Next Article in Journal
Nutrient Uptake and Utilization by Fragrant Rosewood (Dalbergia odorifera) Seedlings Cultured with Oligosaccharide Addition under Different Lighting Spectra
Previous Article in Journal
A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Forests 2018, 9(1), 28; https://doi.org/10.3390/f9010028

Predicting Volume and Biomass Change from Multi-Temporal Lidar Sampling and Remeasured Field Inventory Data in Panther Creek Watershed, Oregon, USA

1
Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, 280 Peavy Hall, Corvallis, OR 97331, USA
2
9320 40th Ave. NE, Seattle, WA 98115, USA
*
Author to whom correspondence should be addressed.
Received: 2 October 2017 / Revised: 10 January 2018 / Accepted: 11 January 2018 / Published: 12 January 2018
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
View Full-Text   |   Download PDF [3176 KB, uploaded 12 January 2018]   |  

Abstract

Using lidar for large-scale forest management can improve operational and management decisions. Using multi-temporal lidar sampling and remeasured field inventory data collected from 78 plots in the Panther Creek Watershed, Oregon, USA, we evaluated the performance of different fixed and mixed models in estimating change in aboveground biomass ( AGB ) and cubic volume including top and stump ( CVTS ) over a five-year period. Actual values of CVTS and AGB were obtained using newly fitted volume and biomass equations or the equations used by the Pacific Northwest unit of the Forest Inventory and Analysis program. Estimates of change based on fixed and mixed-effect linear models were more accurate than change estimates based on differences in LIDAR-based estimates. This may have been due to the compounding of errors in LIDAR-based estimates over the two time periods. Models used to predict volume and biomass at a given time were, however, more precise than the models used to predict change. Models used to estimate CVTS were not as accurate as the models employed to estimate AGB . Final models had cross-validation root mean squared errors as low as 40.90% for AGB and 54.36% for CVTS . View Full-Text
Keywords: LiDAR; Pacific Northwest; aboveground biomass; cubic volume; change estimation LiDAR; Pacific Northwest; aboveground biomass; cubic volume; change estimation
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Poudel, K.P.; Flewelling, J.W.; Temesgen, H. Predicting Volume and Biomass Change from Multi-Temporal Lidar Sampling and Remeasured Field Inventory Data in Panther Creek Watershed, Oregon, USA. Forests 2018, 9, 28.

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]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top