Theses and Dissertations from UMD
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Item A 20-YEAR CLIMATOLOGY OF GLOBAL ATMOSPHERIC METHANE FROM HYPERSPECTRAL THERMAL INFRARED SOUNDERS WITH SOME APPLICATIONS(2022) Zhou, Lihang; Warner, Juying; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Atmospheric Methane (CH4) is the second most important greenhouse gas after carbon dioxide (CO2), and accounts for approximately 20% of the global warming produced by all well-mixed greenhouse gases. Thus, its spatiotemporal distributions and relevant long-term trends are critical to understanding the sources, sinks, and global budget of atmospheric composition, as well as the associated climate impacts. The current suite of hyperspectral thermal infrared sounders has provided continuous global methane data records since 2002, starting with the Atmospheric Infrared Sounder (AIRS) onboard the NASA EOS/Aqua satellite launched on 2 May 2002. The Cross-track Infrared Sounder (CrIS) was launched onboard the Suomi National Polar Orbiting Partnership (SNPP) on 28 October 2011 and then on NOAA-20 on 18 November 2017. The Infrared Atmospheric Sounding Interferometer (IASI) was launched onboard the EUMETSAT MetOp-A on 19 October 2006, followed by MetOp-B on 17 September 2012, then Metop-C on 7 November 2018. In this study, nearly two decades of global CH4 concentrations retrieved from the AIRS and CrIS sensors were analyzed. Results indicate that the global mid-upper tropospheric CH4 concentrations (centered around 400 hPa) increased significantly from 2003 to 2020, i.e., with an annual average of ~1754 ppbv in 2003 and ~1839 ppbv in 2020. The total increase is approximately 85 ppbv representing a +4.8% change in 18 years. More importantly, the rate of increase was derived using satellite measurements and shown to be consistent with the rate of increase previously reported only from in-situ observational measurements. It further confirmed that there was a steady increase starting in 2007 that became stronger since 2014, as also reported from the in-situ observations. In addition, comparisons of the methane retrieved from the AIRS and CrIS against in situ measurements from NOAA Global Monitoring Laboratory (GML) were conducted. One of the key findings of this comparative study is that there are phase shifts in the seasonal cycles between satellite thermal infrared measurements and ground measurements, especially in the middle to high latitudes in the northern hemisphere. Through this, an issue common in the hyperspectral thermal sensor retrievals were discovered that was unknown previously and offered potential solutions. We also conducted research on some applications of the retrieval products in monitoring the changes of CH4 over the selected regions (the Arctic and South America). Detailed analyses based on local geographic changes related to CH4 concentration increases were discussed. The results of this study concluded that while the atmospheric CH4 concentration over the Arctic region has been increasing since the early 2000s, there were no catastrophic sudden jumps during the period of 2008-2012, as indicated by the earlier studies using pre-validated retrieval products. From our study of CH4 climatology using hyperspectral infrared sounders, it has been proved that the CH4 from hyperspectral sounders provide valuable information on CH4 for the mid-upper troposphere and lower stratosphere. Future approaches are suggested that include: 1) Utilizing extended data records for CH4 monitoring using AIRS, CrIS, and other potential new generation hyperspectral infrared sensors; 2). Improving the algorithms for trace gas retrievals; and 3). Enhancing the capacity to detect CH4 changes and anomalies with radiance signals from hyperspectral infrared sounders.Item MAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSION(2011) Swatantran, Anuradha; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation explored the combined use of lidar and other remote sensing data for improved forest structure and habitat mapping. The objectives were to quantify aboveground biomass and canopy dynamics and map habitat characteristics with lidar and /or fusion approaches. Structural metrics from lidar and spectral characteristics from hyperspectral data were combined for improving biomass estimates in the Sierra Nevada, California. Addition of hyperspectral metrics only marginally improved biomass estimates from lidar, however, predictions from lidar after species stratification of field data improved by 12%. Spatial predictions from lidar after species stratification of hyperspectral data also had lower errors suggesting this could be viable method for mapping biomass at landscape level. A combined analysis of the two datasets further showed that fusion could have considerably more value in understanding ecosystem and habitat characteristics. The second objective was to quantify canopy height and biomass changes in in the Sierra Nevada using lidar data acquired in 1999 and 2008. Direct change detection showed overall statistically significant positive height change at footprint level (ΔRH100 = 0.69 m, +/- 7.94 m). Across the