Training Area Concept in a Two-Phase Biomass Inventory Using Airborne Laser Scanning and RapidEye Satellite Data

Remote Sensing

Parvez Rana¹  Timo Tokola¹  Lauri Korhonen¹  Qing Xu¹  Timo Kumpula²  Petteri Vihervaara³ 

[1] School of Forest Sciences, University of Eastern Finland, P.O. Box-111, FI-80101 Joensuu, Finland; E-Mails:;Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistonkatu 7, FI-80101 Joensuu, Finland; E-Mail:;Finnish Environment Institute (SYKE), Natural Environment Centre, Ecosystem Change Unit, P.O. Box 111, Yliopistokatu 7 (Natura), FI-80101 Joensuu, Finland; E-Mails:

关键词: remote sensing;    ALS;    biomass;    RapidEye;    boreal forest;   

DOI  :  10.3390/rs6010285

来源: mdpi

【 摘 要 】

This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated by assessing the bias and the root mean square error (RMSE). In the phase I, ALS data from 50 field plots were used to predict AGB and volume for the 200 surrogate plots. In the phase II, the ALS-simulated surrogate plots were used as a ground-truth to estimate AGB and volume from the RapidEye data for the study area. The resulting RapidEye models were validated against a separate set of 28 plots. The RapidEye models showed a promising accuracy with a relative RMSE of 19%–20% for both volume and AGB. The evaluated concept of biomass inventory would be useful to support future forest monitoring and decision making for sustainable use of forest resources.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland