Document Type
Article
Publication Title
Carbon Balance and Management
Publication Date
10-2011
Volume
6
Issue
9
First Page
1
Last Page
17
Abstract
Background
The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland. Monitoring, reporting, and verifying carbon stocks and fluxes at a project level is the single largest direct cost of a forest carbon offset project. There are now many methods for estimating forest stocks with high accuracy that use both Airborne Laser Scanning (ALS) and high-resolution optical remote sensing data. However, many of these methods are not appropriate for use under existing carbon offset standards and most have not been field tested.
Results
This paper presents a pixel-based forest stratification method that uses both ALS and optical remote sensing data to optimally partition the variability across an ~10,000 ha forest ownership in Mendocino County, CA, USA. This new stratification approach improved the accuracy of the forest inventory, reduced the cost of field-based inventory, and provides a powerful tool for future management planning. This approach also details a method of determining the optimum pixel size to best partition a forest.
Conclusions
The use of ALS and optical remote sensing data can help reduce the cost of field inventory and can help to locate areas that need the most intensive inventory effort. This pixel-based stratification method may provide a cost-effective approach to reducing inventory costs over larger areas when the remote sensing data acquisition costs can be kept low on a per acre basis.
DOI
http://dx.doi.org/10.1186/1750-0680-6-9
Rights
© 2011 Golinkoff et al; licensee BioMed Central Ltd.
Recommended Citation
Golinkoff et al: The use of airborne laser scanning to develop a pixel-based stratification for a verified carbon offset project. Carbon Balance and Management, 2011, 6:9. doi: 10.1186/1750-0680-6-9
Comments
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.