Uncertainty [MGD Sections]

UNFCCC decisions and requirements
IPCC good practice guidance
Relationship to UNFCCC
GHGI coverage, approaches, methods and tiers
Design decisions relevant to national forest monitoring systems
Land cover, land use and stratification
Forest reference emission levels and forest reference levels
Quality assurance and quality control
Guiding principles – Requirements and design decisions
Estimation methods for REDD+ activities
Integration frameworks for estimating emission and removals
Selecting an integration framework
Activity data x emission/removal factor tools
Fully integrated tools
Practical considerations in choosing an integration tool
Guiding principles – Methods and approaches
Remote sensing observations
Coarse resolution optical data
Medium resolution optical data
High resolution optical data
L-band Synthetic aperture radar
C-band and X-band SAR
LIDAR
Global forest cover change datasets
Ground-based observations
National forest inventories
Auxiliary data
Guiding principles – Remote sensing and ground-based observations
Activity data
Methods for estimating activity data
Maps of forest/non-forest, land use, or forest stratification
Detecting areas of change
Additional map products from remote sensing
Estimating uncertainty of area and change in area
Estimating total emissions/removals and its uncertainty
REDD+ requirements and procedures
Reporting forest reference emission levels and forest reference levels
Technical assessment of forest reference emission levels and forest reference levels
Reporting results of REDD+ activities
Technical analysis of the REDD+ annex to the BUR
Additional advice on REDD+ reporting and verification
Guiding Principles – Reporting and verification of emissions and removals
Financial considerations
Country examples – Tier 3 integration
Use of global forest change map data
Relative efficiencies
Developing and using allometric models to estimate biomass

Record Keeping [MGD Sections]

Integration + Estimation [MGD Sections]

Ground Based Observations [MGD Sections]

4.1.4   L-band synthetic aperture radar Previous topic Parent topic Child topic Next topic

The potential ability of imaging radar (also referred to as Synthetic Aperture Radar, SAR) to provide activity data has been demonstrated at the subnational (Mitchell et al., 2012) and regional (project) level and could be useful, particularly in areas of persistent cloud cover, as well as in combination with optical data. In a volume (such as a forest canopy), the radar signal tends to interact more strongly with scatterers of comparable size or larger than the wavelength; shorter wavelengths, such as X- and C-band, respond more to the leaves and twigs, while L-band will hardly see the leaves but will see larger branches.
Current and near-future SAR systems have multi-polarisation capacities which, like the different spectral bands of optical data, provide additional information. SAR systems can provide information that is not visible in optical data (and vice versa) and the two data sources are therefore to be regarded as complementary, not competing.
An additional advantage of the cloud independence of SAR is that large regions can be acquired within relatively short time windows (few weeks – few months), reducing the need to fill in data gaps with data from different years or different seasons. Consistent archives of global or regional wall-to-wall data exist for some historical SAR missions for certain time periods (JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2), and through the CEOS Data Strategy for GFOI, such systematic acquisition strategies are becoming standard for several of the near-future core and non-core SAR missions (Sentinel-1, SAOCOM-1, ALOS-2, RCM). L-band SAR.
With a wavelength of about 23.5 cm, L-band SAR penetrates through the forest canopy and generally provides clear distinction between vegetated and non-vegetated areas. It is commonly used for mapping of forest/non-forest, and with time series of data, for detection of forest cover changes. At least two polarisations are preferred, because the cross-polarisation channel is particularly sensitive to forest structural parameters, such as twigs, branches and stems, and thus indirectly to forest types and age classes. L-band SAR is also linked to above-ground biomass up to a level of about 100 tonnes per hectare, although this is an area of research (Lucas et al., 2010; GEO, 2011) and accuracy levels are currently insufficient for use for GHGI estimates.
Semi-annual wall-to-wall observations over the global forest cover were undertaken by ALOS L-band SAR (PALSAR) between 2007 and 2011 and by ALOS-2 PALSAR-2 since 2014. 25m resolution PALSAR and PALSAR-2 global mosaic data are open to the public and can be accessed from JAXA. The SAOCOM-1 L-band SAR constellation (launch 2017/2018) will feature a similar systematic acquisition strategy that will provide cloud-free coverage over the pan-tropical regions several times per year.
High temporal frequency, coarse (100 m) resolution L-band SAR data acquired in so-called ScanSAR mode have demonstrated potential for early warning of forest clearings (e.g., INDICAR system of IBAMA, Brazil (de Mesquita, 2011). L-band SAR is considered to have operational capacity to map forest cover and changes (GEO, 2011; Walker et al., 2010), and to be pre-operational for deriving land cover (GEO, 2011), activity data (Mitchell et al., 2012; Lucas et al., 2010), forest sub-stratification (GEO, 2011; Hoekman, 2012) products as input to emissions estimation. Combined use of different sensor types (e.g. L-band SAR and optical, L- and C-band SAR) can improve discrimination of forest and land cover types (Holecz et al., 2010).