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Santoro, M., Beer, C., Cartus, O., Schmullius, C., Shvidenko, A., McCallum, I. 
ORCID: https://orcid.org/0000-0002-5812-9988, Wegmueller, U., & Wiesmann, A.
(2010).
The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data.
In: Proceedings of ESA Living Planet Symposium, 28 June-2 July 2010.
Abstract
Retrieval of forest growing stock volume (GSV) is a major topic of investigation in the remote sensing community due to the necessity of accurate and updated information on forest resources, which is not achievable using traditional survey methods at the regional and global level. Radar remote sensing has the advantage of being able to acquire images over any part of the Earth with a high repetition frequency. While an image can be formed regardless of the cloud cover and the solar illumination, the environmental conditions can play a significant role on the measurements collected by the sensor. This aspect is of particular relevance in case of forest-related studies and for high frequencies. C-band SAR backscatter is generally deemed as useless when aiming at forest resources assessment due to the weak sensitivity with respect to biophysical properties. Furthermore, the strong sensitivity to the dielectric properties of the scattering objects make C-band SAR backscatter an unreliable tool for monitoring forests. With increasing number of studies on extraction of forest biophysical properties from SAR data, the research community tends toward a clear focus on the use of low frequency SAR.
In this paper we demonstrate that accurate estimates of forest GSV can be obtained also from C-band backscatter data under the requirement that large stack of observations are available. The estimation of the GSV is carried out by means of the BIOMASAR algorithm, which combines conventional SAR processing techniques in the case of multi-temporal data stacks (calibration, co-registration, multi-temporal speckle filter), the inversion of Water-Cloud-like model relating the GSV to the forest backscatter, and a multi-temporal combination of GSV estimates from each image. While the single parts forming the BIOMASAR algorithm approach are well known (Askne et al, IEEE TGRS, 1995; Kurvonen et al, IEEE TGRS, 1999), the implementation in an automated approach to retrieve GSV is a novel aspect. Model training, which is traditionally based on in situ measurements of forest GSV and corresponding forest backscatter measurements, is carried out by consideration of the backscattering for unvegetated and dense forest areas. These are identified by means of the MODIS Vegetation Continuous Fields product. For each pixel the corresponding measures of central tendency in a finite-size window are computed. The multi-temporal combination exploits the different sensitivities of the forest backscatter to GSV, which can be derived from the estimates of the a priori unknown model parameters.
The BIOMASAR algorithm was first presented in (Santoro et al, Proc ENVISAT Symposium, 2007) and has now been validated in the case of ENVISAT ASAR ScanSAR data using in situ information from five test sites within the boreal zone. The validation activities were carried out within an ESA Support to Science Element (STSE) Project. The algorithm performs well for all validation sites. The retrieval RMSE is generally below 40% for full resolution data and below 20% for aggregated versions at reduced spatial resolution. The most prominent result is that the retrieved GSV was never affected by saturation, with estimates of GSV in line with in situ data up to 300 m3/ha. This unexpected result opens up the possibility of exploiting the extensive archives of ENVISAT ASAR ScanSAR data for pan-boreal forest GSV retrieval at a spatial resolution required by ecological and carbon accounting models.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Research Programs: | Forestry (FOR) |
| Bibliographic Reference: | In:; Proceedings of ESA Living Planet Symposium; 28 June-2 July 2010, CD-ROM, Oslo, Norway (July 2010) |
| Related URLs: | |
| Depositing User: | IIASA Import |
| Date Deposited: | 15 Jan 2016 08:44 |
| Last Modified: | 27 Aug 2021 17:21 |
| URI: | https://pure.iiasa.ac.at/9430 |
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