東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Monitoring tropical and montane fore...

Greenberg, Jonathan Asher.

Linked to FindBook

Google Book

Amazon

博客來

Monitoring tropical and montane forest dynamics and structure using remote sensing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Monitoring tropical and montane forest dynamics and structure using remote sensing./
Author:	Greenberg, Jonathan Asher.
Description:	93 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1850.
Contained By:	Dissertation Abstracts International66-04B.
Subject:	Biology, Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3171878
ISBN:	9780542085369

Monitoring tropical and montane forest dynamics and structure using remote sensing.
Greenberg, Jonathan Asher.

Monitoring tropical and montane forest dynamics and structure using remote sensing. - 93 p.

Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1850.

Thesis (Ph.D.)--University of California, Davis, 2005.

Uncertainties in our understanding of the basic inputs and dynamics at work in the global carbon cycle severely restrict our ability to address why climate change is happening and how best to mitigate it. I focused on advances in regional and global climate change model inputs, addressing two major uncertainties: (1) what are the anthropogenic factors influencing deforestation and (2) what is the carbon load of an ecosystem? Analysis of anthropogenic factors leading to land use changes are presented in an evaluation of deforestation at the UNESCO Biosphere Reserve, Parque National Yasuni, located in the rainforest of eastern Ecuador, using multitemporal Landsat satellite imagery. Using survival analysis, I assessed current and future trends in deforestation rates and investigated the impact of spatial, cultural, and economic factors on deforestation. I found the annual rate of deforestation is currently only 0.11%, but is increasing with time, so that by 2063, 50% of the forest within 2 km of a major oil access road will be lost due to unhindered colonization and anthropogenic conversion. To improve accuracy in estimating landscape level carbon sequestration, I developed a new approach to generating regional aboveground biomass estimates for tree species of the Lake Tahoe Basin, California using hyperspatial (<1m2) remote sensing imagery. I demonstrate how, with accurate classification maps and allometric equations relating DBH or crown area to biomass, that crown parameters can be used to estimate regional biomass. I show that biomass estimated with fine-scale optical sensors does not saturate at high biomass levels as does coarse-scale optical and RADAR sensors. Finally, I address a technical problem to improve quantitative comparison of remote sensing datasets. I present a modification of the empirical line method for normalizing the radiance or reflectance scales of two images. Radiometric normalization of multitemporal remote sensing datasets is a critical step in accurate analyses of land cover change. The method for correcting radiance differences among datasets is almost entirely automated and easily implemented on any number of data analysis packages, which substantially reduces the time it takes to preprocess imagery for use in change detection research.

ISBN: 9780542085369Subjects--Topical Terms:

1017726
Biology, Ecology.

Monitoring tropical and montane forest dynamics and structure using remote sensing.
LDR:03251nmm 2200301 4500 001 1823440
005 20061130142447.5
008 130610s2005 eng d
020 $a 9780542085369
035 $a (UnM)AAI3171878
035 $a AAI3171878
040 $a UnM $c UnM
100 1 $a Greenberg, Jonathan Asher. $3 1912547
245 1 0 $a Monitoring tropical and montane forest dynamics and structure using remote sensing.
300 $a 93 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1850.
500 $a Adviser: Susan L. Ustin.
502 $a Thesis (Ph.D.)--University of California, Davis, 2005.
520 $a Uncertainties in our understanding of the basic inputs and dynamics at work in the global carbon cycle severely restrict our ability to address why climate change is happening and how best to mitigate it. I focused on advances in regional and global climate change model inputs, addressing two major uncertainties: (1) what are the anthropogenic factors influencing deforestation and (2) what is the carbon load of an ecosystem? Analysis of anthropogenic factors leading to land use changes are presented in an evaluation of deforestation at the UNESCO Biosphere Reserve, Parque National Yasuni, located in the rainforest of eastern Ecuador, using multitemporal Landsat satellite imagery. Using survival analysis, I assessed current and future trends in deforestation rates and investigated the impact of spatial, cultural, and economic factors on deforestation. I found the annual rate of deforestation is currently only 0.11%, but is increasing with time, so that by 2063, 50% of the forest within 2 km of a major oil access road will be lost due to unhindered colonization and anthropogenic conversion. To improve accuracy in estimating landscape level carbon sequestration, I developed a new approach to generating regional aboveground biomass estimates for tree species of the Lake Tahoe Basin, California using hyperspatial (<1m2) remote sensing imagery. I demonstrate how, with accurate classification maps and allometric equations relating DBH or crown area to biomass, that crown parameters can be used to estimate regional biomass. I show that biomass estimated with fine-scale optical sensors does not saturate at high biomass levels as does coarse-scale optical and RADAR sensors. Finally, I address a technical problem to improve quantitative comparison of remote sensing datasets. I present a modification of the empirical line method for normalizing the radiance or reflectance scales of two images. Radiometric normalization of multitemporal remote sensing datasets is a critical step in accurate analyses of land cover change. The method for correcting radiance differences among datasets is almost entirely automated and easily implemented on any number of data analysis packages, which substantially reduces the time it takes to preprocess imagery for use in change detection research.
590 $a School code: 0029.
650 4 $a Biology, Ecology. $3 1017726
650 4 $a Environmental Sciences. $3 676987
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Remote Sensing. $3 1018559
690 $a 0329
690 $a 0768
690 $a 0478
690 $a 0799
710 2 0 $a University of California, Davis. $3 1018682
773 0 $t Dissertation Abstracts International $g 66-04B.
790 1 0 $a Ustin, Susan L., $e advisor
790 $a 0029
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3171878