运用改进Richards模型预测马尾松人工林立地指数——以广西高峰林场为例
|
摘要
以广西高峰林场的马尾松人工林为研究对象,选取森林资源二类调查中的海拔高、坡度、坡向、坡位、土层厚度为立地影响因子,采用打分法和灰色关联度分析法计算各立地因子的权重和总得分,将立地因子的总得分与Richards立地指数模型相结合,借助SPSS对模型进行参数估计。结果表明:模型改进后的平均误差和均方根误差比改进前分别降低了7.96%和10.59%,说明改进后的模型,预测林分的优势木高更精准。因此,考虑立地因子影响的Richards模型,为立地质量评价提供了技术支撑。
Taking Pinus massoniana plantation in Gaofeng Forest Farm of Guangxi as the research object, considering the influence of site factors synthetically, we selected elevation, slope gradient, slope direction, slope position and soil layer thickness as the main site impact factors in the second type of forest resources survey. The weight and total score of site factors were calculated by scoring method and grey correlation analysis method, and the total score of site factors was calculated. Combining with Richards site index model and using SPSS to estimate the parameters of the model and establish the improved model, the test results show that the average error and root mean square error after the model improvement were reduced by 7.96% and 10.59%, respectively, before the improvement. The Richards model can predict the height of dominant trees more accurately. Considering the influence of site factors, Richards model can provide theoretical support for site quality evaluation.
Taking Pinus massoniana plantation in Gaofeng Forest Farm of Guangxi as the research object, considering the influence of site factors synthetically, we selected elevation, slope gradient, slope direction, slope position and soil layer thickness as the main site impact factors in the second type of forest resources survey. The weight and total score of site factors were calculated by scoring method and grey correlation analysis method, and the total score of site factors was calculated. Combining with Richards site index model and using SPSS to estimate the parameters of the model and establish the improved model, the test results show that the average error and root mean square error after the model improvement were reduced by 7.96% and 10.59%, respectively, before the improvement. The Richards model can predict the height of dominant trees more accurately. Considering the influence of site factors, Richards model can provide theoretical support for site quality evaluation.
引文
[1] 郭艳荣,刘洋,吴保国.福建省宜林地立地质量的分级与数量化评价[J].东北林业大学学报,2014,42(10):54-59.
[2] 蔡会德,吴树刚,杨彦臣,等.桉树立地评价及其决策支持技术的实现[J].北京林业大学学报,2009,31(S2):36-46.
[3] MARTíN-BENITO D,GEA-IZQUIERDO G,DEL RíO M,et al.Long-term trends in dominant-height growth of Black pine using dynamic models[J].Forest Ecology and Management,2008,256(5):1230-1238.
[4] WANG Y,LEMAY V M,BAKER T G.Modelling and prediction of dominant height and site index of Eucalyptus globulus plantations using a nonlinear mixed-effects model approach[J].Canadian Journal of Forest Research,2007,37(8):1390-1403.
[5] 王冬至,张冬燕,蒋凤玲,等.塞罕坝华北落叶松人工林地位指数模型[J].应用生态学报,2015,26(11):3413-3420.
[6] 杨传强,李士美,孔雨光,等.山东省松类人工林立地指数表的编制与应用[J].林业资源管理,2018(2):43-47,118.
[7] 潘知星,罗丽婵.广西国有高峰林场不同树种林下土壤的特性研究[J].绿色科技,2017(3):116-118.
[8] 陈晨,刘光武,曹玲.Richards方程在马尾松人工林地位指数建模方面的研究[J].绿色科技,2016(1):5-6.
[9] 林狄显.杉木人工林立地质量评价方法的研究[J].内蒙古林业调查设计,2011,34(2):41-46,49.
[10] 邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002.
[11] LAUER D K,KUSH J S.Dynamic site index equation for thinned stands of even-aged natural longleaf pine[J].Southern Journal of Applied Forestry,2010,34(1):28-37.
[12] JOHANSSON T.Site index curves for Young hybrid larch growing on former farmland in Sweden[J].Forests,2012,3(3):723-735.
[13] 李春明,张会儒.利用非线性混合模型模拟杉木林优势木平均高[J].林业科学,2010,46(3):89-95.
[14] 李春明.基于纵向数据非线性混合模型的杉木林优势木平均高研究[J].林业科学研究,2011,24(1):68-73.
[15] 符利勇,张会儒,李春明,等.非线性混合效应模型参数估计方法分析[J].林业科学,2013,49(1):114-119.
[16] 国家林业局.中国森林资源报告:第七次全国森林资源清查[M].北京:中国林业出版社,2009.
