Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning

Torabzadeh, Hossein; Leiterer, Reik; Hueni, Andreas; Schaepman, Michael E.; Morsdorf, Felix

doi:10.1016/j.agrformet.2019.107744

Cited by 43 publications

(35 citation statements)

References 114 publications

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“…In some cases, with 1 m 2 spatial resolution remote sensing allows differentiation of different genotypes of poplar clones (Madritch et al, 2014). Tree canopies are likely to be well distinguished if functional information on morphology and physiology at species level is available (Torabzadeh et al, 2019). In recent years, the use of remote sensing has enabled great advances in both functional as well as scaling-based approaches (Gamon et al, 2019;Malenovský et al, 2019).…”

Section: Direct Detection and Sampling Of Species And Their Traitsmentioning

confidence: 99%

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Randin

Ashcroft

Bolliger

et al. 2020

Remote Sensing of Environment

181

142

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Section: Direct Detection and Sampling Of Species And Their Traitsmentioning

confidence: 99%

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Randin

Ashcroft

Bolliger

et al. 2020

Remote Sensing of Environment

181

142

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“…Tree species classification is highly significant for sustainable forest management and ecological environmental protection [1]. High-spatial-resolution remote sensing images are preferred for detailed tree classification because of their better spatial characteristics.…”

Section: Introductionmentioning

confidence: 99%

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020

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Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China.

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“…The current carrying means of the laser system include airborne laser scanning (ALS), terrestrial laser scanning (TLS) and mobile laser scanning (MLS). ALS is always used to detect individual trees [39] [40] measure tree stem radii [41] and classify tree species [42]. Ramiya et al [39] proposed an object-based labelling framework for delineating individual tree canopy clusters in urban green spaces from an airborne LiDAR point cloud.…”

Section: A Backgroundmentioning

confidence: 99%

“…It is desirable to automatically detect each tree and analyse its structural parameters, which cannot provide accurate information about beam intensity. Torabzadeh et al [42] fused imaging spectroscopy (IS) data with leaf-on and leaf-off small-footprint airborne laser scanning (ALS) data for tree species identification in a dense temperate forest in Switzerland. Although ALS has the advantage of a wide scanning range and fast scanning speed, some trunk information is easily lost in layered forests.…”

Section: A Backgroundmentioning

confidence: 99%

Trunk Model Establishment and Parameter Estimation for a Single Tree Using Multistation Terrestrial Laser Scanning

Pan

Zhang

et al. 2020

IEEE Access

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Parameter estimation and effective tree trunk model establishment are significant to tree biomass measurements and three-dimensional (3D) reconstruction. In this study, we proposed a trunk model establishment and parameter estimation method for a single tree through multistation terrestrial laser scanning (TLS). This approach obtains a more accurate and dense point cloud and yields several improvements. First, to correct the tree position, the angle between the trunk centreline and the vertical line was extracted to normalize the tree point clouds. Subsequently, some tree model parameters were accurately calculated. The tree height was extracted based on individual differences rather than directly adopting the Digital Elevation Model (DEM) model; The B-Spline curve fitting was added to the diameters at breast height (DBH) measure, which better determined the shape of the DBH compared with circle fitting; The CBH was measured by multi-view voxel projection instead of Single-view fixed threshold segmentation, which confirmed the position of the CBH more accurately. To highlight the effectiveness and pertinence of the proposed method, we analysed three plots that the proposed method can achieve more accurate results, where the mean relative error (MRE) of the tree height, DBH and CBH were 1.89%, 1.74% and 2.91% satisfying the accuracy standard specified by the technical indicators (± 5%). The experimental results validate the trunk model accuracy and the effectiveness of the parameter measurements (close to the field measurements). The proposed method is significantly different from the accuracy of the previous method. The proposed method has the best accuracy in Multistation Terrestrial Laser Scanning. Hence the proposed method has outstanding performance in the high-precision extraction of trunk parameters and the construction of trunk models which will perform forest inventory and construction of virtual forest more accurately.

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Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning

Cited by 43 publications

References 114 publications

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

Trunk Model Establishment and Parameter Estimation for a Single Tree Using Multistation Terrestrial Laser Scanning

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