The use of airborne laser scanning to develop a pixel-based stratification for a verified carbon offset project

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

The forest of information: beating paths through the jungle

In this article an outline is given of the history of information and publishing in forestry, communications technology is described (including mobile computing, GIS and satellite observations), and the future exchange of information via the Internet and web-based services is discussed. Two examples show the use of computer technology and decision support systems to prepare a forest management plan and to purchase a hardwood table for the garden. A section is included on The Global Forest Information Service (GFIS), being developed by IUFRO, which is based on a distributed network of metadatabases which catalogue the information resources (digitalized or not) of contributing partners

Model documentation for the European Forest Information Scenario model (EFISCEN 3.1.3)

EFISCEN is a forest resource projection model, used to gain insight into the future development of European forests. It has been used widely to study issues such as sustainable management regimes, wood production possibilities, nature oriented management, climate change impacts, natural disturbances and carbon balance issues. This report describes the history of EFISCEN and the current state of the model, version 3.1.3. It contains a user guide as well as a description of past validations and an uncertainty analysi

Seizure Onset Detection in EEG Signals Based on Entropy from Generalized Gaussian PDF Modeling and Ensemble Bagging Classifier

This paper proposes a new algorithm for epileptic seizure onset detection in EEG signals. The algorithm relies on the measure of the entropy of observed data sequences. Precisely, the data is decomposed into different brain rhythms using wavelet multi-scale transformation. The resulting coefficients are represented using their generalized Gaussian distribution. The proposed algorithm estimates the parameters of the distribution and the associated entropy. Next, an ensemble bagging classifier is used to performs the seizure onset detection using the entropy of each brain rhythm, by discriminating between seizure and non-seizure. Preliminary experiments with 105 epileptic events suggest that the proposed methodology is a powerful tool for detecting seizures in epileptic signals in terms of classification accuracy, sensitivity and specificity