塞罕坝华北落叶松人工林生产力及其空间分布预测
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摘要
准确预测森林立地生产力是进行高效森林经营的关键。立地指数是森林生产力可靠的评价指标之一。基于地形、气候和土壤因子以及220块样地解析木数据,采用回归克里格(RK)模型对塞罕坝机械林场华北落叶松(Larix principis-rupprechtii)人工林立地指数(SI)进行空间插值预测,并分析了不同半变异函数对RK模型精度的影响。拟合结果表明:基于高斯半变异函数的RK模型精度优于球状和指数RK模型,且具有较小的残差(RMSE=0.82 m,MAE=0.66 m),表明高斯RK模型具有很强的预测SI能力;高斯半变异函数分析表明研究区华北落叶松人工林SI存在较强的空间自相关性,且在724.89 m变程内差异显著;影响华北落叶松立地指数分布的主要环境因子有土壤全氮、土壤pH、夏季降水量和春季降水量;立地生产力较高区域一般分布在春季降水适中、夏季降水较多、土壤为中性及偏酸性且全氮含量较高的东南部地区,占研究区总面积的32.00%,而在春、夏季降水量少或者春季降水量过多、土壤全氮含量过低且偏碱性的北部边缘地区立地生产力较低,仅占研究区总面积的8.90%。研究区土壤、气候因子与树木生长习性共同决定了华北落叶松人工林生产力的分布格局。通过降低土壤酸碱度和适当施加氮肥等措施,可以提高华北落叶松人工林生产力。
The accurate prediction of forest site productivity is crucial for the effective forest management. Site index(SI) is one of the main measures of forest productivity. In this study,we integrated 220 field inventory, topography, climate and soil factors to predict SI of Larix principis-rupprechtii using regression Kriging(RK) model in Saihanba Mechanized Forest Farm, Hebei province. The influence of different semivariograms on the accuracy of RK model was also analyzed. Fitting results showed that the accuracy of RK model based on Gaussian semivariogram was higher than that based on spherical and exponential semivariogram, and had little residual variation(RMSE=0.82 m and MAE=0.66 m), indicating RK model based on Gaussian semivariogram had a highly predictive power to predict SI in the study area. Gaussian semivariogram analysis showed that there was a strong spatial autocorrelation in SI in the study area, and the spatial variation was significant in the range of 724.89 m; The major environmental factors affecting spatial variation in SI of L. principis-rupprechtii plantations included: soil total nitrogen(TN), soil pH, mean summer precipitation(SUP) and mean spring precipitation(SPP). The sites with high productivity of L. principis-rupprechtii might tend to potentially occur in the southeast part with suitable SPP, relatively high SUP, neutral or acidic soil and relatively high TN, accounting for 32.00% of the total area of the study region.However, sites with low productivity of L. principis-rupprechtii were typically found at the northern edge with excessive SPP or lower SUP, high soil pH and extremely low TN, only accounting for 8.90% of the whole region. Accordingly, the distribution patterns of productivity for L. principis-rupprechtii plantation were jointly determined by climatic and soil factors as well as tree growth characteristics in the study area. Improving productivity of L. principis-rupprechtii plantation can be realized by soil pH reduction and appropriate nitrogen increase in the study area.
