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| Adaptive cluster sampling (ACS) has widely been used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study a k-step ACS based on Horvitz-Thompson (HT) estimator was developed and an unbiased estimator was derived. The k-step ACS Horvitz-Thompson (ACS-HT) was examined first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz (ACS-HH) and Horvitz-Thompson (ACS-HT) estimators, and k-step ACS-HT estimator. The effectiveness of using different k-step sizes was also compared. The results showed that k-step ACS-HT estimator was most effective and ACS-HH was least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species. K-step ACS without replacement was slightly more effective than that with replacement. In K-step ACS, the variance estimate of 1-step ACS is more big than other K-step ACS (k>1), but it is more small than ACS. This implies that -step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters. |
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| Keywords:Forest management;Finalized sample size; K-step adaptive cluster sampling; Sampling designs, Horvitz-Thompson estimator |
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