A comparison of different methods for forest resource estimation using information from airborne laser scanning and CIR orthophotos

Abstract

This article compares three methods for forest resource estimation based on remote sensing features extracted from Airborne laser scanning and CIR orthophotos. The estimation was made exemplarily for the total stem volume of trees for a given area, measured in cubic metres per hectare [m³ ha−1] (as one of the most important quantitative parameters to characterise a forest stand). The following methods were compared: Regression Analysis (RA), k-NN (nearest neighbour) method and a method that utilises regional yield tables, referred to as the yield table method (YT-method). The estimation of stem volume was examined in a mixed forest in Southern Germany using 300 circular inventory plots, each with a size of 452 m². Remote sensing features relating to vegetation height and structures were extracted and used as input variables in the different approaches. The accuracy of the estimation was analysed using scatter plots and quantified using absolute and relative root mean square errors (RMSE). The comparison was made for all plots, as well as for averaged plot values located within forest stands that have the same age class. On “plot level” the RMSE yielded 79.79 m³ ha−1 (RA), 81.93 m³ ha−1 (k-NN) and 81.78 m³ ha−1 (YT-method) and for the averaged values 35.75 m³ ha−1 (RA), 35.06 m³ ha−1 (k-NN) and 42.98 m³ ha−1 (YT-method). Advantages and disadvantages, as well as requirements, of the methods are discussed.