The accurate prediction of forest site productivity is crucial for the effective forest management. Site index(SI) is one of the main measures of forest productivity. In this study,we integrated 220 field inventory, topography, climate and soil factors to predict SI of Larix principis-rupprechtii using regression Kriging(RK) model in Saihanba Mechanized Forest Farm, Hebei province. The influence of different semivariograms on the accuracy of RK model was also analyzed. Fitting results showed that the accuracy of RK model based on Gaussian semivariogram was higher than that based on spherical and exponential semivariogram, and had little residual variation(RMSE=0.82 m and MAE=0.66 m), indicating RK model based on Gaussian semivariogram had a highly predictive power to predict SI in the study area. Gaussian semivariogram analysis showed that there was a strong spatial autocorrelation in SI in the study area, and the spatial variation was significant in the range of 724.89 m; The major environmental factors affecting spatial variation in SI of L. principis-rupprechtii plantations included: soil total nitrogen(TN), soil pH, mean summer precipitation(SUP) and mean spring precipitation(SPP). The sites with high productivity of L. principis-rupprechtii might tend to potentially occur in the southeast part with suitable SPP, relatively high SUP, neutral or acidic soil and relatively high TN, accounting for 32.00% of the total area of the study region.However, sites with low productivity of L. principis-rupprechtii were typically found at the northern edge with excessive SPP or lower SUP, high soil pH and extremely low TN, only accounting for 8.90% of the whole region. Accordingly, the distribution patterns of productivity for L. principis-rupprechtii plantation were jointly determined by climatic and soil factors as well as tree growth characteristics in the study area. Improving productivity of L. principis-rupprechtii plantation can be realized by soil pH reduction and appropriate nitrogen increase in the study area.
引文
[1] HUANG S L, RAMIREZ C, CONWAY S, et al. Mapping site index and volume increment from forest inventory, Landsat,and ecological variables in Tahoe National Forest, California, USA. Canadian Journal of Forest Research, 2017, 47(1):113-124.
[2] BUEIS T, BRAVO F, PANDO V, et al. Site factors as predictors for Pinus halepensis Mill. productivity in Spanish plantations. Annals of Forest Science, 2017, 74(1):6.
[3] LATTA G, TEMESGEN H, BARRETT T M. Mapping and imputing potential productivity of pacific northwest forests using climate variables. Canadian Journal of Forest Research, 2009, 39(6):1197-1207.
[4] MCKENNEY D W, PEDLAR J H. Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. Forest Ecology and Management, 2003, 175(1-3):497-507.
[5] NIGH G D, YING C C, QIAN H. Climate and productivity of major conifer species in the interior of British Columbia,Canada. Forest Science, 2004, 50(5):659-671.
[6]吴恒,党坤良,田相林,等.秦岭林区天然次生林与人工林立地质量评价.林业科学, 2015, 51(4):78-88.[WU H,DANG K L, TIAN X L, et al. Evaluating site quality for secondary forests and plantation in Qinling mountains. Scientia Silvae Sinicae, 2015, 51(4):78-88.]
[7] LITTKE K M, HARRISON R B, ZABOWSKI D. Determining the effects of biogeoclimatic properties on different site index systems of ouglas-fir in the Coastal Pacific northwest. Forest Science, 2016, 62(5):503-512.
[8] ANTóN-FERNáNDEZ C, MOLA-YUDEGO B, DALSGAARD L, et al. Climate-sensitive site index models for Norway. Canadian Journal of Forest Research, 2016, 46(6):794-803.
[9] BEAULIEU J, RAULIER F, PRéGENT G, et al. Predicting site index from climatic, edaphic, and stand structural properties for seven plantation-grown conifer species in Quebec. Canadian Journal of Forest Research, 2011, 41(4):682-693.
[10] JIANG H Q, RADTKE P J, WEISKITTEL A R, et al. Climate-and soil-based models of site productivity in Eastern US tree species. Canadian Journal of Forest Research, 2015, 45(3):325-342.
[11] FALKOWSKI M J, WULDER M A, WHITE J C, et al. Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery. Progress in Physical Geography, 2009, 33(3):403-423.
[12] PARRESOL B R, SCOTT D A, ZARNOCH S J, et al. Modeling forest site productivity using mapped geospatial attributes within a South Carolina landscape, USA. Forest Ecology and Management, 2017, 406:196-207.
[13] BRAVO-OVIEDO A, ROIG S, BRAVO F, et al. Environmental variability and its relationship to site index in Mediterranean maritine pine. Forest Systems, 2011, 20(1):50-64.
[14] WARING R H, MILNER K S, JOLLY W M, et al. Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U.S.A. with a MODIS satellite-derived vegetation index. Forest Ecology and Management,2006, 228(1-3):285-291.
