基于角尺度、混交度和胸径优势度的杉木混交林空间结构优化

陈超; 赵吟吟; 俞家聪; 李雪建; 方庆; 张晔华; 宋美萱; 杜华强

doi:10.13466/j.cnki.lczyyj.2025.01.005

林草资源研究 >

2025 , Vol. 0 >Issue 1: 38 - 47

DOI: https://doi.org/10.13466/j.cnki.lczyyj.2025.01.005

科学研究

基于角尺度、混交度和胸径优势度的杉木混交林空间结构优化

陈超 ,
赵吟吟 ,
俞家聪 ,
李雪建 ,
方庆 ,
张晔华 ,
宋美萱 ,
杜华强

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1.浙江农林大学环境与资源学院,浙江临安 311300
2.省部共建亚热带森林培育国家重点实验室,浙江临安 311300
3.浙江省森林资源监测中心,杭州 310020
4.建德市建德林场,浙江建德 311604

陈超,硕士研究生,主要研究方向为森林资源遥感监测与信息技术。Email:chenchao1916@stu.zafu.edu.cn

收稿日期: 2024-06-30

修回日期: 2024-11-17

网络出版日期: 2025-08-06

基金资助

浙江省“尖兵”“领雁”研发攻关计划“浙江主要森林类型提质增汇关键技术研究与应用”(2023C02035)

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Optimization of spatial structure of Cunninghamia lanceolata mixed forests based on uniform,dominance and mingling

Chao CHEN ,
Yinyin ZHAO ,
Jiacong YU ,
Xuejian LI ,
Qing FANG ,
Yehua ZHANG ,
Meixuan SONG ,
Huaqiang DU

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1. School of Environmental and Resources Science Zhejiang A & F University,Lin'an 311300,Zhejiang,China
2. State Key Laboratory of Subtropical Silviculture,Lin'an 311300,Zhejiang,China
3. Zhejiang Forest Resource Monitoring Center,Hangzhou 310020,Zhejiang,China
4. Jiande forest farm,Jiande 311604,Zhejiang,China

Received date: 2024-06-30

Revised date: 2024-11-17

Online published: 2025-08-06

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摘要

优化林分空间结构不仅能够提高森林质量,还可以进一步发挥森林的功能效益。以浙江省建德市梅城林场的杉木混交林为研究对象,采用乘除法对角尺度(W)、胸径优势度(U)和混交度(M)进行多目标规划,构建起林分空间结构优化模型;使用模拟退火算法对模型求解,获得最优采伐方案;对比分析模拟择伐前后林分空间结构参数及其二元分布的变化。结果表明:经过模拟择伐,林分角尺度提高了2.34%,胸径优势度下降了0.82%,树种混交度提高了11.36%,目标函数值增大了12.42%,一定程度上改善了林分的空间结构。经过择伐,林分空间结构二元分布特征更加趋向于合理。由此可见,基于角尺度、混角度和胸径优势度所构建的林分空间结构优化体系能够有效改善林分空间结构特征,为制定后续森林经营方案提供参考。

关键词： 林分空间结构; 择伐; 优化模型; 模拟退火算法; 二元分布

本文引用格式

陈超 , 赵吟吟 , 俞家聪 , 李雪建 , 方庆 , 张晔华 , 宋美萱 , 杜华强 . 基于角尺度、混交度和胸径优势度的杉木混交林空间结构优化[J]. 林草资源研究, 2025 , 0(1) : 38 -47 . DOI: 10.13466/j.cnki.lczyyj.2025.01.005

Abstract

Optimizing the spatial structure of forest stands can significantly enhance forest quality and further unlock the functional and economic benefits of forests.This study focuses on the Cunninghamia lanceolata mixed forests in Meicheng Forest Farm,Jiande City,Zhejiang Province.Firstly,the uniform(W),dominance(U),and mingling(M)indices integrated into a multi-objective optimization framework using multiplication and division methods,thereby constructing a spatial structure optimization model for the forest stand.Then,the simulated annealing was applied to solve the model derive the selection cutting plan.Finally,a comparative analysis was conducted on the variations in spatial structure parameters and binary distribution of the forest stand before and after the simulated selection cutting.The results showed that,following the simulation,the uniformity increased by 2.34%,dominance decreased by 0.82%,mingling among mixed tree species increased by 11.36%,and the objective function value rose by 12.42%,which collectively improved the spatial structure of the forest stand.After selection cutting,the binary distribution characteristics of the spatial structure became more reasonable.Therefore,the forest stand spatial structure optimization system developed in this study,based on W,U,and M indices,effectively enhances the spatial structure characteristics of the forest and offers valuable insights for the formulation of future forest management strategies.

