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

被引：2

作者：

Li W. ^{[1
,2
]}

Lü Z. ^{[1
,3
]}

Huang X. ^{[1
]}

Zhang Z. ^{[1
]}

机构：

[1] Hebei Province Key Laboratory of Forest Trees Germplasm Resources and Forest Protection, College of Forestry, Agricultural University of Hebei, Baoding

[2] Henan Provincial Institute of Forest Inventory and Planning, Zhengzhou

[3] College of Resource and Environment, Huazhong Agricultural University, Wuhan

来源：

Linye Kexue/Scientia Silvae Sinicae | 2021年 / 57卷 / 03期

关键词：

Environmental factors; Geostatistics; Larix principis-rupprechtii; Random forest; Site index;

D O I：

10.11707/j.1001-7488.20210308

中图分类号：

学科分类号：

摘要：

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. © 2021, Editorial Department of Scientia Silvae Sinicae. All right reserved.

引用

页码：79 / 89

页数：10

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