Unit-level Small Area Estimation of Forest Inventory with GEDI Auxiliary Information

International audience; National Forest Inventories (NFIs) play an important role in understanding the state of forests at thenational and regional levels. Forest inventory for small territorial areas, such as municipalities, is alsoimportant for decision-makers. However, information is relatively limited at this level. As a result,developing small area estimation (SAE) approaches has gained increasing popularity in the field offorest inventory. It enables prediction of forest attributes for sub-populations using regression modelsbased on auxiliary data commonly derived from remote sensing techniques over an area of interest (AOI).It has been reported that SAE can improve the precision of forest inventory without increasing costs(Mandallaz, Breschan and Hill 2013) and may produce reliable predictions of forest attributes locally,even when field plots are not available (Rao 2014).Tomppo (2006) is a pioneer in the use of auxiliary data for multi-source forest inventory. Previously,common sources of auxiliary data often came from satellite-based imagery (McRoberts et al. 2007),digital aerial photogrammetry (Breidenbach et al. 2018), and airborne laser scanning (Magnussen et al.2014). NASA’s newly-launched Global Ecosystem Dynamics Investigation (GEDI) is a full waveformLiDAR instrument aboard the International Space Station (ISS). Its products consist of footprintmeasurements projected to cover 4% of the global land surface by the end of its mission (Dubayah et al.2020). This will provide an unprecedented opportunity to systematically collect samples of forestinformation that can be used in SAE on a large scale.The objective of this study is to explore the possibility of using GEDI auxiliary data to improve theaccuracy of forest inventory for a large natural area in central France (Sologne), as well as for smallersub-areas defined by French administrative boundaries (departments). The results will then be comparedagainst estimates obtained from simple random sampling (SRS), to assess the efficiency of the auxiliarydata.