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Enhancing Ecological Inference About Forest Species With Remote Sensing and Statistical Advances.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing Ecological Inference About Forest Species With Remote Sensing and Statistical Advances./
作者:	Spiers, Anna Isabel.
面頁冊數:	1 online resource (141 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Ecology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30314696click for full text (PQDT)
ISBN:	9798379529291

Enhancing Ecological Inference About Forest Species With Remote Sensing and Statistical Advances.
Spiers, Anna Isabel.

Enhancing Ecological Inference About Forest Species With Remote Sensing and Statistical Advances. - 1 online resource (141 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2023.

Includes bibliographical references

Human-caused disturbances and climate change are affecting forest-dependent communities in myriad ways, including through habitat clearing, habitat degradation, and shifted natural disturbance regimes. Our ability to account for impacts to ecological communities relies on long-term monitoring in the face of global change as well as leveraging technological and theoretical advancements to make better use of existing data. This dissertation explores the ecology of forest species in disturbed and pristine environments. Throughout my dissertation, I use a combination of observational, experimental, and statistical methods to characterize ecological communities in temperate forests and examine how these communities respond to disturbance. A theme across these chapters is a focus on how recent advances in remote sensing technologies and statistical modeling allow ecologists to study forests at a scale relevant to a tree's biology and better account for uncertainty in ecological inferences. In my first chapter, I review what forest traits can be measured with drone technology through a literature research, and describe a case study of how ecologists use drone-derived data to estimate aboveground carbon stock. This review illustrates the utility and underutilization of drones in forest and spatial ecology. In chapter two, I describe the interaction of two disturbances (fragmentation and fire) on a Eucalyptus community by combining ground-, satellite-, and drone-based surveys. I assess how prior forest fragmentation changed forest structure and fuel availability so that a subsequent wildfire burned more severely in fragmented forest remnants compared to continuous forest. Finally, in my third chapter, I contribute a statistical framework that better accounts for species classification uncertainty in community datasets with observation error. In this framework, we leverage observed species abundance to estimate occupancy, focusing on a carabid community. These studies demonstrate that drone remote sensing is a helpful tool in forest ecology, disturbance interactions can create unexpected and nonlinear outcomes, and observation error in community surveys can be accounted for more accurately with the advanced statistical tools.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379529291Subjects--Topical Terms:

516476
Ecology.
Subjects--Index Terms:

Ecological inferenceIndex Terms--Genre/Form:

542853
Electronic books.

Enhancing Ecological Inference About Forest Species With Remote Sensing and Statistical Advances.
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520 $a Human-caused disturbances and climate change are affecting forest-dependent communities in myriad ways, including through habitat clearing, habitat degradation, and shifted natural disturbance regimes. Our ability to account for impacts to ecological communities relies on long-term monitoring in the face of global change as well as leveraging technological and theoretical advancements to make better use of existing data. This dissertation explores the ecology of forest species in disturbed and pristine environments. Throughout my dissertation, I use a combination of observational, experimental, and statistical methods to characterize ecological communities in temperate forests and examine how these communities respond to disturbance. A theme across these chapters is a focus on how recent advances in remote sensing technologies and statistical modeling allow ecologists to study forests at a scale relevant to a tree's biology and better account for uncertainty in ecological inferences. In my first chapter, I review what forest traits can be measured with drone technology through a literature research, and describe a case study of how ecologists use drone-derived data to estimate aboveground carbon stock. This review illustrates the utility and underutilization of drones in forest and spatial ecology. In chapter two, I describe the interaction of two disturbances (fragmentation and fire) on a Eucalyptus community by combining ground-, satellite-, and drone-based surveys. I assess how prior forest fragmentation changed forest structure and fuel availability so that a subsequent wildfire burned more severely in fragmented forest remnants compared to continuous forest. Finally, in my third chapter, I contribute a statistical framework that better accounts for species classification uncertainty in community datasets with observation error. In this framework, we leverage observed species abundance to estimate occupancy, focusing on a carabid community. These studies demonstrate that drone remote sensing is a helpful tool in forest ecology, disturbance interactions can create unexpected and nonlinear outcomes, and observation error in community surveys can be accounted for more accurately with the advanced statistical tools.
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