RacineCoopsSt-OngeEtAl2014

Référence

Racine, E.B., Coops, N.C., St-Onge, B. and Begin, J. (2014) Estimating forest stand age from LiDAR-derived predictors and nearest neighbor imputation. Forest Science, 60(1):128-136. (Scopus )

Résumé

Within the managed boreal forest of Quebec (Canada), there is a need to ensure the maintenance of age diversity across the forest resource, and, as a result, forest age has become an important variable in forest management plans. Forest age is a major driver of stand height, with site factors such as climate, water, and light availability, modifying height based on local conditions. The link between stand age and height is used to evaluate the site index, a commonly used measure of productivity, which is the basis of many forest management decisions. Stand age is extremely costly and laborious to measure in situ and, as a result, new technologies are required to provide added predictive power, such as light detection and ranging (LiDAR) which can provide insights into forest structure and site characteristics. In this article, we examined the utility of airborne LiDAR data to estimate stand age across 158 sample plots within a naturally regenerated boreal forest in Quebec, Eastern Canada. A suite of forest structure (height, first return penetration rate, and corresponding Weibull distribution parameters) and site attributes (elevation, slope, aspect, solar radiation, wetness index, catchment area, and flow path length) was derived from the LiDAR data. The k-nearest neighbor approach allowed imputation of stand age across the forest area, as well as providing key information on the predictive power of LiDAR metrics for estimating age in this forest region. Results indicate that LiDAR-derived predictors can estimate age well (R2 = 0.83, root mean square error [RMSE] = 19%) with a predicted error of less than 10 years. Estimations using only forest structure variables were lower (R2 = 0.74, RMSE = 22%). The most important forest structural predictors were stand height and Weibull crown shape parameters. Site predictors, in contrast, which had less predictive power, included elevation, slope, and catchment area with low to moderate importance. © 2014 Society of American Foresters.

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@ARTICLE { RacineCoopsSt-OngeEtAl2014,
    AUTHOR = { Racine, E.B. and Coops, N.C. and St-Onge, B. and Begin, J. },
    TITLE = { Estimating forest stand age from LiDAR-derived predictors and nearest neighbor imputation },
    JOURNAL = { Forest Science },
    YEAR = { 2014 },
    VOLUME = { 60 },
    PAGES = { 128-136 },
    NUMBER = { 1 },
    ABSTRACT = { Within the managed boreal forest of Quebec (Canada), there is a need to ensure the maintenance of age diversity across the forest resource, and, as a result, forest age has become an important variable in forest management plans. Forest age is a major driver of stand height, with site factors such as climate, water, and light availability, modifying height based on local conditions. The link between stand age and height is used to evaluate the site index, a commonly used measure of productivity, which is the basis of many forest management decisions. Stand age is extremely costly and laborious to measure in situ and, as a result, new technologies are required to provide added predictive power, such as light detection and ranging (LiDAR) which can provide insights into forest structure and site characteristics. In this article, we examined the utility of airborne LiDAR data to estimate stand age across 158 sample plots within a naturally regenerated boreal forest in Quebec, Eastern Canada. A suite of forest structure (height, first return penetration rate, and corresponding Weibull distribution parameters) and site attributes (elevation, slope, aspect, solar radiation, wetness index, catchment area, and flow path length) was derived from the LiDAR data. The k-nearest neighbor approach allowed imputation of stand age across the forest area, as well as providing key information on the predictive power of LiDAR metrics for estimating age in this forest region. Results indicate that LiDAR-derived predictors can estimate age well (R2 = 0.83, root mean square error [RMSE] = 19%) with a predicted error of less than 10 years. Estimations using only forest structure variables were lower (R2 = 0.74, RMSE = 22%). The most important forest structural predictors were stand height and Weibull crown shape parameters. Site predictors, in contrast, which had less predictive power, included elevation, slope, and catchment area with low to moderate importance. © 2014 Society of American Foresters. },
    ADDRESS = { Université Laval, Canada },
    COMMENT = { Export Date: 20 February 2014 Source: Scopus },
    KEYWORDS = { Boreal forest, Forest structure, K-NN imputation, LiDAR remote sensing, Random forest },
    OWNER = { Luc },
    TIMESTAMP = { 2014.02.20 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84893369892&partnerID=40&md5=e69f44ff5e3a85ff0bdc68fe8392ed61 },
}

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