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- Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in
Mixed Hardwoods of West Virginia
Authors: Henry Liebermann et al.
Abstract: 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.
PubDate: Fri, 19 Jan 2018 11:55:27 PST
- A Multiscale Investigation of Habitat Use and Within-river Distribution of
Sympatric Sand Darter Species
Authors: Patricia A. Thompson et al.
Abstract: 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.
PubDate: Fri, 19 Jan 2018 11:55:22 PST
- Predicting Post-Fire Change in West Virginia, USA from Remotely-Sensed
Data
Authors: Michael Strager P. Strager et al.
Abstract: 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.
PubDate: Mon, 28 Nov 2016 06:40:14 PST
- Spatial Analysis of Forest Crimes in Mark Twain National Forest, Missouri
Authors: Karun Pandit et al.
Abstract: Forest crime mitigation has been identified as a challenging issue in forest management in the United States. Knowledge of the spatial pattern of forest crimes would help in wisely allocating limited enforcement resources to curb forest crimes. This study explores the spatial pattern of three different types of forest crimes: fire crime, illegal timber logging crime, and occupancy use crime in the Salem-Patosi Ranger District of Mark Twain National Forest. Univariate and bivariate Ripley’s K-functions were applied to explore the spatial patterns in crime events, like clustering and attraction among forest crime types. Results reveal significant clustering for each forest crime type and the combined events. Peak clustering was observed at 2.3 km, 2.7 km and 3.6 km for fire, timber and occupancy use crimes, respectively. For better forest crime mitigation, when there is an event of a given forest crime type, monitoring should be intensified around its respective spatial scale of peak clustering to avert future crime events. Significant attraction was observed between i) fire crime and illegal timber logging crime, and ii) fire crime and occupancy use crime, at spatial scales of 0.3 km and 0.2 km, respectively. At the respective spatial scales, occurrence of one type of crime increases the chances of occurrence of another type of crime, thus we recommend allocating available resources accordingly to minimize crime events. Further study could help establish any association of clustering or attraction of forest crimes with different socio-economic and bio-physical factors prevalent in and around the area.
PubDate: Thu, 14 Jan 2016 09:00:14 PST
- Discordant Data and Interpretation of Results from Wildlife Habitat Models
Authors: Anita T. Morzillo et al.
Abstract: Wildlife habitat management is an important part of natural resource management. As a result, there are a large number of models and tools for wildlife habitat assessment. A consequence of the many assessment tools is inconsistency when comparing results between tools, which may lead to potential confusion management decisions. Our objective was to compare results from two wildlife habitat models – one being relatively coarse (HUC5) scale and not spatially dynamic and the other being finer scale spatial data based on a 30 m spatial resolution –for habitat assessment of three species across the West Cascades of Oregon: Northern spotted owl, pileated woodpecker, and western bluebird. The coarse-scale model predicted more habitat for the two specialist species (owl and bluebird), whereas the fine-scale model predicted more habitat for the generalist (woodpecker). Spatial evaluation of fine-scale models suggested habitat pattern that was not revealed by coarse-scale models. Differences in model assumptions, variables used, and flexibility of variable treatment account for differences in model performance. These findings suggest that cautious interpretation of results is needed given the constraints of each model. Coarse-scale models may help prioritize management treatments across space, but further fine-scale and site-specific analyses enhance information needed for making habitat management decisions.
PubDate: Thu, 14 Jan 2016 09:00:12 PST
- Comparison of Terrain Indices and Landform Classification Procedures in
Low-Relief Agricultural Fields
Authors: Derek A. Evans et al.
Abstract: Landforms control the spatial distribution of numerous factors associated with agronomy and water quality. Although curvature and slope are the fundamental surface derivatives used in landform classification procedures, methodologies for landform classifications have been performed with other terrain indices including the topographic position index (TPI) and the convergence index (CI). The objectives of this study are to compare plan curvature, the convergence index, profile curvature, and the topographic position index at various scales to determine which better identifies the spatial variability of soil phosphorus (P) within three low relief agricultural fields in central Illinois and to compare how two methods of landform classification, e.g. Pennock et al. (1987) and a modified approach to the TPI method (Weiss 2001, Jenness 2006), capture the variability of spatial soil P within an agricultural field. Soil sampling was performed on a 0.4 ha grid within three agricultural fields located near Decatur, IL and samples were analyzed for Mehlich-3 phosphorus. A 10-m DEM of the three fields was also generated from a survey performed with a real time kinematic global positioning system. The DEM was used to generate rasters of profile curvature, plan curvature, topographic position index, and convergence index in each of the three fields at scales ranging from 10 m to 150 m radii. In two of the three study sites, the TPI (r ≥ -0.42) was better correlated to soil P than profile curvature (r ≤ 0.41), while the CI (r ≥ -0.52) was better correlated to soil P than plan curvature (r ≥ -0.45) in all three sites. Although the Pennock method of landform classification failed to identify footslopes and shoulders, which are clearly part of these fields’ topographic framework, the Pennock method (R² = 0.29) and TPI method (R² = 0.30) classified landforms that captured similar amounts of soil P spatial variability in two of the three study sites. The TPI and CI should be further explored when performing terrain analysis at the agricultural field scale to create solutions for precision management objectives.
PubDate: Thu, 14 Jan 2016 09:00:09 PST
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