Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes

Xiaowei Yu,Markus Holopainen,Juha Hyyppä,Mikko Vastaranta

doi:10.3390/rs2061481

Xiaowei Yu, Markus Holopainen + Show 2 more

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https://doi.org/10.3390/rs2061481

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Abstract

Approaches to deriving forest information from laser scanner data have generally made use of two methods: the area-based and individual tree-based approaches. In this paper, these two methods were evaluated and compared for their abilities to predict forest attributes at the plot level using the same datasets. Airborne laser scanner data were collected over the Evo forest area, southern Finland, with an averaging point density of 2.6 points/m2. Mean height, mean diameter and volume were predicted from laser-derived features for plots (area-based method) or tree height, diameter at breast height and volume for individual trees (individual tree-based method) using random forests technique. To evaluate and compare the two forest inventory methods, the root-mean-squared error (RMSE) and correlation coefficient (R) between the predicted and observed plot-level values were computed. The results indicated that both area-based method (with an RMSE of 6.42% for mean height, 10.32% for mean diameter and 20.90% for volume) and individual tree-based method (with an RMSE of 5.69% for mean height, 10.77% for mean diameter and 18.55% for volume) produced promising and compatible results. Increase in point density is expected to increase the accuracy of the individual tree-based technique more than that of the area-based technique.

Highlights

Airborne laser scanning (ALS) is an active remote-sensing technique that provides three-dimensional (3D) high-precision measurements of targets in the form of a point cloud (x,y,z, intensity of the backscattered power), based on laser-ranging measurements supported by the position and orientation information derived with use of a differential Global Positioning Systemdevice and an inertial measurement unit (IMU) [1,2]
The features derived from laser data are maximum height, mean height calculated as the arithmetic mean of laser heights, standard deviation of laser heights, coefficient of variation, penetration computed as the proportions of ground hits to total number of hits, percentiles calculated from 0% to 100% of canopy height distribution at 10% intervals and canopy cover percentiles expressed as the proportion of first returns below a given percentage of total height from 10% to 90% of the heights with 10% intervals
The test results suggested that similar accuracy was achieved with both methods for mean diameter and slightly better accuracy for mean height and volume using the individual tree-based method

Summary

Introduction

Airborne laser scanning (ALS) is an active remote-sensing technique that provides three-dimensional (3D) high-precision measurements of targets in the form of a point cloud (x,y,z, intensity of the backscattered power), based on laser-ranging measurements supported by the position and orientation information derived with use of a differential Global Positioning System (dGPS)device and an inertial measurement unit (IMU) [1,2]. Rapid technical advances in laser scanning currently make ALS one of the most promising technologies for the retrieval of detailed information on forests at different levels, i.e., from the individual tree to the plot/stand and nationwide. Approaches to deriving forest information from laser scanner data can be mainly divided into two groups: area-based (or distribution-based) and individual tree-based approaches [4]. In area-based methods, quantiles (percentiles) and other nonphysical distribution-related features of reflected laser canopy height are used to predict forest characteristics, such as mean tree height, mean diameter, basal area, volume and biomass, at the plot or stand level or for other areas of interest, typically using regression, discriminant analysis or nonparametric estimation techniques. Previous studies have indicated that mean height [e.g., 5-7], basal area [e.g., 7-9], mean volume [e.g., 9-14] and biomass [15] can be accurately predicted with area-based methods. Regression models produced a coefficient of determination (R2) of 0.93, 0.97 and 0.95 for mean height, stand volume and basal area, respectively

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