Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Guerra-Hernández Guerra-Hernández,González-Ferreiro González-Ferreiro,Botequim Botequim,Díaz-Varela Díaz-Varela,Cardil Cardil,Soares Soares,Cosenza Cosenza,Silva Silva

doi:10.3390/f10100905

Guerra-Hernández Guerra-Hernández, González-Ferreiro González-Ferreiro + Show 6 more

Open Access

https://doi.org/10.3390/f10100905

Copy DOI

Journal: Forests	Publication Date: Oct 15, 2019
Citations: 15	License type: CC BY 4.0

Affiliation: University of Leon, University of Lisbon

Abstract

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

Highlights

Sustainable forest management demands accurate information that can be obtained efficiently and rapidly [1] in order to describe forest structure and quantify forest resources [2]
The Unmanned Aerial Vehicles (UAV)-based Digital Aerial Photogrammetry (DAP) method tends to underestimate tree height relative to field measurements, there was no appreciable bias throughout the observed diameter (Figure 5a,b)
D was not directly measured in CHMs derived from UAV and airborne laser scanning (ALS), hSfM and hALS were, and these variables were significant in the Structure from Motion (SfM) and ALS equations

Summary

Introduction

Sustainable forest management demands accurate information that can be obtained efficiently and rapidly [1] in order to describe forest structure and quantify forest resources [2]. Accurate, traditional forest inventory is resource- and time-consuming, indicating the need for either alternative or complementary methods that may overcome the drawbacks of field data acquisition [3]. A number of alternatives to traditional field-based measurement of morphological parameters for characterizing three-dimensional (3D) structure of trees and canopies have emerged [4,7]. Laser Scanning (ALS), Digital Aerial Photogrammetry (DAP) and Terrestrial Laser Scanning (TLS) have become widely established as forest mapping and monitoring methods [8,9,10,11]. In the last two decades, DAP, ALS (e.g., [12,13,14]) or a combination of these methods (e.g., [15,16,17]) have been increasingly used to support forest inventories at different scales

Objectives

Methods

Results

Discussion

Conclusion