자료유형  

 학위논문

Control Number  

0016935188

International Standard Book Number  

9798380714242

Dewey Decimal Classification Number  

621.3678

Main Entry-Personal Name  

McGregor, Ian Ramsey.

Publication, Distribution, etc. (Imprint  

[S.l.] : North Carolina State University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(158 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

General Note  

Advisor: Gray, Joshua M.

Dissertation Note  

Thesis (Ph.D.)--North Carolina State University, 2023.

Restrictions on Access Note  

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

Summary, Etc.  

요약Preserving forests and mitigating deforestation have been primary goals of tropical remote sensing for decades. From landscape change estimates to recent near real-time (NRT) monitoring alerts, methods have continuously evolved with the help of an increasing amount of satellite imagery, the shift from static to time series approaches, and the near-instantaneous availability of satellite data. However, as it has developed, NRT monitoring with remote sensing has encountered challenges in two main areas: technical improvements, and bridging the gap between model development and clear impacts on the ground. The former includes issues with detecting small-scale change over periods with extensive cloud cover, and the use of short training periods due to either data availability or computational intensity. In addition, previous methods have indicated the presence of trade-offs may preclude an optimization of both detection accuracy and latency, yet there have been few dedicated discussions about the implications for users. Meanwhile, the ability of remote sensing analyses to directly influence policy is hampered by the exclusion of external spatial data describing landscape processes, as well as its inability to incorporate data users as part of the system.For the first chapter, we proposed a novel, multi-source, NRT monitoring algorithm that harmonizes data from three sensors, two optical and one synthetic aperture radar. We chose our study region to be Myanmar, as despite high levels of forest cover and biodiversity, deforestation rates are high in the region, with spatial scales of forest removal ranging from shifting cultivation to the expansion of commercial agriculture. In creating the algorithm, we generated training data and also focused on whether or not we could optimize detection latency and accuracy. Trade-offs between the two were characterized for different parameterizations to understand the impacts on decision-making by users, such as forest managers deciding to prioritize fast or accurate detections. We then demonstrated the application of the method at landscape scale by creating daily probability maps at 10 m resolution for two sub-regions and assessed the outputs by validating positive detections within them.In the second chapter we developed an extension to Chapter 1 for which we incorporated external spatial data from landscape processes into the probability estimates. We first identified proximate drivers of deforestation in the study region and modeled their influence on forest disturbance while explicitly accounting for spatial covariance. These estimated effects were then combined with the remote sensing probabilities from the first chapter using a Bayesian methodology, resulting in a more representative and confident detection estimate. We conducted another sensitivity analysis to understand how the trade-offs from the first chapter changed, and subsequently created new daily probability estimates for the sub-regions to determine if incorporating the external data improved the NRT detection metrics.Finally, the third chapter introduced a new method for incorporating users into the workflow. We proposed an iterative approach in which field validation data was directly used by the algorithm to update the probability equation, allowing for continuous improvement over time. We explored the impact of varying the different inputs, and discussed the implications of them for users in an operational setting.

Subject Added Entry-Topical Term  

Remote sensing.

Subject Added Entry-Topical Term  

Partial differential equations.

Subject Added Entry-Topical Term  

Sensitivity analysis.

Subject Added Entry-Topical Term  

Funding.

Subject Added Entry-Topical Term  

Wildlife sanctuaries.

Subject Added Entry-Topical Term  

Sensors.

Subject Added Entry-Topical Term  

Probability.

Subject Added Entry-Topical Term  

Time series.

Subject Added Entry-Topical Term  

Forests.

Subject Added Entry-Topical Term  

Environmental policy.

Subject Added Entry-Topical Term  

Deforestation.

Subject Added Entry-Topical Term  

Forestry.

Subject Added Entry-Topical Term  

Wildlife conservation.

Added Entry-Corporate Name  

North Carolina State University.

Host Item Entry  

Dissertations Abstracts International. 85-05B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641111