ZhouLeiPengEtAl2016

Référence

Zhou, X., Lei, X., Peng, C., Wang, W., Zhou, C., Liu, C., Liu, Z. (2016) Correcting the overestimate of forest biomass carbon on the national scale. Methods in Ecology and Evolution, 7(4):447-455. (Scopus )

Résumé

For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume-biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling-up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume-biomass equation. This is based on analysing the effect of power-law function convexity on scaling-up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling-up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling-up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national-scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling-up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling-up error. Generally, the presented methods will help to translate fine-scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors. © 2016 British Ecological Society.

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@ARTICLE { ZhouLeiPengEtAl2016,
    AUTHOR = { Zhou, X. and Lei, X. and Peng, C. and Wang, W. and Zhou, C. and Liu, C. and Liu, Z. },
    TITLE = { Correcting the overestimate of forest biomass carbon on the national scale },
    JOURNAL = { Methods in Ecology and Evolution },
    YEAR = { 2016 },
    VOLUME = { 7 },
    NUMBER = { 4 },
    PAGES = { 447-455 },
    NOTE = { cited By 0 },
    ABSTRACT = { For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume-biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling-up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume-biomass equation. This is based on analysing the effect of power-law function convexity on scaling-up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling-up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling-up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national-scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling-up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling-up error. Generally, the presented methods will help to translate fine-scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors. © 2016 British Ecological Society. },
    AFFILIATION = { Ecological Modeling and Carbon Science, Department of Biology Science, University of Quebec at Montreal, Montreal, QC H3C 3P8, Canada; Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada; Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth of Chinese Academy of Sci. and Beijing Normal Univ., Beijing, 100101, China; School of Geomatics, Institute for Remote Sensing Science and Application, Liaoning Technical University, Fuxin, Liaoning, China },
    AUTHOR_KEYWORDS = { Aggregation error; Allometric equation; Error compensation; Expectation of function; Extrapolation; Nonlinear function; Scaling-up error; Volume-biomass equation },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/2041-210X.12505 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950266496&doi=10.1111%2f2041-210X.12505&partnerID=40&md5=3ee1b0ed8ab410269173e96fddbc53f1 },
}

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