We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empiri...We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.展开更多
Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests ...Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree（BRT） model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region（Wuyishan National Nature Reserve, China）. Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass（t） increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1（mean ± standard error） to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass（in decreasing order of importance） were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales.展开更多
基金supported by the Major Research Development Program of China(2016YFC0502704)National Science Foundation of China(31670645,31470578 and 31200363)+4 种基金National Forestry Public Welfare Foundation of China(201304205)Fujian Provincial Department of S&T Project(2013YZ0001-1,2015Y0083,2016Y0083,2016T3037 and 2016T3032)Key Laboratory of Urban Environment and Health of CAS(KLUEH-C-201701)Youth Innovation Promotion Association CAS(2014267)Key Program of the Chinese Academy of Sciences(KFZDSW-324)
文摘We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.
基金supported by National Forestry Public Welfare Foundation of China(201304205)National Science Foundation of China(31470578 and 31200363)+2 种基金Fujian Provincial Department of S&T Project(2016Y0083,2013YZ0001-1,2014J05044 and 2015Y0083)Xiamen Municipal Department of Science and Technology(3502Z20130037 and 3502Z20142016)Youth Innovation Promotion Association CAS
文摘Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree（BRT） model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region（Wuyishan National Nature Reserve, China）. Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass（t） increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1（mean ± standard error） to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass（in decreasing order of importance） were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales.
