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Theses and Dissertations

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Browsing Theses and Dissertations by Subject "biomass"

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    Evaluation of UAS flight parameters for rapid monitoring of forest characteristics
    (Colorado State University. Libraries, 2020) Swayze, Neal, author; Tinkham, Wade T., advisor; Vogeler, Jody, committee member; Hudak, Andrew, committee member
    Forest managers are increasingly turning to finer spatial and temporal resolution data for monitoring forest structure in a rapidly changing world. Traditionally utilized networks of field plots for inventorying forest resources require significant time and financial investments; in response to this, remote sensing techniques have been investigated for providing inventory data across large extents. These methods, including light detection and ranging (LiDAR), require significant financial investment that limits the frequency of repeated surveys. Unmanned Aerial Systems (UAS) have emerged as potential alternatives for generating fine spatial and temporal resolution 2D and 3D data for modeling forest structure. The use of Structure from Motion (SfM) photogrammetry has made it possible to use UAS to collect aerial images and generate point clouds that can be used to model vertical forest structure information in a cost-effective way. Recent research has indicated that UAS-derived SfM point clouds are comparable to LiDAR point clouds for forest structure characterization through both area-based and individual tree observations. However, substantial knowledge gaps exist regarding the influence of UAS flight parameters on SfM-derived forest attributes. This thesis presents two studies to address these knowledge gaps. Specifically, Chapter 1 investigates the influence of UAS altitude and flight speed on modeling aboveground forest biomass through an area-based approach and Chapter 2 evaluates the influence of UAS altitude, camera angle, and flight pattern on extracted tree level and summarized plot and stand level attributes. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling, with UAS predictions increasingly outperforming (2-24% increased variance explained) contemporary LiDAR strategies as acquisition altitude increased from 80-120 m. When monitored at the individual tree level, UAS acquisitions conducted using a combination of crosshatch flight paths and off-nadir camera angles (20-30°) maximized tree detection rates (F-score of 0.77), correlations between stem mapped and extracted tree heights and DBHs (0.995 and 0.910, respectively), and estimates of stand and plot level basal area per hectare and TPH. These results indicate that UAS can be utilized to accurately summarize tree, plot, and stand level forest structure to assist in monitoring and planning of management prescriptions.
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    ItemOpen Access
    Mapping Tamarix: new techniques for field measurements, spatial modeling and remote sensing
    (Colorado State University. Libraries, 2009) Evangelista, Paul H., author; Romme, William, advisor; Stohlgren, Thomas, advisor
    Native riparian ecosystems throughout the southwestern United States are being altered by the rapid invasion of Tamarix species, commonly known as tamarisk. The effects that tamarisk has on ecosystem processes have been poorly quantified largely due to inadequate survey methods. I tested new approaches for field measurements, spatial models and remote sensing to improve our ability measure and to map tamarisk occurrence, and provide new methods that will assist in management and control efforts. Examining allometric relationships between basal cover and height measurements collected in the field, I was able to produce several models to accurately estimate aboveground biomass. The best two models were explained 97% of the variance (R 2 = 0.97). Next, I tested five commonly used predictive spatial models to identify which methods performed best for tamarisk using different types of data collected in the field. Most spatial models performed well for tamarisk, with logistic regression performing best with an Area Under the receiver-operating characteristic Curve (AUC) of 0.89 and overall accuracy of 85%. The results of this study also suggested that models may not perform equally with different invasive species, and that results may be influenced by species traits and their interaction with environmental factors. Lastly, I tested several approaches to improve the ability to remotely sense tamarisk occurrence. Using Landsat7 ETM+ satellite scenes and derived vegetation indices for six different months of the growing season, I examined their ability to detect tamarisk individually (single-scene analyses) and collectively (time-series). My results showed that time-series analyses were best suited to distinguish tamarisk from other vegetation and landscape features (AUC = 0.96, overall accuracy = 90%). June, August and September were the best months to detect unique phenological attributes that are likely related to the species' extended growing season and green-up during peak growing months. These studies demonstrate that new techniques can further our understanding of tamarisk's impacts on ecosystem processes, predict potential distribution and new invasions, and improve our ability to detect occurrence using remote sensing techniques. Collectively, the results of my studies may increase our ability to map tamarisk distributions and better quantify its impacts over multiple spatial and temporal scales.