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
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]

4.1.3   High resolution optical data Previous topic Parent topic Child topic Next topic

High resolution (finer than 10 metres) data can improve detection of changes associated with degradation, and allow REDD+ activity data generally to be monitored more accurately and with greater differentiation than medium resolution data. There are some examples of countries making use of high resolution data for wall-to-wall mapping for REDD+ including Mexico and Guyana (Box 15), but acquisition and processing costs are higher than with medium resolution data. Also high resolution data may not be available for entire countries for a sufficient number of time periods to allow direct estimation of REDD+ activity data from wall-to-wall coverage.
Consequently, so far high resolution optical data have been used mainly in sample-based verification or accuracy assessment, for sampling transects or local areas or regions of interest, and for assessment of hot spots where changes are occurring or are more likely to occur. High resolution data may also be valuable for providing training data for change detection algorithms and can be used to produce emission and removal estimates and factors – e.g. the application of LIDAR (see below) to estimating depth of peat combusted by fire in Indonesia, and hence emissions of carbon dioxide and non-carbon dioxide greenhouse gases (Ballhorn et al., 2009). The use of high resolution data continues to be the subject of research.

Box 15: Use of high resolution data in the mapping context– Guyana

Guyana has developed a MRV process which provides the basis for performance measurement which has drawn on a capacity building Roadmap spanning the period 2010 to 2013, and includes the forest carbon monitoring system and forest cover assessment. The work has been supported under the terms of the Joint Concept Note which Guyana and Norway signed in 2009a. Guyana began developing its historical (1990) land cover change baselines from freely-available 30 m Landsat imagery. A review by the Guyana Forestry Commission (GFC)b after the first year of operation (2011), lead to opting for high resolution RapidEye imagery to cover the most active change areas. Today the MRV processes conform to IPCC approach 3. All post 1990 land cover changes (including non-anthropogenic changes) greater than 1 ha as detected are mapped and stored as a GIS. From 2011 onwards the MRV process included the mapping and monitoring of forest degradation (or canopy disturbance) surrounding deforestation events at a national-scale. Overall change areas are estimated using a combination of mapped and reference data and the spatially explicit data is also used for assessing the effect of drivers. An independent accuracy assessment conducted in 2013 quantified the accuracy of the deforestation and forest degradation mapping at 99% and 80% respectively.
The process designed and adopted by GFC has developed over time, and integrates good practice linked to operational research focused on developing methods appropriate for the forest degradation drivers. The MRV design recognises the problem of persistent cloud cover, the spatial scale and the intensity of the land cover change. To address these, frequent coverage of high resolution imagery is used. As with many countries, considerable expertise in Guyana resides in the use of GIS rather than in remote sensing technologies. Given these challenges, a GIS-based MRV process has the advantage of being adaptable, user friendly and flexible enough to incorporate a range of different data types required to meet IPCC requirements.
The change detection processing chain is semi-automated with each satellite image assimilated and batch processed. The processing includes conversion of images to reflectance, atmospheric normalisation, detection and delineation of land cover change using vegetation indices, and conversion of these changes to a GIS format. The quality of the change delineation is systematically assessed and edited by trained analysts who also attribute a change driver to each polygon. The attribution options are illustrated in mapping documentation with the attribution process controlled by the use of a customised GIS toolbar. The toolbar stores all relevant attributes and assists the operator to ensure appropriate land cover change and driver combinations are selected. Image 1.1 provides an overview of the mapping flow, from satellite images (A) to creation of a pre-processed change layer (B) to the generation of a multi-temporal forest change products (C).
Forest degradation mapping is undertaken in conjunction with deforestation mapping. The scale (<1 ha) and intensity of degradation is known to vary by driver (i.e. mining prospecting, timber extraction, or shifting cultivation). Degraded forest is identified from temporal persistence of canopy disturbance. Further monitoring is used to determine if the changes in the canopy can be considered forest degradation, linked to a significant percentage reduction in carbon stocks in the areas affected, or just temporary disturbances that recover in a short time period. To detect forest degradation on satellite imagery the disturbance must occur at a scale that causes a visible change in the canopy. Using the method adopted, the pixel resolution and temporal frequency of sensors such as Landsat and disaster monitoring constellation are insufficient to detect forest degradation related to canopy disturbance.
Notes: a. The JCN sets out a series of interim measures that are intended to be used whilst the full MRV functionality is being developed. b. The implementing Agency with technical assistance provided by Indufor Asia Pacific