Peer review report 2 On “Describing forest canopy gaps efficiently, accurately, and objectively: new prospects through the use of terrestrial laser scanning”

2016 ◽  
Vol 217 ◽  
pp. 139
Author(s):  
Steven Hancock
2018 ◽  
Vol 210 ◽  
pp. 452-472 ◽  
Author(s):  
Lixia Ma ◽  
Guang Zheng ◽  
Xiaofei Wang ◽  
Shiming Li ◽  
Yi Lin ◽  
...  

2015 ◽  
Vol 5 (4) ◽  
pp. 114-122
Author(s):  
Стариков ◽  
Aleksandr Starikov ◽  
Батурин ◽  
Kirill Baturin

Now for the decision of tasks of monitoring and evaluation of forest plantations the use of methods and means of laser scanning is one of the most act-sexual and priorities. Laser scanning can be performed independently, or in combination with digital aerial and space photos and video, and can also be carried out ground research on the sample areas. A number of indicators laser scanning is superior to other, currently known, remote evaluation methods qualitative and quantitative characteristics of the forest Fund Laser scanning of forest cover based on the use of modern tech-nologies of digital photogrammetry and geoinformation systems, as well as methods of digital processing and multidimensional modeling of the reflected signals. The article provides analysis of modern methods and means of aerial and terrestrial laser scanning of forest stands. The use of air-borne laser scanning will allow achieving high precision in the determination of basic inventory pa-rameters that are comparable to land-based taxation. Main advantages of laser ranging to other me-thods of monitoring of forest plantations is that the laser beam is able to penetrate the forest canopy, thereby scanning all the tiers of the stand. High density scanning (5-10 points per 1 m2) allows ob-taining three-dimensional images of individual trees with high accuracy. The obtained three-dimensional model requires no processing, unlike aerospace methods of remote sensing that are as-sociated with long and arduous races-encryption of the images. Terrestrial laser scanning, in fact, similar to traditional photogrammetric methods, but it allows you to get the coordinates from one point of standing with the possibility of control measurements directly in the field, while providing higher measurement accuracy, compared with photogrammetric methods.


2021 ◽  
Vol 875 (1) ◽  
pp. 012083
Author(s):  
N Begliarov ◽  
E Mitrofanov ◽  
V Kiseleva

Abstract Modern geodetic technologies of gathering three-dimensional spatial data incorporate terrestrial laser scanning and aerial photo survey from unmanned aerial vehicles. The combination of these technologies and joint result of survey provide the data of 3D point model and accurate information on trunks and crowns of individual trees. The paper examines the experiment with the application of method of formation of 3D measuring scene in the form of dense cloud of points combining the results of terrestrial laser scanning and materials of photogrammetric processing of UAV-provided data. The method eliminates basic shortcomings of each technology, enhances their advantages, and opens the way to the compilation of more representative 3D measuring scenes. A specific advantage of the method is the outcropping of detailed information on the form, size and condition of individual tree crowns. This option finds a practical application in landscape evaluation and design, remote measuring of trunk parameters excluding the felling of model trees for the compilation of regional timber account tables. The closest perspectives of method development are related to increasing the accuracy of combined survey by specifying flight missions and working with the light regime under forest canopy.


2020 ◽  
Vol 12 (6) ◽  
pp. 1010 ◽  
Author(s):  
Bingxiao Wu ◽  
Guang Zheng ◽  
Yang Chen

Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa ≥ 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA > 84.26% (Kappa ≥ 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy.


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