基于地理加权回归拓展模型的天然次生林碳储量空间分布

doi:10.13287/j.1001-9332.202104.002

摘要/Abstract

摘要： 为精准获取区域尺度天然次生林的碳储量及其空间分布格局,以吉林省汪清林业局浪溪林场的天然次生林为研究对象,基于165块局级固定样地,以林分因子、地形因子和土壤因子为影响因子,将普通地理加权回归模型(GWR)作为基础,从空间维度、参数异质性特征和残差空间自相关性3个方面进行改进,构建7类拓展模型,即地理海拔加权回归模型(GAWR)、半参数地理加权回归模型(SGWR)、半参数地理海拔加权回归模型(SGAWR)、地理加权回归克里格模型(GWRK)、地理海拔加权回归克里格模型(GAWRK)、半参数地理加权回归克里格模型(SGWRK)和半参数地理海拔加权回归克里格模型(SGAWRK)。运用7类拓展模型对研究区的森林碳储量及其分布情况进行模拟估测,采用决定系数(R²)、均方误差(MSE)和赤池信息准则(AIC)对各种模型的拟合效果进行评价;最后,运用最优回归模型的拟合结果绘制森林碳储量空间分布图,分析研究区森林碳储量的分布规律。结果表明: 林分因子和地形因子对天然次生林碳储量产生了较大的影响,其中林分平均胸径是影响最大的变量,两者呈显著正相关;SGWR和SGAWR模型能够进一步降低GWR模型残差的空间自相关性;地理加权回归拓展模型能进一步提升GWR模型的拟合效果。其中,SGWRK模型具有最高的R²和最低的MSE和AIC。将海拔作为空间权重未能有效提高模型的拟合效果;浪溪林场森林总碳储量为205×10⁴ t,碳密度为8.56～145.74 t·hm^-2,平均值57.98 t·hm^-2,整体上呈现西北高、东南低,边缘高、内部低的分布格局。通过改进地理加权回归基础模型对参数异质性特征和残差空间自相关性的处理,可以更好地揭示研究区森林碳储量与相关变量间的空间关系,提升模型对区域尺度森林碳储量及其空间分布的估测精度。

关键词: 天然次生林, 碳储量, 地理加权, 拓展模型, 空间分布

Abstract: To accurately assess carbon storage and its spatial distribution in natural secondary forest at the regional scale, we constructed seven expansion models by modifying the geographically weighted regression (GWR) in aspects of spatial dimension, parameter heterogeneity and residual spatial autocorrelation, based on data collected from 165 bureau level permanent plots in Langxi Forest Farm of Wangqing Forestry Bureau in Jilin Province. Stand factor, topography factor, and soil factor were selected as the influencing factors. The expansion models included geographically and altitudinal weighted regression (GAWR), semiparametric geographically weighted regression (SGWR), semiparametric geographically and altitudinal weighted regression (SGAWR), geographically weighted regression Kriging (GWRK), geographically and altitudinal weighted regression Kriging (GAWRK), semiparametric geographically weighted regression Kriging (SGWRK), and semiparametric geographically and altitudinal weighted regression Kriging (SGAWRK). Coefficient of determination (R²), mean square error (MSE) and Akaike’s Information Criterion (AIC) were used to evaluate the fitness of these models. Finally, the spatial distribution diagram of forest carbon storage was drawn with the fitting results of the optimal regression model, and the distribution pattern of forest carbon storage in the research area was analyzed. The stand factor and topographic factor had strong influence on carbon storage of natural secondary forests, among which the average diameter at breast height (DBH) of stands was the dominant variable. There was positive correlation between stand factor and topographic factor. SGWR and SGAWR model could reduce the spatial autocorrelation of the GWR model residual. The geographically regression expansion model could improve the fitting effect of GWR model. Among them, the SGWRK model had the highest R² and the lowest MSE and AIC. The method with altitude as the spatial weight did not effectively improve the fitting effect of the model. The total forest carbon storage of Langxi Forest Farm was 205×10⁴ t, and the carbon density ranged from 8.56 to 145.74 t·hm^-2, with a mean value of 57.98 t·hm^-2. Overall, the distribution pattern of carbon storage was high in the northwest and low in the southeast, while high in the edge and low in the interior. By improving the parameter heterogeneity and residual spatial autocorrelation in the GWR model, we can accurately assess the spatial relationship between forest carbon storage and relevant variables in the study area, and improve the estimation accuracy of the forest carbon storage and its spatial distribution at the regional scale.

