TY - JOUR

T1 - High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach

AU - Schwartz, Martin

AU - Ciais, Philippe

AU - Ottlé, Catherine

AU - De Truchis, Aurelien

AU - Vega, Cedric

AU - Fayad, Ibrahim

AU - Brandt, Martin

AU - Fensholt, Rasmus

AU - Baghdadi, Nicolas

AU - Morneau, François

AU - Morin, David

AU - Guyon, Dominique

AU - Dayau, Sylvia

AU - Wigneron, Jean Pierre

N1 - Publisher Copyright: © 2024 The Author(s)

PY - 2024

Y1 - 2024

N2 - In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10–––20 m) is needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-sensor remote sensing measurements to create a high-resolution canopy height map over the “Landes de Gascogne” forest in France, a large maritime pine plantation of 13,000 km2 with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-Net models based on combinations of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each sensor in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole “Landes de Gascogne” forest area for 2020 with a mean absolute error of 2.02 m on the test dataset. The best predictions were obtained using all available bands from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.

AB - In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10–––20 m) is needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-sensor remote sensing measurements to create a high-resolution canopy height map over the “Landes de Gascogne” forest in France, a large maritime pine plantation of 13,000 km2 with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-Net models based on combinations of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each sensor in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole “Landes de Gascogne” forest area for 2020 with a mean absolute error of 2.02 m on the test dataset. The best predictions were obtained using all available bands from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.

KW - 3D Stereo

KW - Deep Learning

KW - Forest height

KW - Forest Inventory

KW - GEDI

KW - Landes forest

KW - Sentinel-1

KW - Sentinel-2

KW - U-Net

U2 - 10.1016/j.jag.2024.103711

DO - 10.1016/j.jag.2024.103711

M3 - Journal article

AN - SCOPUS:85185914484

VL - 128

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 1569-8432

M1 - 103711

ER -