國立虎尾科技大學 |

Advancing Near Real-Time Deforestation Monitoring Via Multi-Source Remote Sensing Data, Landscape Processes, and End-User Contributions.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Advancing Near Real-Time Deforestation Monitoring Via Multi-Source Remote Sensing Data, Landscape Processes, and End-User Contributions./
作者:	McGregor, Ian Ramsey.
面頁冊數:	1 online resource (158 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
標題:	Wildlife conservation. -
電子資源:	click for full text (PQDT)
ISBN:	9798380714242

Advancing Near Real-Time Deforestation Monitoring Via Multi-Source Remote Sensing Data, Landscape Processes, and End-User Contributions.
McGregor, Ian Ramsey.

Advancing Near Real-Time Deforestation Monitoring Via Multi-Source Remote Sensing Data, Landscape Processes, and End-User Contributions. - 1 online resource (158 pages)

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

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

Includes bibliographical references

Preserving 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380714242Subjects--Topical Terms:

654486
Wildlife conservation.
Index Terms--Genre/Form:

554714
Electronic books.

Advancing Near Real-Time Deforestation Monitoring Via Multi-Source Remote Sensing Data, Landscape Processes, and End-User Contributions.
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504 $a Includes bibliographical references
520 $a Preserving 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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