Key words: natural secondary forest, carbon storage, geographically weighted, expansion model, spatial distribution

陈科屹, 张会儒, 张博, 何友均. 基于地理加权回归拓展模型的天然次生林碳储量空间分布[J]. 应用生态学报, 2021, 32(4): 1175-1183.

CHEN Ke-yi, ZHANG Hui-ru, ZHANG Bo, HE You-jun. Spatial distribution of carbon storage in natural secondary forest based on geographically weighted regression expansion model.[J]. Chinese Journal of Applied Ecology, 2021, 32(4): 1175-1183.

参考文献

[1] 陈大珂, 周晓峰, 祝宁. 天然次生林——结构·功能·动态与经营. 哈尔滨: 东北林业大学出版社, 1994: 1-5 [Chen D-K, Zhou X-F, Zhu N. Natural Secondary Forest: Structure, Function, Dynamics and Management. Harbin: Northeast Forestry University Press, 1994: 1-5]
[2] 赵利群, 翁国盛, 高秀芹. 次生林综述. 防护林科技, 2006(5): 47-49 [Zhao L-Q, Weng G-S, Gao X-Q. A review of secondary forests. Protection Forest Science and Technology, 2006(5): 47-49]
[3] 国家林业局森林资源管理司. 第七次全国森林资源清查及森林资源状况. 林业资源管理, 2010(1): 1-8 [Department of Forest Resources Management, State Forestry Administration. The 7th National Forest Inventory and Status of Forest Resources. Forest Resources Management, 2010(1): 1-8]
[4] Réjou-Méchain M, Muller-Landau HC, Detto M, et al. Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks. Biogeosciences, 2014, 11: 6827-6840
[5] 刘畅. 黑龙江省森林碳储量空间分布研究. 博士论文. 哈尔滨: 东北林业大学, 2014 [Liu C. Spatial Distribution of Forest Carbon Storage in Heilongjiang Pro-vince. PhD Thesis. Harbin: Northeast Forest University, 2014]
[6] 章皖秋, 岳彩荣, 袁华. 林木调查数据的随机、空间、时间特征的模型处理. 西北林学院学报, 2016, 31(5): 230-237 [Zhang W-Q, Yu C-R, Yuan H. Solutions for random effect, spatial and temporal correlation and heterogeneity in the observed forest data. Journal of Northwest Forestry University, 2016, 31(5): 230-237]
[7] Brunsdon C, Fotheringham AS, Charlton M. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 1996, 28: 281-298
[8] Zhang LJ, Bi HQ, Cheng PF, et al. Modeling spatial variation in tree diameter-height relationships. Forest Ecology and Management, 2004, 189: 317-329
[9] Zhang LJ, Shi HJ. Local modeling of tree growth by geographically weighted regression. Forest Science, 2004, 50: 225-244
[10] Zhen Z, Li FR, Liu ZG, et al. Geographically local modeling of occurrence, count, and volume of downwood in Northeast China. Applied Geography, 2013, 37: 114-126
[11] 杜一尘, 李明泽, 范文义, 等. 基于地理加权回归模型与林火遥感数据估算森林年龄. 林业科学, 2019, 55(6): 184-194 [Du Y-C, Li M-Z, Fan W-Y, et al. Estimation of forest stand age based on GWR model and forest fire remote sensing data. Scientia Silvae Sinicae, 2019, 55(6): 184-194]
[12] Liu C, Zhang LJ, Li FR, et al. Spatial modeling of the carbon stock of forest trees in Heilongjiang Province, China. Journal of Forestry Research, 2014, 25: 269-280
[13] 刘正显. 基于GWR模型的凉水自然保护区森林生物量空间分布研究. 硕士论文. 哈尔滨: 东北林业大学, 2015 [Liu Z-X. Research on the Spatial Distribution of Liangshui Nature Reserve Forest Biomass Based on Geographically Weighted Regression. Master Thesis. Harbin: Northeast Forest University, 2015]
