جستجوی مقالات مرتبط با کلیدواژه « مدل رقومی سطح » در نشریات گروه « جنگلداری »

Estimation of qualitative and quantitative characteristics of plants on UAV images is considered a challenge in remote sensing. Therefore, this study aimed to present a method to estimate crown area, height, and species in a mixed Pistacia-Amygdalus stand in UAV-derived point clouds. To this aim, 100 Pistacia atlantica Desf. trees and 100 Amygdalus spp < em>. shrubs were randomly selected. Point cloud was obtained by UAV-derived imagery with 50 points per m2 in a 64-ha study area in Baneh Research Forest, Fars province. The quantitative characteristics were then estimated on the point cloud. Additionally, species type was classified using random forest and 37 quantitative attributes measured on point cloud, canopy height model, and orthomosaic. Crown area and height of Pistacia (R2= 0.91 and 0.83, PRMSE=4.7% and 3.2%, respectively) and Amygdalus (R2= 0.89 and 0.47, PRMSE=22.1% and 21.5%, respectively) were also estimated. By application of quantitative attributes and random forest, species type was classified with an accuracy of 0.92 and κ of 0.98. All in all, results indicated that UAV point clouds can be efficiently applied to estimate a set of qualitative and quantitative attributes of Pistacia and Amygdalus within the study area. However, inaccurate and imprecise results were observed for estimated heights of Amygdalus.

Estimation of allometric tree attributes such as heights that are not directly observed on unmanned aerial vehicle (UAV) imagery is challenging. Therefore, this study aimed to introduce a method to estimate the height of wild pistachio (Pistacia atlantica Desf.) single trees in the Zagros region. Therefore, a 45-ha area in Baneh Research Forest of Fars province was captured by a Phantom IV UAV. An algorithm was then suggested to consider the difference between pixels of ground and crown top as tree height on the digital surface model (DSM) following automatic single tree detection. The heights of 100 trees were estimated on DSMs with spatial resolutions of 3.47, 10, 20, 40, 60, 80, and 100 cm. The results showed that the highest coefficient of determination of 0.89 and the lowest relative root mean square error of 11.8% were returned for heights estimated on DSM with 3.47 cm spatial resolution. Moreover, no significant difference was observed among measured and estimated height values on spatial resolutions of 3.47, 10, and 20 cm, respectively. The tree heights were overestimated on DSM with a spatial resolution of 3.47 cm (bias score 1.15), while they were close to the measured values on 10 cm spatial resolution (bias score 1.01) and were underestimated in other spatial resolutions. In general, the results showed the feasibility to estimate heights of wild pistachio trees on Phantom IV imagery, in particular on UAV imagery with a 10 cm spatial resolution.

Ground inventory provides most accurate data for management purposes in forestry، but it is costly and time consuming. Up to now، various approaches has been proposed for reducing the amount of ground inventory. In this paper، a new approach had proposed and tested for estimating forest stand volume. The approach is based on this idea that forest volume can be estimated by variation of trees height at sample plots. For this aim 150 circular sample plots with systematic random design were collected. The DSM has been extracted from UltraCamD images with 1 to 10 m size by 1m span. Corresponded to ground samples، standard deviation and range of DSM had extracted. In order to modeling، different regression methods and one layer perceptron were used. The results showed that standard deviation of 5 m DSM was the most appropriate data for modeling. Perceptron and linear regression were better than other modeling methods. At best، Bias and RMSE of forest volume were 2 and 47 percent، respectively.