Regresar

Introduction and Rationale

Abstract:

1.1 Background As highlighted by the Global Forest Observation Initiative (GFOI) in their Methods and Guidance Document (MGD)(GFOI 2017), SAR datasets have proven useful as a source of activity data for forest Monitoring, Reporting, and Verification (MRV) systems at national and regional levels. SAR datasets can provide substantial aid to MRV systems, particularly in areas with persistent cloud cover, due to the sensor’s all-weather capability (Reich et al. 2016). The MGD also highlights the potential of SAR datasets with higher spatial resolution (< 5 m) to characterize forest canopy. SAR captures different target parameters than optical sensors, therefore providing unique information that complements standard optical remote sensing methods. A helpful analogy would be that while the energy captured by optical sensors of a green leaf relates to its amount of chlorophyll (or “greenness”), the amount of microwave energy (part of the electromagnetic spectrum used in SAR) scattered by the leaf would be proportional to its size, shape, and water content (Woodhouse 2006). SAR indeed has a strong sensitivity to forest structure and biomass (Saatchi 2015). GFOI’s MGD also documents the limitations of optical data such as Landsat and MODIS to estimate forest biomass and to detect early regrowth of secondary vegetation, including a limited ability to expose small disturbances such as removal of individual trees (GFOI 2017). The main premise and recommendation of GFOI’s MGD is to combine both remote sensing technologies, optical and SAR. However, the main limitation faced over and over by users is how to start ingesting SAR when its …