Lidar-based individual tree species classification using convolutional neural network

Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

doi:10.1117/12.2270123

Cited by 29 publications

(26 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-resolution images are also widely used in tree species classification [30][31][32]. In addition, LiDAR data were successfully used to distinguish different tree species [33][34][35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Meng

Cao

Mao

et al. 2020

Forests

View full text Add to dashboard Cite

Located in the Mount Tai state-owned forest farm, this study adopted Landsat multispectral remote sensing data in 2000 and 2016 on the GEE (Google Earth Engine) platform and selected four phases of images each year according to the phenological period. By dealing with the current situation map of forestry resources in 2000 and the field survey data in 2016, the samples of tree species distribution in 2000 and 2016 were obtained. On the basis of topographic correction with the empirical rotation model, this study used the random forest (RF) classifier to classify tree species from remote sensing images in 2000 and 2016, achieving high classification accuracy. The results showed that, after 16 years of evolution, the percentage of pine species in the forest decreased from 55.69% to 50.22%, with a percentage decrease as high as 5.47%. The percentage of black locust (Robinia pseudoacacia) increased from 10.15% in 2000 to 13.75% in 2016, with an increase of 3.60%. Quercus also had a positive growth in the area. This result reflected the expansion of black locust.

show abstract

Section: Discussionmentioning

confidence: 99%

Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Meng

Cao

Mao

et al. 2020

Forests

View full text Add to dashboard Cite

show abstract

“…For example, SVM supports high dimensionality while classifying data and Zhao et al [65] showed that SVM performs better than the maximum likelihood classifier and linear regression models for estimating various forest parameters. Furthermore, with the increased computational capabilities of computers neural networks are used in recent literature for advancing forest inventories [66,67]. Working with small classification datasets there is a risks that neural networks may conclude that the relevant features within the training dataset are noise.…”

Section: Discussionmentioning

confidence: 99%

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

Miltiadou

Αγαπίου

Aracil

et al. 2020

Forests

View full text Add to dashboard Cite

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.

show abstract

“…Deep learning has also been employed for tree identification from bark information, but using a different type of image. In their work, [22] used LiDAR scans instead of RGB images. They used a point cloud with a spatial resolution of 5 mm at a 10 m distance, from which they generated a depth image of size 256x256.…”

Section: Related Workmentioning

confidence: 99%

Tree Species Identification from Bark Images Using Convolutional Neural Networks

Carpentier

Giguère

Gaudreault

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Tree species identification using bark images is a challenging problem that could prove useful for many forestry related tasks. However, while the recent progress in deep learning showed impressive results on standard vision problems, a lack of datasets prevented its use on tree bark species classification. In this work, we present, and make publicly available, a novel dataset called BarkNet 1.0 containing more than 23,000 high-resolution bark images from 23 different tree species over a wide range of tree diameters. With it, we demonstrate the feasibility of species recognition through bark images, using deep learning. More specifically, we obtain an accuracy of 93.88% on single crop, and an accuracy of 97.81% using a majority voting approach on all of the images of a tree. We also empirically demonstrate that, for a fixed number of images, it is better to maximize the number of tree individuals in the training database, thus directing future data collection efforts.

show abstract

Lidar-based individual tree species classification using convolutional neural network

Cited by 29 publications

References 11 publications

Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

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

Tree Species Identification from Bark Images Using Convolutional Neural Networks

Contact Info

Product

Resources

About