Open AccessOn Using Adaptive Cluster Sampling Design in Forest Inventory: It's Implication to Forest Biodiversity Status Reporting
Author(s) -
Dennis P. Peque
Publication year - 2009
Publication title -
deleted journal
Language(s) - English
Resource type - Journals
ISSN - 0119-4909
DOI - 10.47773/shj.1998.091.5
Subject(s) - systematic sampling , sampling (signal processing) , cluster sampling , statistics , sampling design , stratified sampling , estimator , simple random sample , biodiversity , forest inventory , multistage sampling , population , variance (accounting) , adaptive sampling , environmental science , mathematics , geography , forestry , ecology , biology , forest management , computer science , accounting , demography , filter (signal processing) , sociology , monte carlo method , business , computer vision
This paper presents adaptive cluster sampling (ACS) as a method of assessing forest biodiversity. In this study, ACS was used to estimate the abundance of ecologically sparse population of Diospyros philippinensis (Desrousseaux) within the Visayas State University Forest Reserve. Its statistical efficiency were analyzed by comparing them to the conventional systematic sampling (Syst) estimator. Results indicated that adaptive cluster sampling (ACS) plots captured more trees into the sample compared to systematic sampling (Syst) plots. In addition, ACS estimates for mean and total numbers of individuals per ha was higher than systematic sampling estimates and in terms of variance ACS gave substantially lower variance than systematic sampling. However, the ratio of the adjusted SE of ACS to the adjusted SE of systematic sampling for each species and the combined data of the two species was generally lesser than 1 which means that ACS was not a better design than systematic sampling.