DIRS Laboratory 76-3215
December 17, 2018 at 10:00am
TIMOTHY RUPRIGHT
M.S. Thesis Defense
Abstract: 

The invasive species Agrilus planipennis (Emerald Ash Borer or EAB) is currently impacting ash trees in a large part of the continental United States.  One way of measuring the effect of this infestation on the US markets is to determine the spread of the species and the biomass destruction/loss due to this invasive pest.  In order to assess such impacts, it is necessary to determine how and where ash trees are located toward accurately measuring the relevant ash species biomass and tree count.

 

This study utilizes data captured over the campus of RIT in Rochester, NY.  Hyperspectral data, captured by SpecTIR, and discrete LiDAR data collected by an ALS 50 are used in an attempt to accomplish two tasks:  1) estimate ash biomass and 2) create a discriminant model to determine what types of trees (genera) are located within our surveyed plots.  We surveyed four deciduous plots on the RIT campus and integrated the surveyed areas, the LiDAR data, and the hyperspectral data into a single comprehensive dataset.  Our assessment incorporated different underlying models, including hyperspectral data only, LiDAR data only, and a combination of hyperspectral and LiDAR data.

 

The results indicate that we can predict biomass with an R2 value between 0.55-0.69, at an α=0.01 statistical threshold and an R2 value between 0.85-0.92 (α=0.05 threshold) with the best models.  The results indicate that smaller plot radius hyperspectral plus LiDAR and larger radius hyperspectral approaches scored best for R2 values, but the best RMSE was returned by the model utilizing the larger-radius hyperspectral data plus LiDAR returns. 

 

The genus-level classification analysis utilized a stepwise discriminant model to extract relevant variables, followed by a linear discriminant classification which classified each tree based on the stepwise results.  These models found that one-meter hyperspectral data plus LiDAR could accurately assess the genus level of the trees 86% of the time, with a KHAT score of 0.86.  User and producer accuracies on that model vary from 73-100%, depending on the genus.

 

This study contributes to the effort for combining hyperspectral and LiDAR data to assess deciduous tree stands.  Such an approach to biomass modeling is often used in coniferous forests with high accuracy; however, the variability in uneven-aged and complex deciduous forest typically leads to poorer structural assessment outcomes, even though such species are spectrally more differentiable. Results here indicate that utilizing more robust LiDAR scans (point density) and techniques (data fusion), these methods could yield valuable genus-level biomass or carbon maps per forest genus. Such maps in turn could prove useful to decision-makers from policy experts to conservationists.