St-OngeVepakommaHuEtAl2005

Référence

St-Onge, B., Vepakomma, U., Hu, Y., Cobello, M. (2005) Estimation of specific aboveground forest biomass using a combination of lidar data and Quickbird imagery. In Proc. Can. Symp. Remote Sens.. Department of Geography, University of Quebec at Montreal, succ. Centre-Ville, Montreal, Que. H3C 3P8, pages 353-361. (Scopus )

Résumé

Estimating the aboveground biomass of forests still remains a difficult task. Empirical models based on image reflectance or radar backscattering saturate above a certain biomass level. It was shown that the 3D data generated by scanning lidars (Light Detection and Ranging) overcomes the saturation problem and yield accurate results. Regression of metrics derived from the lidar-measured canopy height distribution against field estimates of biomass gives promising results. However, most lidar-based biomass prediction studies so far were performed on monospecific stands. Because of the differences in crown shape between species, the lidar metrics are influenced by species composition. We hypothesize that biomass prediction equations must be recalibrated for different species. In this fine scale study, we propose to derive species information from a QuickBird image. Lidar data and a concomitant QuickBird image set were acquired over a 200 km2 region of the southern boreal forest of western Quebec. The specific diameter at breast height of trees was measured in the field for 40 plots. Regionally calibrated specific allometric equations were used to estimate in situ biomass. The plots were classified into pure deciduous, pure conifers, and mixed based on the field data. Canopy height derived from the lidar data was integrated with the QuickBird spectral signatures to automatically classify the tree species into deciduous and conifer. The plots were then classified into the deciduous, conifer and mixed classes based on the results of the multispectral classification. These in situ estimates of biomass were regressed against lidar metrics derived from the canopy height model. Different prediction models were developed: a general model for all species, specific models, and a mixed model (combination of specific model). The use of specific models largely increased the prediction accuracy for the conifer and mixed class. Predicting the biomass of deciduous trees was however relatively innaccurate. When the models were chosen based on the image classification, the overall biomass prediction error was only slightly better than that of the general model because of classification error.

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@INPROCEEDINGS { St-OngeVepakommaHuEtAl2005,
    AUTHOR = { St-Onge, B. and Vepakomma, U. and Hu, Y. and Cobello, M. },
    TITLE = { Estimation of specific aboveground forest biomass using a combination of lidar data and Quickbird imagery },
    BOOKTITLE = { Proc. Can. Symp. Remote Sens. },
    YEAR = { 2005 },
    PAGES = { 353--361 },
    ADDRESS = { Department of Geography, University of Quebec at Montreal, succ. Centre-Ville, Montreal, Que. H3C 3P8 },
    ABSTRACT = { Estimating the aboveground biomass of forests still remains a difficult task. Empirical models based on image reflectance or radar backscattering saturate above a certain biomass level. It was shown that the 3D data generated by scanning lidars (Light Detection and Ranging) overcomes the saturation problem and yield accurate results. Regression of metrics derived from the lidar-measured canopy height distribution against field estimates of biomass gives promising results. However, most lidar-based biomass prediction studies so far were performed on monospecific stands. Because of the differences in crown shape between species, the lidar metrics are influenced by species composition. We hypothesize that biomass prediction equations must be recalibrated for different species. In this fine scale study, we propose to derive species information from a QuickBird image. Lidar data and a concomitant QuickBird image set were acquired over a 200 km2 region of the southern boreal forest of western Quebec. The specific diameter at breast height of trees was measured in the field for 40 plots. Regionally calibrated specific allometric equations were used to estimate in situ biomass. The plots were classified into pure deciduous, pure conifers, and mixed based on the field data. Canopy height derived from the lidar data was integrated with the QuickBird spectral signatures to automatically classify the tree species into deciduous and conifer. The plots were then classified into the deciduous, conifer and mixed classes based on the results of the multispectral classification. These in situ estimates of biomass were regressed against lidar metrics derived from the canopy height model. Different prediction models were developed: a general model for all species, specific models, and a mixed model (combination of specific model). The use of specific models largely increased the prediction accuracy for the conifer and mixed class. Predicting the biomass of deciduous trees was however relatively innaccurate. When the models were chosen based on the image classification, the overall biomass prediction error was only slightly better than that of the general model because of classification error. },
    COMMENT = { Export Date: 24 August 2007 Source: Scopus Language of Original Document: English Correspondence Address: St-Onge, B.; Department of Geography; University of Quebec at Montreal; succ. Centre-Ville Montreal, Que. H3C 3P8, Canada; email: St-Onge.Benoit@courrier.uqam.ca },
    KEYWORDS = { Forest biomass, High resolution imagery, Lidar, Backscattering, Forestry, Image segmentation, Optical radar, Radar, Spectrum analyzers, Classification error, Forest biomass, High resolution imagery, Prediction error, Biomass, Biomass, Forestry },
    OWNER = { brugerolles },
    TIMESTAMP = { 2007.12.05 },
    URL = { http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-33745208668&partnerID=40&rel=R6.5.0 },
}

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