Analysis of LiDAR point data and derived elevation models for mapping and characterizing bouldery landforms

This thesis assessed the viability of using LiDAR-derived elevation data in accurately mapping and characterizing bouldery geomorphic features in a study area in the Allegheny Mountains. This study showed that the ground returns classification process conducted by the Canaan Valley Institute (CVI) for their property using the TerraScan software generally removed 5 to 10 m scale local topographic variability and bouldery landforms in creating the CVI classified ground returns data. In open areas, last returns elevation and intensity data were successfully used in this study to map bouldery landforms in the study area. Identifying and describing boulders under a tree canopy required a relatively reliable ground classification of LiDAR points. This study\u27s classifications conducted within Prologic LiDAR Explorer provided a more useful representation than the CVI classified ground data for mapping bouldery landforms and generalized rugged topography. Index overlay for likelihood of presence of bouldery landforms using supervised classified aerial imagery and LiDAR-derived parameters in a raster environment was explored as an alternative means of detecting bouldery landforms because hillshade imagery derived from CVI classified ground data were inadequate for mapping bouldery landforms

Remote Sensing for Monitoring the Mountaintop Mining Landscape: Applications for Land Cover Mapping at the Individual Mine Complex Scale

The aim of this dissertation was to investigate the potential for mapping land cover associated with mountaintop mining in Southern West Virginia using high spatial resolution aerial- and satellite-based multispectral imagery, as well as light detection and ranging (LiDAR) elevation data and terrain derivatives. The following research themes were explored: comparing aerial- and satellite-based imagery, combining data sets of multiple dates and types, incorporating measures of texture, using nonparametric, machine learning classification algorithms, and employing a geographical object-based image analysis (GEOBIA) framework. This research is presented as four interrelated manuscripts.;In a comparison of aerial National Agriculture Imagery Program (NAIP) orthophotography and satellite-based RapidEye data, the aerial imagery was found to provide statistically less accurate classifications of land cover. These lower accuracies are most likely due to inconsistent viewing geometry and radiometric normalization associated with the aerial imagery. Nevertheless, NAIP orthophotography has many characteristics that make it useful for surface mine mapping and monitoring, including its availability for multiple years, a general lack of cloud cover, contiguous coverage of large areas, ease of availability, and low cost. The lower accuracies of the NAIP classifications were somewhat remediated by decreasing the spatial resolution and reducing the number of classes mapped.;Combining LiDAR with multispectral imagery statistically improved the classification of mining and mine reclamation land cover in comparison to only using multispectral data for both pixel-based and GEOBIA classification. This suggests that the reduced spectral resolution of high spatial resolution data can be combated by incorporating data from another sensor.;Generally, the support vector machines (SVM) algorithm provided higher classification accuracies in comparison to random forests (RF) and boosted classification and regression trees (CART) for both pixel-based and GEOBIA classification. It also outperformed k-nearest neighbor, the algorithm commonly used for GEOBIA classification. However, optimizing user-defined parameters for the SVM algorithm tends to be more complex in comparison to the other algorithms. In particular, RF has fewer parameters, and the program seems robust regarding the parameter settings. RF also offers measures to assess model performance, such as estimates of variable importance and overall accuracy.;Textural measures were found to be of marginal value for pixel-based classification. For GEOBIA, neither measures of texture nor object-specific geometry improved the classification accuracy. Notably, the incorporation of additional information from LiDAR provided a greater improvement in classification accuracy then deriving complex textural and geometric measures.;Pre- and post-mining terrain data classified using GEOBIA and machine learning algorithms resulted in significantly more accurate differentiation of mine-reclaimed and non-mining grasslands than was possible with spectral data. The combination of pre- and post-mining terrain data or just pre-mining data generally outperformed post-mining data. Elevation change data were shown to be of particular value, as were terrain shape parameters. GEOBIA was a valuable tool for combining data collected using different sensors and gridded at variable cell sizes, and machine learning algorithms were particularly useful for incorporating the ancillary data derived from the digital elevation models (DEMs), since these most likely would not have met the basic assumptions of multivariate normality required for parametric classifiers.;Collectively, this research suggests that high spatial resolution remotely sensed data are valuable for mapping and monitoring surface mining and mine reclamation, especially when elevation and spectral data are combined. Machine learning algorithms and GEOBIA are useful for integrating such diverse data

