Academic literature on the topic 'Geographic information science and geodesy|Agriculture|Remote sensing'

<p> Remotely sensed data has been discussed as a possible alternative to the standard precision agriculture systems of combine-mounted yield monitors because of the burden, cost, end of season use, and inherent errors that are associated with these systems. Due to the potential quantitative use of remote sensing in precision agriculture, the primary focus of this study was to test the relationship between multitemporal/multispectral digital aerial imagery with corn (<i>Zea mays</i> L.) and soybean (<i>Glycine max </i> L.) yield. Digital aerial imagery was gathered on nine different dates throughout the 2015 growing season from two fields (one corn and one soybean) located on a farm in Story County, Iowa. To begin assessing this relationship, the digital aerial imagery was radiometrically calibrated. The radiometric calibration process used calibration tarps with known reflectance values (3, 6, 12, 22, 44, and 56 percent). The calibrated imagery was then used to calculate and output 12 different vegetation indices (VIs) and three calibrated wavebands (red, green, and near-infrared). </p><p> Next, the calibrated VIs and wavebands from the 2015 growing season were used to examine their relationship with the corn and soybean yield data collected from a combine yield monitor system. This relationship between multitemporal/multispectral digital aerial imagery with corn and soybean yield was investigated with principal component analysis and spatial modeling techniques. The results from spatial modeling of corn revealed that VIs utilizing the green waveband performed strongly. VIs such as, chlorophyll index-green, chlorophyll vegetation index, and green normalized difference vegetation index accounted for 81.6, 83.0, and 82.4 percent of the yield variability, respectively. Strong modeling relationships were also found in soybean using just the near-infrared waveband or VIs that utilized the near-infrared waveband. The near-infrared waveband captured 89.1 percent of the yield variation, while VIs such as, difference vegetation index, triangular vegetation index, soil adjusted vegetation index, and optimized soil adjusted vegetation index accounted for 87.3, 87.3, 83.9, and 83.8 percent of soybean yield variability, respectively. The temporal assessment of the remotely sensed data also identified certain VIs and wavebands that captured pivotal growth stages for detecting potential yield limiting factors. These specific growth stages varied for different VIs and wavebands for both corn and soybean. Overall, the results from this study identified that mid-to-late vegetative growth stages (prior to tasseling) and late-season reproductive stages were important parameters that provided unique information in the modeling of corn yield variability, while the later reproductive stages (just prior to senescence) were essential to capturing soybean yield variability. </p><p> Lastly, this research produced corn and soybean yield maps from the digital aerial imagery. The digital aerial imagery yield maps were then compared with maps that used kriging interpolation of the combine yield monitor data gathered from the same corn and soybean fields. The results indicated that both corn and soybean yield maps produced with multitemporal/multispectral digital aerial imagery were comparable with a standard method of kriging interpolation from yield monitor data.</p><p>

<p> Although savanna ecosystems cover approximately 20 % of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. This study explored the applicability of a past spaceborne Lidar mission and the potential of future missions to estimate canopy height and carbon storage in these biomes. </p><p> The research used data from two Oak savannas in California, USA: the Tejon Ranch Conservancy in Kern County and the Tonzi Ranch in Santa Clara County. In the first paper we used non-parametric regression techniques to estimate canopy height from waveform parameters derived from the Ice Cloud and land Elevation Satellite&rsquo;s Geoscience Laser Altimeter System (ICESat-GLAS) data. Merely adopting the methods derived for forests did not produce adequate results but the modeling was significantly improved by incorporating canopy cover information and interaction terms to address the high structural heterogeneity inherent to savannas. Paper 2 explored the relationship between canopy height and aboveground biomass. To accomplish this we developed generalized models using the classical least squares regression modeling approach to relate canopy height to above ground woody biomass and then employed Hierarchical Bayesian Analysis (HBA) to explore the implications of using generalized instead of species composition-specific models. Models that incorporated canopy cover proxies performed better than those that did not. Although the model parameters indicated interspecific variability, the distribution of the posterior densities of the differences between composition level and global level parameter values showed a high support for the use of global parameters, suggesting that these canopy height-biomass models are universally (large scale) applicable. </p><p> As the spatial coverage of spaceborne lidar will remain limited for the immediate future, our objective in paper 3 was to explore the best means of extrapolating plot level biomass into wall-to-wall maps that provide more ecological information. We evaluated the utility of three spatial modeling approaches to address this problem: deterministic methods, geostatistical methods and an image segmentation approach. Overall, the mean pixel biomass estimated by the 3 approaches did not differ significantly but the output maps showed marked differences in the estimation precision and ability of each model to mimic the primary variable&rsquo;s trend across the landscape. The results emphasized the need for future satellite lidar missions to consider increasing the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary. </p><p> We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard to simulate ICESat-2 data. We segmented each transect into different block sizes and calculated canopy top and mean ground elevation based on the structure of the histogram of the block&rsquo;s aggregated photons. Our algorithm was able to compute canopy height and generate visually meaningful vegetation profiles at MABEL&rsquo;s signal and noise levels but a simulation of the expected performance of ICESat-2 by adjusting MABEL data's detected number of signal and noise photons to that predicted using ATLAS instrument model design cases indicated that signal photons will be substantially lower. The lower data resolution reduces canopy height estimation precision especially in areas of low density vegetation cover. </p><p> Given the clear difficulties in processing simulated ATLAS data, it appears unlikely that it will provide the kind of data required for mapping of the biophysical properties of savanna vegetation. Rather, resources are better concentrated on preparing for the Global Ecosystem Dynamics Investigation (GEDI) mission, a waveform lidar mission scheduled to launch by the end of this decade. In addition to the full waveform technique, GEDI will collect data from 25 m diameter contiguous footprints with a high across track density, a requirement that we identified as critically necessary in paper 3. (Abstract shortened by UMI.)</p>

