Paper Info
Title
Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation.
Abstract
Abstract The European Space Agency (ESA) is currently implementing the BIOMASS mission as 7th Earth Explorer satellite. BIOMASS will provide for the first time global forest aboveground biomass estimates based on P-band synthetic aperture radar (SAR) imagery. This paper addresses an often overlooked element of the data processing chain required to ensure reliable and accurate forest biomass estimates: accurate identification of forest areas ahead of the inversion of radar data into forest biomass estimates. The use of the P-band data from BIOMASS itself for the classification into forest and non-forest land cover types is assessed in this paper. For airborne data in tropical, hemi-boreal and boreal forests we demonstrate that classification accuracies from 90 up to 97% can be achieved using radar backscatter and phase information. However, spaceborne data will have a lower resolution and higher noise level compared to airborne data and a higher probability of mixed pixels containing multiple land cover types. Therefore, airborne data was reduced to 50 m, 100 m and 200 m resolution. The analysis revealed that about 50–60% of the area within the resolution level must be covered by forest to classify a pixel with higher probability as forest compared to non-forest. This results in forest omission and commission leading to similar forest area estimation over all resolutions. However, the forest omission resulted in a biased underestimated biomass, which was not equaled by the forest commission. The results underline the necessity of a highly accurate pre-classification of SAR data for an accurate unbiased aboveground biomass estimation.
Year
DOI
Venue
2017
10.1016/j.jag.2016.12.001
International Journal of Applied Earth Observation and Geoinformation
Keywords
Field
DocType
P-band synthetic aperture radar (SAR),Forestry,Classification,Aboveground biomass,BIOMASS
Radar,Biomass,Satellite,Data processing,Inversion (meteorology),Synthetic aperture radar,Remote sensing,Taiga,Land cover,Geography
Journal
Volume
ISSN
Citations 
56
0303-2434
2
PageRank 
References 
Authors
0.40
21
3
Name
Order
Citations
PageRank
Michael Schlund133.13
Klaus Scipal29617.22
Malcolm W. J. Davidson311813.35