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

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3&thinsp;&times;&thinsp;3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA&thinsp;=&thinsp;85.12&thinsp;%, Kc&thinsp;=&thinsp;0.90) performed better than LiDAR-metrics method (OA&thinsp;=&thinsp;79.86&thinsp;%, Kc&thinsp;=&thinsp;0.81) and spectral-metircs method (OA&thinsp;=&thinsp;71.26, Kc&thinsp;=&thinsp;0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.

Download Full-text

A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

Remote Sensing ◽

10.3390/rs11242948 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2948 ◽

Cited By ~ 2

Author(s):

Hoang Minh Nguyen ◽

Begüm Demir ◽

Michele Dalponte

Keyword(s):

Tree Species ◽

Remote Sensing Data ◽

Classification Problem ◽

Learning Phase ◽

Support Vector ◽

Lidar Data ◽

Species Classification ◽

Individual Tree ◽

Tree Species Classification ◽

Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

Download Full-text

NEON NIST data science evaluation challenge: methods and results of team FEM

10.7287/peerj.preprints.26973v1 ◽

2018 ◽

Cited By ~ 2

Author(s):

Michele Dalponte ◽

Lorenzo Frizzera ◽

Damiano Gianelle

Keyword(s):

Data Science ◽

Near Infrared ◽

Remote Sensing Data ◽

Region Growing ◽

Support Vector ◽

Infrared Band ◽

Species Classification ◽

Individual Tree ◽

Data Alignment ◽

Selection Of

An international data science challenge, called NEON NIST 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) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (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 segmentation 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 segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

Download Full-text

Remote sensing pipeline for tree segmentation and classification in a mixed softwood and hardwood system

PeerJ ◽

10.7717/peerj.5837 ◽

2019 ◽

Vol 6 ◽

pp. e5837 ◽

Cited By ~ 1

Author(s):

Conor A. McMahon

Keyword(s):

Remote Sensing ◽

Data Science ◽

Vegetation Index ◽

Remote Sensing Data ◽

Watershed Segmentation ◽

Stem Height ◽

Plant Identification ◽

Species Classification ◽

Individual Tree ◽

Height Model

The National Institute of Standards and Technology data science evaluation plant identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. I created a pipeline to perform three remote sensing tasks. First, a marker-controlled watershed segmentation thresholded by vegetation index and height was performed to identify individual tree crowns within the canopy height model. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset’s dimensionality through principle component analysis and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

Download Full-text

Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning

Sensors ◽

10.3390/s19061284 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1284 ◽

Cited By ~ 29

Author(s):

Sean Hartling ◽

Vasit Sagan ◽

Paheding Sidike ◽

Maitiniyazi Maimaitijiang ◽

Joshua Carron

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Tree Species ◽

Training Sample ◽

Lidar Data ◽

Species Classification ◽

Individual Tree ◽

Urban Tree ◽

Tree Species Classification ◽

Fusion Approach

Urban areas feature complex and heterogeneous land covers which create challenging issues for tree species classification. The increased availability of high spatial resolution multispectral satellite imagery and LiDAR datasets combined with the recent evolution of deep learning within remote sensing for object detection and scene classification, provide promising opportunities to map individual tree species with greater accuracy and resolution. However, there are knowledge gaps that are related to the contribution of Worldview-3 SWIR bands, very high resolution PAN band and LiDAR data in detailed tree species mapping. Additionally, contemporary deep learning methods are hampered by lack of training samples and difficulties of preparing training data. The objective of this study was to examine the potential of a novel deep learning method, Dense Convolutional Network (DenseNet), to identify dominant individual tree species in a complex urban environment within a fused image of WorldView-2 VNIR, Worldview-3 SWIR and LiDAR datasets. DenseNet results were compared against two popular machine classifiers in remote sensing image analysis, Random Forest (RF) and Support Vector Machine (SVM). Our results demonstrated that: (1) utilizing a data fusion approach beginning with VNIR and adding SWIR, LiDAR, and panchromatic (PAN) bands increased the overall accuracy of the DenseNet classifier from 75.9% to 76.8%, 81.1% and 82.6%, respectively. (2) DenseNet significantly outperformed RF and SVM for the classification of eight dominant tree species with an overall accuracy of 82.6%, compared to 51.8% and 52% for SVM and RF classifiers, respectively. (3) DenseNet maintained superior performance over RF and SVM classifiers under restricted training sample quantities which is a major limiting factor for deep learning techniques. Overall, the study reveals that DenseNet is more effective for urban tree species classification as it outperforms the popular RF and SVM techniques when working with highly complex image scenes regardless of training sample size.

