2 research outputs found
Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning
This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study was to explore the use of globally consistent ARD for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 188 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 54.3% (map-level image classification efficacy (MICE) = 0.433). Accuracy increased to a mean OA of 64.8% (MICE = 0.496) when the Oak/Hickory and Oak/Pine classes were combined into an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 65.3% (MICE = 0.570), while the accuracy for differentiating six classes increased to 76.2% (MICE = 0.660). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic predictions are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to those trained using spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks
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Development of a White Oak Chronology Using Live Trees and a Post-Civil War Cabin in South-Central Virginia
A 280-year old white oak chronology was developed for south-central Virginia to verify the timber harvesting and construction dates of a cabin located on the Reynolds Homestead Research Center. A plaque on the cabin stated that the logs were harvested in 1814. However, the outer rings of the logs dated to 1875 and 1876. From the land-use history of the area, the cabin was most likely constructed to house tenant farmers after the Civil War. Most of the periods of below average growth identified in the 280-year chronology were related to drought events. Correlations between the radial growth of the white oak with temperature and precipitation data from a local weather station were examined. Precipitation had more influence on radial growth than temperature, and significant correlations (p = 0.05) existed between radial growth and precipitation from the previous September, the current April, and the current June.This item is part of the Tree-Ring Research (formerly Tree-Ring Bulletin) archive. It was digitized from a physical copy provided by the Laboratory of Tree-Ring Research at The University of Arizona. For more information about this peer-reviewed scholarly journal, please email the Editor of Tree-Ring Research at [email protected]