Characterizing the calibration domain of remote sensing models using convex hulls

Renaud, Jean-Pierre; Sagar, Ankit; Barbillon, P.; Bouriaud, Olivier; Deleuze, Christine; Vega, Cédric

doi:10.1016/j.jag.2022.102939

Cited by 1 publication

(1 citation statement)

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“…The error of a map (or, its difference compared to a reference dataset) can be distinguished in two components: the systematic error (or, bias) and the random error of pixel-level predictions. In the case of biomass maps, the bias is often due to calibration data with systematic errors in their estimates or extrapolation issues 44 , inaccurate model parameters and the limited sensitivity of the remote sensing data to biomass variability. Several studies have reported that forest biomass maps tend to overestimate the stock in areas with low biomass density and underestimate the stock in areas with high biomass density, thus they are affected by different systematic errors at different biomass ranges 25 , 45 , 46 .…”

Section: Methodsmentioning

confidence: 99%

Harmonised statistics and maps of forest biomass and increment in Europe

Avitabile,

Pilli,

Migliavacca

et al. 2024

Sci Data

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Forest biomass is an essential resource in relation to the green transition and its assessment is key for the sustainable management of forest resources. Here, we present a forest biomass dataset for Europe based on the best available inventory and satellite data, with a higher level of harmonisation and spatial resolution than other existing data. This database provides statistics and maps of the forest area, biomass stock and their share available for wood supply in the year 2020, and statistics on gross and net volume increment in 2010–2020, for 38 European countries. The statistics of most countries are available at a sub-national scale and are derived from National Forest Inventory data, harmonised using common reference definitions and estimation methodology, and updated to a common year using a modelling approach. For those counties without harmonised statistics, data were derived from the State of Europe’s Forest 2020 Report at the national scale. The maps are coherent with the statistics and depict the spatial distribution of the forest variables at 100 m resolution.

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