<p> The Truckee Meadows (Reno, NV) sits in a tectonically complex area of western Nevada, where Walker Lane-style transtension is dominant throughout the region. A new Light Detection and Ranging (LiDAR) study focuses on the Truckee Meadows region of western Nevada, including the Reno/Sparks metropolitan area in Washoe County. We use the airborne LiDAR imagery (1485 sq. km) to create high quality, bare-earth topographic maps that were previously unattainable in vegetated, populated or alpine terrain. This approach gives us an opportunity to improve fault maps that may be outdated or incomplete in the area. Here we provide LiDAR imagery of a large section of Washoe County and an updated fault map of the greater Truckee Meadows region. </p><p> We also use this new LiDAR survey of the Truckee Meadows and nearby basins to constrain geometry, length, distribution, and slip rates along faults imaged by this new dataset. Estimated slip rates are compared to those derived from a geodetic block model constrained by Global Positioning Station (GPS) data to test for consistency. GPS station data and geologic mapping show that both east-west oriented extension and northwest-oriented right-lateral strike slip accommodate transtension as a backdrop for tectonics studies of region, with some northeast-oriented left-lateral strike slip. This study aims to better understand how this transtension is partitioned along remapped faults and newly identified structures in this urban setting, as the framework for strain accommodation in this area remains poorly understood. </p><p> Faults with normal offset were measured along strike using bare-earth LiDAR returns to determine the amount of vertical separation across geomorphic surfaces, and then converted to extension assuming a fault dip of 60 (+/-10) degrees. Since the primary geomorphic surfaces in this region are the result of Sierra Nevadan glacial outwash episodes, we use previously published geologic maps to link each surface to an associated date. When integrated across several basin perpendicular transects within the Mt. Rose pediment, we calculate a total extension rate of 0.87 (+0.40/-0.48) mm/yr for the southern Truckee Meadows basin. Integrated slip rates from fault scarp offsets are within the bounds of 1.23 (+/-0.70) mm/yr suggested by geodetic modeling. Block modeling highlights that north-striking faults primarily accommodate east-west extension, and so northwest-striking faults and/or block rotations must accommodate the northwest-directed shear seen in GPS velocities. This trend is bolstered by the discovery of a new northwest-oriented fault on Peavine Mountain 6 km east of the Mogul (2008) seismicity trend. Our study provides further evidence that the Truckee Meadows sits at a critical transition from north-striking normal faults in the southern part of the basin to northwest-oriented strike-slip faults to the north, an observation that mimics regional tectonics and geomorphology of the adjacent Lake Tahoe/Truckee system to the west.</p>

