Next Article in Journal
Influence of Hemlock Woolly Adelgid Infestation Levels on Water Stress in Eastern Hemlocks within the Great Smoky Mountains National Park, U.S.A.
Previous Article in Journal
Special Issue: The Potential Role for Community Monitoring in MRV and in Benefit Sharing in REDD+
Article Menu

Export Article

Open AccessReview
Forests 2015, 6(1), 252-270;

Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects

Finnish Geospatial Research Institute, Geodeetinrinne 2, Masala FI-02431, Finland
Department of Forest Sciences, University of Helsinki, P.O. Box 27, Helsinki FI-00014, Finland
Biospheric Sciences Laboratory, University Space Research Association, NASA GSFC, Greenbelt, MD 20771, USA
Author to whom correspondence should be addressed.
Academic Editors: Peter Beets and Eric J. Jokela
Received: 28 February 2014 / Accepted: 9 January 2015 / Published: 16 January 2015
Full-Text   |   PDF [7614 KB, uploaded 16 January 2015]   |  


Research activities combining lidar and radar remote sensing have increased in recent years. The main focus in combining lidar-radar forest remote sensing has been on the retrieval of the aboveground biomass (AGB), which is a primary variable related to carbon cycle in land ecosystems, and has therefore been identified as an essential climate variable. In this review, we summarize the studies combining lidar and radar in estimating forest AGB. We discuss the complementary use of lidar and radar according to the relevance of the added value. The most promising prospects for combining lidar and radar data are in the use of lidar-derived ground elevations for improving large-area biomass estimates from radar, and in upscaling of lidar-based AGB data across large areas covered by spaceborne radar missions. View Full-Text
Keywords: lidar; radar; forest biomass; remote sensing; data fusion lidar; radar; forest biomass; remote sensing; data fusion

Figure 1

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 (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Kaasalainen, S.; Holopainen, M.; Karjalainen, M.; Vastaranta, M.; Kankare, V.; Karila, K.; Osmanoglu, B. Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects. Forests 2015, 6, 252-270.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top