Abstract
BackgroundThe 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.ResultsThis 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.ConclusionsThe 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.
Highlights
The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland
This paper will focus on the requirements of the Climate Action Reserve Forest Project Protocol as this protocol is substantially similar to what will likely be adopted by the state of California for their compliance carbon market system
10 Kilometers predictor variables used in these regressions are several topographic and Light Detection and Ranging data (LiDAR) tree crown variables and the principle components of the color-infrared (CIR) and RGB imagery data sets as well as the Principle Component Analysis (PCA) rotations for a suite of variables derived from the LiDAR data
Summary
The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland. In part due to the dearth of climate change policies, a vibrant voluntary carbon offset market has sprung up centered around a suite of different carbon project standards [6,7,8,9], and managing forests for carbon offsets can provide an important income stream for landowners willing to undertake the costs and requirements of these standards These standards all have slightly different requirements regarding how to quantify the amount of carbon offsets generated, but generally all require periodic ground-based installation and measurement of plots to monitor project level carbon storage. These sample-based estimates of forest carbon storage are extrapolated across the full project, often through a stratification approach, whereby unsampled areas receive estimates from areas with similar characteristics based on their remotely sensed attributes [14]
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