Skip all navigation and jump to content Jump to site navigation
NASA Logo - Goddard Space Flight Center

+ NASA Homepage

    
Goddard Space Flight Center
About MODIS News Data Tools /images2 Science Team Science Team Science Team

   + Home
ABOUT MODIS
MODIS Publications Link
MODIS Presentations Link
MODIS Biographies Link
MODIS Science Team Meetings Link
 

 

 

Santoro, M; Beer, C; Cartus, O; Schmullius, C; Shvidenko, A; McCallum, I; Wegmuller, U; Wiesmann, A (2011). Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements. REMOTE SENSING OF ENVIRONMENT, 115(2), 490-507.

Abstract
Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100 m and I km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Quebec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300 m(3)/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1 km pixel size. Larger errors were obtained at 100 m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10 x 10 pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data. (C) 2010 Elsevier Inc. All rights reserved.

DOI:
10.1016/j.rse.2010.09.018

ISSN:
0034-4257

FirstGov logo Privacy Policy and Important Notices NASA logo

Curator: Brandon Maccherone
NASA Official: Shannell Frazier

NASA Home Page Goddard Space Flight Center Home Page