arXiv subjects

Jean-Pierre Renaud

Publications and source records attributed to Jean-Pierre Renaud.

3 recordsLinked to original sources

arXiv2026

Optimizing GEDI Simulator Configuration for European Temperate Forests

Accurate estimation of aboveground biomass density is essential for quantifying forest carbon stocks. NASA's GEDI mission provides valuable canopy structure data, but its sparse sampling necessitates the use of simulators to calibrate biomass models at field inventory locations. The widely used simulator of Hancock et al. (2019) emulates GEDI waveforms from airborne LiDAR point clouds, yet it has never been validated over European temperate forests. Here, we compare approximately 9,500 pairs of observed and simulated GEDI relative height (RH) profiles across French forests using the national airborne LiDAR program as input. We separate two sources of error: waveform modeling differences, assessed by referencing both simulated and real RH metrics to a common ALS-derived ground elevation, and ground detection bias, evaluated by comparing each GEDI L2A processing algorithm against the ALS reference. Under the baseline configuration, the mean absolute bias across the full RH profile reaches 0.69 m in leaf-on and 1.28 m in leaf-off conditions. Switching to intensity-based return weighting and selecting the a3 L2A algorithm reduces these biases to 0.44 m and 0.40 m respectively. The a3 algorithm also achieves near-unbiased ground detection (-0.03 m versus -0.88 m for the default), directly reducing a previously overlooked source of error. We also show that leaf-off acquisitions and low-sensitivity shots, both typically excluded from standard biomass products, are simulated as reliably as their counterparts, substantially expanding the potential calibration and inference datasets.

eess.SP↗

arXiv2025

FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series

Current large-scale satellite-based forest disturbance monitoring systems operate at 10-30~m resolution, too coarse to detect changes at the scale of individual trees and resulting in a systematic underestimation of forest disturbances. Here, we introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024), country-scale mapping of forest canopy height at 1.5 m resolution over France, together with FORMSpoT-$\Delta$, annual disturbance polygons derived from height differences in the FORMSpoT time series. Canopy heights were derived from annual SPOT-6/7 composites using a hierarchical transformer model (PVTv2) trained on high-resolution airborne laser scanning (ALS) data. To enable robust change detection, we developed a post-processing pipeline combining co-registration and spatio-temporal total variation denoising. We find that (1) the French disturbance regime is dominated by small events. Sub-100 m$^{2}$ disturbances alone represent 72% of all events, and disturbances below 0.1 ha account for 97% of events and 39% of the disturbed area. These events are largely missed by Sentinel-1/2 and Landsat-based products. (2) Validated against successive ALS revisits across 19 sites and 5,087 NFI plot revisits, FORMSpoT-$\Delta$ provides reliable detection (F1>0.8) above 100 m$^{2}$ while retaining sensitivity to finer events that coarser products do not capture. (3) At the national scale, FORMSpoT-$\Delta$ resolves contrasted disturbance regimes, from clear-cut-dominated dynamics in maritime pine plantations to diffuse, smaller disturbance events in mountain forests, and captures their temporal dynamics, including the salvage-logging signature of the 2017-2022 bark beetle crisis in northeastern France

cs.CV↗

arXiv2024

How reliable are remote sensing maps calibrated over large areas? A matter of scale?

Remote sensing data are increasingly available and frequently used to produce forest attributes maps. The sampling strategy of the calibration plots may directly affect predictions and map qualities. The aim of this manuscript is to evaluate models transferability at different spatial scales according to the sampling efforts and the calibration domain of these models. Forest inventory plots from locals and regionals networks were used to calibrate randomForest (RF) models for stand basal area predictions. Auxiliary data from ALS flights and a Sentinel-2 image were used. Model transferability was assessed by comparing models developed over a given area and applied elsewhere. Performances were measured in terms of precision (RMSE and bias), coefficient of determination (R2) and the proportion of extrapolated predictions. Regional networks were also thinned to evaluate the effect of sampling efforts on models' performances. Local models showed large bias and extrapolation issues when applied elsewhere. Local issues of regional models were also observed, raising transferability and extrapolation concerns. An increase in sampling efforts was shown to reduce extrapolation issues. The outcoming results of this study underline the importance of considering models' validity domain while producing forest attribute maps, since their transferability is of crucial importance from a forest management perspective.

stat.AP↗