To determine a suitable method, based on Precision and Cost, of inventory in Western oak forests, a random-systematic sampling with circle shape (already used, in the region) as well as transect method (recommended method in these kinds of forests) were selected for comparison. Parameters evaluated were number per hectare, crown cover and basal area (suitable parameters for these forest types).Considering all the sampling factors, a grid of 100×100 m and plot areas of 2000m2 were determined for random-systematic sampling. Length of transect was determined as 50m in transect method.To compare the results of these two sampling methods with the real population statistical parameters, a 100% inventory was taken over a 37.03-hectare area while using both methods over the same area.The results show that the population mean was similar to those of samplings for all parameters, the means being within the range of confidence intervals. However in both sampling methods and in all cases, the inventory errors were greater than those at the acceptalbe level (10%).To compare the related Costs, the total man-hour employed in each method was evaluated. Total time used in transect method was 714 minutes and in random-systematic method it was 3027.5 minutes.With respect to Precision, random-systematic sampling with circle shape plots is of less error than transect method in all cases.To precisely evaluate the two methods, the product of E2 (Square of sampling error) times T (total inventory time) was used. This ceofficienct was less in transect method than in randome-systematic sampling with circle shape plots in all cases.

The present study aimed to investigate the accuracy and Precision estimation of the number, basal area and volume of the standing trees by methods of random and systematic random sampling in the forests of West Guilan. The Cost or inventory time was determined using the criteria (E%2 × T). Inventory was carried out by complete sampling (census) in an area of 52 hectares. The study area (section 11, district 2, Nav forests, Asalem) was divided into 520 rectangular plots (50m×20m) and each plot was measured separately. Measured characteristics were the kind of tree species, diameter at breast height and height. After inventory operation, the study area was stratified based on forest density. In each stratum, sampling was carried out by simple random sampling and systematic random sampling. The results showed that implementation of stratified sampling has reduced the sampling error and increased the sampling time for estimating the characteristics of abundance, basal area and standing volume of trees per ha in the study area. Amount of criterion (E%2 × T) in stratified sampling was less than sampling without stratification and between stratified samplings; stratified random systematic sampling had the lowest value. Therefore, among the studied methods, stratified random systematic sampling, due to high Precision and minimum criteria of (E%2 × T), was a suitable method for evaluation of the number, Basal area and volume of standing trees in the study area.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low Cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-Cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.