Estimating Single Tree Stem Volume of Pinus sylvestris Using Airborne Laser Scanner and Multispectral Line Scanner Data

Christoph Straub,Barbara Koch

doi:10.3390/rs3050929

Abstract

So far, only a few studies have been carried out in central European forests to estimate individual tree stem volume of pine trees from high resolution remote sensing data. In this article information derived from airborne laser scanner and multispectral line scanner data were tested to predict the stem volume of 178 pines (Pinus sylvestris) in a study site in the south-west of Germany. First, tree crowns were automatically delineated using both multispectral and laser scanner data. Next, tree height, crown diameter and crown volume were derived for each crown segment. All combinations of the derived tree features were used as explanatory variables in allometric models to predict the stem volume. A model with tree height and crown diameter had the best performance with respect to the prediction accuracy determined by a leave-one-out cross-validation: Root Mean Square Error (RMSE) = 24.02% and Bias = 1.36%.

Highlights

In general two main approaches are distinguished to estimate forest inventory attributes from airborne laser scanner (ALS) data [1]: (1) Area based methods, described as canopy height
The main modification is that multispectral line scanner data was integrated in the procedure as an additional dataset with the objective to improve the separation of coniferous and deciduous trees: 1. In the first step the study area is classified into the classes: (a) coniferous trees and (b) deciduous trees
The t-statistic is shown to determine if a variable is making a significant contribution to the model for stem volume estimation

Summary

Introduction

In general two main approaches are distinguished to estimate forest inventory attributes from airborne laser scanner (ALS) data [1]: (1) Area based methods, described as canopy heightRemote Sens. 2011, 3 distribution (CHD) approaches; and (2) Individual-tree-detection (ITD) methods. In general two main approaches are distinguished to estimate forest inventory attributes from airborne laser scanner (ALS) data [1]: (1) Area based methods, described as canopy height. 2011, 3 distribution (CHD) approaches; and (2) Individual-tree-detection (ITD) methods. Often the CHD approaches are associated with low-resolution and the ITD methods with high-resolution data. In Central Europe, the majority of studies have concentrated on CHD approaches to estimate forest inventory attributes such as stand heights, basal area or timber volume per hectare. Metrics related to canopy height and densities have been used as predictors in regression models and nonparametric methods (e.g., [2,3,4,5]). Possibilities to detect individual tree crowns in deciduous and mixed temperate forests in Germany are described in [6]

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Conclusion