Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated ...Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.展开更多
采用标准地调查和生物量实测方法,研究了长沙市区4种人工林生态系统生物量、碳储量及其分布特征。结果表明:马尾松林、杉木林、毛竹林和杨树林生态系统生物量分别为135.390、100.578、64.497、63.381 t/hm^2;林下植被及死地被物层分别为...采用标准地调查和生物量实测方法,研究了长沙市区4种人工林生态系统生物量、碳储量及其分布特征。结果表明:马尾松林、杉木林、毛竹林和杨树林生态系统生物量分别为135.390、100.578、64.497、63.381 t/hm^2;林下植被及死地被物层分别为18.374、22.321、1.847 t/hm^2和2.602 t/hm^2。乔木层林木各器官含碳率为0.405—0.551 g C/g,林下植被层为0.421—0.518 g C/g,死地被物层为0.230—0.545 g C/g,土壤层有机碳含量为15.669—19.163 g C/kg。4种人工林生态系统总碳储量为208.671、176.723、149.168 t/hm^2和164.735 t/hm^2,其中植被层为32.789—67.8661 t/hm^2;死地被物层为0.394—6.163 t/hm^2;土壤层为134.642、116.911、115.985 t/hm^2和126.860 t/hm^2。4种森林年净固碳量为15.167 t hm-2a-1,固定CO_2量55.602 t hm-2a-1。研究结果可为深入研究城市森林碳平衡提供基础数据。展开更多
基金financially supported by the Scientific Research Funds for Forestry Public Welfare of China(Granted No.201004026)the Program for Changjiang Scholars and Innovative Research Team in University(IRT1054)
文摘Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.
文摘采用标准地调查和生物量实测方法,研究了长沙市区4种人工林生态系统生物量、碳储量及其分布特征。结果表明:马尾松林、杉木林、毛竹林和杨树林生态系统生物量分别为135.390、100.578、64.497、63.381 t/hm^2;林下植被及死地被物层分别为18.374、22.321、1.847 t/hm^2和2.602 t/hm^2。乔木层林木各器官含碳率为0.405—0.551 g C/g,林下植被层为0.421—0.518 g C/g,死地被物层为0.230—0.545 g C/g,土壤层有机碳含量为15.669—19.163 g C/kg。4种人工林生态系统总碳储量为208.671、176.723、149.168 t/hm^2和164.735 t/hm^2,其中植被层为32.789—67.8661 t/hm^2;死地被物层为0.394—6.163 t/hm^2;土壤层为134.642、116.911、115.985 t/hm^2和126.860 t/hm^2。4种森林年净固碳量为15.167 t hm-2a-1,固定CO_2量55.602 t hm-2a-1。研究结果可为深入研究城市森林碳平衡提供基础数据。