[15] WEISKITTEL A R, CROOKSTON N L, RADTKE P J. Linking climate, gross primary productivity, and site index across forests of the Western United States. Canadian Journal of Forest Research, 2011, 41(8):1710-1721.
[16] FARRELLY N, Ní-DHUBHáINá, NIEUWENHUIS M. Site index of Sitka spruce(Picea sitchensis)in relation to different measures of site quality in Ireland. Canadian Journal of Forest Research, 2011, 41(2):265-278.
[17] HLáSNY T, TROMBIK J, BO?E?A M, et al. Climatic drivers of forest productivity in Central Europe. Agricultural and Forest Meteorology, 2017, 234-235:258-273.
[18] SHARMA R P. Modelling height, height growth and site index from National Forest Inventory Data in Norway. Oslo,Norway:Norwegian University of Life Sciences, 2013.
[19]曾春阳,唐代生,唐嘉锴.森林立地指数的地统计学空间分析.生态学报, 2010, 30(13):3465-3471.[ZENG C Y,TANG D S, TANG J K. Spatial pattern of forest ecoystem site index using geostatistical technology. Acta Ecologica Sinica, 2010, 30(13):3465-3471.]
[20]王海宾,彭道黎,范应龙,等.基于辅助信息的森林蓄积量空间模拟.农业机械学报, 2016, 47(6):283-289.[WANG H B, PENG D L, FAN Y L, et al. Spatial modeling of forest stock volume based on auxiliary information. Transactions of the CSAM, 2016, 47(6):283-289.]
[21]赵安玖,陈昆,郭世刚.基于不同空间插值模型的川西南山地常绿阔叶林叶面积指数估测.自然资源学报, 2014, 29(4):598-609.[ZHAO A J, CHEN K, GUO S G. Estimation LAI of montane evergreen broad-leaved forest in Southwest Sichuan using different spatial prediction models. Journal of Natural Resources, 2014, 29(4):598-609.]
[22] HENGL T, HEUVELINK G B M, ROSSITER D G. About regression-kriging:From equations to case studies. Computers&Geosciences, 2007, 33(10):1301-1315.
[23]张树梓,李梅,张树彬,等.塞罕坝华北落叶松人工林天然更新影响因子.生态学报, 2015, 35(16):5403-5411.[ZHANG S Z, LI M, ZHANG S B, et al. Factors affecting natural regeneration of Larix principis-rupprechtii plantations in Saihanba of Hebei, China. Acta Ecologica Sinica, 2015, 35(16):5403-5411.]
[24]段劼,马履一,贾黎明,等.北京低山地区油松人工林立地指数表的编制及应用.林业科学, 2009, 45(3):7-12.[DUAN J, MA L Y, JIA L M, et al. Establishment and application of site index table for Pinus tabulaeformis plantation in the low elevation area of beijing. Scientia Silvae Sinicae, 2009, 45(3):7-12.]
[25]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.应用生态学报, 2015, 26(11):3413-3420.[WANG D Z, ZHANG D Y, JANG F L, et al. A site index model for Larix principis-rupprechtii plantation in Saihanba,North China. Chinese Journal of Applied Ecology, 2015, 26(11):3413-3420.]
[26] WANG T L, WANG G Y, INNES J, et al. Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia-Pacific region. Forest Ecology and Management, 2016, 360:357-366.
[27]范顺祥,郑建伟,魏士凯,等.河北省森林草原区主要草本植物功能群适宜分布预测.草业学报, 2018, 27(3):24-32.[FAN S X, ZHENG J W, WEI S K, et al. Predicting suitable distribution of dominant herbaceous plant functional groups in a forest-steppe zone of Hebei, China. Acta Prataculturae Sinica, 2018, 27(3):24-32.]
[28] WEI S G, DAI Y J, LIU B Y, et al. A China data set of soil properties for land surface modeling. Journal of Advances in Modeling Earth Systems, 2013, 5(2):212-224.
[29] JOHNSON J W. Factors affecting relative weights:The influence of sampling and measurement error. Organizational Research Methods, 2004, 7(3):283-299.