Download Full-text

NEON NIST data science evaluation challenge: methods and results of team Conor

10.7287/peerj.preprints.26977 ◽

2018 ◽

Author(s):

Conor A McMahon

Keyword(s):

Remote Sensing ◽

Data Science ◽

Remote Sensing Data ◽

Watershed Segmentation ◽

Stem Height ◽

Plant Identification ◽

Species Classification ◽

Individual Tree ◽

Crown Area ◽

Tree Crowns

The NIST DSE Plant Identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. To compete in the competition, I created a pipeline to perform three remote sensing tasks. First, a NDVI- and height-thresholded watershed segmentation was performed to identify individual tree crowns using LIDAR height measurements. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset's dimensionality through PCA and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

Download Full-text

NEON NIST data science evaluation challenge: methods and results of team FEM

10.7287/peerj.preprints.26973 ◽

2018 ◽

Author(s):

Michele Dalponte ◽

Lorenzo Frizzera ◽

Damiano Gianelle

Keyword(s):

Data Science ◽

Near Infrared ◽

Remote Sensing Data ◽

Region Growing ◽

Support Vector ◽

Infrared Band ◽

Species Classification ◽

Individual Tree ◽

Data Alignment ◽

Selection Of

An international data science challenge, called NEON NIST 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) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (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 segmentation 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 segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

Download Full-text

Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator

10.7287/peerj.preprints.27052v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sheng Zou ◽

Paul Gader ◽

Alina Zare

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Tree Species ◽

Tree Crown ◽

Species Classification ◽

Ecological Information ◽

Tree Species Classification ◽

Testing Data ◽

Ace Algorithm ◽

Species Variability

Tree species classification using hyperspectral imagery is a challenging task due to the high spectral similarity between species and large intra-species variability. This paper proposes a solution using the Multiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a discriminative target signature to differentiate between a pair of tree species while accounting for label uncertainty. Additionally, the performance of MI-ACE does not rely on parameter settings that require tuning resulting in a method that is easy to use in application. Results presented are using training and testing data provided by a data analysis competition aimed at encouraging the development of methods for extracting ecological information through remote sensing obtained through participation in the competition.

Download Full-text

Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator

10.7287/peerj.preprints.27052 ◽

2018 ◽

Author(s):

Sheng Zou ◽

Paul Gader ◽

Alina Zare

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Tree Species ◽

Tree Crown ◽

Species Classification ◽

Ecological Information ◽

Tree Species Classification ◽

Testing Data ◽

Ace Algorithm ◽

Species Variability

Tree species classification using hyperspectral imagery is a challenging task due to the high spectral similarity between species and large intra-species variability. This paper proposes a solution using the Multiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a discriminative target signature to differentiate between a pair of tree species while accounting for label uncertainty. Additionally, the performance of MI-ACE does not rely on parameter settings that require tuning resulting in a method that is easy to use in application. Results presented are using training and testing data provided by a data analysis competition aimed at encouraging the development of methods for extracting ecological information through remote sensing obtained through participation in the competition.

Download Full-text

NEON NIST data science evaluation challenge: methods and results of team Conor

10.7287/peerj.preprints.26977v1 ◽

2018 ◽

Cited By ~ 2

Author(s):

Conor A McMahon

Keyword(s):

Remote Sensing ◽

Data Science ◽

Remote Sensing Data ◽

Watershed Segmentation ◽

Stem Height ◽

Plant Identification ◽

Species Classification ◽

Individual Tree ◽

Crown Area ◽

Tree Crowns

The NIST DSE Plant Identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. To compete in the competition, I created a pipeline to perform three remote sensing tasks. First, a NDVI- and height-thresholded watershed segmentation was performed to identify individual tree crowns using LIDAR height measurements. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset's dimensionality through PCA and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

Download Full-text