Using spatial distribution patterns and sample plot design to improve the accuracy of forest resource sampling survey

Hu Yingxin; Mei Anqi; Xu Qing; Hou Zhengyang

doi:10.12171/j.1000-1522.20230061

Journal of Beijing Forestry University > 2024 > 46(2): 155-165. > DOI: 10.12171/j.1000-1522.20230061

Hu Yingxin, Mei Anqi, Xu Qing, Hou Zhengyang. Using spatial distribution patterns and sample plot design to improve the accuracy of forest resource sampling survey[J]. Journal of Beijing Forestry University, 2024, 46(2): 155-165. DOI: 10.12171/j.1000-1522.20230061

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Using spatial distribution patterns and sample plot design to improve the accuracy of forest resource sampling survey

Hu Yingxin^{1, 2,},
Mei Anqi^{1, 2},
Xu Qing³,
Hou Zhengyang^{1, 2, ,}

1.
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
2.
Ecological Observation and Research Station of Heilongjiang Sanjiang Plain Wetlands, National Forestry and Grassland Administration, Shuangyashan 518000, Heilongjiang, China
3.
International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration/ Beijing for Bamboo & Rattan Science and Technology, Beijing 100102, China

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Received Date: March 16, 2023
Revised Date: January 04, 2024
Available Online: January 10, 2024

Graphical Abstract

Abstract

Abstract

Objective
In forest resource survey, sampling design under the spatial distribution pattern of forest attributes was studied to break through regional limitations and provide generalizable empirical rules for sampling survey.
Method
Artificial forest populations were constructed with the data field survey at the Beijing Jiufeng National Forest Park. Tree mortality rate was used as a proxy for expressing forest spatial autocorrelation. Systematic sampling design was adopted, and Monte Carlo simulations were implemented to evaluate the effects of spatial autocorrelation, sample plot size and systematic sampling on sampling precision.
Result
(1) The coefficients of variation for the four different spatial distribution patterns increased in the following order: 0% mortality, 20% mortality in random pattern, 10% mortality in aggregated pattern, and 20% mortality in aggregated pattern. When mortality rate was 20% and sampling intensity was 2.73%, the coefficient of variation for random pattern was 1.3% lower than that for aggregated pattern. (2) The coefficients of variation for three different sample plot sizes increased in the following order: 20 m × 20 m, 30 m × 30 m, and 40 m × 40 m. The coefficient of variation of 40 m × 40 m was significantly higher than that of 20 m × 20 m and 30 m × 30 m. (3) With increasing sampling intensity, under the random pattern, the coefficient of variation for the artificial population designed with 8 × 8 main units was about 0.02% higher than that for 4 × 4, and about 0.15% higher than that for 16 × 16. Under the clustered pattern, the coefficient of variation for the artificial population designed with 8 × 8 main units was about 0.32% higher than that for 4 × 4, and about 0.54% lower than that for 16 × 16.
Conclusion
(1) Different degrees of spatial autocorrelation reduce sampling accuracy, among which aggregated pattern has a more significant impact than random pattern. (2) Smaller sample plots help improve sampling accuracy and convergence rate, but reasonable sample plot design can effectively enhance sampling efficiency. (3) The number of main units in systematic sampling has little impact on sampling accuracy. In practice, systematic sampling designs with a sample size of 1 should be avoided because sampling errors would be hard to quantify.
- sampling survey,
- spatial model,
- sample plot size,
- systematic sampling

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References (30)

