The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

Abstract

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Spatial validation reveals poor predictive performance of large-scale ecological mapping models

“…Sampling of the territory by airborne or UAV-LiDAR scanning is also becoming denser across the tropics 43 – 47 . At the same time, this study shows that a better valorization of commercial forest inventory data is possible, while concerted efforts are ongoing to develop national forest inventories and improve the coordination between scientific plot networks 48 , 49 . We argue that the unprecedented amount of data produced by these efforts will provide a unique opportunity to clarify, develop, and extensively validate a conceptual modeling framework for biomass mapping from high- or medium-resolution multispectral data, which could allow leveraging decades of image archives.…”

A map of African humid tropical forest aboveground biomass derived from management inventories

“…For each Monte Carlo simulation, errors associated to each computational step are calculated and propagated throughout the computation chain. This approach has been described elsewhere, notably in the BIOMASS R package 27 , and is now used as a standard for generating calibration AGB data for satellite missions 31 . The procedure outputs a vector of AGB estimates for each plot (of length 1000 here) from which we extracted the mean and the variance.…”

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…However, ground plot survey data are still indispensable for remote sensing modeling [74]. The costs of ground plot surveys have always been high, which has presented some obstacles to the estimation of the provincial FSV by remote sensing.…”