Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest

Miltiadou, Milto; Campbell, Neill D. F.; Aracil, Susana Gonzalez; Brown, T R; Grant, Michael

doi:10.1016/j.jag.2018.01.008

Cited by 13 publications

(14 citation statements)

References 25 publications

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“…The proposed methodology is a pipeline of processing steps that leads towards the detection of the dead standing eucalypt trees. Table 1 gives an overview of proposed pipeline and highlights the differences between the new multi-scale 3D-windows approach and the single size 3D-windows approach [53]. The following sub-sections explain each step of the processing pipeline with more details.…”

Section: Methodsmentioning

confidence: 99%

“…The following sub-sections explain each step of the processing pipeline with more details. For an intensive explanation of the single 3D-windows approach and the overlapping steps (e.g., weighted k-Nearest Neighbour (k-NN) algorithm, seed growth algorithm), please refer to this article [53].…”

Section: Methodsmentioning

confidence: 99%

“…Height and radius are divisible by 0.8, which is the length of a voxel. Using several tests, as reference [53] and the fact that the diameter should contain an odd number of voxel lengths for DASOS to work [45], the sizes of the windows are defined as follow: (1)…”

Section: Extraction Of Features From 3d-windowsmentioning

confidence: 99%

“…This paper attempts to address the limitations of working with eucalypt trees in a native Australian forest. Detection of dead standing eucalypt trees from full-waveform LiDAR without tree delineation has been proposed before and it was shown that it performs better than a random prediction [53]. In image processing and computer vision, scientists try to identify if objects, like faces, exist within a 2D image and detect them by extracting features [54].…”

Section: Introductionmentioning

confidence: 99%

“…In image processing and computer vision, scientists try to identify if objects, like faces, exist within a 2D image and detect them by extracting features [54]. Similarly, Miltiadou et al [53] extract 3D structural features from local areas around dead trees (positive samples) and live trees (negative samples) using single size 3D-windows and trains an object detection classifier. For tackling height and size variations of trees, this study adds on existing knowledge by introducing the usage of multi-scale 3D-windows.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

Miltiadou

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Aracil

et al. 2020

Forests

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In southern Australia, many native mammals and birds rely on hollows for sheltering, while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse Woody Debris—CWD) is a very different task from a classification perspective. This study focuses on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees have irregular shapes making delineation of them challenging. Additionally, since the study area is a native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods that will be resistant to those challenges. Previous study showed that detection of dead standing trees without tree delineation is possible. This was achieved by using single size 3D-windows to extract structural features from voxelised full-waveform LiDAR and characterise dead (positive samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the single 3D-windows approach and the new multi-scale 3D-windows approach were implemented for comparison purposes. The accuracy of the results was calculated using the precision and recall parameters and it was proven that the multi-scale 3D-windows approach performs better than the single size 3D-windows approach. This open ups possibilities for applying the proposed approach on other native forest related applications.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%