林业科学 ›› 2021, Vol. 57 ›› Issue (3): 79-89.doi: 10.11707/j.1001-7488.20210308
李文博1,2,吕振刚1,3,黄选瑞1,张志东1,*
收稿日期:
2019-03-11
出版日期:
2021-03-01
发布日期:
2021-04-07
通讯作者:
张志东
基金资助:
Wenbo Li1,2,Zhengang Lü1,3,Xuanrui Huang1,Zhidong Zhang1,*
Received:
2019-03-11
Online:
2021-03-01
Published:
2021-04-07
Contact:
Zhidong Zhang
摘要:
目的: 探究河北省北部华北落叶松人工林立地生产力空间分布格局及其与环境因子的关系,为高效森林经营提供依据。方法: 基于研究区1 179块森林资源二类调查小班数据,建立以地形、气候和土壤因子为辅助变量的多元线性回归(MLR)、随机森林(RF)、回归克里格(RK)和地理加权回归克里格(GWRK)模型。通过模型评价,选择最优模型预测研究区华北落叶松人工林立地指数(SI)空间分布,采用相关和偏相关分析方法分析SI分布格局与主要环境因子的关系。结果: 海拔、最干月降水、土壤全氮和全磷是影响研究区华北落叶松人工林立地生产力的主要因子;地统计学的2种模型(RK和GWRK)拟合优度相近且均显著高于MLR和RF模型,与全局回归的RK模型相比,局部回归的GWRK模型具有较高的R2(0.780)及较低的AIC(160.533)、RMSE(1.593 m)和MAE(1.113 m),为最优预测模型;不同立地生产力等级地区内SI对地形和土壤因子的变化表现更为敏感,对气候因子的变化反应较弱。结论: 海拔、最干月降水较高以及土壤氮磷含量适中的研究区西北部,是华北落叶松人工林适生区,而海拔、最干月降水较低以及土壤氮磷含量不平衡的东、南部,华北落叶松人工林的适应性较差,可通过造林活动或适当添加氮磷肥削弱环境因子对树木的不利影响。
中图分类号:
李文博,吕振刚,黄选瑞,张志东. 河北省北部华北落叶松人工林立地指数空间分布预测[J]. 林业科学, 2021, 57(3): 79-89.
Wenbo Li,Zhengang Lü,Xuanrui Huang,Zhidong Zhang. Predicting Spatial Distribution of Site Index for Larix principis-rupprechtii Plantations in the Northern Hebei Province[J]. Scientia Silvae Sinicae, 2021, 57(3): 79-89.
表1
森林调查数据描述性统计结果"
数据来源 Data source | 项目 Items | 样本量 Number | 最小值 Min. | 最大值 Max. | 平均值 Mean | 标准差 Std. | 变异系数 CV(%) | K-S检验 K-S test |
解析木 Analytical trees | 平均木高 Average height/m | 92 | 9.80 | 21.60 | 15.66 | 3.06 | 19.53 | 0.71 |
优势木高 Dominant height/m | 92 | 10.42 | 23.50 | 16.59 | 3.17 | 18.73 | 0.91 | |
森林调查 Forest inventory | 立地指数 Site index/m | 1 179 | 6.00 | 20.90 | 13.40 | 3.02 | 22.56 | 0.06 |
表2
环境因子描述性统计①"
因子类型 Factor type | 因子 Factors | 最小值 Min. | 最大值 Max. | 平均值 Mean | 标准差 Std. | 变异系数 CV(%) |
气候 Climate | BIO1/℃ | 1.18 | 6.94 | 3.87 | 1.70 | 43.93 |
BIO2/℃ | 9.88 | 14.42 | 12.50 | 0.83 | 6.64 | |
BIO3 | 22.65 | 29.72 | 27.03 | 1.42 | 5.25 | |
BIO5/℃ | 19.90 | 28.90 | 25.97 | 1.72 | 6.62 | |
BIO6/℃ | -28.00 | -16.50 | -20.26 | 2.20 | 10.86 | |
BIO8/℃ | 13.52 | 21.43 | 18.54 | 1.44 | 7.77 | |
BIO9/℃ | -17.88 | -7.07 | -11.91 | 2.17 | 18.22 | |
BIO11/mm | -17.89 | -9.13 | -12.03 | 2.03 | 16.87 | |
BIO14/mm | 1.00 | 5.00 | 1.76 | 0.77 | 43.75* | |
BIO15 | 105.18 | 120.15 | 113.77 | 2.60 | 2.29 | |
BIO17/mm | 5.00 | 17.00 | 8.28 | 2.14 | 25.85 | |
土壤 Soil | SOM/(g·kg-1) | 0.24 | 10.66 | 3.63 | 2.06 | 56.75 |
TN/(g·kg-1) | 0.01 | 0.50 | 0.20 | 0.09 | 45.00* | |
TP/(g·kg-1) | 0.03 | 0.11 | 0.06 | 0.02 | 33.33*** | |
地形 Terrain | DEM/m | 663.00 | 2 042.00 | 1 272.36 | 221.17 | 17.38*** |
表3
模型残差描述性统计和正态性检验"
预测模型 Prediction models | 最小值 Min. | 最大值 Max. | 平均值 Mean | 标准差 Std. | K-S检验∶P K-S test∶P-value | 空间相关关系检验 Spatial correlation test | ||
Moran’I | Z | P | ||||||
MLR | -6.096 | 7.886 | -0.002 | 2.356 | 0.428 | 0.219 | 7.291 | < 0.01 |
RF | -7.203 | 7.539 | 0.012 | 2.201 | 0.229 | 0.116 | 4.084 | 0.031 |
RK | -6.068 | 5.942 | -0.014 | 1.665 | 0.274 | 0.074 | 2.306 | 0.085 |
GWRK | -6.167 | 6.040 | -0.046 | 1.589 | 0.055 | 0.062 | 1.699 | 0.208 |
表5
GWR模型描述性统计"
因子 Factors | 平均值 Mean | 中位数 Median | 标准差 Std. | 空间相关关系检验 Spatial correlation test | ||
Moran’I | Z | P | ||||
截距 Intercept | 8.710 | 11.194 | 7.219 | 0.842 | 79.268 | < 0.01 |
DEM | 0.003 | 0.002 | 0.004 | 0.649 | 61.088 | < 0.01 |
BIO14 | -0.273 | -0.211 | 0.542 | 0.384 | 36.284 | < 0.01 |
TP | 26.045 | 9.710 | 51.983 | 0.653 | 61.550 | < 0.01 |
TN | -3.528 | -1.318 | 7.958 | 0.278 | 26.448 | < 0.01 |
表6
各立地生产力等级SI与环境因子的偏相关关系①"
因子 Factors | SI数据集 SI dataset | 立地生产力等级 Class of site productivity | ||||||
相关系数 Correlation coefficient | 偏相关系数 Partial correlation coefficient | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ | ||
TN | 0.318** | -0.074* | -0.128** | -0.044 | 0.221** | -0.057 | -0.229* | |
TP | 0.408** | 0.181** | -0.011 | 0.158** | -0.118 | -0.040 | 0.184 | |
BIO14 | 0.578** | 0.063* | -0.052 | 0.021 | 0.095 | 0.126 | -0.047 | |
DEM | 0.640** | 0.331** | 0.056 | 0.183** | 0.242** | -0.157 | -0.078 |
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