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

More Information

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

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