V
Valerie A. Thomas
Researcher at Virginia Tech
Publications - 71
Citations - 1723
Valerie A. Thomas is an academic researcher from Virginia Tech. The author has contributed to research in topics: Lidar & Canopy. The author has an hindex of 20, co-authored 63 publications receiving 1384 citations. Previous affiliations of Valerie A. Thomas include Queen's University & Virginia Tech College of Natural Resources and Environment.
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Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density
TL;DR: In this article, the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables was investigated and validated for the entire study area from the low-density models.
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Fitting the Multitemporal Curve: A Fourier Series Approach to the Missing Data Problem in Remote Sensing Analysis
TL;DR: A Fourier regression algorithm is illustrated, here on time series of normalized difference vegetation indices (NDVIs) for Landsat pixels with 30-m resolution, which indicates that Fourier regressors may be used to interpolate missing data for multitemporal analysis at the Landsat scale, especially for annual or longer studies.
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On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data
TL;DR: A method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover, which is able to detect changes from both deforestation and subtler forest degradation and thinning.
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Image classification of a northern peatland complex using spectral and plant community data
Valerie A. Thomas,Paul Treitz,Dennis E. Jelinski,John R. Miller,Peter M. Lafleur,J. Harry McCaughey +5 more
TL;DR: In this paper, the authors explored the relationship between classification of species cover and community data and reflectance values and found that two-way indicator species analysis (TWINSPAN) clusters did not correspond well to spectral reflectance and gave the lowest classification results of the methods investigated.
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Approximating Prediction Uncertainty for Random Forest Regression Models
TL;DR: A Monte Carlo approach to quantify prediction uncertainty for random forest regression models by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors.