution LiDAR and multispectral satellite data to derive estimates plant production across a heterogeneous landscape in northern Wisconsin. Together, these papers indicate that light- and carbon-use efficiency models driven by remote sensing and surface meteorology data are capable of providing accurate estimates of plant production within stands and across landscapes of the Great Lakes Region. It is demonstrated that model results can be improved using spatial inputs derived from a combination of LiDAR data and multispectral imagery, and uncertainties can be reduced by accounting for variations in leaf area and cloudiness. Land cover generalizations and failure to account for wetlands and wetland processes were less important for predicting plant production in this predominantly forested region.
590 $a School code: 0130.
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Atmospheric Sciences. $3 1019179
650 4 $a Biogeochemistry. $3 545717
650 4 $a Remote Sensing. $3 1018559
690 $a 0425
690 $a 0478
690 $a 0725
690 $a 0799
710 2 $a University of Minnesota. $b Natural Resources Science and Management. $3 1027864
773 0 $t Dissertation Abstracts International $g 69-03B.
790 $a 0130
790 1 0 $a Bauer, Marvin $e committee member
790 1 0 $a Bolstad, Paul V., $e advisor
790 1 0 $a Davis, Kenneth J. $e committee member
790 1 0 $a Griffis, Tim $e committee member
790 1 0 $a Reich, Peter $e committee member
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3303938