河北省北部华北落叶松人工林立地指数空间分布预测

doi:10.11707/j.1001-7488.20210308

Abstract

Abstract:

Objective: Exploring the spatial distribution of site productivity and analyzing its relationships with environmental factors for Larix principis-rupprechtii plantations are crucial for the effective forest management in the north area of Hebei Province. Method: Based on data from 1 179 forest inventory plots distributed across this area, we established multiple linear regression (MLR), random forest(RF), regression Kriging(RK) and geographical weighted regression Kriging(GWRK) models with topographic, climate and soil factors. Through model evaluation, the optimal model was selected to predict the spatial distribution patterns of site index(SI) for L. principis-rupprechtii plantations. The relationships between the distribution pattern of SI and environmental factors were analyzed by correlation and partial correlation analysis. Result: The major environmental factors influencing site productivity of L. principis-rupprechtii plantations in the study area included elevation(DEM), precipitation of the driest month(BIO14), total soil nitrogen and phosphorus(TN and TP). The values of goodness of fit of the two geostatistical models(RK and GWRK) were similar and both significantly higher than those of MLR and RF models. In contrast to RK model based on global regression, the GWRK model based on local regression had a higher fitting accuracy with R²=0.780, AIC=160.533, RMSE=1.593 m and MAE=1.113 m. The GWRK model based on local regression had a highly predictive power to predict SI of L. principis-rupprechtii plantations in the study area. SI was more sensitive to the variation of topographic and soil factors and less sensitive to the variation of the climate factors across regions with different site productivity classes. Conclusion: The site with a high growth suitability for L. principis-rupprechtii might tend to occur in the northwest area with a higher elevation, relatively high BIO14 and suitable soil TN and TP contents. However, the site with a low growth suitability for L. principis-rupprechtii were found in the eastern and southern areas with a lower elevation, relatively low BIO14, and unbalanced TN and TP contents. It might be feasible to mitigate the negative effects of environmental factors on growth and productivity of L. principis-rupprechtii plantations through silvicultural activities or appropriate nitrogen and phosphorus addition in the study area.

Key words: site index, environmental factors, geostatistics, random forest, Larix principis-rupprechtii

CLC Number:

S757

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.

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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
解析木 Analytical trees	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

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^***

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
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	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

预测模型 Prediction models	评价指标平均值 Mean value of validation indicators
预测模型 Prediction models	R²	RMSE	MAE	AIC
MLR	0.376	2.364	1.830	416.702
RF	0.544	2.205	1.657	332.770
RK	0.751	1.669	1.232	190.450
GWRK	0.780	1.593	1.113	160.533

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
因子 Factors	平均值 Mean	中位数 Median	标准差 Std.	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

Predicting Spatial Distribution of Site Index for Larix principis-rupprechtii Plantations in the Northern Hebei Province

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因子 Factors	SI数据集 SI dataset		立地生产力等级 Class of site productivity
因子 Factors	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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