[30] JAMES M L, SCOTT T. Multivariate relative importance:Extending relative weight analysis to multivariate criterion spaces. Journal of Applied Psychology, 2008, 93(2):329-345.
[31]张冬峰,石英.区域气候模式RegCM3对华北地区未来气候变化的数值模拟.地球物理学报, 2012, 55(9):2854-2866.[ZHANG D F, SHI Y. Numerical simulation of climate changes over North China by the RegCM3 model. Chinese Journal of Geophysics, 2012, 55(9):2854-2866.]
[32]汝海丽,张海东,焦峰,等.黄土丘陵区微地形梯度下草地群落植物与土壤碳、氮、磷化学计量学特征.自然资源学报, 2016, 31(10):1752-1763.[RU H L, ZHANG H D, JIAO F, et al. Plant and soil C, N, P stoichiometric characteristics in relation to micro-topography in the hilly Loess Plateau region, China. Journal of Natural Resources, 2016, 31(10):1752-1763.]
[33]张杰,李栋梁,王文.夏季风期间青藏高原地形对降水的影响.地理科学, 2008, 28(2):235-240.[ZHANG J, LI D L,WANG W. Influence of terrain on precipitation in Qinghai-Tibet Plateau during summer monsoon. Scientia Geographica Sinica, 2008, 28(2):235-240.]
[34]杨昕,汤国安,王春, et al.基于DEM的山区气温地形修正模型:以陕西省耀县为例.地理科学, 2007, 27(4):525-530.[YANG X, TANG G A, WANG C, et al. Terrain-revised ground surface temperature model of mountain area based on DEM:A case study in Yaoxian county of Shananxi province. Scientia Geographica Sinica, 2007, 27(4):525-530.]
[35]邱乐丰,杨超,林芬芳,等.基于环境辅助变量的拔山茶园土壤肥力空间预测.应用生态学报, 2010, 21(12):3099-3104.[QIU L F, YANG C, LIN F F, et al. Spatial pattern of soil fertility in Bashan tea garden:A prediction based on environmental auxiliary variables. Chinese Journal of Applied Ecology, 2010, 21(12):3099-3104.]
[36]张东秋,石培礼,张宪洲.土壤呼吸主要影响因素的研究进展.地球科学进展, 2005, 20(7):778-785.[ZHANG D Q,SHI P L, ZHANG X Z. Some advance in the main factors controlling soil respiration. Advances in Earth Science, 2005,20(7):778-785.]
[37]王丹丹,岳书平,林芬芳,等.东北地区旱地土壤全氮空间变异性对幅度拓展的响应.土壤学报, 2012, 49(4):625-635.[WANG D D, YUE S P, LIN F F, et al. Response of spatial variability of soil total nitrogen to expansion of uplands in scale in Northeast China. Acta Pedologica Sinica, 2012, 49(4):625-635.]
[2] BUEIS T, BRAVO F, PANDO V, et al. Site factors as predictors for Pinus halepensis Mill. productivity in Spanish plantations. Annals of Forest Science, 2017, 74(1):6.
[3] LATTA G, TEMESGEN H, BARRETT T M. Mapping and imputing potential productivity of pacific northwest forests using climate variables. Canadian Journal of Forest Research, 2009, 39(6):1197-1207.
[4] MCKENNEY D W, PEDLAR J H. Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. Forest Ecology and Management, 2003, 175(1-3):497-507.
[5] NIGH G D, YING C C, QIAN H. Climate and productivity of major conifer species in the interior of British Columbia,Canada. Forest Science, 2004, 50(5):659-671.
[6]吴恒,党坤良,田相林,等.秦岭林区天然次生林与人工林立地质量评价.林业科学, 2015, 51(4):78-88.[WU H,DANG K L, TIAN X L, et al. Evaluating site quality for secondary forests and plantation in Qinling mountains. Scientia Silvae Sinicae, 2015, 51(4):78-88.]
[7] LITTKE K M, HARRISON R B, ZABOWSKI D. Determining the effects of biogeoclimatic properties on different site index systems of ouglas-fir in the Coastal Pacific northwest. Forest Science, 2016, 62(5):503-512.
