The Impact of Forest Management Inventory Factors on the Ecological Service Value of Forest Water Conservation Based on Machine Learning Algorithms

Author:

Chen Zhefu12,Lü Yong1,Liu Yang3,Chen Duanlv2,Peng Baofa2

Affiliation:

1. College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China

2. College of School of Geographical Sciences and Tourism, Hunan University of Arts and Science, Changde 415000, China

3. Engineering Research Center for Smart Agricultural Machinery Beidou Navigation Adaptation Technology and Equipment of Hunan Province, Hunan Automotive Engineering Vocational University, Zhuzhou 412000, China

Abstract

Based on forest management inventory data, this study applies machine learning algorithms to explore the relationships between forest water conservation capacity and forest management inventory factors, thus providing more extensive insights into forest water conservation services. By integrating the InVEST model and machine learning algorithms, this study identifies the key factors related to water conservation services based on forest management inventory factors and investigates the differences in and accuracy of forest water conservation models using the random forest algorithm. The results are as follows: (1) The determination coefficients (R2) of the three machine learning models range from 0.508 to 0.869, with root mean square errors (RMSEs) ranging from 28.380 to 69.339. The performance of these models is generally satisfactory, with the random forest algorithm showing superior results. (2) By leveraging the advantages of the three machine learning algorithms in handling categorical data, this study analyzes the contributions of forest management inventory factors, revealing the impact mechanisms of forest-type water conservation services. (3) The integration of machine learning algorithms allows for better processing of the scale and correlation of independent variables, providing more objective information on the main controlling factors of forest water conservation. (4) Predictions of water conservation capacity using machine learning are consistent with that of the InVEST model. The water conservation per unit area shows a variation trend as follows: slow-growing broadleaf forests > shrub forests > middle-growing broadleaf forests > cunninghamia lanceolata forests > fast-growing broadleaf forests > pine forests > bamboo forests. (5) Since this study considers only the factors available in the forest management inventory, which does not encompass all relevant influencing factors, it is difficult to fully address the complexities of how forest water conservation services interact with forest structure. Therefore, further research is needed to investigate the intrinsic mechanisms underlying the interactions between water conservation and forest management inventory factors.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

MDPI AG

Reference60 articles.

1. Expert Knowledge Based Valuation Method of Ecosystem Services in China;Xie;J. Nat. Resour.,2018

2. Evaluation of the spatial patterns of the water retention function of the forest ecosystem in the Dongjiang River Watershed;Zhang;Acta Ecol. Sin.,2016

3. Research on water conservation function of forest ecosystem: Propress and prospect;Liu;Chin. J. Ecol.,2022

4. Changes in vegetation structure and diversity after grass-to-forest succession in a Southern;Katherine;Am. Midl. Nat.,1998

5. Study on the Coupling Relationship between Structure and Function of Water Conservation Forests in Mountainous Area of Beijing;Luo;For. Resour. Manag.,2011

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3