Session: ASME Undergraduate Student Design Expo

Paper Number: 164870

Engineering-Driven Remote Sensing for Analyzing Transpiration Dynamics in Drought-Stressed Conifer Forests 

Despite the ecological significance of prominent coniferous forests surrounding Rocky Mountain Biological Laboratory in western Colorado, there are few comprehensive studies on these systems, particularly regarding transpiration dynamics. Their critical role in carbon sequestration, microclimate dynamics, and water resource management in the Upper Colorado River Basin render them subjects of great research interest. With that being said, sustained drought conditions throughout the region are changing energy fluxes within these forests and bringing into question their resilience in increasingly water-limited circumstances. Examining the physiological adaptations and water-use strategies among different conifer species will aid in our understanding of the ways in which forests contribute to a balanced energy budget in the face of changing climatic conditions. The specific focus of this study is on Engelmann Spruce (Picea engelmannii), Subalpine Fir (Abies lasiocarpa), and Lodgepole Pine (Pinus contorta), three dominant conifers within the Upper East River Watershed. This research aims to investigate the variation in transpiration rates among drought stressed conifers in stands with varying species composition and forest structure. The work combines stem-scale trait measurements and high-resolution aerial imagery with unmanned aerial vehicles (drones) to study transpiration dynamics at a landscape level.

The study employs unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors to capture high-resolution imagery of entire coniferous forests. The acquired data is processed using Agisoft Metashape for orthomosaic generation and analyzed via custom algorithms in RStudio to segment individual tree crowns and extract thermal data. Radiometric temperature correction techniques allow for indirect transpiration measurement of said crowns, while GIS-based spatial analyses correlate transpiration rates with environmental and structural factors to create comprehensive data sets for individual trees. Multiple linear regression models are used to quantify the relationships between transpiration and variables such as snow persistence, slope, canopy height, and understory cover. Preliminary findings indicate that transpiration rates vary significantly among species and are influenced by both environmental and structural covariates. Lodgepole Pine demonstrates heightened sensitivity to canopy height and landscape slope, suggesting superior drought-adaptive mechanisms, while Engelmann Spruce and Subalpine Fir exhibit lower transpiration rates and a more muted response to external factors, potentially indicating vulnerability to prolonged drought. In any case, the nuanced responses of conifers underscore the intricate interplay between species traits, physiological adaptations, and forest structure and composition in determining transpiration dynamics within forested landscapes. Additionally, the results highlight the research potential of UAV-based remote sensing applications for landscape/catchment-scale ecological monitoring. This work illustrates an intersection of engineering applications and environmental science, integrating remote sensing, computational modeling, and statistical analyses to provide an effective methodology for accurately assessing forest characteristics. As such, the interdisciplinary approach enhances predictive capabilities in climate impact studies and informs forest management strategies. Future work will explore the integration of machine learning techniques for enhanced predictive modeling (for tree species identification, environmental covariate determination, etc.) furthering the application of engineering methodologies in ecological studies.

Presenting Author: Benjamin Golla University of Tulsa

Presenting Author Biography: Benjamin Golla is a mechanical engineering student with an environmental focus at The University of Tulsa. His professional interests lie in integrating engineering principles and applications into ecological and sustainability challenges. His latest research integrates said engineering methodologies with ecological studies, leveraging UAV-based remote sensing applications to investigate ecological phenomena. He is passionate about interdisciplinary problem-solving and advancing technologies that support environmental resilience and resource management.

Authors:

Benjamin Golla University of Tulsa