VegaSt-Onge2005

Référence

Vega, C., St-Onge, B. (2005) Integration of photogrammetric, lidar and multi-spectral remote sensing data for forest growth estimation and analysis. In Proceedings of the 26th Canadian Symposium on Remote Sensing. Pages 211-221. (Scopus )

Résumé

Estimation of forest height and growth is based on repeated measurements of site-specific biophysical characteristics. Conventional assessment is made on field measurements which are uneconomical and inaccurate. Moreover, height estimation remains a difficult task. Several studies have shown that precise height metrics can be derived through surface elevation models using digital photogrammetry (DP) and airborne laser scanning altimetry (ALSA). A time serie of Canopy Height Models (CHM), which is the difference between Digital Surface model (DSM) and Digital Terrain Model (DIM), has potential for assessing height increment and growth. As DP is limited to DSMs (parallax measurements are restricted to visible surface objects), few studies could address historical height measurements. ALSA overcomes this limitation as laser pulses can propagate through canopy openings to the ground, producing both precise DSM and DTM. Combining existing ALSA DTM with photogrammetric DSMs can help reconstruct historical Photo-ALSA CHMs (PACHM). In this study we reconstructed time series of PACHM over a mixed boreal forest in Quebec (Canada) based on a 2003 ALSA survey (DTM, DSM, CHM) and photogrammetric DSM's extracted from a set of archive photographs (1945, 1965, 1983), and used forest type classification over continuoulsy growing stands based on QuickBrid imagery. Height differences were used to estimate growth, age and site index (SI) at the plot level. Topographic parameters (elevation, slope, aspect, curvature) were extracted from the ALSA DTM at different scales to analyse their effect on height incrment, growth and site index. Derived estimates are in good agreement with available local measuremenrs. The method adopted shows potential to analyse and model stand height growth over a broad range of forest conditions.

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@INPROCEEDINGS { VegaSt-Onge2005,
    AUTHOR = { Vega, C. and St-Onge, B. },
    TITLE = { Integration of photogrammetric, lidar and multi-spectral remote sensing data for forest growth estimation and analysis },
    BOOKTITLE = { Proceedings of the 26th Canadian Symposium on Remote Sensing },
    YEAR = { 2005 },
    PAGES = { 211-221 },
    ABSTRACT = { Estimation of forest height and growth is based on repeated measurements of site-specific biophysical characteristics. Conventional assessment is made on field measurements which are uneconomical and inaccurate. Moreover, height estimation remains a difficult task. Several studies have shown that precise height metrics can be derived through surface elevation models using digital photogrammetry (DP) and airborne laser scanning altimetry (ALSA). A time serie of Canopy Height Models (CHM), which is the difference between Digital Surface model (DSM) and Digital Terrain Model (DIM), has potential for assessing height increment and growth. As DP is limited to DSMs (parallax measurements are restricted to visible surface objects), few studies could address historical height measurements. ALSA overcomes this limitation as laser pulses can propagate through canopy openings to the ground, producing both precise DSM and DTM. Combining existing ALSA DTM with photogrammetric DSMs can help reconstruct historical Photo-ALSA CHMs (PACHM). In this study we reconstructed time series of PACHM over a mixed boreal forest in Quebec (Canada) based on a 2003 ALSA survey (DTM, DSM, CHM) and photogrammetric DSM's extracted from a set of archive photographs (1945, 1965, 1983), and used forest type classification over continuoulsy growing stands based on QuickBrid imagery. Height differences were used to estimate growth, age and site index (SI) at the plot level. Topographic parameters (elevation, slope, aspect, curvature) were extracted from the ALSA DTM at different scales to analyse their effect on height incrment, growth and site index. Derived estimates are in good agreement with available local measuremenrs. The method adopted shows potential to analyse and model stand height growth over a broad range of forest conditions. },
    COMMENT = { Export Date: 1 June 2009 Source: Scopus },
    KEYWORDS = { Canopy height model, Height growth model, Lidar, Multi-spectral image classification, Photogrammetry, Data reduction, Image processing, Mathematical models, Optical radar, Photogrammetry, Remote sensing, Canopy height model, Forest conditions, Height growth model, Multi-spectral image classification, Forestry, Forestry, Photogrammetry },
    OWNER = { Luc },
    TIMESTAMP = { 2009.06.01 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-33745223842&partnerID=40 },
}

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