[14] 戚玉娇, 李凤日. 基于KNN方法的大兴安岭地区森林地上碳储量遥感估算. 林业科学, 2015, 51(5): 46-55 [Qi Y-J, Li F-R. Remote sensing estimation of aboveground forest carbon storage in Daxing’an Mountains based on KNN method. Scientia Silvae Sinicae, 2015, 51(5): 46-55]
[15] Chen L, Ren CY, Zhang B, et al. Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests, 2018, 9: 582
[16] Guo L, Ma ZH, Zhang LJ. Comparison of bandwidth selection in application of geographically weighted regression: A case study. Canadian Journal of Forest Research, 2008, 38: 2526-2534
[17] 张会儒, 唐守正. 东北天然林可持续经营技术研究. 北京: 中国林业出版社, 2011: 28-30 [Zhang H-R, Tang S-Z. Research on Sustainable Management Technology of Natural Forest in Northeast China. Beijing: China Forestry Press, 2011: 28-30]
[18] Fotheringham AS, Brunsdon C, Charlton M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. New York: John Wiley and Sons, 2002: 27-64
[19] 陈科屹, 张会儒, 雷相东. 不同群落蒙古栎种群空间格局的地统计学分析. 应用生态学报, 2018, 29(5): 1542-1550 [Chen K-Y, Zhang H-R, Lei X-D. Geostatistical analysis on the spatial pattern of Quercus mongo-lica population in different communities. Chinese Journal of Applied Ecology, 2018, 29(5): 1542-1550]
[20] Cambardella CA, Moorman TB, Novak J. et al. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 1994, 58: 1501-1511
[21] Wheeler D, Tiefelsdorf M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 2005, 7: 161-187
[22] 吴国训. 江西省森林植被净初级生产力及碳储量估算. 博士论文. 南京: 南京林业大学, 2015 [Wu G-X. Net Primary Productivity and Carbon Storage Estimation of Forest in Jiangxi Province, China. PhD Thesis. Nanjing: Nanjing Forest University, 2015]
[23] 王海宾, 侯瑞萍, 郑冬梅, 等. 基于地理加权回归模型的亚热带地区乔木林生物量估算. 农业机械学报, 2018, 49(6): 184-190 [Wang H-B, Hou R-P, Zheng D-M, et al. Biomass estimation of arbor forest in subtropical region based on geographically weighted regression model. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6): 184-190]
[24] Propastin P. Multiscale analysis of the relationship between topography and aboveground biomass in the tropical rainforests of Sulawesi, Indonesia. International Journal of Geographical Information, 2011, 25: 455-472
[25] Propastin P. Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data. International Journal of Applied Earth Observations and Geoinformation, 2012, 18: 82-90
[26] 郭含茹. 基于地理加权回归的区域森林碳储量估计. 硕士论文. 杭州: 浙江农林大学, 2015 [Guo H-R. Geographically Weighted Regression Based on Estimation of Regional Forest Carbon Storage. Master Thesis. Hangzhou: Zhejiang Agriculture and Forest University, 2015]
[27] 张国峰, 杨立荣, 瞿明凯, 等. 基于地理加权回归克里格的日平均气温插值. 应用生态学报, 2015, 26(5): 1531-1536 [Zhang G-F, Yang L-R, Qu M-K, et al. Interpolation of daily mean temperature by using geographically weighted regression-Kriging. Chinese Journal of Applied Ecology, 2015, 26(5): 1531-1536]
[28] 李豪, 刘涛, 徐精文. 基于混合地理加权回归与克里格的区域降水量空间插值方法. 中国农业气象, 2018, 39(10): 674-684 [Li H, Liu T, Xu J-W. Spatial interpolation of regional precipitation based on mixed geographical weighted regression combined with Kriging interpolation. Chinese Journal of Agrometeorology, 2018, 39(10): 674-684]