Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Forest parameter mapping based on lidar and SAR data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Zhang, Z. ; Remote Sensing & GIS Res. Center, Beijing Normal Univ., Beijing, China ; Zhang, L. ; Ni, W. ; Guo, Z.
more authors

Vegetation spatial structure including plant height, biomass, vertical and horizontal heterogeneity, is an important factor influencing the exchanges of matter and energy between the landscape and atmosphere, and the biodiversity of ecosystems. Estimation of boreal forest canopy height is an extremely urgent research because it is essential for understanding ecosystems changing by human activities and climate change. Data from lidar and radar contain information relevant to different aspects of the biophysical properties of the vegetation canopy. GLAS (Geoscience Laser Altimeter System) and ALOS PALSAR data were used to test the combined use of lidar samplings and radar images for canopy height and stand biomass mapping in our test area. The result showed that maximum tree height and biomass in gLAS footprints can be predicted by GLAS waveform data. By using these sampling data parameters retrieval models using SAR data only can be developed. These models were applied to the entire SAR image, and the results were assessed using large-scale forest inventory data.

Published in:

Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009  (Volume:5 )

Date of Conference:

12-17 July 2009