This thesis investigated multispectral detection of the invasive floating macrophyte, European Frog-bit, using Quickbird imagery and fuzzy image classification. To determine if the spectral signature of European Frog-bit were separable from other wetland vegetation, a species level land cover classification was conducted on a 6km section of the South Nation River in Ontario, Canada. Supervised and unsupervised imagery classification approaches were evaluated using the fuzzy classifiers, Fuzzy Segmentation for Object Based Image Classification (FS) and Fuzzy C-Means (FCM). Both approaches were sufficiently robust to detect European Frog-bit. User’s and producer’s accuracies for the European Frog-bit class were 81.0% and 77.9% for the FS classifier and 63.5% and 73.0% for the FCM classifier. These accuracies indicated that the spectral signature of EFB was sufficiently different to permit detection and separation from other wetland vegetation and fuzzy image classifiers were capable of detecting EFB in Quickbird imagery.

Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed “cohorts”, and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground based classifications and to extend structural cohort typing from plot to stand-levels. In both mixedwood and black spruce forest types, stand age and age related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using LiDAR were similar between boreal mixedwood and black spruce forest types; the best LiDAR-based models of cohort type relied upon combinations of tree size, size heterogeneity, and tree density related variables. The methods described here to measure, classify, and predict cohort-related structural complexity assist in translating the conceptual three cohort model to a more precise, measurement based management system. In addition, the approaches presented here to measure and classify stand structural complexity promise to significantly enhance the detail of structural information in operational forest inventories in support of a wide array of forest management and conservation applications.

Master of Science / Department of Mechanical and Nuclear Engineering / Dale E. Schinstock / The goal of this research is to develop methods and tools for generating flight plans for an unmanned aerial vehicle (UAV). A method of generating flight plans is needed to describe data collection missions, such as taking aerial photographs. The flight plans are two-dimensional and exist in a plane a fixed distance above the Earth. Since the flight areas are typically small, the Earth's curvature is not accounted for in flight plan generation. Designed to completely cover a specified field area, the plans consist of a series of line and arc segments and are described in a format that is recognized by the Piccolo autopilot used by the Kansas State University Autonomous Vehicle Systems (AVS) Lab. Grids are designed to cover the field area, and turn maneuvers are designed to ensure efficient flight plans. The flight plan generation process is broken into several parts. Once a field area is defined, path lines covering this area are calculated. Optimal turn maneuvers are calculated to smoothly connect the path lines in a continuous flight plan. Two methods of determining path line order are discussed. One method flies the lines in the order that they are arranged spatially; the other method decides line order by calculating the shortest turn maneuver to another path line. After the flight plan is generated, a text file is created in a format that is readable for the autopilot. In order to easily generate flight plans, a graphical user interface (GUI) has been created. This GUI allows a user to easily generate a flight plan without modifying any code. The flight plan generation software is used to build example flight plans for this thesis. These flight plans were flown with an UAV and test results are presented.

Master of Arts / Geography / Marcellus M. Caldas / As concerns increase over climate change, energy independence, and higher fuel prices, Brazilian sugarcane ethanol is seen as a part of a clean energy future. Brazilian sugarcane ethanol was developed with a long history of government support, and with the introduction of the flex fueled car in 2003, demand rose dramatically. These factors have helped sugarcane expand beyond its traditional regions of Brazil into the Cerrado. More recently however, private capital from both domestic and foreign companies have started investing in Brazilian agriculture and these investments have helped fuel the sugarcane expansion into the Cerrado in the last 15 years. Over 22 sugarcane mills have been constructed in the Brazilian state of Goiás, located in the heart of the Cerrado. The increased investments driving the expansion of sugarcane into the Cerrado brings numerous questions regarding its environmental and social impacts. Thus, the goal of this thesis is to understand how the structural organization of the sugarcane ethanol mills’ affects development at a municipality level in the state of Goiás, Brazil. More specifically, this thesis has two objectives; to evaluate the effects of the sugarcane mills’ influence on land use and land cover change in these municipalities; and to compare how domestic owned mills, foreign owned mills, and jointly owned mills affect socioeconomic development on the municipalities. Three municipalities were analyzed, Edéia, Caçu, and Quirinópolis. Results showed that land use and land cover change varied by municipality. The majority of Edéia’s sugarcane expansion came from lands already in agricultural use. On the other hand, Caçu’s and Quirinópolis’s sugarcane expansion came from pasture lands. However, throughout all the municipalities, sugarcane expansion over native vegetation was small. All three municipalities increased their socioeconomic development levels over the past 20 years as reported on the Human Development Index. In addition, urban survey responses revealed that the residents of Edéia perceived the sugarcane mill had made their lives better than respondents in either Quirinópolis or Caçu. However, this analysis covers only a brief period of time, and future analysis of these, and other municipalities that host sugarcane mills throughout the Cerrado will be needed.

