Next Article in Journal
Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors
Previous Article in Journal
Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series
A correction was published on 11 August 2015, see Remote Sens. 2015, 7(8), 10242.

Remote Sens. 2014, 6(1), 285-309; doi:10.3390/rs6010285
Article

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

1,* , 1
,
1
,
1
,
2
,
3
 and
3
1 School of Forest Sciences, University of Eastern Finland, P.O. Box-111, FI-80101 Joensuu, Finland 2 Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistonkatu 7, FI-80101 Joensuu, Finland 3 Finnish Environment Institute (SYKE), Natural Environment Centre, Ecosystem Change Unit, P.O. Box 111, Yliopistokatu 7 (Natura), FI-80101 Joensuu, Finland
* Author to whom correspondence should be addressed.
Received: 12 November 2013 / Revised: 16 December 2013 / Accepted: 18 December 2013 / Published: 27 December 2013
View Full-Text   |   Download PDF [950 KB, uploaded 19 June 2014]   |   Browse Figures

Abstract

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.
Keywords: remote sensing; ALS; biomass; RapidEye; boreal forest remote sensing; ALS; biomass; RapidEye; boreal forest
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Rana, P.; Tokola, T.; Korhonen, L.; Xu, Q.; Kumpula, T.; Vihervaara, P.; Mononen, L. Training Area Concept in a Two-Phase Biomass Inventory Using Airborne Laser Scanning and RapidEye Satellite Data. Remote Sens. 2014, 6, 285-309.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert