Next Article in Journal
Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks
Previous Article in Journal
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(3), 442; https://doi.org/10.3390/rs10030442

Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA

1
RedCastle Resources, Inc. Contractor to USDA Forest Service Geospatial Technology and Applications Center, Salt Lake City, UT 84119, USA
2
Department of Urban Design and Planning, College of the Built Environment, University of Washington, Seattle, WA 98195, USA
3
USDA Forest Service, Southwestern Regional Office, Albuquerque, NM 87102, USA
4
USDA Forest Service, Coconino National Forest, Flagstaff, AZ 86001, USA
*
Author to whom correspondence should be addressed.
Received: 29 January 2018 / Revised: 1 March 2018 / Accepted: 6 March 2018 / Published: 12 March 2018
Full-Text   |   PDF [4302 KB, uploaded 12 March 2018]   |  

Abstract

Historical forest management practices in the southwestern US have left forests prone to high-severity, stand-replacement fires. Reducing the cost of forest-fire management and reintroducing fire to the landscape without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to increase ability of managers to monitor AGB and other forest metrics at reduced cost. We developed a regional lidar-based statistical model to estimate AGB for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. Model selection was performed using Bayesian model averaging (BMA) to reduce researcher bias, fully explore the model space, and avoid overfitting. The selected model includes measures of canopy height, canopy density, and height distribution. The model selected with BMA explains 71% of the variability in field-estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model is structured in accordance with previously described local models, and performs equivalently to these smaller scale models. We have demonstrated the effectiveness of lidar for developing cost-effective, robust regional AGB models for monitoring and planning adaptively at the landscape scale. View Full-Text
Keywords: forest biomass; aboveground biomass; airborne lidar; monitoring; regional forest inventory; variable selection; Bayesian model averaging; multiple linear regression forest biomass; aboveground biomass; airborne lidar; monitoring; regional forest inventory; variable selection; Bayesian model averaging; multiple linear regression
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Tenneson, K.; Patterson, M.S.; Mellin, T.; Nigrelli, M.; Joria, P.; Mitchell, B. Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Remote Sens. 2018, 10, 442.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top