References

[1]	白降丽, 彭道黎, 庾晓红. 我国森林资源调查技术发展研究[J]. 山西林业科技, 2005(1): 4−7. Bai J L, Peng D L, Yu X H. Study on the development of forest resource inventory technology in China[J]. Shanxi Forestry Science and Technology, 2005(1): 4−7.
[2]	张会儒, 雷相东, 李凤日. 中国森林经理学研究进展与展望[J]. 林业科学, 2020, 26(9): 130−142. Zhang H R, Lei X D, Li F R. Research progress and prospects of forest management science in China[J]. Scientia Silvae Sinicae, 2020, 26(9): 130−142.
[3]	陈盼盼. 地面摄影测量在森林资源调查中的关键技术研究[D]. 北京: 北京林业大学, 2020. Chen P P. Study on key technologies of ground photogrammetry in forest inventory[D]. Beijing: Beijing Forestry University, 2020.
[4]	冯仲科. “互联网+”推进森林资源调查走进精准林业新时代[J]. 国土绿化, 2019(2): 16−19. Feng Z K. “Internet+” promotes the forest resource survey to enter the era of precision forestry[J]. Land Greening, 2019(2): 16−19.
[5]	罗仙仙, 亢新刚, 杨华. 我国森林资源综合监测抽样理论研究综述[J]. 西北林学院学报, 2008, 23(6): 187−193. Luo X X, Kang X G, Yang H. A review on the sampling theory of forest resources comprehensive monitoring[J]. Journal of Northwest Forestry University, 2008, 23(6): 187−193.
[6]	曾伟生, 黄国胜, 党永峰, 等. 全国森林资源宏观监测的抽样设计与估计方法探索[J]. 林业资源管理, 2016(3): 1−6. Zeng W S, Huang G S, Dang Y F, et al. Discussion on sampling design and estimation methods of national forest resources macro-monitoring[J]. Forest Resources Management, 2016(3): 1−6.
[7]	高宏. 森林资源抽样调查技术方法[J]. 林业勘查设计, 2018, 188(4): 87−88. doi: 10.3969/j.issn.1673-4505.2018.04.040 Gao H. Technical methods of sampling survey of forest resources[J]. Forest Investigation Design, 2018, 188(4): 87−88. doi: 10.3969/j.issn.1673-4505.2018.04.040
[8]	李春干, 代华兵. 中国森林资源调查: 历史、现状与趋势[J]. 世界林业研究, 2021, 34(6): 72−80. Li C G, Dai H B. Forest management inventory in China: history, current status and trend[J]. World Forestry Research, 2021, 34(6): 72−80.
[9]	Tomppo E. Forest inventory: methodology and applications[M]. Dordrecht: Springer, 2006: 179−194.
[10]	李明阳, 姜文倩, 徐婷, 等. 基于总体表面属性特征的森林资源抽样调查方法比较[J]. 东北林业大学学报, 2011, 39(9): 49−51, 64. doi: 10.3969/j.issn.1000-5382.2011.09.016 Li M Y, Jiang W Q, Xu T, et al. Comparison of sampling survey methods of forest resources for populations with different surface attributes[J]. Journal of Northeast Forestry University, 2011, 39(9): 49−51, 64. doi: 10.3969/j.issn.1000-5382.2011.09.016
[11]	Annika K M. Forest inventory: methodology and applications [M]. New York: Springer-Verlag, 2006.
[12]	Cochran W G. Sampling techniques[M]. 3rd ed. Hoboken: John Wiley & Sons, 1977: 227.
[13]	Mcroberts R E, Westfall J A. Propagating uncertainty through individual tree volume model predictions to large-area volume estimates[J]. Annals of Forest Science, 2016, 73(3): 625−633. doi: 10.1007/s13595-015-0473-x
[14]	Särndal C E T, Hoem L, Lindley J M, et al. Design-based and model-based inference in survey sampling[J]. Scandinavian Journal of Statistics, 1978(5): 27−52.
[15]	Schreuder H T, Gregoire T G, Wood G B. Sampling methods for multiresource forest inventory [M]. New York: John Wiley & Sons, 1993: 446.
[16]	金勇进, 郝一炜. 非概率样本的模型推断[J]. 数学的实践与认识, 2019, 49(5): 246−255. Jin Y J, Hao Y W. Model-based inference for non-probability sample[J]. Mathematics in Practice and Theory, 2019, 49(5): 246−255.
[17]	Thompson S K. Sampling[M]. 3rd ed. Hoboken: John Wiley & Sons, 2012.
[18]	Legendre P. Spatial autocorrelation: trouble or new paradigm[J]. Ecology, 1993, 74(6): 1659−1673. doi: 10.2307/1939924
[19]	Reed D, Burkhart H. Spatial autocorrelation of individual tree characteristics in loblolly pine stands[J]. Forest Science, 1985, 31(3): 575−587.
[20]	Penttinen A , Henttonen H M. Marked point processes in forest statistics[J]. Forest Science, 1992, 38(4): 806−824.
[21]	Tomppo E. Models and methods for analyzing spatial pattern of trees[J]. Communicationes Instituti Forestalis Fenniae, 1986(138): 65.
[22]	Chou Y H. Spatial patterns and spatial autocorrelation[J]. Lecture Notes in Computer Science, 1995(988): 365−376.
[23]	Zeng W S, Tomppo E, Healey S P, et al. The national forest inventory in China: history-results-international context[J]. Forest Ecosystems, 2015(2): 23.
[24]	宋新民, 李金良. 抽样调查技术[M]. 北京: 中国林业出版社, 2007. Song X M, Li J L. Sampling survey technology[M]. Beijing: China Forestry Publishing House, 2007.
[25]	Hou Z, Xu Q, Hartikainen S, et al. Impact of plot size and spatial pattern of forest attributes on sampling efficacy[J]. Forest Science, 2015, 61(5): 847−860. doi: 10.5849/forsci.14-197
[26]	庞丽峰, 雷渊才, 陆元昌, 等. 森林经营单位级系统群团抽样设计比较[J]. 西北农林科技大学学报(自然科学版), 2015, 43(6): 141−152. Pang L F, Lei Y C, Lu Y C, et al. Comparison of cluster systematic sapling at management unit level[J]. Journal of Northwest A & F University (Natural Science Edition), 2015, 43(6): 141−152.
[27]	嘎拉泰, 朱双双, 徐道春, 等. 鹫峰森林多层土壤温度变化规律的研究[J]. 林业工程学报, 2018, 3(3): 136−141. Ga L T, Zhu S S, Xu D C, et al. Investigation of temperature changing patterns of multi-layer soil in Jiufeng forest[J]. Journal of Forestry Engineering, 2018, 3(3): 136−141.
[28]	孟芮萱. 城郊型森林公园游憩功能评价及结构优化方法研究: 以鹫峰国家森林公园为例[D]. 北京: 北京林业大学, 2020. Meng R X. The study on recreation function evaluation and structure optimization methods in the suburban forest park: take Jiufeng National Forest Park as a case[D]. Beijing: Beijing Forestry University, 2020.
[29]	蒲莹, 曾伟生, 阳帆. 北京市树高胸径回归模型研建及一元立木材积表检验[J]. 林业资源管理, 2021(3): 62−66. Pu Y, Zeng W S, Yang F. Tree height-diameter regression models development and one-variable tree volume tables examination in Beijing[J]. Forest Resources Management, 2021(3): 62−66.
[30]	Berrill J P, O’hara K L. Influence of tree spatial pattern and sample plot type and size on inventory estimates for leaf area index, stocking, and tree size parameters[C]//Proceedings of coast redwood forests in a changing California: A symposium for scientists and managers. Albany, CA: Pacific Southwest Research Station, 2012: 485– 497.

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Using spatial distribution patterns and sample plot design to improve the accuracy of forest resource sampling survey