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

A Multiscale Investigation of Habitat Use and Within-river Distribution of Sympatric Sand Darter Species

The western sand darter Ammocrypta clara, and eastern sand darter Ammocrypta pellucida are sand-dwelling fishes of conservation concern. Past research has emphasized the importance of studying individual populations of conservation concern, while recent research has revealed the importance of incorporating landscape scale processes that structure habitat mosaics and local populations. We examined habitat use and distributions of western and eastern sand darters in the lower Elk River of West Virginia. At the sandbar habitat use scale, western sand darters were detected in sandbars with greater area, higher proportions of coarse grain sand and faster bottom current velocity, while the eastern sand darter used a wider range of sandbar habitats. The landscape scale analysis revealed that contributing drainage area was an important predictor for both species, while sinuosity, which presumably represents valley type also contributed to the western sand darter’s habitat suitability. Sandbar quality (area, grain size, and velocity) and fluvial geomorphic variables (drainage area and valley type) are likely key driving factors structuring sand darter distributions in the Elk River. This multiscale study of within-river species distribution and habitat use is unique, given that only a few sympatric populations are known of western and eastern sand darters

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

A Two-Stage GIS-Based Suitability Model for Siting Biomass-to-Biofuel Plants and its Application in West Virginia, USA

Woody biomass has been considered of low value because the cost of removal generally exceeded market price. New, valued-added markets to offset removal costs are necessary for utilization to be effective. In recent years the use of biomass as feedstock for biofuel production in the United States has been on the rise. A variety of liquid fuels can be produced from woody biomass; ethanol is one of the most promising. This study presents a two-stage approach to selecting woody biomass-based biofuel plants using Geographical Information System (GIS) spatial analysis and the multi-criteria analysis ranking algorithm of compromise programming. Site suitability was evaluated to minimize direct cost for investors and potential negative environmental impacts. The first step was to create a site suitability index using a linear fuzzy logic prediction model. The model involved 15 variables in three factor groups: (1) general physical conditions, (2) costs, and (3) environmental factors. The weights of the cost factors were determined using pairwise comparisons in the Analytical Hierarchy Process (AHP). The value of site suitability was reclassified into three categories (non-suitable, low-suitable, and high-suitable) using different classification methods. With a feasible plant location defined as an industrial site within the most suitable area, the second stage of the analysis used compromise programming to compare the potential sites. The criteria used to rank the potential sites included fuzzy distance to woody biomass, highways, railways, commercial airports, communities, and available parcel size. The AHP was used to compute the relative importance of each criterion. The top ten suitable sites were determined, and sensitivity analyses were conducted to derive the most preferred sites. The approach was successful in taking a large amount of non-commensurate spatial data and integrating a site-based ranking algorithm to find the top locations for biomass plants. It also has great potential and applicability to other suitability and site selection studies

TREEDETECTION: AUTOMATIC TREE DETECTION USING UAV-BASED DATA

In this study it is presented a toolbox built in ArcGIS using ArcPy designed to automatically detect trees in high resolution data obtained from Unmanned Aerial Vehicles (UAV). The toolbox, TreeDetection, contains a tool called TreeDetect, which requires three parameters: a raster input, a conversion factor and an output folder. Three other optional parameters can be changed to improve the detection according to characteristics of the forest and raster source. We tested the TreeDetect tool in three study sites: a young Eucalyptus plantation; adult Eucalyptus and Pinus stands; and a Mixed Hardwood natural forest. We also tested distinct raster inputs, according to the data availability in each site. The tool was considered efficient to detect the trees in the three study areas. The detection accuracy was lower in the natural stand, as expected considering the complex structure of this forest type. All the raster input rested provided satisfactory results, but in the homogeneous stand the Digital Surface Model (DSM) was not as effective as the spectral bands. Furthermore, research can be performed with emphasis in different sensors and band combinations, as well in the parameters’ selection

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents

Evaluating Impacts of Anthropogenic Disturbance to Wetland Water Quality Functions

Wetland ecosystems play fundamental roles in regulating our freshwater resources, yet they are not comprehensively protected from degradation and loss. West Virginia, USA has wetlands across diverse landscapes and geography that feed into both the Chesapeake Bay and Gulf of Mexico. The state is also comprised of diverse anthropogenic land-use practices. We are assessing 200 wetlands over 2 years to evaluate how anthropogenic disturbance impact wetland water quality functions. Select water quality parameters (20), and relative diversity and abundance of vegetation and macroinvertebrates will be used as bioindicators. They will be compared with GIS assessments of watershed land cover/ land-use practices and climate data to evaluate relationships and determine how they impact a wetland’s ability to carry out select water quality functions. Preliminary results after one year of sampling indicate that wetlands at higher elevation with fewer watershed land-use practices generally had lower E. Coli, heavy metal (Lead and Zinc), and nutrient (Phosphorus and Nitrogen) concentrations relative to wetlands at lower elevations with greater watershed land-use practices. Seasonal conductivity readings increased following precipitation events. Conductivity and salinity readings also decreased along its drainage gradient, indicative of the wetland performing its water quality functions. We also observed that conductivity and nutrient concentrations were highest during the winter and lowest during the summer, coinciding with the bottom and peak periods of primary productivity. The results of this project will be used to develop wetland water quality standards for West Virginia and help advance more comprehensive wetland regulations

Spatial variation in the impact of dragonflies and debris on recreational ecosystem services in a floodplain wetland

Recreation is an important ecosystem service. The interaction between people and habitat components is rarely considered in the analyses of recreational experiences, making it difficult to predict what people will experience. In this study we develop a modelling framework that describes three stages of interaction between people and habitats. This framework considers: (1) the distribution of habitat components in the environment, (2) the proportion of the available components that visitors notice, and (3) the net impact of multiple components on the quality of the recreational experience. The model was applied to a case study river floodplain, and was used to estimate visitor exposure to a combination of positive habitat components (dragonflies) and negative components (debris). The model provided an index of net impacts on experience quality that showed spatial variation across the floodplain, and this analysis highlighted areas that would deliver more positive experiences to visitors. The results of a sensitivity analysis indicated that neglecting the noticeability (observation rate) of habitat components resulted in different predictions. It is therefore important that the noticeability of habitat components is considered during analyses of recreational experiences, and recreational ecosystem service valuations