جستجوی مقالات مرتبط با کلیدواژه "پهپاد" در نشریات گروه "جنگلداری"

Forest canopy gaps play an important role in forest dynamics. Unmanned aerial vehicle (UAV) data provide demonstrated capacity to systematically and accurately detect and map canopy gaps and have been considered as an alternative way to describe the forest stands. This study aims to extract canopy gaps using UAV data and compare the performance of different canopy gap extraction methods in a part of the replanted forest in the Arab Dagh Region, Golestan Province, Iran.

After the acquisition of UAV images and initial preprocessing, the digital terrain model (DTM), digital surface model (DSM), Canopy height model (CHM), and orthophoto mosaic were produced. CHM classification performs to extract forest gaps by different methods of height thresholding on CHM, CHM slope thresholding, and object-based classification. For performance evaluation of used methods and accuracy assessment of the canopy gap maps, the central position and boundary of some gaps were measured by DGPS. Finally, the point and polygon base accuracy of delineated gaps were assessed for each of the methods..

The results of the point accuracy assessment showed that the canopy gap map obtained by object-based classification method with applying the support vector machine (SVM) algorithm with 99% overall accuracy and 0.98 kappa coefficient had the best performance compared to other algorithms and methods. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using 3 m height thresholding.

The results showed that with aerial images of the UAV and its outputs, as well as the use of automated methods, the map of the canopy gap can be accurately extracted. Of course, the degree of accuracy depends on several factors such as the type of drone and cameras used, flight parameters and so on. Given the results, it is hoped that this approach will gradually be used as a cheap and accurate method in forest surveying.

Canopy gap delineation is essential for achieving a better comprehension of forest structure. This study aims to (a) extract canopy gaps using UAV data and (b) compare the performance of different canopy gap extraction methods in a managed stand in the northeast of Iran. A canopy height model (CHM) was produced by subtracting LIDAR digital terrain model from the UAV digital surface model. CHM classification performs to extract gaps by thresholding CHM (fixed height and CHM slope and relative height thresholds) and object-based classification on the UAV CHM and orthophoto. Ground truth is produced in the point and polygon forms through field measurements and visual interpretation of the UAV orthophoto. The geometry of the canopy gaps (Area, perimeter, and shape complexity) was calculated. Finally, the point and polygon base accuracy of delineated gaps assess for each of the methods. Point accuracy assessment suggests that 60% CHM slope produces the highest overall accuracy and Kappa coefficient of 91.7% and 0.874, respectively. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using relative height and 60% CHM slope thresholds. The lowest mean errors of GSCI produced by 70% CHM slope (0.15). Moreover, object-based classification showed the lowest mean error of area (33.76 m2) and perimeter (16.80 m). In conclusion, while area accuracy is considered the best fit of the delineated gap's geometry is gained by the object-based classification.

Orthphoto mosaic, and digital elevation models (DEMs) that created from UAV imagery can be used to delineate tree crowns. The goal of this research is to compare two segmentation techniques in the estimation of crown diameter. In this regard, two successful flights were carried out in two different seasons (leaf-off and leaf-on). Then we generated accurate CHM through a photogrammetric workflow using DTM and DSM. We used invert watershed (IWS) and multiresolution segmentation (MRS) to detect tree crowns on CHM and orthophoto mosaic, respectively. To compare the estimates of mean crown diameter from UAV images with actual values, 95 trees were measured. The results of comparing the estimated and field measured values showed that all two algorithms effectively delineate tree crowns. The results of linear regression showed there is a high agreement between estimated and measured values, which were (R2=0.88) for MRS and (R2=0.92) for IWS. The best result was obtained using IWS techniques (RMSE= 7.02 % and MAE = 5.97 %). T-test results showed that there are no significant differences between the field measurement and IWS estimated values. Although the t-test result showed there are significant differences between the means of MRS estimated and measured crown diameters, but based on RMSE (8.74 %) and MAE (8.11 %) of the MRS technique, the error of crown diameter estimation was small and therefore acceptable. Finally, the results showed that this methodology, as an accurate and low-cost process, could be used to estimate mean tree crown diameter.

