Browsing by Author "Davenport, Frances, committee member"

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Open Access
Comparative analysis of remote sensing platforms for assessing maize crop biophysical characteristics and evapotranspiration estimation
(Colorado State University. Libraries, 2024) Al-Majali, Zaid, author; Chávez, José L., advisor; Davenport, Frances, committee member; O'Connel, Jessica, committee member
The rapid growth in population, climate variability, and decreasing water resources necessitate innovative agricultural practices to ensure food security and resource conservation. This study investigates the effectiveness of various multispectral imagery from remote sensing (RS) platforms (such as Unmanned Aircraft Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5) in the appropriate estimation of crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aimed at evaluating the accuracy of vegetation indices (VIs) derived from different sources of RS data in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional cover (Fc), as well as the ETa. Field experiments were conducted at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, in 2022. Different irrigation strategies were used to assess the maize's water use response. Surface reflectance data was collected using the MSR sensor, and observed LAI, Hc, and Fc values served as ground truth for validating remote sensing estimates. The study applied various statistical analyses to compare the performance of different remote sensing platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than other satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of remote sensing models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced remote sensing technologies in enhancing precision agriculture practices and optimizing water resource management.
Open Access
Novel applications of data-driven approaches for understanding the impacts of household energy interventions
(Colorado State University. Libraries, 2025) Brehmer, Collin, author; Carter, Ellison, advisor; Davenport, Frances, committee member; Keller, Kayleigh, committee member; Sharvelle, Sybil, committee member
Air pollution from household solid fuel combustion is associated with premature death, disease, and radiative climate forcing. Beginning in 2015, the Chinese government implemented the Clean Heating Policy in Northern China (CHP) with the goal to transition 70% of homes in the Beijing region from coal-based space heating to natural gas or electric-powered space heating. Studies of the impact of the CHP on air pollution and the potential mechanisms of action are limited. The continued use of a secondary solid fuel or heating device after the primary solid fuel heating stove is replaced with a cleaner alternative could weaken the impacts of the effort to replace the primary solid fuel stove. In Chapter 1, we identified heating events from biomass kang stoves as a proxy for stove use using a combination of manually labeled data and XGBoost modeling. We showed that biomass kang stove usage did not change because of the CHP and agreed with self-reported measures of heating duration. Our results demonstrated the capability of XGBoost to identify stove use events when trained on manually labeled event data and provided evidence that self-reported measures of stove use may be sufficient for understanding how secondary stove use changes as a result of a household energy intervention. Fine particulate matter air pollution (PM2.5) is of particular interest when evaluating household energy transitions since it is a product of incomplete combustion and is related to several health outcomes. We evaluated the impacts of the CHP on seasonal indoor, seasonal outdoor, and 24-hr personal PM2.5 exposure in 50 villages, 300 homes, and 500 participants during three years over a four-year period. The CHP had high uptake, with a significant decrease in coal usage in treated groups. We also observed a significant reduction in seasonal average indoor PM2.5 (22.2 [4.2, 40.3] µg/m3). Seasonal outdoor and 24-hr personal PM2.5 exposure did decrease over time but the decrease could not be attributed to the CHP due to similar decreases in treated and untreated groups. Our study suggests that the CHP yielded promising results in reducing indoor PM2.5 and provided valuable insights for household energy transitions worldwide. Given that most household energy interventions target one source of air pollution, using a mixture of sources as an outcome, like PM2.5, when only one of the sources of air pollution is targeted by the policy can make it hard to disentangle the effects of the policy if the variability in the non-targeted sources is high. Chapter 3 identified sources and their contributions to outdoor and personal PM2.5 exposure using chemical analysis and source apportionment. We used the concentration of the coal-containing source in outdoor and personal exposure measurements as the outcome in policy analysis models and compared the findings to the models where total PM2.5 was the outcome. We found a significant reduction in personal exposure to the coal containing source (-7.75 [-13.4, -2.14] µg m-3), which contrasts with our findings that the CHP had no impact on personal exposure to total PM2.5. This work demonstrates how additional granularity in the air pollution outcome can serve as a better outcome than a mixture of sources.