جستجوی مقالات مرتبط با کلیدواژه « hierarchical clustering » در نشریات گروه « منابع طبیعی »

Nowadays, the application of clustering methods is widely increased, although choosing the right method due to the existence of different method and effective factors is difficult. The present study aimed to compare the results of widely used clustering algorithms and to determine the most effective methods according to the different evaluators and evaluate the effective distance measurement method for clustering algorithms. The data of Hyrcanian beech forests were examined in an area protected by the department of natural resources of Nowshahr. Random-systematic sampling method with regular grid of 100×200 m was used for determining the center of sample plots; 100-m2 (10×10 m) sample plots had been used to check the shrub species and 400-m2 (20×20 m) to check the herbaceous species. A total of 120 sample plots were measured. The abundance and coverage of tree, shrub and herbaceous species were estimated based on Braun-Blanquette scale. Three distance methods of measuring distance Bray Curtis, Hellinger and Manhattan were used and five clustering methods (Average method clustering methods, Ward method, flexible beta method with beta values ​​of -0.1, -0.25, -0.4) with six evaluation indicators (silhouette evaluation criterion, PARATNA criterion, Indval criterion, ISAMIC criterion, MRPP criterion and Phi correlation coefficient) were examined. Different clustering algorithms were arranged from best to worst for each dataset. The comparison analysis revealed that Ward’s and flexible-beta with beta value of -0.1 had the best performance. The present findings illustrated that Hellinger distance measurement method is better in homogeneous data than other distance measurement methods.

Soil salinity is considered as one of the potential environmental hazards. The purpose of this study was to find the best index and the most suitable relationship for estimating soil salinity and its mapping using remote sensing data. At the first step, random sampling was performed using fishnet method and surface soil electrical conductivity (EC) measurements. Then, the threshold levels (92%, 95%, and 98%) were applied to the output images of each indicator. The methodology included using the least squares fitting (LS-fit) technique and principal components analysis (PCA) for halite and gypsum minerals, determining the correlation between the output of indices and ground data, and performing clustering and factor analysis between EC and output images. In order to select the best model derived from Landsat-8 band combinations and the amount of salinity, collinearity test, Durbin-Watson test, and backward multivariate regression were employed. The Cohen‘s kappa coefficient was also applied to evaluate the multivariate regression formed by Landsat-8 bands. The performance of the indicators was evaluated based on four criteria of root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) and R-squared (R2). The results of the factor analysis showed the smallest distance between the EC, salinity index (SI) and brightness index (BI). The SI with an amount of 0.89 had the highest Pearson correlation with EC. In the dendrogram diagram, SI index with EC was placed in a cluster, and the RMSE, MBE, MAE and R2 values of the SI index were estimated to be 0.16, 0.11, 0.12, and 0.76, respectively. Compared to the rest of the indicators and linear, multivariate regression (with Cohen‘s kappa coefficient of 60%,), the SI index has provided better outcomes.