[8] ANTóN-FERNáNDEZ C, MOLA-YUDEGO B, DALSGAARD L, et al. Climate-sensitive site index models for Norway. Canadian Journal of Forest Research, 2016, 46(6):794-803.
[9] BEAULIEU J, RAULIER F, PRéGENT G, et al. Predicting site index from climatic, edaphic, and stand structural properties for seven plantation-grown conifer species in Quebec. Canadian Journal of Forest Research, 2011, 41(4):682-693.
[10] JIANG H Q, RADTKE P J, WEISKITTEL A R, et al. Climate-and soil-based models of site productivity in Eastern US tree species. Canadian Journal of Forest Research, 2015, 45(3):325-342.
[11] FALKOWSKI M J, WULDER M A, WHITE J C, et al. Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery. Progress in Physical Geography, 2009, 33(3):403-423.
[12] PARRESOL B R, SCOTT D A, ZARNOCH S J, et al. Modeling forest site productivity using mapped geospatial attributes within a South Carolina landscape, USA. Forest Ecology and Management, 2017, 406:196-207.
[13] BRAVO-OVIEDO A, ROIG S, BRAVO F, et al. Environmental variability and its relationship to site index in Mediterranean maritine pine. Forest Systems, 2011, 20(1):50-64.
[14] WARING R H, MILNER K S, JOLLY W M, et al. Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U.S.A. with a MODIS satellite-derived vegetation index. Forest Ecology and Management,2006, 228(1-3):285-291.
[15] WEISKITTEL A R, CROOKSTON N L, RADTKE P J. Linking climate, gross primary productivity, and site index across forests of the Western United States. Canadian Journal of Forest Research, 2011, 41(8):1710-1721.
[16] FARRELLY N, Ní-DHUBHáINá, NIEUWENHUIS M. Site index of Sitka spruce(Picea sitchensis)in relation to different measures of site quality in Ireland. Canadian Journal of Forest Research, 2011, 41(2):265-278.
[17] HLáSNY T, TROMBIK J, BO?E?A M, et al. Climatic drivers of forest productivity in Central Europe. Agricultural and Forest Meteorology, 2017, 234-235:258-273.
[18] SHARMA R P. Modelling height, height growth and site index from National Forest Inventory Data in Norway. Oslo,Norway:Norwegian University of Life Sciences, 2013.
[19]曾春阳,唐代生,唐嘉锴.森林立地指数的地统计学空间分析.生态学报, 2010, 30(13):3465-3471.[ZENG C Y,TANG D S, TANG J K. Spatial pattern of forest ecoystem site index using geostatistical technology. Acta Ecologica Sinica, 2010, 30(13):3465-3471.]
[20]王海宾,彭道黎,范应龙,等.基于辅助信息的森林蓄积量空间模拟.农业机械学报, 2016, 47(6):283-289.[WANG H B, PENG D L, FAN Y L, et al. Spatial modeling of forest stock volume based on auxiliary information. Transactions of the CSAM, 2016, 47(6):283-289.]
[21]赵安玖,陈昆,郭世刚.基于不同空间插值模型的川西南山地常绿阔叶林叶面积指数估测.自然资源学报, 2014, 29(4):598-609.[ZHAO A J, CHEN K, GUO S G. Estimation LAI of montane evergreen broad-leaved forest in Southwest Sichuan using different spatial prediction models. Journal of Natural Resources, 2014, 29(4):598-609.]
[22] HENGL T, HEUVELINK G B M, ROSSITER D G. About regression-kriging:From equations to case studies. Computers&Geosciences, 2007, 33(10):1301-1315.
[23]张树梓,李梅,张树彬,等.塞罕坝华北落叶松人工林天然更新影响因子.生态学报, 2015, 35(16):5403-5411.[ZHANG S Z, LI M, ZHANG S B, et al. Factors affecting natural regeneration of Larix principis-rupprechtii plantations in Saihanba of Hebei, China. Acta Ecologica Sinica, 2015, 35(16):5403-5411.]