Unmanned aerial vehicles (UAVs) images have high spatial resolution. They are a valuable source of information for mapping land cover and thematic information, particularly in the identification of tree species. The aim of this study was to investigate the capability of drone images and the base object method for detecting tree species in the Hyrcanian forests. For this purpose, part of an area in parcel 24 of district one in Mazandaran Darabkola forest was selected. The ground truth map was prepared through accurate recording with geographic coordinate’s algorithm using distance and azimuth in MATLAB software. Proper processing was done on the images and classification performed on images at three flight height; 55, 75 and 100 meters in two categories of one-step and hierarchical classifications. In object-based classification, the nearest neighbor method was used to classify three species. The accuracy of the maps derived from classifications was evaluated using 50% of the ground truth map. The results showed that the map of the hierarchical classification by the object based method at a flight height of 55 meters has the best ability to detect tree species in the three heights. These comparisons showed Kappa's coefficient of 0.81 accuracy of tree species classification in 55-meter height by UAV.

Crown cover of trees in arid and semi-arid regions is of great importance as the parts of ground under tree canopies are favourable environments for regeneration establishment and survival of other organisms. Therefore, it is essential to be aware of crown cover status of trees and shrubs, monitor their changes and assess their health. Remote sensing data obtained by most of satellites do not make unbiased measurement of crown area of single trees possible as their spatial resolution is not suitable for this purpose. On the other hand, these data are not available at the time researchers need, and if available, they are expensive. Continuous progress in remote sensing results in access of researchers to unmanned aerial vehicle (UAV) that imagery taken by this device have not only very high spatial resolution for precise study of biometric characteristics of single trees, but also availability of images at favourable time for researchers. Considering this issue, this study was aimed to evaluate UAV imagery and corresponding digital surface model (DSM) resulted from stereo images to estimate crown area of wild pistachio single trees in Zagros vegetation zone. Moreover, it was also aimed to investigate the impact of spatial resolution of DSM on accuracy and precision of estimating crown area of the trees.
A part of Baneh Research Site with area of 45 ha was selected that was purely cover by wild pistachio. In October 2016, the study area was covered by 1076 aerial images with 3 cm spatial resolution taken by a UAV flown at 70 m above the study area. The images were geo-referenced using 12 ground control points collected by Leica Viva GS15 three-frequency global positioning system. The DSM extracted using bundle adjustment method was then resampled to three different spatial resolutions of 3, 50, and 100 cm. An object-based processing method was proposed in order to automatically extract the crown boundaries from DSM. Number of 100 wild pistachio trees were randomly selected that their spatial position were registered and their crown areas were measured before. The mean crown area obtained by DSM of UAV imagery and the observed mean were compared by paired sample t-test. In addition, three indices of root mean squared error (RMSE), model efficiency (ME), and bias score (BS) were applied to assess the precision of results.
The orhtophoto mosaic of the study area was obtained with RMSE of 8 cm. Among 100 wild pistachio trees randomly selected for this study, 100, 89, and 80 trees were recognized on DSMs with spatial resolution of 3, 50, and 100 cm, respectively. Although there was no significant difference between observed mean crown area of 80 trees (51.3 m2) recognized on all DSMs and mean crown area estimated on DSMs with 3 (42.6 m2), 50 (44.5 m2), and 100 cm (39.2 m2) (α=0.05), but their correlation decreased. Moreover, RMSE increased and ME and BS decreased with decreasing spatial resolution of DSM.
In general, it was concluded that DSM of UAV imagery is an appropriate means to recognize and measure crown area of wild pistachio single trees in the study area that obviously separated tree crowns from their shadow and other objects. Moreover, it was revealed that with decreasing spatial resolution of DSM, data processing became easier and there was no significant difference between observations and measurements but the precision of results decreased.