Predicting Post-Fire Change in West Virginia, USA from Remotely-Sensed Data

Prescribed burning is used in West Virginia, USA to return the important disturbance process of fire to oak and oak-pine forests. Species composition and structure are often the main goals for re-establishing fire with less emphasis on fuel reduction or reducing catastrophic wildfire. In planning prescribed fires land managers could benefit from the ability to predict mortality to overstory trees. In this study, wildfires and prescribed fires in West Virginia were examined to determine if specific landscape and terrain characteristics were associated with patches of high/moderate post-fire change. Using the ensemble machine learning approach of Random Forest, we determined that linear aspect was the most important variable associated with high/moderate post-fire change patches, followed by hillshade, aspect as class, heat load index, slope/aspect ratio (sine transformed), average roughness, and slope in degrees. These findings were then applied to a statewide spatial model for predicting post-fire change. Our results will help land managers contemplating the use of prescribed fire to spatially target landscape planning and restoration sites and better estimate potential post-fire effects

Land-Surface Parameters for Spatial Predictive Mapping and Modeling

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Adaptation by Western Corn Rootworm (Coleoptera: Chrysomelidae) to Bt Maize: Inheritance, Fitness Costs, and Feeding Preference

We examined inheritance of resistance, feeding behavior, and fitness costs for a laboratory-selected strain of western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), with resistance to maize (Zea maize L.) producing the Bacillus thuringiensisBerliner (Bt) toxin Cry3Bb1. The resistant strain developed faster and had increased survival on Bt maize relative to a susceptible strain. Results from reciprocal crosses of the resistant and susceptible strains indicated that inheritance of resistance was nonrecessive. No fitness costs were associated with resistance alleles in the presence of two entomopathogenic nematode species, Steinernema carpocapsae Weiser and Heterorhabditis bacteriophora Poinar. Larval feeding studies indicated that the susceptible and resistant strains did not differ in preference for Bt and non-Bt root tissue in choice assays

Field-Evolved Resistance to Bt Maize by Western Corn Rootworm

Crops engineered to produce insecticidal toxins derived from the bacterium Bacillus thuringiensis (Bt) are planted on millions of hectares annually, reducing the use of conventional insecticides and suppressing pests. However, the evolution of resistance could cut short these benefits. A primary pest targeted by Bt maize in the United States is the western corn rootwormDiabrotica virgifera virgifera (Coleoptera: Chrysomelidae). We report that fields identified by farmers as having severe rootworm feeding injury to Bt maize contained populations of western corn rootworm that displayed significantly higher survival on Cry3Bb1 maize in laboratory bioassays than did western corn rootworm from fields not associated with such feeding injury. In all cases, fields experiencing severe rootworm feeding contained Cry3Bb1 maize. Interviews with farmers indicated that Cry3Bb1 maize had been grown in those fields for at least three consecutive years. There was a significant positive correlation between the number of years Cry3Bb1 maize had been grown in a field and the survival of rootworm populations on Cry3Bb1 maize in bioassays. However, there was no significant correlation among populations for survival on Cry34/35Ab1 maize and Cry3Bb1 maize, suggesting a lack of cross resistance between these Bt toxins. This is the first report of field-evolved resistance to a Bt toxin by the western corn rootworm and by any species of Coleoptera. Insufficient planting of refuges and non-recessive inheritance of resistance may have contributed to resistance. These results suggest that improvements in resistance management and a more integrated approach to the use of Bt crops may be necessary