Key words： stand spatial structure; selection cutting; optimization models; simulated annealing; binary distribution

参考文献

[1]	GAO Wenqiang, LEI Xiangdong, LIANG Maowei, et al. Biodiversity increased both productivity and its spatial stability in temperate forests in northeastern China[J]. Science of The Total Environment, 2021,780:146674.
[2]	惠刚盈, 胡艳波. 混交林树种空间隔离程度表达方式的研究[J]. 林业科学研究, 2001, 14(1):23-27.
[3]	AHMAD B, WANG Yanhui, HAO Jia, et al. Variation of carbon density components with overstory structure of larch plantations in northwest China and its implication for optimal forest management[J]. Forest Ecology and Management, 2021,496:119399.
[4]	LI Yuanfa, YE Shaoming, HUI Gangying, et al. Spatial structure of timber harvested according to structure-based forest management[J]. Forest Ecology and Management, 2014,322:106-116.
[5]	张晓红, 周超凡, 张状, 等. 崇礼冬奥核心区华北落叶松人工林结构特征与优化模拟[J]. 林业科学, 2022, 58(10):79-88.
[6]	赵文菲, 曹小玉, 谢政锠, 等. 基于结构方程模型的杉木公益林林分空间结构评价[J]. 林业科学, 2022, 58(8):76-88.
[7]	汤孟平, 唐守正, 雷相东, 等. 林分择伐空间结构优化模型研究[J]. 林业科学, 2004, 40(5):25-31.
[8]	KAYA A, BETTINGER P, BOSTON K, et al. Optimisation in forest management[J]. Current Forestry Reports, 2016,2:1-17.
[9]	HOFSTAD O, ARAYA M M. Optimal wood harvest in miombo woodland considering REDD+payments — A case study at Kitulangalo Forest Reserve,Tanzania[J]. Forest Policy and Economics, 2015,51:9-16.
[10]	LIU Wanyu, LIN Chuncheng, SU Kehong. Modelling the spatial forest-thinning planning problem considering carbon sequestration and emissions[J]. Forest Policy and Economics, 2017,78:51-66.
[11]	HEINONEN T, M?KINEN A, RASINM?KI J, et al. Aggregating microsegments into harvest blocks by using spatial optimization and proximity objectives[J]. Canadian Journal of Forest Research, 2018,48:1184-1193.
[12]	PACKALEN P, PUKKALA T, PASCUAL A. Combining spatial and economic criteria in tree-level harvest planning[J]. Forest Ecosystems, 2020, 7(2):234-246.
[13]	汤孟平, 徐文兵, 陈永刚, 等. 毛竹林空间结构优化调控模型[J]. 林业科学, 2013, 49(1):120-125.
[14]	QIU Hanqing, ZHANG Huaiqing, LEI Kexin, et al. A new tree-level multi-objective forest harvest model(MO-PSO):Integrating neighborhood indices and PSO algorithm to improve the optimization effect of spatial structure[J]. Forests, 2023, 14(3):441.
[15]	DONG Lingbo, BETTINGER P, LIU Zhaogang. Optimizing neighborhood-based stand spatial structure:Four cases of boreal forests[J]. Forest Ecology and Management, 2022, 506(2):119965.
[16]	SUN Yusen, JIN Xingji, PUKKALA T, et al. Two-level optimization approach to tree-level forest planning[J]. Forest Ecosystems, 2022,9:100001.
[17]	XUAN Shuai, WANG Jianming, CHEN Yuling. Reinforcement learning for stand structure optimization of pinus yunnanensis secondary forests in southwest China[J]. Forests, 2023, 14(12):2456.
[18]	DONG Lingbo, LU Wei, LIU Zhaogang. Developing alternative forest spatial management plans when carbon and timber values are considered:A real case from northeastern China[J]. Ecological Modelling, 2018,385:45-57.
