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Estimation and modeling of forest at...

Golinkoff, Jordan Seth.

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Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data./
作者:	Golinkoff, Jordan Seth.
面頁冊數:	203 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
標題:	Agriculture, Forestry and Wildlife. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3568103
ISBN:	9781303217333

Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data.
Golinkoff, Jordan Seth.

Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data. - 203 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (Ph.D.)--University of Montana, 2013.

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

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time.

ISBN: 9781303217333Subjects--Topical Terms:

783690
Agriculture, Forestry and Wildlife.

Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data.
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100 1 $a Golinkoff, Jordan Seth. $3 3170918
245 1 0 $a Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data.
300 $a 203 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Steven W. Running.
502 $a Thesis (Ph.D.)--University of Montana, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time.
520 $a This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.
590 $a School code: 0136.
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Remote Sensing. $3 1018559
650 4 $a Statistics. $3 517247
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710 2 $a University of Montana. $b Forestry. $3 1031083
773 0 $t Dissertation Abstracts International $g 74-10B(E).
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