# Bagging NFI, GEDI and Sentinel-2 data to produce high-resolution maps of forest volume

Anouk Schleich<fnref n="1" />, Cédric Vega², Jean-Pierre Renaud³, Sylvie Durrieu<fnref n="1" />

INRAE

<figure><img src="image_1.png" /></figure>

IGN
INSTITUT NATIONAL
DE L'INFORMATION
GÉOGRAPHIQUE
ET FORESTIÈRE

ENSG
Géomatique

ÉCOLE NATIONALE
DES SCIENCES
GÉOGRAPHIQUES

tetis
TERRITOIRE ENVIRONNEMENT TELEDETECTION
INFORMATION SPATIALE

## I. Context, Objective & Data

GEDI samples forest structures at a very high density at an almost global scale, and is a good candidate to improve Multisource forest inventories. However, one key issue is the difficulty to obtain spatial matching between GEDI and field reference data for both calibrating and validating models.

We propose a solution based on a k-nearest neighbour (KNN) bagging approach [1],[2], to bridge the gap between GEDI and national forest inventory (NFI) data. It relies on the use of continuous auxiliary information, here Sentinel-2 (S2).

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The approach was tested over the Vosges mountains in France to predict growing stock volume (GSV) at a 30 m plot level. The data vector included 10 GEDI RH values, from GEDI RH derived variables, S2 spectral band textures, S2 vegetation indices, and S2 temporal differences.

## II. Method

<figure><img src="image_6.png" /><figcaption>Step 1: KNN Bagging
Predict GEDI for NFI plots using S2</figcaption></figure>

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Estimate GEDI variables based on S2 for every 30 m grid cell matching NFI plots :
Model trained on 109 500 GEDI footprints and tested on 500 test footprints

1. **Variable selection** : choose S2 variables correlated with GEDI variables

2. **Bagging** (n=1000) → 1000 predicted GEDI candidates per grid cell

3. **Select** best GEDI footprint candidate per cell by minimising residuals to the mean prediction

4. **Evaluation** : assess quality on test data set by comparing predicted and real GEDI variables

<figure><img src="image_8.png" /><figcaption>Step 2: KNN Bagging
Predict NFI for all S2-GEDI 30 m grid cells</figcaption></figure>

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Estimate NFI GSV for any cell having S2 and GEDI variables :
Model trained on 80 % of the NFI plots and tested on remaining 20 %

1. **KNN Bagging with variable selection** (n=1000) → 1000 predicted GSVᵢ per grid cell

2. **Define** per grid cell final predicted GSV = $\frac{1}{n} \sum_{i=1}^{n} GSV_{i}$

3. **Evaluation** : assess quality on the 20 % test data set by comparing predicted and real NFI volumes

## III. Results

Residuals between real and predicted GEDI variables

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Observed and predicted GSV

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57 % of the GSV variance can be explained by our approach.

$$ 
\begin{aligned}
\text{RMSE} &= 137 \text{ m}^3/\text{ha} \\
\text{bias} &= 6.8 \text{ m}^3/\text{ha}
\end{aligned}
 $$

## IV. Perspectives

* Improve predictions by optimizing variables (e.g. add LAI) and variable selection

* Expand to entire area using existing GEDI height maps (e.g. [3],[4],[5])

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Final output goal : NFI GSV prediction map on 30 m grid

References : [1] Sagar et al. 2022 (10.1016/j.isprsjprs.2022.08.016), [2] Breiman 1996 (10.1007/BF00058655), [3] Schwartz et al. 2023 (10.5194/essd-2023-196), [4] Potapov et al. 2021 (10.1016/j.rse.2020.112165), [5] Lang et al. 2022 (10.48550/arXiv.2204.08322)

This work is supported by the SLIM project, founded by the TOSCA CNES Grant 4500066524.

It is also supported by the cofounding of INRAE and IGN for the PhD thesis of Anouk Schleich.

<footnote n="1">¹ UMR TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE - 34196 Montpellier, France (anouk.schleich@inrae.fr, sylvie.durrieu@inrae.fr)
² ENSG, IGN, Laboratoire d'inventaire forestier - 54042 Nancy, France (cedric.vega@ign.fr)
³ Office National des Forêts, Pôle Recherche et Développement Innovation - 54600 Villier-les-Nancy, France (jean-pierre.renaud-02@onf.fr)</footnote>