scholarly journals Typical Landscape Tree Species Recognition Based on RedEdge-MX: Suitability Analysis of Two Texture Extraction Forms under Random Forest Supervision

2021 ◽  
Author(s):  
Huaipeng Liu ◽  
Xiaoyan Su ◽  
Huijun An
Keyword(s):  
Random Forest ◽  
Tree Species ◽  
Species Recognition ◽  
Suitability Analysis ◽  
Texture Extraction

scholarly journals Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

2019 ◽  
Vol 11 (23) ◽  
pp. 2788 ◽  
Author(s):  
Uwe Knauer ◽  
Cornelius Styp von Rekowski ◽  
Marianne Stecklina ◽  
Tilman Krokotsch ◽  
Tuan Pham Minh ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
Classification Performance ◽  
Majority Voting ◽  
Imaging Data ◽  
Species Classification ◽  
Weighted Voting ◽  
Linear Discriminant

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

scholarly journals Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1218 ◽  
Author(s):  
Julia Maschler ◽  
Clement Atzberger ◽  
Markus Immitzer
Keyword(s):  
Random Forest ◽  
Scots Pine ◽  
Tree Species ◽  
Near Infrared ◽  
Mean Shift ◽  
Hyperspectral Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Crowns

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.

scholarly journals Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

2016 ◽  
Vol 49 (1) ◽  
pp. 239-259 ◽  
Author(s):  
Anton Kuzmin ◽  
Lauri Korhonen ◽  
Terhikki Manninen ◽  
Matti Maltamo
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Species Recognition

scholarly journals Land Suitability Analysis of Alvar Grassland Vegetation in Estonia Using Random Forest

GI_Forum ◽  
2020 ◽  
Vol 1 ◽  
pp. 63-72
Author(s):  
Irada Ismayilova ◽  
Evelyn Uuemaa ◽  
Aveliina Helm ◽  
Christian Röger ◽  
Sabine Timpf
Keyword(s):  
Random Forest ◽  
Land Suitability ◽  
Suitability Analysis ◽  
Grassland Vegetation ◽  
Land Suitability Analysis

scholarly journals Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets

2021 ◽  
Vol 13 (22) ◽  
pp. 4657
Author(s):  
Rafael Hologa ◽  
Konstantin Scheffczyk ◽  
Christoph Dreiser ◽  
Stefanie Gärtner
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Tree Species ◽  
Abies Alba ◽  
Tree Level ◽  
Mountain Forest ◽  
Silver Fir ◽  
Forest Landscape ◽  
Species Classification ◽  
Tree Species Composition

For monitoring protected forest landscapes over time it is essential to follow changes in tree species composition and forest dynamics. Data driven remote sensing methods provide valuable options if terrestrial approaches for forest inventories and monitoring activities cannot be applied efficiently due to restrictions or the size of the study area. We demonstrate how species can be detected at a single tree level utilizing a Random Forest (RF) model using the Black Forest National Park as an example of a Central European forest landscape with complex relief. The classes were European silver fir (Abies alba, AA), Norway spruce (Picea abies, PA), Scots pine (Pinus sylvestris, PS), European larch (Larix decidua including Larix kampferii, LD), Douglas fir (Pseudotsuga menziesii, PM), deciduous broadleaved species (DB) and standing dead trees (snags, WD). Based on a multi-temporal (leaf-on and leaf-off phenophase) and multi-spectral mosaic (R-G-B-NIR) with 10 cm spatial resolution, digital elevation models (DTM, DSM, CHM) with 40 cm spatial resolution and a LiDAR dataset with 25 pulses per m2, 126 variables were derived and used to train the RF algorithm with 1130 individual trees. The main objective was to determine a subset of meaningful variables for the RF model classification on four heterogeneous test sites. Using feature selection techniques, mainly passive optical variables from the leaf-off phenophase were considered due to their ability to differentiate between conifers and the two broader classes. An examination of the two phenological phases (using the difference of the respective NDVIs) is important to clearly distinguish deciduous trees from other classes including snags (WD). We also found that the variables of the first derivation of NIR and the tree metrics play a crucial role in discriminating PA und PS. With this unique set of variables some classes can be differentiated more reliably, especially LD and DB but also AA, PA and WD, whereas difficulties exist in identifying PM and PS. Overall, the non-parametric object-based approach has proved to be highly suitable for accurately detecting (OA: 89.5%) of the analyzed classes. Finally, the successful classification of complex 265 km2 study area substantiates our findings.

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