[17] 李贤伟,李守剑,张健,等.四川盆周西缘水杉人工林林地立地质量评价研究[J].四川农业大学学报,2002,20(2):106-109.
[18] 李元元,聂华.北京市林业产业结构发展的灰色动态关联分析[J].林业调查规划,2006,31(3):94-97.
[19] 叶林,刘成斌,常方圆,等.伊春市落叶松人工林立地质量评价[J].林业勘查设计,2006(2):49-52.
[20] 严会超.生态公益林质量评价与可持续经营研究[D].北京:中国农业大学,2005:34-85.
[21] 惠淑荣,李丽锋,刘强,等.辽东地区日本落叶松立地分类和立地质量研究[J].西北林学院学报,2011,26(3):139-142.
[22] 邓聚龙.灰色系统基本方法[M].武汉:华中工学院出版社,1987.
[23] 郭雪.太原市区空气质量影响因素的灰色关联分析[C]//中国学气象会.第35届中国气象学会年会S12大气成分与天气、气候变化与环境影响暨环境气象预报及影响评估.北京:中国气象学会,2018:167-171.
[2] 蔡会德,吴树刚,杨彦臣,等.桉树立地评价及其决策支持技术的实现[J].北京林业大学学报,2009,31(S2):36-46.
[3] MARTíN-BENITO D,GEA-IZQUIERDO G,DEL RíO M,et al.Long-term trends in dominant-height growth of Black pine using dynamic models[J].Forest Ecology and Management,2008,256(5):1230-1238.
[4] WANG Y,LEMAY V M,BAKER T G.Modelling and prediction of dominant height and site index of Eucalyptus globulus plantations using a nonlinear mixed-effects model approach[J].Canadian Journal of Forest Research,2007,37(8):1390-1403.
[5] 王冬至,张冬燕,蒋凤玲,等.塞罕坝华北落叶松人工林地位指数模型[J].应用生态学报,2015,26(11):3413-3420.
[6] 杨传强,李士美,孔雨光,等.山东省松类人工林立地指数表的编制与应用[J].林业资源管理,2018(2):43-47,118.
[7] 潘知星,罗丽婵.广西国有高峰林场不同树种林下土壤的特性研究[J].绿色科技,2017(3):116-118.
[8] 陈晨,刘光武,曹玲.Richards方程在马尾松人工林地位指数建模方面的研究[J].绿色科技,2016(1):5-6.
[9] 林狄显.杉木人工林立地质量评价方法的研究[J].内蒙古林业调查设计,2011,34(2):41-46,49.
[10] 邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002.
[11] LAUER D K,KUSH J S.Dynamic site index equation for thinned stands of even-aged natural longleaf pine[J].Southern Journal of Applied Forestry,2010,34(1):28-37.
[12] JOHANSSON T.Site index curves for Young hybrid larch growing on former farmland in Sweden[J].Forests,2012,3(3):723-735.
[13] 李春明,张会儒.利用非线性混合模型模拟杉木林优势木平均高[J].林业科学,2010,46(3):89-95.
[14] 李春明.基于纵向数据非线性混合模型的杉木林优势木平均高研究[J].林业科学研究,2011,24(1):68-73.
[15] 符利勇,张会儒,李春明,等.非线性混合效应模型参数估计方法分析[J].林业科学,2013,49(1):114-119.
[16] 国家林业局.中国森林资源报告:第七次全国森林资源清查[M].北京:中国林业出版社,2009.
[17] 李贤伟,李守剑,张健,等.四川盆周西缘水杉人工林林地立地质量评价研究[J].四川农业大学学报,2002,20(2):106-109.
[18] 李元元,聂华.北京市林业产业结构发展的灰色动态关联分析[J].林业调查规划,2006,31(3):94-97.
[19] 叶林,刘成斌,常方圆,等.伊春市落叶松人工林立地质量评价[J].林业勘查设计,2006(2):49-52.
[20] 严会超.生态公益林质量评价与可持续经营研究[D].北京:中国农业大学,2005:34-85.
[21] 惠淑荣,李丽锋,刘强,等.辽东地区日本落叶松立地分类和立地质量研究[J].西北林学院学报,2011,26(3):139-142.
[22] 邓聚龙.灰色系统基本方法[M].武汉:华中工学院出版社,1987.
[23] 郭雪.太原市区空气质量影响因素的灰色关联分析[C]//中国学气象会.第35届中国气象学会年会S12大气成分与天气、气候变化与环境影响暨环境气象预报及影响评估.北京:中国气象学会,2018:167-171.