Master of Science / Department of Clinical Sciences / Robert L. Larson / Bradley J. White / We performed research that investigated the ability of three dimensional accelerometers to classify cattle behavior and also describe the circadian patterns within that behavior. The first of three studies (validation study) tested a decision tree classification system and its ability to describe behaviors of lying, standing, and walking. Classification accuracies for lying, standing, and walking behaviors were 99.2%, 98.0%, and 67.8% respectively, with walking behavior having significantly lower accuracy (P<0.01). This study also tested the accuracy of classifying the above behaviors using different device reporting intervals, or epochs. Reporting intervals of 3, 5, and 10 seconds (s) were evaluated in their ability to describe cattle behaviors of lying, standing, and walking. Classification accuracies for the 3s, 5s, and 10s reporting interval were 98.1%, 97.7%, and 85.4% respectively, with no difference in classification accuracy of the 3s and 5s epochs (P=0.73) while the 10s epoch exhibited significantly lower overall accuracy (P<0.01). This validated accelerometer monitoring system was then implemented in two studies (Winter 2007 and Spring 2008) where the devices were used to describe behavior patterns of beef calves in a drylot production setting. Lying behavior of the cattle was analyzed and found to be significantly associated (P<0.001) with hour of the day. Calves in these studies spent most (> 55%) of the nighttime hours (2000 to 0400) involved in lying behavior and spent the least percentage of time lying (<30%) during periods of time where feed was presented at the bunk (0700 and 1700). Mean lying time was also associated with trial day (P<0.01) and most trial days (67.5%) calves spending between 45% and 55% of time lying. Variation of lying behavior was found between individuals (range 29% to 66%); however, consistency in lying behavior was found within individual calves across study periods. The accelerometer monitoring system studies presented here provide evidence these devices have utility in recording behaviors (lying, standing, and walking) of individual beef calves raised in typical production settings.

