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Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height

hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
hal.structure.identifierModélisation des Surfaces et Interfaces Continentales [MOSAIC]
hal.structure.identifierKayrros
dc.contributor.authorFAYAD, Ibrahim
hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
dc.contributor.authorCIAIS, Philippe
hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
hal.structure.identifierModélisation des Surfaces et Interfaces Continentales [MOSAIC]
dc.contributor.authorSCHWARTZ, Martin
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, Jean-Pierre
hal.structure.identifierTerritoires, Environnement, Télédétection et Information Spatiale [UMR TETIS]
dc.contributor.authorBAGHDADI, Nicolas
hal.structure.identifierKayrros
dc.contributor.authorDE TRUCHIS, Aurélien
hal.structure.identifierKayrros
dc.contributor.authorD'ASPREMONT, Alexandre
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorFRAPPART, Frederic
hal.structure.identifierJet Propulsion Laboratory [JPL]
dc.contributor.authorSAATCHI, Sassan
hal.structure.identifierKayrros
dc.contributor.authorSEAN, Ewan
hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
hal.structure.identifierModélisation des Surfaces et Interfaces Continentales [MOSAIC]
dc.contributor.authorPELLISSIER-TANON, Agnes
hal.structure.identifierMathématiques et Informatique Appliquées [MIA Paris-Saclay]
dc.contributor.authorBAZZI, Hassan
dc.date.accessioned2024-04-08T11:39:31Z
dc.date.available2024-04-08T11:39:31Z
dc.date.issued2024-03
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195036
dc.description.abstractEnAccurate and timely monitoring of forest canopy height is critical for assessing forest dynamics, biodiversity, carbon sequestration as well as forest degradation and deforestation. Recent advances in deep learning techniques, coupled with the vast amount of spaceborne remote sensing data offer an unprecedented opportunity to map canopy height at high spatial and temporal resolutions. Current techniques for wall-to-wall canopy height mapping correlate remotely sensed information from optical and radar sensors in the 2D space to the vertical structure of trees using lidar's 3D measurement abilities serving as height proxies. While studies making use of deep learning algorithms have shown promising performances for the accurate mapping of canopy height, they have limitations due to the type of architectures and loss functions employed. Moreover, mapping canopy height over tropical forests remains poorly studied, and the accurate height estimation of tall canopies is a challenge due to signal saturation from optical and radar sensors, persistent cloud cover, and sometimes limited penetration capabilities of lidar instruments. In this study, we map heights at 10 m resolution across the diverse landscape of Ghana with a new vision transformer (ViT) model, dubbed Hy-TeC, optimized concurrently with a classification (discrete) and a regression (continuous) loss function. This model achieves significantly higher accuracy than previously employed convolutional-based approaches (ConvNets) optimized with only a continuous loss function. Hy-TeC results show that our proposed discrete/continuous loss formulation significantly increases the sensitivity for very tall trees (i.e., > 35 m). Overall, Hy-TeC has significantly reduced bias (0.8 m) and higher accuracy (RMSE = 6.6 m) over tropical forests for which other approaches show poorer performance and oftentimes a saturation effect. The height maps generated by Hy-TeC also have better ground sampling distance and better sensitivity to sparse vegetation. Over these areas, Hy-TeC showed an RMSE of 3.1 m in comparison to a reference dataset while the baseline ConvNet model had an RMSE of 4.3 m. Hy-TeC, which was used to generate a height map of Ghana using free and open access remotely sensed data with Sentinel-2 and Sentinel-1 images as predictors and GEDI height measurements as calibration data, has the potential to be used globally.
dc.language.isoen
dc.publisherElsevier
dc.subject.enCanopy height
dc.subject.enGEDI
dc.subject.enSentinel-1
dc.subject.enSentinel-2
dc.subject.enVision transformers, deep learning, knowledge distillation
dc.title.enHy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2023.113945
dc.subject.halPlanète et Univers [physics]/Océan, Atmosphère
dc.subject.halPlanète et Univers [physics]/Interfaces continentales, environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page113945
bordeaux.volume302
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04372506
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04372506v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2024-03&rft.volume=302&rft.spage=113945&rft.epage=113945&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=FAYAD,%20Ibrahim&CIAIS,%20Philippe&SCHWARTZ,%20Martin&WIGNERON,%20Jean-Pierre&BAGHDADI,%20Nicolas&rft.genre=article


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