Comparison of WORLD VIEW2 , PLEIDES2 and IRS LISSIII satellites capability for estimating stand volume of forest ( case study: Darabkola Experimental Forest)

Investigation on quantitative characteristics of forest such as Stand volume is one of the most important principles in planning and forest management decision. The aim of this study is comparison of various satellites data capability and non-parametric methods for estimating stand volume of forest. The studied area is district 1 Darabkola forest in Mazandaran province in southeast of Sari with 2612 hectares which is located in 74 basin of Sari natural recourses Department. Using systematic-random with 10 R.sample plots with 300m×500m sampling net system were measured150 circular sample plots. The necessary preprocessing and processing include ratio, vegetation index, Principal Component Analysis and texture analyse were done on WorldView-2¡ Pleiades-2 and IRS-LISS III imagery . For modeling in this study be used different regression methods include different variants of k-Nearest Niebuhr, kernel machine support vector and random forest . The results of modeling the stand volume using machine support vector showed that the best kernel in order for worldview- 2,IRS-LISS III and Pleiades-2 satellites was Polynomial,RBFand Polynomial with %RMSE equal to 34/57,49/5 and 43/03.The best variant in k-Nearest Niebuhr in order for said satellites was chebychev,chebychev and City block with %RMSE equal to 41/18,55/09 and 46/97. %RMSE in random forest method in order for said satellites was 31/33,48/91 and 45/68. Results showed random forest was the best model for estimation stand volume and WorldVeiw-2 satellite data has the best result with percent root mean square error and bias of estimation equal to 31.33 and 2.8 percent.Because of more bands and less width of them, WorldView-2 satellite has better outcomes than Pleiades-2 satellite; since if there are more bands and width of them is narrower, information can be saved in different bands and ratio of signal to noise will be increased. Therefore, phenomenon detects better and accuracy of outcomes increases. The results did not show much difference between the non-parametric algorithms in terms of Percent Root Mean Square Error, but a large difference was observed in terms of sensors. Overall results of this study showed sensors and Regression methods used in this study have a relatively high capability in estimation of forest stand volume . The results also show in addition to the spatial resolution of satellites their spectral resolution has a significant impact on raising the accuracy of the forest stand volume modeling results using satellite images .