Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning

Remote Sensing

Aaron E. Maxwell¹  E. Louise Loudermilk²  Luis Andrés Guillén³  Nicholas S. Skowronski⁴  Michael R. Gallagher⁵  Alexis Everland⁶ 

[1] Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA;New Jersey Department of Environmental Protection, Forest Fire Service, P.O. Box 239, New Lisbon, NJ 08064, USA;Southern Swedish Forest Research Centre, Swedish University of Agricultural Science, P.O. Box 49, SE-230 53 Alnarp, Sweden;USDA Forest Service, Northern Research Station, Morgantown, WV 26506, USA;USDA Forest Service, Northern Research Station, New Lisbon, NJ 08064, USA;USDA Forest Service, Southern Research Station, Athens Fire Laboratory, 320 Green Street, Athens, GA 30602, USA;

关键词: burn severity;    fire effects;    machine learning;    terrestrial laser scanning;   

DOI  :  10.3390/rs13204168

来源: DOAJ

【 摘 要 】

Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data but must be calibrated to field data to derive meaning. The composite burn index (CBI) is the most widely used field-based method used to calibrate satellite-based burn severity data but important limitations of this approach have yet to be resolved. The objective of this study was focused on predicting CBI from point cloud and visible-spectrum camera (RGB) metrics derived from single-scan terrestrial laser scanning (TLS) datasets to determine the viability of TLS data as an alternative approach to estimating burn severity in the field. In our approach, we considered the predictive potential of post-scan-only metrics, differenced pre- and post-scan metrics, RGB metrics, and all three together to predict CBI and evaluated these with candidate algorithms (i.e., linear model, random forest (RF), and support vector machines (SVM) and two evaluation criteria (R-squared and root mean square error (RMSE)). In congruence with the strata-based observations used to calculate CBI, we evaluated the potential approaches at the strata level and at the plot level using 70 TLS and 10 RGB independent variables that we generated from the field data. Machine learning algorithms successfully predicted total plot CBI and strata-specific CBI; however, the accuracy of predictions varied among strata by algorithm. RGB variables improved predictions when used in conjunction with TLS variables, but alone proved a poor predictor of burn severity below the canopy. Although our study was to predict CBI, our results highlight that TLS-based methods for quantifying burn severity can be an improvement over CBI in many ways because TLS is repeatable, quantitative, faster, requires less field-expertise, and is more flexible to phenological variation and biomass change in the understory where prescribed fire effects are most pronounced. We also point out that TLS data can also be leveraged to inform other monitoring needs beyond those specific to wildland fire, representing additional efficiency in using this approach.

【 授权许可】

Unknown