Single Tree Classification Using Multi-Temporal ALS Data and CIR Imagery in Mixed Old-Growth Forest in Poland

Tree species classification is important for a variety of environmental applications, including biodiversity monitoring, wildfire risk assessment, ecosystem services assessment, and sustainable forest management. In this study we used a fusion of three remote sensing (RM) datasets including ALS (leaf-on and leaf-off) and colour-infrared (CIR) imagery (leaf-on), to classify different coniferous and deciduous tree species, including dead class, in a mixed temperate forest in Poland. We used intensity and structural variables from the ALS data and spectral information derived from aerial imagery for the classification procedure. Additionally, we tested the differences in classification accuracy of all the variants included in the data integration. The random forest classifier was used in the study. The highest accuracies were obtained for classification based on both point clouds and including image spectral information. The mean values for overall accuracy and kappa were 84.3% and 0.82, respectively. Analysis of the leaf-on and leaf-off alone is not sufficient to identify individual tree species due to their different discriminatory power. Leaf-on and leaf-off ALS point cloud features alone gave the lowest accuracies of 72% ≤ OA ≤ 74% and 0.67 ≤ κ ≤ 0.70. Classification based on both point clouds was found to give satisfactory and comparable results to classification based on combined information from all three sources (83% ≤ OA ≤ 84% and 0.81 ≤ κ ≤ 0.82). The classification accuracy varied between species. The classification results for coniferous trees were always better than for deciduous trees independent of the datasets. In the classification based on both point clouds (leaf-on and leaf-off), the intensity features seemed to be more important than the other groups of variables, especially the coefficient of variation, skewness, and percentiles. The NDVI was the most important CIR-based feature.

Download Full-text

Tree segmentation and classification of deciduous park trees in Sanssouci Park, Potsdam, Germany, using airborne and terrestrial lidar point clouds

10.5194/egusphere-egu21-8689 ◽

2021 ◽

Author(s):

Aljoscha Rheinwalt ◽

Bodo Bookhagen

Keyword(s):

Tree Species ◽

Supervised Classification ◽

Spatial Scales ◽

Point Clouds ◽

Airborne Lidar ◽

Deciduous Tree ◽

Individual Tree ◽

Terrestrial Lidar ◽

Common Tree

While automated, lidar-based tree delineation has proven successful for conifer-dominated forests, deciduous tree stands remain a challenge. &#160;But automatic and reliable segmentation of trees at large spatial scales is a prerequisite for a supervised classification into tree species. We propose an aspect driven tree segmentation that clusters local elevation minima across different aspects. These clusters define tree outlines that respect tree inherent local elevation minima. We validate this approach with more than 25.000 mapped trees of the Sanssouci Park, Potsdam, using an airborne lidar point cloud collected in 2018, and various terrestrial lidar scans for a large fraction of the same park. Further, we demonstrate the tree segmentation by supervised tree species classifications for the most common tree species using random forests and Gaussian process classifiers with geometric parameters derived from individual tree crowns.

Download Full-text

A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

Forests ◽

10.3390/f10010001 ◽

2018 ◽

Vol 10 (1) ◽

pp. 1 ◽

Cited By ~ 22

Author(s):

Kepu Wang ◽

Tiejun Wang ◽

Xuehua Liu

Keyword(s):

Tree Species ◽

Classification Accuracy ◽

Urban Environments ◽

Airborne Lidar ◽

Lidar Data ◽

Species Classification ◽

Individual Tree ◽

Urban Tree ◽

Tree Species Classification ◽

Optical Imagery

With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs.

Download Full-text

RESNET-BASED TREE SPECIES CLASSIFICATION USING UAV IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-475-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 475-481 ◽

Cited By ~ 11

Author(s):

S. Natesan ◽

C. Armenakis ◽

U. Vepakomma

Keyword(s):

Neural Networks ◽

High Resolution ◽

Tree Species ◽

Classification Accuracy ◽

Forest Tree ◽

Tree Level ◽

Species Classification ◽

Individual Tree ◽

Tree Species Classification ◽

Rgb Images

Abstract. Tree species classification at individual tree level is a challenging problem in forest management. Deep learning, a cutting-edge technology evolved from Artificial Intelligence, was seen to outperform other techniques when it comes to complex problems such as image classification. In this work, we present a novel method to classify forest tree species through high resolution RGB images acquired with a simple consumer grade camera mounted on a UAV platform using Residual Neural Networks. We used UAV RGB images acquired over three years that varied in numerous acquisition parameters such as season, time, illumination and angle to train the neural network. To begin with, we have experimented with limited data towards the identification of two pine species namely red pine and white pine from the rest of the species. We performed two experiments, first with the images from all three acquisition years and the second with images from only one acquisition year. In the first experiment, we obtained 80% classification accuracy when the trained network was tested on a distinct set of images and in the second experiment, we obtained 51% classification accuracy. As a part of this work, a novel dataset of high-resolution labelled tree species is generated that can be used to conduct further studies involving deep neural networks in forestry.