[24]段劼,马履一,贾黎明,等.北京低山地区油松人工林立地指数表的编制及应用.林业科学, 2009, 45(3):7-12.[DUAN J, MA L Y, JIA L M, et al. Establishment and application of site index table for Pinus tabulaeformis plantation in the low elevation area of beijing. Scientia Silvae Sinicae, 2009, 45(3):7-12.]
[25]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.应用生态学报, 2015, 26(11):3413-3420.[WANG D Z, ZHANG D Y, JANG F L, et al. A site index model for Larix principis-rupprechtii plantation in Saihanba,North China. Chinese Journal of Applied Ecology, 2015, 26(11):3413-3420.]
[26] WANG T L, WANG G Y, INNES J, et al. Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia-Pacific region. Forest Ecology and Management, 2016, 360:357-366.
[27]范顺祥,郑建伟,魏士凯,等.河北省森林草原区主要草本植物功能群适宜分布预测.草业学报, 2018, 27(3):24-32.[FAN S X, ZHENG J W, WEI S K, et al. Predicting suitable distribution of dominant herbaceous plant functional groups in a forest-steppe zone of Hebei, China. Acta Prataculturae Sinica, 2018, 27(3):24-32.]
[28] WEI S G, DAI Y J, LIU B Y, et al. A China data set of soil properties for land surface modeling. Journal of Advances in Modeling Earth Systems, 2013, 5(2):212-224.
[29] JOHNSON J W. Factors affecting relative weights:The influence of sampling and measurement error. Organizational Research Methods, 2004, 7(3):283-299.
[30] JAMES M L, SCOTT T. Multivariate relative importance:Extending relative weight analysis to multivariate criterion spaces. Journal of Applied Psychology, 2008, 93(2):329-345.
[31]张冬峰,石英.区域气候模式RegCM3对华北地区未来气候变化的数值模拟.地球物理学报, 2012, 55(9):2854-2866.[ZHANG D F, SHI Y. Numerical simulation of climate changes over North China by the RegCM3 model. Chinese Journal of Geophysics, 2012, 55(9):2854-2866.]
[32]汝海丽,张海东,焦峰,等.黄土丘陵区微地形梯度下草地群落植物与土壤碳、氮、磷化学计量学特征.自然资源学报, 2016, 31(10):1752-1763.[RU H L, ZHANG H D, JIAO F, et al. Plant and soil C, N, P stoichiometric characteristics in relation to micro-topography in the hilly Loess Plateau region, China. Journal of Natural Resources, 2016, 31(10):1752-1763.]
[33]张杰,李栋梁,王文.夏季风期间青藏高原地形对降水的影响.地理科学, 2008, 28(2):235-240.[ZHANG J, LI D L,WANG W. Influence of terrain on precipitation in Qinghai-Tibet Plateau during summer monsoon. Scientia Geographica Sinica, 2008, 28(2):235-240.]
[34]杨昕,汤国安,王春, et al.基于DEM的山区气温地形修正模型:以陕西省耀县为例.地理科学, 2007, 27(4):525-530.[YANG X, TANG G A, WANG C, et al. Terrain-revised ground surface temperature model of mountain area based on DEM:A case study in Yaoxian county of Shananxi province. Scientia Geographica Sinica, 2007, 27(4):525-530.]
[35]邱乐丰,杨超,林芬芳,等.基于环境辅助变量的拔山茶园土壤肥力空间预测.应用生态学报, 2010, 21(12):3099-3104.[QIU L F, YANG C, LIN F F, et al. Spatial pattern of soil fertility in Bashan tea garden:A prediction based on environmental auxiliary variables. Chinese Journal of Applied Ecology, 2010, 21(12):3099-3104.]
[36]张东秋,石培礼,张宪洲.土壤呼吸主要影响因素的研究进展.地球科学进展, 2005, 20(7):778-785.[ZHANG D Q,SHI P L, ZHANG X Z. Some advance in the main factors controlling soil respiration. Advances in Earth Science, 2005,20(7):778-785.]
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