Matching Items (3)
Description
Globally, land use change is the primary driver of biodiversity loss (IPBES, 2019). Land use change due to agricultural expansion is driving bird species to the brink of extinction in the Peruvian Amazon rainforest. Agriculture is one of the primary threats to bird species in the region, and agroforestry is being pursued in some communities as a potential solution to reduce agriculture's impacts on species, as agroforestry provides improved habitat for wildlife while also enabling livelihoods for people. Understanding how anthropogenic land use choices affect imperiled species is an important prerequisite for conservation policy and practice in the region. In this thesis, I develop a spatial model for quantifying expected threat abatement from shifting agricultural land use choices towards agroforestry. I used this model explored how agricultural land use impacts imperiled bird species in the Peruvian Amazon. My approach builds on the species threat abatement and restoration (STAR) metric to make the expected consequences of reducing agricultural threats spatially explicit. I then analyzed results of applying the metric to alternative scenarios with and without agroforestry conversion. I found that agroforestry could result in up to 18.68% reduction in mean bird projected population decline. I found that converting all terrestrial agriculture in the Peruvian Amazon to agroforestry could produce a benefit of up to 83% to imperiled birds in the region in terms of improvement in Red List status. This use of the STAR metric to model alternative scenarios presents a novel usage for the STAR metric and a promising approach to understand how to address terrestrial biodiversity challenges efficiently and effectively.
ContributorsPoe, Katherine (Author) / Iacona, Gwen (Thesis advisor) / Gerber, Leah (Thesis advisor) / Mair, Louise (Committee member) / Arizona State University (Publisher)
Created2023
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects on ecosystems on both a local and global scale, making it one of the most environmentally threatening phenomena occurring today. In order to minimize deforestation in the Amazon and its consequences, it is helpful to analyze its occurrence using machine learning architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that has shown significant capability in performing semantic segmentation. It is built upon a symmetric series of downsampling and upsampling layers that propagate feature information into higher spatial resolutions, allowing for the precise identification of features on the pixel scale. Such an architecture is well-suited for identifying features in satellite imagery. In this thesis, we construct and train a U-Net to identify deforested areas in satellite imagery of the Amazon through semantic segmentation.
ContributorsGiel, Joshua (Author) / Douglas, Liam (Co-author) / Espanol, Malena (Thesis director) / Cochran, Douglas (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Sustainability (Contributor)
Created2024-05
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects
on ecosystems on both a local and global scale, making it one of the most environmentally
threatening phenomena occurring today. In order to minimize deforestation in the Ama-
zon and its consequences, it is helpful to analyze its occurrence using machine learning
architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that
has shown significant capability in performing semantic segmentation. It is built upon
a symmetric series of downsampling and upsampling layers that propagate feature infor-
mation into higher spatial resolutions, allowing for the precise identification of features
on the pixel scale. Such an architecture is well-suited for identifying features in satellite
imagery. In this thesis, we construct and train a U-Net to identify deforested areas in
satellite imagery of the Amazon through semantic segmentation.
ContributorsDouglas, Liam (Author) / Giel, Joshua (Co-author) / Espanol, Malena (Thesis director) / Cochran, Douglas (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05