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Study of Adaptability of the Primary Afforestation Species in Chongyi County,Jiangxi Province Based on Random Forest

  • Jincheng HUANG ,
  • Hongsheng LIU ,
  • Jinkui NING ,
  • Xunzhi OUYANG ,
  • Hao ZANG
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  • 1. Forestry Bureau of Chongyi,Ganzhou,Jiangxi 341300,China
    2. College of Forestry,Jiangxi Agricultural University,Nanchang 330045,China

Received date: 2022-01-21

  Revised date: 2022-04-06

  Online published: 2022-06-13

Abstract

Based on forest management inventory data and five primary forestation species (Cunninghamia lanceolata,Pinus massoniana,Schima superba,Melia azedarach and Choerospondias axillaris),the adaptability models of forestation species were constructed to predict adaptability for afforestation sites in Chongyi County,Jiangxi Province. The input variables contained elevation,slope,aspect,position,soil type,parent rock,soil thickness,and thickness of soil humus,the output variable was growth adaptability. The results showed that the training accuracy of adaptability models for 5 forestation species were 88.69%,93.13%,95.54%,93.86% and 98.92%,respectively. In addition,the generalization accuracy of adaptability models for 5 species were 72.79%,84.18%,77.99%,81.22% and 80.56%,respectively. Site factors greatly affecting the adaptability of coniferous species were thickness of soil humus,elevation and soil thickness,while the driver factors for broad-leaved species depended on species. The species adaptability models based on random forest could analyze the adaptability for forestation species,and extract the comfortable growth environment. Thus,the established models could provide support to the problem of matching species to sites and the improvement of regional forest quality.

Cite this article

Jincheng HUANG , Hongsheng LIU , Jinkui NING , Xunzhi OUYANG , Hao ZANG . Study of Adaptability of the Primary Afforestation Species in Chongyi County,Jiangxi Province Based on Random Forest[J]. Forest and Grassland Resources Research, 2022 , 0(2) : 117 -125 . DOI: 10.13466/j.cnki.lyzygl.2022.02.016

