Characterizing Prescribed Fire with Terrestrial LiDAR in the New Jersey Pine Barrens

Author

Samuel Rhule StocktonFollow

Semester

Fall

Date of Graduation

2024

Document Type

Thesis

Degree Type

MA

College

Eberly College of Arts and Sciences

Committee Chair

Brenden McNeil

Committee Member

Aaron Maxwell

Committee Member

Michael Gallagher

Abstract

Prescribed burning has become a commonly used tool in the mitigation of wildfire, though monitoring the way it changes ecosystems has historically been a time-intensive process. Rapid change across ecosystems has necessitated advancements in remote sensing technologies to quantify the changes taking place. Single-scan terrestrial LiDAR scanning is one such method of monitoring these changes through the quantification of ecosystem structural characteristics. Acute disturbance events such as fire can transform the structure of an ecosystem, and by extension, change the way that ecosystem functions. The purpose of this study is to analyze the changes in vegetation density and distribution following a prescribed burn in the New Jersey Pine Barrens, and then compare these data to the current standard for evaluating fire severity: the Composite Burn Index. Scans were collected before prescribed fire, after prescribed fire, and annually for three subsequent years at 38 plots within the Wharton State Forest in New Jersey. These scans were divided into vegetation types and then height bins to determine the effects of fire on different forest strata. I found that the increase in LiDAR hits 3 years after prescribed burning was directly related to the percent of pre-fire points which were absent post-fire. This was true of each forest strata (p < 0.01), save for ground fuels (p = 0.404), which likely experienced uneven point gain due to mortality patterns higher in the forest profile. Neither of these changes, percent loss or points gained, were significantly related to CBI. This study shows that TLS data can be used to create time series of forest structural characteristics. Such time series are capable of capturing vegetation changes due disturbance events, as well as differences in regeneration thereafter. Relative to CBI, TLS data also provide a more precise and accurate method of quantifying forest change.. We suggest that further examinations of TLS time series could productively examine other forest structural characteristics and compare these to other traditional methods of forest monitoring.

Recommended Citation

Stockton, Samuel Rhule, "Characterizing Prescribed Fire with Terrestrial LiDAR in the New Jersey Pine Barrens" (2024). Graduate Theses, Dissertations, and Problem Reports. 12671.
https://researchrepository.wvu.edu/etd/12671