Assessment of leaf and canopy chlorophyll content provides information on plant physiological status; it is related to nitrogen content and hence, photosynthesis process, net primary productivity and carbon budget. In this study, a method is developed for the retrieval of total chlorophyll content (Chlorophyll a+b) per unit leaf and per unit ground area based on improved vegetation structural parameters which are derived using multispectral multi-angle remote sensing data. Structural characteristics such as clumping and gaps within a canopy affect its solar radiation absorption and distribution and impact its reflected radiance acquired by a sensor. One of the main challenges for the remote sensing community is to accurately estimate vegetation structural parameters, which inevitably influence the retrieval of leaf chlorophyll content. Multi-angle optical measurements provide a means to characterize the anisotropy of surface reflectance, which has been shown to contain information on vegetation structural characteristics. Hyperspectral optical measurements, on the other hand, provide a fine spectral resolution at the red-edge, a narrow spectral range between the red and near infra-red spectra, which is particularly useful for retrieving chlorophyll content. This study explores a new refined measurement concept of combining multi-angle and hyperspectral remote sensing that employs hyperspectral signals only in the vertical (nadir) direction and multispectral measurements in two additional (off-nadir) directions within two spectral bands, red and near infra-red (NIR). The refinement has been proposed in order to reduce the redundancy of hyperspectral data at more than one angle and to better retrieve the three-dimensional vegetation structural information by choosing the two most useful angles of measurements. To illustrate that hyperspectral data acquired at multiple angles exhibit redundancy, a radiative transfer model was used to generate off-nadir hyperspectral reflectances. It has been successfully demonstrated that the off-nadir hyperspectral simulations could be closely reconstructed based on the nadir hyperspectral reflectance and off-nadir multi-spectral reflectance in the red and NIR bands. This is shown using the Compact High-resolution Imaging Spectrometer (CHRIS) and Compact Airborne Spectrographic Imager (CASI) data acquired over a forested area in the Sudbury region (Ontario, Canada). Through intensive validation using field data, it is demonstrated that the combination of reflectances at two angles, the hotspot and darkspot, through the Normalized Difference between Hotspot and Darkspot (NDHD) index has the strongest response to changes in vegetation clumping, an important structural component of canopy. Clumping index (Ω) and Leaf Area Index (LAI) maps are generated based on previous algorithms as well as empirical relationships developed in this study. To retrieve chlorophyll content, inversion of the 5-Scale model is performed by developing Look-Up Tables (LUTs) that are based on the improved structural characteristics developed using multi-angle data. The generated clumping index and LAI maps are used in the LUTs to estimate leaf reflectance. Inversion of the leaf reflectance model, PROSPECT, is further employed to estimate chlorophyll content per unit leaf area. The estimated leaf chlorophyll contents are in good agreement with field-measured values. The refined measurement concept of combining hyperspectral with multispectral multi-angle data provides the opportunity for simultaneous retrieval of vegetation structural and biochemical parameters.

Doctor of Philosophy / Department of Horticulture, Forestry, and Recreation Resources / Dale J. Bremer / Jack Fry / Increasing water scarcity may result in greater irrigation restrictions for turfgrass. Drought tolerance and recovery of Kentucky bluegrasses (Poa. pratensis L.) (KBG) were evaluated during and after 88 and 60 day dry downs in 2010 and 2011, respectively, under a rainout shelter. Changes in green coverage were evaluated with digital images. Green coverage declined slowest during dry downs and increased fastest during recoveries in the cultivar ‘Apollo’, indicating it had superior drought tolerance. Electrolyte leakage, photosynthesis, and leaf water potential were evaluated in 7 KBG cultivars during and after the dry downs. Soil moisture at 5 and 20 cm was measured. There were generally no differences in physiological parameters among cultivars during or after dry down. The highest reduction in soil moisture at 5 and 20 cm was in Apollo, suggesting it had a better developed root system for mining water from the profile during drought. Weed prevention and turfgrass establishment of ‘Legacy’ buffalograss (Buchloe dactyloides [Nutt.] Engelm.) and ‘Chisholm’ zoysiagrass (Zoysia japonica Steud.) grown on turf reinforcement mats (TRM) was evaluated. ‘Chisholm’ zoysiagrass stolons grew under the TRM; as such, use of TRM for this cultivar is not practical. Buffalograss had 90% or greater coverage when established on TRM in 2010 and 65% or greater coverage in 2011; coverage was similar to that in oxadiazon-treated plots at the end of each year. ‘Legacy’ buffalograss plugs were established on TRM over plastic for 3 weeks, stored in TRM under tree shade for 7, 14, or 21 days, and evaluated for establishment after storage. In 2010, plugs on mats stored for 7 days had similar coverage to the control, but in 2011 displayed similar coverage to plugs stored on TRM for 14 or 21 day treatments. Green leaf are index (LAI) is an important indicator of turfgrass performance, but its measurement is time consuming and destructive. Measurements using hyperspectral radiometry were compared with destructive measurements of LAI. Results suggest spectral radiometry has potential to accurately predict LAI. The robustness of prediction models varied over the growing season. Finding one model to predict LAI across and entire growing season still seems unrealistic.