References

[1] Eckes-Shephard A H, Tiavlovsky E, Chen Y, et al. Direct response of tree growth to soil water and its implications for terrestrial carbon cycle modelling[J]. Global Change Biology, 2021, 27(1):121-135.
[2] Zhao Qiong, Zeng Dehui. Nitrogen addition effects on tree growth and soil properties mediated by soil phosphorus availability and tree species identity[J]. Forest Ecology and Management, 2019, 449:117478.
[3] 陆海飞, 刘望舒, 徐建民, 等. 广西中南部尾巨桉人工林立地类型划分及立地质量评价[J]. 林业科学, 2021, 57(5):13-24.
[4] 郭艳荣, 刘洋, 吴保国. 福建省宜林地立地质量的分级与数量化评价[J]. 东北林业大学学报, 2014, 42(10):54-59.
[5] 雷相东, 唐守正, 符利勇, 等. 森林立地质量定量评价——理论、方法、应用[M]. 北京: 中国林业出版社, 2020:3-9.
[6] 唐诚, 王春胜, 庞圣江. 广西大青山西南桦人工林立地类型划分及评价[J]. 西北林学院学报, 2018, 33(4):52-57.
[7] 朱光玉, 康立. 森林立地生产力评价指标与方法[J]. 西北林学院学报, 2016, 31(6):275-281.
[8] Weiskittel A R, Hann D W, Kershaw J A, et al. Forest growth and yield modeling[M]. Chichester: John Wiley & Sons, Ltd, 2011:38-47.
[9] 刘洵, 曾思齐, 龙时胜, 等. 湖南省栎类天然次生林胸径地位指数表研制[J]. 森林与环境学报, 2019, 39(3):265-272.
[10] 李斌成, 许业洲, 袁慧, 等. 湖北省日本落叶松差分型立地指数模型构建[J]. 森林与环境学报, 2020, 40(4):433-441.
[11] 张博, 陈科屹, 周来, 等. 基于分位数回归的杉木人工林地位级划分方法研究[J]. 林业科学研究, 2021, 34(4):103-110.
[12] Liu Xianzhao, Duan Guangshuang, Chhin S, et al. Evaluation of potential versus realized site productivity of Larix principis-rupprechtii plantations across northern China[J]. Forest Ecology and Management, 2021, 479:118608.
[13] 李凤日. 测树学[M]. 4版. 北京: 中国林业出版社, 2019:138-140.
[14] 顾云春, 李永武, 杨承栋. 森林立地分类与评价的立地要素原理与方法[M]. 北京: 科学出版社, 1993:13-16.
[15] 姚茂和, 盛炜彤, 熊有强. 杉木人工林林下植被对立地的指示意义[J]. 林业科学, 1992, 28(3):208-212.
[16] Jumwong N, Wachrinrat C, Sungkaew S, et al. Site indicator species for predicting the productivity of teak plantations in Phrae Province,Thailand[J]. Biotropia, 2020, 27(5):104-114.
[17] 刘聘, 乔一娜, 李静文, 等. 安溪县福建柏人工林立地质量数量化评价[J]. 云南农业大学学报:自然科学版, 2021, 36(2):324-329.
[18] Fiandino S, Plevich J, Tarico J, et al. Modeling forest site productivity using climate data and topographic imagery in Pinus elliottii plantations of central Argentina[J]. Annals of Forest Science, 2020, 77:95.
[19] Afif-Khouri E, Álvarez-Álvarez P, Fernández-López M J, et al. Influence of climate,edaphic factors and tree nutrition on site index of chestnut coppice stands in north-west Spain[J]. Forestry, 2011, 84(4):385-396.
[20] Kint V, Vos B D, Deckers J, et al. Predicting forest site productivity in temperate lowland from forest floor,soil and litterfall characteristics using boosted regression trees[J]. Plant and Soil, 2012, 354:157-172.
[21] Sabatia C O, Burkhart H E. Predicting site index of plantation loblolly pine from biophysical variables[J]. Forest Ecology and Management, 2014, 326:142-156.
[22] Coomes D A, Allen R B. Effects of size,competition and altitude on tree growth[J]. Journal of Ecology, 2007, 95:1084-1097.
[23] 雷相东. 机器学习算法在森林生长收获预估中的应用[J]. 北京林业大学学报, 2019, 41(12):1-14.
[24] Jevšenak J, Skudnik M. A random forest model for basal area increment predictions from national forest inventory data[J]. Forest Ecology and Management, 2021, 479:118601.
[25] Jiang Huiquan, Radtke P J, Weiskittel A R, et al. Climate- and soil-based models of site productivity in eastern US tree species[J]. Canadian Journal of Forest Research, 2015, 45:325-342.
[26] 高若楠, 苏喜友, 谢阳生, 等. 基于随机森林的杉木适生性预测研究[J]. 北京林业大学学报, 2017, 39(12):36-43.
[27] 杜雨菲, 吴保国, 陈玉玲. 基于机器学习算法的广西桉树适宜性研究[J]. 浙江农林大学学报, 2020, 37(1):122-128.
[28] 郭鸿郡. 基于GWR的黑龙江大兴安岭森林立地质量的遥感分析[D]. 哈尔滨: 东北林业大学, 2017,15-18.
[29] 郭颖婕, 刘晓燕, 郭茂祖, 等. 植物抗性基因识别中的随机森林分类方法[J]. 计算机科学与探索, 2012, 6(1):67-77.
[30] 李嘉珏, 于洪波. 甘肃黄土高原立地分类与适地适树[M]. 北京: 北京科学技术出版社, 1990:91-93.
[31] 谢阳生, 陆元昌, 刘宪钊, 等. 多功能森林经营方案编制技术及案例[M]. 北京: 中国林业出版社, 2019.
[32] 刘丹. 基于分布适宜性和潜在生产力的定量适地适树研究[D]. 北京: 中国林业科学研究院, 2018:1-3.
[33] Chen Yuling, Wu Baoguo, Chen Dong, et al. Using machine learning to assess site suitability for afforestation with particular species[J]. Forests, 2019, 10(9):739.
[34] 蔡学林, 张志云, 欧阳勋志. 江西森林立地质量数量化评价研究[J]. 江西农业大学学报, 1997, 19(6):81-89.
[35] 李绍忠, 孟康敏, 赵冰, 等. 东北珍贵阔叶树适地适树的研究[J]. 应用生态学报, 1992, 3(3):195-201.
[36] 罗也, 王君, 杨雨春, 等. 利用随机效应模型模拟东北三省胡桃楸地位指数[J]. 应用生态学报, 2020, 31(8):2549-2557.
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