[19]	LI Yuanfa, HUI Gangying, ZHAO Zhonghua, et al. Spatial structural characteristics of three hardwood species in Korean pine broad-leaved forest—Validating the bivariate distribution of structural parameters from the point of tree population[J]. Forest Ecology and Management, 2014,314:17-25.
[20]	黄露波, 甄贞, 赵颖慧. 基于多源LiDAR的阔叶红松林林分空间结构特征分析[J]. 中南林业科技大学学报, 2023, 43(8):36-50.
[21]	王奕茹, 李际平, 曹小玉, 等. 运用空间结构参数二元分布对杉木—闽楠混交林林分结构分析[J]. 东北林业大学学报, 2020, 48(7):55-59.
[22]	万盼, 刘文桢, 刘瑞红, 等. 结构化经营对栎松混交林林分空间结构及稳定性的影响[J]. 林业科学, 2020, 56(4):35-45.
[23]	王小东, 陆长华, 黄庆丰. 基于RS与GIS的东至县梅城林场森林景观格局动态分析[J]. 安徽农业大学学报, 2011, 38(4):522-527.
[24]	AGUIRRE O, HUI Gangying, GADOW K V, et al. An analysis of spatial forest structure using neighbourhood-based variables[J]. Forest Ecology and Management, 2003, 183(1-3):137-145.
[25]	ZHANG Gongqiao, HUI Gangying, ZHAO Zhonghua, et al. Composition of basal area in natural forests based on the uniform angle index[J]. Ecological Informatics, 2018,45:1-8.
[26]	HUI Gangying, ALBERT M, GADOWW K V. DasUmgebungsmaβ als Parameter zur Nachbildung von Bestandesstrukturen[J]. Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch, 1998,117:258-266.
[27]	PETRITAN A M, BIRIS I A, MERCE O, et al. Structure and diversity of a natural temperate sessile oak(Quercus petraea L.)-European Beech(Fagus sylvatica L.) forest[J]. Forest Ecology and Management, 2012,280:140-149.
[28]	DONG Lingbo, WEI Hongyang, LIU Zhaogang. Optimizing forest spatial structure with neighborhood-based indices:Four case studies from Northeast China[J]. Forests, 2020, 11(4):413.
[29]	惠刚盈, GADOW K. 森林空间结构量化分析方法[M]. 北京: 中国科学技术出版社, 2003.
[30]	刘帅, 张江, 李建军, 等. 森林空间结构分析中基于Voronoi图的样地边缘校正[J]. 林业科学, 2017, 53(1):28-37.
[31]	李建军, 张会儒, 刘帅, 等. 基于改进PSO的洞庭湖水源涵养林空间优化模型[J]. 生态学报, 2013, 33(13):4031-4040.
[32]	XUAN Shuai, WANG Jianming, CHEN Yuling. Reinforcement learning for stand structure optimization of pinus yunnanensis secondary forests in Southwest China[J]. Forests, 2023, 14(12):2456.
[33]	REEVES C.R. Modern heuristic techniques for combinatorial problems(advanced topics in computer science)[M]. New York: John Wiley & Sons, Inc.,1993.
[34]	沈康, 杨廷栋, 张怀清, 等. 基于模拟退火算法的林分多目标经营动态可视化模拟[J]. 林业科学研究, 2020, 33(3):99-106.
[35]	孙云霞, 刘兆刚, 董灵波. 基于模拟退火算法逆转搜索的森林空间经营规划[J]. 林业科学, 2019, 55(11):52-62.
[36]	黄维. 油松飞播林林分空间结构特征分析及优化调整[D]. 杨凌: 西北农林科技大学, 2022.
[37]	HUANG Xiang, CHEN Yichen, TAN Hongru, et al. Extraction of the spatial structure of Chinese fir plantations stands based on unmanned aerial vehicle and its effect on AGB[J]. Forest Ecology and Management, 2024,558:121800.
[38]	赵晨昊, 王建军, 周光, 等. 基于空间结构优化的金盆山天然混交林采伐模拟[J]. 西南林业大学学报(自然科学), 2022, 42(5):126-133.
[39]	YOSHIMOTO A, ASANTE P, KONOSHIMA Masashi. Stand-level forest management planning approaches[J]. Current Forestry Reports, 2016,2:163-176.
[40]	CAO Lin, LIU Kun, SHEN Xin, et al. Estimation of forest structural parameters using UAV-LiDAR data and a process-based model in Ginkgo planted forests[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(11):4175-4190.
[41]	LIU Kun, SHEN Xin, CAO Lin, et al. Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,146:465-482.

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