Download Full-text

Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning

Frontiers in Plant Science ◽

10.3389/fpls.2021.635440 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dominik Seidel ◽

Peter Annighöfer ◽

Anton Thielman ◽

Quentin Edward Seifert ◽

Jan-Henrik Thauer ◽

...

Keyword(s):

Tree Species ◽

Classification Accuracy ◽

Laser Scanning ◽

Confusion Matrix ◽

Point Clouds ◽

Small Sample ◽

Training Data ◽

Computationally Efficient ◽

Species Classification ◽

3D Point Clouds

Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach.

Download Full-text

An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs13101868 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1868

Author(s):

Martina Deur ◽

Mateo Gašparović ◽

Ivan Balenović

Keyword(s):

Spatial Resolution ◽

Satellite Imagery ◽

Tree Species ◽

Classification Accuracy ◽

New Technologies ◽

Deciduous Forest ◽

Species Classification ◽

Tree Species Classification ◽

Object Based ◽

Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

Download Full-text

Individual Tree Species Classification by Illuminated—Shaded Area Separation

Remote Sensing ◽

10.3390/rs2010019 ◽

2009 ◽

Vol 2 (1) ◽

pp. 19-35 ◽

Cited By ~ 27

Author(s):

Eetu Puttonen ◽

Paula Litkey ◽

Juha Hyyppä

Keyword(s):

Tree Species ◽

Species Classification ◽

Individual Tree ◽

Tree Species Classification

Download Full-text

Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data

PeerJ ◽

10.7717/peerj.6227 ◽

2019 ◽

Vol 6 ◽

pp. e6227 ◽

Cited By ~ 12

Author(s):

Michele Dalponte ◽

Lorenzo Frizzera ◽

Damiano Gianelle

Keyword(s):

Remote Sensing ◽

Tree Species ◽

Data Science ◽

Remote Sensing Data ◽

Tree Crown ◽

Species Classification ◽

Individual Tree ◽

Sensing Data ◽

Tree Species Classification ◽

Selection Of

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

Download Full-text

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs12071070 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1070 ◽

Cited By ~ 6

Author(s):

Somayeh Nezami ◽

Ehsan Khoramshahi ◽

Olli Nevalainen ◽

Ilkka Pölönen ◽

Eija Honkavaara

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Tree Species ◽

Classification Accuracy ◽

Performance Metrics ◽

Test Site ◽

Biomass Estimation ◽

Species Classification ◽

Tree Species Classification ◽

3D Cnn

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Download Full-text

An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification

Remote Sensing ◽

10.3390/rs11182078 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2078 ◽

Cited By ~ 4

Author(s):

Yuhong He ◽

Jian Yang ◽

John Caspersen ◽

Trevor Jones

Keyword(s):

Tree Species ◽

Near Infrared ◽

Deciduous Forest ◽

Structural Diversity ◽

Species Classification ◽

Individual Tree ◽

Spectral Overlap ◽

Object Based ◽

Early Fall ◽

Crown Delineation

Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual tree crown (ITC) delineation, non-forest gap elimination, and object-based classification. The process of species classification started with ITC delineation using the spectral angle segmentation algorithm, followed by object-based random forest classifications. A total of 672 trees was located along three triangular transects for training and validation. For single-season images, the late-spring, mid-summer, and early-fall images achieve the overall accuracies of 0.46, 0.42, and 0.35, respectively. Combining the spectral information of the early-spring, mid-summer, and early-fall images increases the overall accuracy of classification to 0.79. However, further adding the late-fall image to separate deciduous and coniferous trees as an extra step was not successful. Compared to traditional four-band (Blue, Green, Red, Near-Infrared) images, the four additional bands of WorldView images (i.e., Coastal, Yellow, Red Edge, and Near-Infrared2) contribute to the species classification greatly (OA: 0.79 vs. 0.53). This study gains insights into the contribution of the additional spectral bands and multi-seasonal images to distinguishing species with seemingly high degrees of spectral overlap.

Download Full-text

Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

Remote Sensing ◽

10.3390/rs12203328 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3328

Author(s):

Mohammad Imangholiloo ◽

Ninni Saarinen ◽

Markus Holopainen ◽

Xiaowei Yu ◽

Juha Hyyppä ◽

...

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Sustainable Forest Management ◽

Single Channel ◽

Point Clouds ◽

Tree Density ◽

Airborne Laser Scanning ◽

Tree Species Composition ◽

Coniferous Trees ◽

Airborne Laser

Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.

Download Full-text