Next Article in Journal
Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution
Previous Article in Journal
Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(8), 7303-7319; doi:10.3390/rs6087303

The Penetration Depth Derived from the Synthesis of ALOS/PALSAR InSAR Data and ASTER GDEM for the Mapping of Forest Biomass

State Key Laboratory of Remote Sensing Science, the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agriculture Sciences, Beijing 100081, China
Author to whom correspondence should be addressed.
Received: 28 April 2014 / Revised: 16 July 2014 / Accepted: 16 July 2014 / Published: 5 August 2014
View Full-Text   |   Download PDF [6446 KB, uploaded 5 August 2014]   |  


The Global Digital Elevation Model produced from stereo images of Advanced Spaceborne Thermal Emission and Reflection Radiometer data (ASTER GDEM) covers land surfaces between latitudes of 83°N and 83°S. The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite (ALOS) collected many SAR images since it was launched on 24 January 2006. The combination of ALOS/PALSAR interferometric data and ASTER GDEM should provide the penetration depth of SAR data assuming ASTER GDEM was the elevation of vegetation canopy top. It would be correlated with forest biomass because penetration depth could be affected by forest density and forest canopy height. Their combination held great promises for the forest biomass mapping over large area. The feasibility of forest biomass mapping through the data synthesis of ALOS/PALSAR InSAR data and ASTER GDEM was investigated in this study. A procedure for the extraction of penetration depth was firstly proposed. Then three models were built for biomass estimation: (I) model only using backscattering coefficients of ALOS/PALSAR data; (II) model only using penetration depth; (III) model using both of them. The biomass estimated from Lidar data was taken as reference data to evaluate the three different models. The results showed that the combination of backscattering coefficients and penetration depth gave the best accuracy. The forest disturbance has to be considered in forest biomass estimation because of the long time span of ASTER data for generating ASTER GDEM. The spatial homogeneity could be used to improve estimation accuracy. View Full-Text
Keywords: forest biomass; ALOS/PALSAR; ASTER GDEM; InSAR; photogrammetry; penetration depth forest biomass; ALOS/PALSAR; ASTER GDEM; InSAR; photogrammetry; penetration depth

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ni, W.; Zhang, Z.; Sun, G.; Guo, Z.; He, Y. The Penetration Depth Derived from the Synthesis of ALOS/PALSAR InSAR Data and ASTER GDEM for the Mapping of Forest Biomass. Remote Sens. 2014, 6, 7303-7319.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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