landscape, ~20 % of height and biomass changes were significant with more than 60% being positive, suggesting regeneration from past disturbances and a small net carbon sink. This study added further evidence to the capabilities of waveform lidar in mapping canopy dynamics while highlighting the need for error analysis and rigorous field validation Lastly, fusion applications for habitat mapping were tested with radar, lidar and multispectral data in the Hubbard Brook Experimental Forest, New Hampshire. A suite of metrics from each dataset was used to predict multi-year presence for eight migratory songbirds with data mining methods. Results showed that fusion improved predictions for all datasets, with more than 25% improvement from radar alone. Spatial predictions from fusion were also consistent with known habitat preferences for the birds demonstrating the potential of multi- sensor fusion in mapping habitat characteristics. The main contribution of this research was an improved understanding of lidar and multi-sensor fusion approaches for applications in carbon science and habitat studies.Item THE GENERATION AND APPLICATIONS OF A SPECTRALLY RESOLVED INFRARED RADIANCE CLIMATOLOGY DERIVED FROM THE ATMOSPHERIC INFRARED SOUNDER(2009) Goldberg, Mitchell David; Kalnay, Eugenia; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is growing consensus that persistent and increasing anthropogenic emissions, since the beginning of the industrial revolution in the 19th century, are increasing atmospheric temperatures, increasing sea levels, melting ice caps and glaciers, increasing the occurrence of severe weather, and causing regional shifts in precipitation patterns. Changes in these parameters or occurrences are responses to changes in climate forcing terms, notably greenhouse gases. The NASA Atmospheric InfraRed Sounder (AIRS), launched in May of 2002, is the first high spectral resolution infrared sounder with nearly complete global coverage on a daily basis. High spectral resolution in the infrared provides sensitivity to nearly all climate forcings, responses and feedbacks. The AIRS radiances are sensitive to changes in carbon dioxide, methane, carbon monoxide, ozone, water vapor, temperature, clouds, aerosols, and surface characteristics. This study uses the raw AIRS data to generate the first ever spectrally resolved infrared radiance (SRIR) dataset (2002- 2006) for monitoring changes in atmospheric temperature and constituents and for assessing the accuracy of climate and weather model analyses and forecasts. The SRIR dataset is a very powerful tool. Spectral signatures derived from the dataset confirmed the largest depletion of ozone over the Arctic in 2005, and also verified that the European Center for Medium Range Weather (ECMWF) model analysis water vapor fields are significantly more accurate than the analyses of the National Centers for Environmental Prediction (NCEP). The NCEP moisture fields are generally 20% more moist than those from ECMWF. This research included computations of radiances from NCEP and ECMWF atmospheric states and compared the calculated radiances with those obtained from the SRIR dataset. Comparisons showed very good agreement between the SRIR data and ECMWF simulated radiances, while the agreement with NCEP values was rather poor. Interannual differences of radiances computed from ECMWF analyses were nearly identical to those derived from the SRIR dataset, while the corresponding NCEP interannual differences were in poorer agreement. However, further comparisons with the SRIR dataset in 2006 found degradation in the ECMWF upper tropospheric water vapor fields due to an operational change in ECMWF assimilation procedures. This unexpected result demonstrates the importance of continuous routine monitoring. The SRIR climatology will be extended into the future using AIRS and other high spectral resolution sounders.Item Detection of Fecal Contamination on Cantaloupes and Strawberries Using Hyperspectral Fluorescence Imagery(2006-05-08) Vargas, Angela Maria; Tao, Yang; Kim, Moon; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fluorescence methods are widely used for investigation of biological materials, and in recent years have also been used to monitor food quality and safety. In this research, fluorescence imaging techniques for detecting fecal contamination on cantaloupes and strawberries were evaluated. Fluorescence images at emission peaks were examined for fecal classification. These images were subjected to further analysis utilizing band ratios and principal component analysis. Two-band ratio images and principal component images, compared to the single-band images, enhanced the contrast between the feces-contaminated spots and untreated sample surfaces. The images exhibited useful results for contamination detection, however, false positives resulting from natural color variation on strawberry surfaces present a problem throughout the methods. This study confirmed the capability of hyperspectral fluorescence imaging in detecting fecal matter on cantaloupes and strawberries and the potential for this method to be used for developing on-line applications.