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<p>Accurate and precise wood volume estimates are vital for sustainable forest management and are typically obtained using sample-based estimators. This study explores cluster sampling for estimating wood volume in an Amazonian forest, aiming to: (1) measure the loss of precision and deviation in volume estimates by comparing full samples to reduced samples and (2) analyze how the distance between cluster subunits affects the volume estimates. Data were collected from 22 clusters (model used in the Brazilian National Forest Inventory) installed in the Bom Futuro National Forest, Brazil. Our analyses consisted of examining the effect of reducing the (i) cluster size, in two directions (inward and outward), and (ii) sample size, by reducing the number of sample units from 22 to 4 clusters. We found that reducing the cluster size to 0.56&#xa0;ha still yielded timber volume estimates as accurate and precise as the original 0.80&#xa0;ha clusters, a useful innovation for forest inventories in the Amazon. Precision loss from reduced sampling ranged from 1.2 to 1.5 times, while deviation loss ranged from 7.2 to 19 times. We concluded that the precision was more influenced by the cluster size reduction than the sample size reduction. The sampled area required to stabilize precision of the volume estimate is smaller than that required to stabilize the mean volume estimate. The effect of the distance between subunits on deviation and precision was attenuated as the cluster size increased.</p>

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Optimizing Cluster Sampling Intensity for Wood Volume Estimation in a Brazilian Amazon Tropical Forest

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Matheus da Costa Gondim,
  • Hassan C. David,
  • Luan D. Fiorentin,
  • Ângelo A. Ebling,
  • Vinícius A. Morais,
  • Emanuel J. G. de Araújo,
  • Sylvio Péllico Netto

摘要

Accurate and precise wood volume estimates are vital for sustainable forest management and are typically obtained using sample-based estimators. This study explores cluster sampling for estimating wood volume in an Amazonian forest, aiming to: (1) measure the loss of precision and deviation in volume estimates by comparing full samples to reduced samples and (2) analyze how the distance between cluster subunits affects the volume estimates. Data were collected from 22 clusters (model used in the Brazilian National Forest Inventory) installed in the Bom Futuro National Forest, Brazil. Our analyses consisted of examining the effect of reducing the (i) cluster size, in two directions (inward and outward), and (ii) sample size, by reducing the number of sample units from 22 to 4 clusters. We found that reducing the cluster size to 0.56 ha still yielded timber volume estimates as accurate and precise as the original 0.80 ha clusters, a useful innovation for forest inventories in the Amazon. Precision loss from reduced sampling ranged from 1.2 to 1.5 times, while deviation loss ranged from 7.2 to 19 times. We concluded that the precision was more influenced by the cluster size reduction than the sample size reduction. The sampled area required to stabilize precision of the volume estimate is smaller than that required to stabilize the mean volume estimate. The effect of the distance between subunits on deviation and precision was attenuated as the cluster size increased.