LutherFournierLierEtAl2019

Référence

Luther, J.E., Fournier, R.A., van Lier, O.R., Bujold, M. (2019) Extending ALS-based mapping of forest attributes with medium resolution satellite and environmental data. Remote Sensing, 11(9). (Scopus )

Résumé

Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending ALS-based maps of forest attributes using medium resolution satellite and environmental data. First, we developed ALS-based models and predicted a suite of forest attributes for a 950 km 2 study area covered by wall-to-wall ALS data. Then, we used samples extracted from the ALS-based predictions to model and map these attributes with satellite and environmental data for an extended 5600 km 2 area with similar forest and ecological conditions. All attributes were predicted well with the ALS data (R 2 ≥ 0.83; RMSD% < 26). The satellite and environmental models developed using the ALS-based predictions resulted in increased correspondence between observed and predicted values by 13-49% and decreased prediction errors by 8-28% compared with models developed directly with the ground plots. Improvements were observed for both multiple regression and random forest models, and for the suite of forest attributes assessed. We concluded that the use of ALS-based predictions in this study improved the estimation of forest attributes beyond an approach linking ground plots directly to the satellite and environmental data. © 2019 by the authors.

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@ARTICLE { LutherFournierLierEtAl2019,
    AUTHOR = { Luther, J.E. and Fournier, R.A. and van Lier, O.R. and Bujold, M. },
    TITLE = { Extending ALS-based mapping of forest attributes with medium resolution satellite and environmental data },
    JOURNAL = { Remote Sensing },
    YEAR = { 2019 },
    VOLUME = { 11 },
    NUMBER = { 9 },
    NOTE = { cited By 0 },
    ABSTRACT = { Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending ALS-based maps of forest attributes using medium resolution satellite and environmental data. First, we developed ALS-based models and predicted a suite of forest attributes for a 950 km 2 study area covered by wall-to-wall ALS data. Then, we used samples extracted from the ALS-based predictions to model and map these attributes with satellite and environmental data for an extended 5600 km 2 area with similar forest and ecological conditions. All attributes were predicted well with the ALS data (R 2 ≥ 0.83; RMSD% < 26). The satellite and environmental models developed using the ALS-based predictions resulted in increased correspondence between observed and predicted values by 13-49% and decreased prediction errors by 8-28% compared with models developed directly with the ground plots. Improvements were observed for both multiple regression and random forest models, and for the suite of forest attributes assessed. We concluded that the use of ALS-based predictions in this study improved the estimation of forest attributes beyond an approach linking ground plots directly to the satellite and environmental data. © 2019 by the authors. },
    AFFILIATION = { Natural Resources Canada, Canadian Forest Service-Atlantic Forestry Centre, Corner Brook, NL A2H 5G4, Canada; Department of Applied Geomatics, Centre d'Applications et de Recherches en Télédétection (CARTEL), Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; Natural Resources Canada, Canadian Forest Service-Canadian Wood Fibre Centre, Corner Brook, NL A2H 5G4, Canada },
    ART_NUMBER = { 1092 },
    AUTHOR_KEYWORDS = { Boreal forest; Forest attributes; Imagery; Inventory; LiDAR; Modeling; PALSAR; Random forest; Regression; Sentinel-2 },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/rs11091092 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065697188&doi=10.3390%2frs11091092&partnerID=40&md5=017009a6ba640a74344840fbcfbfca5c },
}

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