<p> Light Detection and Ranging (LiDAR) technology combined with high-resolution differential Global Positioning Systems (dGPS) provide the ability to measure coastal elevation with high precision. This study investigates the use of LiDAR data and GIS to conduct time-series analyses of coastal sediment volume shifts during the 2006-2007 El Ni&ntilde;o winter, Summer of 2007 and following 2007-2008 La Ni&ntilde;a winter in the Oceanside Littoral Cell (OLC). The OLC, located in Southern California, spans from Dana Point to La Jolla and includes over 84 km of coastline. The ability to quantify sediment volume changes contributes to the scientific understanding of the role El Ni&ntilde;o storms play in the OLC sand budget. This study provides a method to analyze LiDAR data to evaluate coastal geomorphologic changes over time. Additionally, identifying specific areas of coastal beach erosion associated with historical El Ni&ntilde;o events can aid beach managers, planners, and scientists in protecting the valuable coastline. LiDAR datasets were prepared and formatted which included ground classifying millions of elevation points. Formatted datasets were inputted into an Empirical Bayesian Kriging (EBK) model, creating high-resolution, 1-meter grid cell, Digital Elevation Models (DEMs). The EBK model also incorporated uncertainty into the workflow by producing prediction error surfaces. LiDAR-derived DEMs were used to calculate sediment volume changes through a technique called DEM differencing. Results were visualized through a series of maps and tables. Overall results show that there was a higher rate of beach sediment erosion during the 2006-2007 El Ni&ntilde;o winter than the 2007-2008 La Ni&ntilde;a winter. Sediment accretion was evident during the intermediary Summer of 2007. Future applications of this study include incorporating bathymetric datasets to understand near-shore sediment transport, evaluating sediment contribution through cliff erosion, and conducting decadal scale studies to evaluate long-term trends with sea level rise scenarios. </p>

<p>Wetland monitoring and preservation efforts have the potential to be enhanced with advanced remote sensing acquisition and digital image analysis approaches. Progress in the development and utilization of Unmanned Aerial Systems (UAS) and Unmanned Aerial Vehicles (UAV) as remote sensing platforms has offered significant spatial and temporal advantages over traditional aerial and orbital remote sensing platforms. Photogrammetric approaches to generate high spatial resolution orthophotos of UAV acquired imagery along with the UAV?s low-cost and temporally flexible characteristics are explored. A comparative analysis of different spectral based land cover maps derived from imagery captured using UAV, satellite, and airplane platforms provide an assessment of the Huntington Beach Wetlands. This research presents a UAS remote sensing methodology encompassing data collection, image processing, and analysis in constructing spectral based land cover maps to augment the efforts of the Huntington Beach Wetlands Conservancy by assessing ecological and temporal changes at the Huntington Beach Wetlands.

xi, 54 p. : ill. (some col.)<br>On the Middle Fork John Day River (MFJD), a low gradient, meandering river in eastern Oregon, restoration includes engineered log structures intended to increase in-stream complexity and habitat diversity. Effects of log structures on riverbed topography can be captured through repeat topographic surveys, digital elevation model (DEM) of differencing (DoD), and aerial imagery. This study evaluates the (1) potential for remote sensing analysis, (2) effect of survey point density on DEMs, and (3) application of DoDs, in monitoring riverbed changes in the MFJD. An average point spacing and density finer than 0.50m and 1.25pts/m<super>2</super> captures riverbed complexities. Although elevation changes were expected to be minimal, DoDs revealed -0.9 to 0.5m elevation changes associated with log structure designs. Incorporating numerical thresholds into future monitoring survey methods will improve the modeling of MFJD riverbed surfaces. Monitoring riverbed changes through DoDs can inform improvements to future restoration design and the effectiveness of log structures.<br>Committee in charge: Patricia McDowell, Chairperson; Andrew Marcus, Member

Master of Science<br>Department of Geography<br>Douglas G. Goodin<br>Volyn Oblast in Western Ukraine has experienced substantial land use/land cover change over the last 25 years as a result of a change in political systems. Remote sensing provides a framework to quantify this change without extensive field work or historical land cover records. In this study, land change is quantified utilizing a post-classification change detection technique comparing Landsat imagery from 1986-2011(Post-Soviet era began 1991). A variety of remote sensing classification methods are explored to take advantage of spectral and spatial variation within this complex study area, and a hybrid scheme is ultimately utilized. Land cover from the CORINE classification scheme is then converted to the EUNIS habitat classification scheme to analyze how land cover change has affected habitat fragmentation. I found large scale agricultural abandonment, increases in forested areas, shifts towards smaller scale farming practices, shifts towards mixed forest structures, and increases in fragmentation of both forest and agricultural habitat types. These changes could have several positive and negative on biodiversity, ecosystems, and human well-being.