SAMPLING PROCESSES FOR Carapa guianensis AUBL. IN THE AMAZON

ABSTRACT The objective of this study was to analyze the adaptive cluster sampling (ACS), simple random sampling (SRS) and systematic sampling (SS) processes to obtain the number of ha-1 trees of Carapa guianensis Aubl. in the Amazon. The data were obtained through 100% inventory and sampling simulations, considering a DBH ≥ 25 cm, a sampling intensity of 4%, a maximum error of 10% and plots of 0.09, 0.16 and 0.25 ha. The last two sizes were only used to analyze their effect on the ACS estimators. The processes were evaluated for accuracy, precision (E%) and confidence interval (CI), while the mean ha-1 of the processes were compared with that of the 100% inventory by the Z test. The ACS process showed no significant difference between its average ha-1 trees and the 100% inventory, and it was also the most accurate and the only one whose CI was true. However, it presented a final sample intensity 3.6 times greater than the simple and systematic random samplings, in addition to E% above 10%, which makes it unacceptable, legally, and economically unfeasible. The other processes had densities significantly higher than the 100% inventory, with sample intensities lower than ACS and E% lower than 10%, making them legally viable. The use of larger plots in the ACS implies larger clusters and a greater tendency to underestimate the number of trees, resulting in larger sample errors and less accuracy.

摘要 本研究旨在分析自适应整群抽样（adaptive cluster sampling, ACS）、简单随机抽样（simple random sampling, SRS）与系统抽样（systematic sampling, SS）三种方法，以获取亚马逊区域圭亚那椌木（Carapa guianensis Aubl.）的每公顷（ha⁻¹）株数。本研究通过全样地清查与抽样模拟获取数据，设定胸径（Diameter at Breast Height, DBH）≥25 cm、抽样强度4%、最大允许误差10%，样地面积分别为0.09、0.16与0.25公顷，其中后两种样地面积仅用于分析其对自适应整群抽样估计量的影响。本研究从准确性、精确度（E%）与置信区间（confidence interval, CI）三个维度对三种抽样方法进行评估，并通过Z检验将各方法的每公顷平均株数与全样地清查结果进行对比。结果显示，自适应整群抽样的平均每公顷株数与全样地清查结果无显著差异，且其准确性最高，同时是唯一置信区间有效的方法。但该方法的最终抽样强度为简单随机抽样与系统抽样的3.6倍，且相对误差率超过10%，既不符合法律规范，也不具备经济可行性。其余两种抽样方法的估测密度显著高于全样地清查结果，且抽样强度低于自适应整群抽样，相对误差率低于10%，因此具备法律合规性。在自适应整群抽样中使用更大样地，会形成更大的整群，进而更容易低估株数，导致抽样误差增大、准确性降低。