Master of Science / Department of Geography / Douglas Goodin / Hyperspectral data has been widely used for estimates of canopy biochemical content over the past decades. Most of these studies were conducted in forests or crops with relatively uniform canopies. Feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures remains uncertain. Spectral data at the canopy level, with mixed background noise, canopy biochemical and biophysical properties create more problems in spectral analysis than that at the leaf level. Complications of heterogeneous canopies make biochemical retrieval through remote sensing even more difficult due to more uneven spatial distribution of biochemical constituents. The objective of my research was to map canopy nitrogen content in tallgrass prairie with mixed canopies by means of hyperspectral data from in-situ and airborne measurements. Research efforts were divided into three steps: (1) the green leaf area index (LAI) retrieval, given LAI is an important parameter in scaling nitrogen content from leaves to canopies; (2) canopy nitrogen modeling from analysis of in-situ hyperspectral data; and (3) canopy nitrogen mapping based on aerial hyperspectral imagery. Research results revealed that a fine chlorophyll absorption feature in the green-yellow region at wavelengths of 562 – 600 nm was sensitive to canopy nitrogen status. Specific spectral features from the normalized spectral data by the first derivative or continuum removal in this narrow spectral region could be selected by multivariate regression for nitrogen modeling. The optimal nitrogen models with high predictive accuracy measured as low values of root-mean-square error (RMSE) were applied to the aerial hyperspectral imagery for canopy nitrogen mapping during the growth seasons from May to September. These maps would be of great value in studies on the interactions between canopy vegetation quality and grazing patterns of large herbivores in tallgrass prairie.

Master of Arts / Department of Geography / J. M. Shawn Hutchinson / Time series of normalized difference vegetation index (NDVI) data from satellite spectral measurements can be used to characterize and quantify changes in vegetation phenology and explore the role of natural and anthropogenic activities in causing those changes. Several programs and methods exist to process phenometric data from remotely-sensed imagery, including TIMESAT, which extracts seasonality parameters from time-series image data by fitting a smooth function to the series. This smoothing function, however, is dependent upon user-defined input parameter settings which have an unknown amount of influence in shaping the final phenometric estimates. To test this, a sensitivity analysis was conducted using MODIS maximum value composite NDVI time-series data acquired for Fort Riley, Kansas during the period 2001-2012. The phenometric data generated from the different input setting files were compared against that from a base scenario using Pearson and Lin’s Concordance Correlation Analyses. Findings show that small changes to parameter settings results in insignificant differences in phenometric estimates, with the exception of end of season data and growing season length. Next, a time-series analysis of the same MODIS NDVI data for Fort Riley and nearby Konza Prairie Biological Station (KPBS) was conducted to determine if significant differences existed in selected vegetation phenometrics. Phenometrics of interest were estimated using TIMESAT and based on a Savitzky-Golay filter with parameter settings found optimal in the previous study. The phenometrics start of season, end of season, length of season, maximum value, and small seasonal integral were compared using Kolmogorov-Smirnov (K-S) and showed significant differences existed for all phenometrics in the comparison of Fort Riley training areas and KPBS, as well as low- versus high-training intensity areas within Fort Riley. Fort Riley and high-intensity training areas have earlier dates for the start and end of the growing season, shorter growing season lengths, lower maximum NDVI values, and lower small seasonal integrals compared to KPBS and low-intensity training areas, respectively. Evidence was found that establishes a link between military land uses and/or land management practices and observed phenometric differences.

Urban forests provide vital services to communities and are crucial for our mental, physical and emotional well-being. Recent research has shown that many variables at a neighbourhood-level are linked to variations in urban forest quantity, however, relationships at the property-level have not been considered. The purpose of this study was to examine the relationships at property-level in four socioeconomically varied neighbourhoods in the City of Mississauga (Ontario, Canada). Percent canopy cover and tree density was calculated using information from a survey, GIS datasets and remote sensing. Regression was used to determine which property-level characteristics are related to variations in the two tree cover variables. The results show that variables dealing with residents attitudes towards trees and space constraints are commonly linked to tree cover variations. The study found differences in relationships between the two tree measures at property-level and it provides greater insight into human-urban forest relationship at the micro-scale.