Presentation a new Joint-point Sampling method estimator for tree density

Joint-point method is one of the distance sampling methods for forest inventory. For estimation of tree density with joint-point sampling method two estimator suggested by Batcheler and also Engeman et al. that biased in nonrandom spatial pattern. Aim of this study was presentation a new unbiased and most efficiency estimator for this method. Forty-four sample size for each of six sampling methods: Circle plot (10ar as true value), Nearest individual method, nearest neighbor method, Compound method, second nearest neighbor method and Joint-point method in forests of Sorkheh dizeh Dalahoo of Kermanshah province with systematic random design (100m*100m) carried out. After that trees density of forest stand calculated with 10 estimators of above methods and new estimator. Then calculated density with new estimator compared with Batcheler and Engeman et al. estimators and other estimators in different spatial patterns (regular, random and aggregate) by precision and accuracy criteria. The results showed that this new estimator was not only most efficient than Batcheler and Engeman et al. estimators but also was best than other estimators.