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Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements by Maurizio Santoro; Christian Beer; Oliver Cartus; Christiane Schmullius; Anatoly Shvidenko; Ian McCallum; Urs Wegmüller; Andreas Wiesmann is a Environmental Science article available to read on EtoBox.
What is Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements about?
## algorithm MODIS Vegetation Continuous Fields 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 The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated.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 ScanSA
Who reads Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Maurizio Santoro; Christian Beer; Oliver Cartus; Christiane Schmullius; Anatoly Shvidenko; Ian McCallum; Urs Wegmüller; Andreas Wiesmann
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0034-4257)
- Published
- 2011
- Language
- EN
- Field
- Environmental Science (Physical Sciences)