Multi 3d Window Dead Tree Detection Of Dead Standing Eucalyptus Camaldulensis From Voxelised Full Waveform Lidar Data For Tackling High Differences In Native Forests
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Multi 3d Window Dead Tree Detection Of Dead Standing Eucalyptus Camaldulensis From Voxelised Full Waveform Lidar Data For Tackling High Differences In Native Forests
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Author : Milto Miltiadou
language : en
Publisher:
Release Date : 2017
Multi 3d Window Dead Tree Detection Of Dead Standing Eucalyptus Camaldulensis From Voxelised Full Waveform Lidar Data For Tackling High Differences In Native Forests written by Milto Miltiadou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Detection of dead trees is an important for managing biodiversity in native Australian forests. Most of the previous work on dead standing trees detection performs single tree crown delineation before health assessment. Nevertheless, classifications at tree level, while working with native forest is a challenge for multiple reasons: big spatial resolution, variance density of trees, different tree heights and sizes. Tree crown delineation is usually done by detecting local maxima from the canopy height model (CHM) and then segmenting trees using the watershed algorithm, but Eucalypt trees has multiple trunk splits making tree delineation difficult. Shendryk et al, 2016, published an interesting Eucalyptus delineation algorithm that performs segmentation from bottom to top, but pulse density was 36 points/m2 around forested areas (expensive to acquire for big spatial resolution). Miltiadou et al, 2018, attempted detection of dead Eucalypt trees without tree delineation using 3D-windows and showed that it is possible, but the methodology can be improved. The presented work, takes that research a step forward and uses multiple 3D windows charactering dead trees. A random forest classifier, a weighted-distance KNN algorithm are used to create a 2D probabilistic field for each 3D-window size. Then the results are merged, and the locations of the dead trees are predicted. It is shown that the multi-3D-window approach improved the results of the original research published work in 2018.
Classification Of Tree Species As Well As Standing Dead Trees Using Triple Wavelength Lidar In A Temperate Forest
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Author : Nina Amiri
language : en
Publisher:
Release Date : 2019
Classification Of Tree Species As Well As Standing Dead Trees Using Triple Wavelength Lidar In A Temperate Forest written by Nina Amiri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Abstract: Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8-17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees
Lidar Voxel Segmentation Using 3d Convolutional Neural Networks
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Author : Yuval H. Levental
language : en
Publisher:
Release Date : 2021
Lidar Voxel Segmentation Using 3d Convolutional Neural Networks written by Yuval H. Levental and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Convolutions (Mathematics) categories.
"Light detection and ranging (lidar) forest models are important for studying forest composition in great detail, and for tracking objects in the understory. In this study we used DIRSIG, a first-principles and physics-based simulation tool, to turn the lidar data into voxels, towards classifying forest voxel types. A voxel is a 3D cube where the dimension represents a certain distance. These voxels are split into categories consisting of background, leaf, bark, ground, and object elements. Voxel content is then predicted from the provided simulated and real National Ecological Observation Network (NEON) data. The inputs are 3D neighborhood cubes which surround each voxel, which contain surrounding lidar signal and content type information. Provided simulated data are from two sources: a VLP-16 drone, which collects discrete lidar data close to the canopy, and the NEON Airborne Observation Platform (AOP), which is attached to an airplane flying 1000 m above ground level and collects both discrete and waveform lidar data. Different machine learning algorithms were implemented, with 3D CNN algorithms shown to be the most effective. The Keras library was used, since creating the layers with the sequential model was regarded as an elegant approach. The simulated VLP-16 waveform data were significantly more accurate than the simulated NEON waveform data, which was attributed to its proximity to the canopy. Leaves and branches exhibited acceptable accuracies, due to their relatively random shapes. However, ground and objects in both cases had very high accuracy due to the high intensities and their rigid shapes, respectively. A sample of real NEON waveform lidar data was used, though the sample primarily focused on the canopy region; however, most of the voxels were correctly predicted as leaves. Additional channels were added to the input voxels in order to improve accuracy. One input parameter which proved to be very useful were the local z-values of each input array. Additionally, the Keras Tuner framework was used to obtain improved hyperparameters. The learning rate was reduced by a factor of 10, which provided slower, but steadier convergence towards accurate predictions. The resulting accuracies from the predictions are promising, but there is room for improvement. Different ML algorithms that use the point cloud should also be considered. Further segmentation of forest classes is another possibility. For example, there are different types of trees and bushes, so each tree or bush could have its own unique classes, which would make predicting the shapes much easier. Overall, discovering a method for accurate object prediction has been the most significant finding. For the ground truth models, the best object precision is approximately 99% and the best recall is 78%."--Abstract.
Radiation Protection Programmes For The Transport Of Radioactive Material
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Author : International Atomic Energy Agency
language : en
Publisher:
Release Date : 2024-02-29
Radiation Protection Programmes For The Transport Of Radioactive Material written by International Atomic Energy Agency and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Technology & Engineering categories.
This Safety Guide provides recommendations on meeting the requirements established in IAEA Safety Standards Series No. SSR-6 (Rev. 1), Regulations for the Safe Transport of Radioactive Material, 2018 Edition, for a radiation protection programme for the transport of radioactive material. The objectives of a radiation protection programme for the transport of radioactive material are to provide for adequate consideration of radiation protection measures in transport; to ensure that the system of radiological protection is adequately applied; to enhance a safety culture in the transport of radioactive material; and to provide practical measures to meet these objectives. The recommendations provided in this Safety Guide are applicable to the transport of radioactive material by all modes on land, water, or in the air, including transport that is incidental to the use of the radioactive material. The intended audience for this Safety Guide includes competent authorities, consignors, carriers, consignees, and operators of ports (e.g. harbours, seaports and airports). It will also be of interest to the employees of public authorities (customs authorities, harbour authorities, port authorities) concerning activities associated with the transport of radioactive material that involve radiation exposure.