Combining remotely sensed estimates of structure and function to improve quantification of forest productivity
| dc.contributor.author | Williams, Paige T. | en |
| dc.contributor.committeechair | Wynne, Randolph H. | en |
| dc.contributor.committeechair | Thomas, Valerie Anne | en |
| dc.contributor.committeemember | Seiler, John R. | en |
| dc.contributor.committeemember | Harding, David John | en |
| dc.contributor.department | Forest Resources and Environmental Conservation | en |
| dc.date.accessioned | 2024-09-20T08:00:20Z | en |
| dc.date.available | 2024-09-20T08:00:20Z | en |
| dc.date.issued | 2024-09-19 | en |
| dc.description.abstract | Gross primary productivity (GPP) describes the total photosynthesis (i.e., carbon fixation) for an ecosystem and is an important component of the global land carbon budget. Accurate measurements of forest carbon sequestration are crucial, as climate, soils, and management practices influence carbon uptake. Disturbances such as wildfires, diseases, insect infestations, and extreme weather events significantly impact forest health and functionality. Remote sensing is useful for monitoring global ecosystems and estimating forest vitality. High-resolution images and lidar (Light Detection and Ranging) data offer enhanced insights into forest light utilization. Combining lidar with spectral data provides a comprehensive view of forest ecosystems, integrating both structural and physiological information. This work includes three separate studies under a common objective to establish an improved understanding of forest productivity in different forest compositions using a combination of physiological function from optical imagery and morphological structure from lidar. The first study investigates the photochemical reflectance index (PRI) and how illumination affects the diurnal and vertical distributions of two managed pine stands with varying ages and row orientations, using airborne hyperspectral and lidar data. We developed a novel method to classify canopy illumination into sunlit, shaded, and mixed light areas using the hyperspectral data and a simulated panchromatic band. PRI values varied between sunlit and shaded foliage throughout the day, reflecting differences in foliage efficiency depending on light conditions and forest structure. The second study evaluates how well remotely quantified plant functional traits predict GPP across U.S. forests. Using data from NEON's airborne remote sensing and in situ flux tower measurements, the study assessed hyperspectral optical indices as physiological traits and lidar-derived products as morphological and environmental traits. By developing multiple linear regression models with separated and combined trait groups, the best prediction model combined morphological, environmental, and physiological traits with a PRESS R2 of 0.75. This underscores the importance of integrating various functional traits for accurate forest productivity predictions. The last study detects insect-induced tree mortality with separated and combined models of structure from lidar and physiology from satellite imagery. Although all models tend to overestimate tree mortality, integrating lidar data enhances predictions by 6% offering valuable structural context. A central theme of this work is that lidar-derived structural measurements were crucial in every chapter. | en |
| dc.description.abstractgeneral | Plant photosynthesis, converting sunlight and carbon dioxide to chemical energy (carbohydrates) and oxygen, is fundamental to all life on Earth. Measuring how much carbon forests capture is essential to understanding the global carbon cycle. Forests must be healthy to be productive, so forest health monitoring is also vital. Remote sensing is a tool to measure objects without physically touching the object you are measuring. From remote sensing, we can take pictures of forests and record information on how much sunlight is reflected. Remote sensing technologies like lidar use lasers to portray trees and forests in three dimensions (3D). Combining pictures with 3D shapes can provide a more comprehensive understanding of how trees store carbon. This work incorporates three separate studies using a combination of pictures and 3D data. The first study looks at the changes in light utilization of a pine plantation throughout the day and within the canopy. The second study uses a range of forest descriptors with different compositions to predict their productivity. The last study shows how imagery from space and 3D data can locate dead and live trees. The overall thrust of the study shows that incorporating 3D portrayals of the forest enhances our understanding of forest productivity. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41434 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121168 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | forest | en |
| dc.subject | remote sensing | en |
| dc.subject | productivity | en |
| dc.subject | structure | en |
| dc.subject | function | en |
| dc.title | Combining remotely sensed estimates of structure and function to improve quantification of forest productivity | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Forestry | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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