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Remote sensing assessment of semi-arid forest structure changes and ecohydrological responses to thinning-based restoration practices

Belmonte, Adam Jackson (2021) Remote sensing assessment of semi-arid forest structure changes and ecohydrological responses to thinning-based restoration practices. Doctoral thesis, Northern Arizona University.

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The expansive ponderosa pine forests across the southwestern U.S. have grown significantly denser over the last century, altering the historical ecological functioning, health, and resilience of the entire ecosystem. Coupled with the ongoing threats posed from climate change, namely hotter and drier regional weather conditions, these forests are increasingly vulnerable to drought-related stress and mortality. To help combat these and other deleterious effects, landscape-scale (400+ ha) forest restoration thinning has been used to promote vegetation health and help stabilize regional ecohydrological systems. Assessing restoration-based forest structure changes and the effects of altered forest structure on surface water resources are key to improving management practices. Remote sensing methodologies and datasets offer accurate, cost-effective, and timely ways to quantify aspects of both forest structure and ecohydrological conditions across multiple spatial scales. In this dissertation, I use high-resolution remote sensing to develop and test novel methodologies for quantifying forest structure, snow cover, and soil moisture conditions in response to a restoration thinning treatment. First, I used unmanned aerial vehicle (UAV) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) and assess specific objectives of a restoration thinning project. I found that estimates of fine-scale forest structure were most accurate in low‐density conditions, with significantly degraded accuracies in high‐density conditions. Mid‐scale estimates of forest structure behaved similarly across the density gradient. Overall, I found that a majority of the prescription objectives were met in the post‐thinning conditions, demonstrating the effectiveness of UAV image data in quantifying forest structure changes from thinning treatments. Next, I use UAV multispectral imagery and SfM models to quantify snow cover dynamics and examine the effects of forest structure shading on persistent snow cover. I first develop a method with 90.2% accuracy to identify persistent snow cover using repeat UAV imagery (n = 11 dates) across the 76-ha forest. Using the SfM-derived trees (98% accuracy, n = 1,280 trees) and forest structure variables, I show that forest canopy shading was a significant driver of persistent snow cover patches (R² = 0.70). Overall, my results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence, providing insight into the importance of forest canopy shading in the amount and distribution of persistent seasonal snow cover. Finally, I use dense soil water potential time-series data across the same thinned forest site to assess soil moisture availability and persistence in response to seasonal drought and forest structure conditions. Using terrestrial lidar data, I assess how fine-scale forest structure components drive differences in the timing, magnitude, and amount of soil drying across soil depths during the seasonal drought period. Results show significant differences in soil moisture response between two abnormally dry years, across all soil depths (25, 50, and 100 cm), and from specific forest structure metrics. Taken together, these studies provide a detailed methodological assessment of the efficacy of high-resolution remote sensing datasets in quantifying forest structure changes from thinning-based restoration and impacts of forest structure on specific ecohydrological components, and importantly how forest management can be used to optimize the availability of water resources in the semi-arid ponderosa pine forests.

Item Type: Thesis (Doctoral)
Publisher’s Statement: © Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords: Remote sensing; Ponderosa pine; Southwest (U.S.); Forest health; Forest thinning
Subjects: S Agriculture > SD Forestry
NAU Depositing Author Academic Status: Student
Department/Unit: Graduate College > Theses and Dissertations
College of Engineering, Informatics, and Applied Sciences > School of Informatics, Computing, and Cyber Systems
Date Deposited: 21 Feb 2022 17:49
Last Modified: 19 May 2022 08:30
URI: https://openknowledge.nau.edu/id/eprint/5725

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