Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data

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.

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Tree Species Classification in Boreal Forests With Hyperspectral Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2012.2216272 ◽

2013 ◽

Vol 51 (5) ◽

pp. 2632-2645 ◽

Cited By ~ 160

Author(s):

Michele Dalponte ◽

Hans Ole Orka ◽

Terje Gobakken ◽

Damiano Gianelle ◽

Erik Naesset

Keyword(s):

Tree Species ◽

Boreal Forests ◽

Hyperspectral Data ◽

Species Classification ◽

Tree Species Classification

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A New Individual Tree Species Recognition Method Based on a Convolutional Neural Network and High-Spatial Resolution Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs13030479 ◽

2021 ◽

Vol 13 (3) ◽

pp. 479

Author(s):

Shijie Yan ◽

Linhai Jing ◽

Huan Wang

Keyword(s):

Remote Sensing ◽

Tree Species ◽

High Spatial Resolution ◽

Tree Crown ◽

Recognition Method ◽

Individual Tree ◽

Tree Crowns ◽

Multi Scale ◽

Crown Delineation ◽

Tree Crown Delineation

Tree species surveys are crucial to forest resource management and can provide references for forest protection policy making. The traditional tree species survey in the field is labor-intensive and time-consuming, supporting the practical significance of remote sensing. The availability of high-resolution satellite remote sensing data enable individual tree species (ITS) recognition at low cost. In this study, the potential of the combination of such images and a convolutional neural network (CNN) to recognize ITS was explored. Firstly, individual tree crowns were delineated from a high-spatial resolution WorldView-3 (WV3) image and manually labeled as different tree species. Next, a dataset of the image subsets of the labeled individual tree crowns was built, and several CNN models were trained based on the dataset for ITS recognition. The models were then applied to the WV3 image. The results show that the distribution maps of six ITS offered an overall accuracy of 82.7% and a kappa coefficient of 0.79 based on the modified GoogLeNet, which used the multi-scale convolution kernel to extract features of the tree crown samples and was modified for small-scale samples. The ITS recognition method proposed in this study, with multi-scale individual tree crown delineation, avoids artificial tree crown delineation. Compared with the random forest (RF) and support vector machine (SVM) approaches, this method can automatically extract features and outperform RF and SVM in the classification of six tree species.

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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.

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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.

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CROWN-LEVEL TREE SPECIES CLASSIFICATION USING INTEGRATED AIRBORNE HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA

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

10.5194/isprs-archives-xlii-3-2629-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2629-2634

Author(s):

Z. Wang ◽

J. Wu ◽

Y. Wang ◽

X. Kong ◽

H. Bao ◽

...

Keyword(s):

Remote Sensing ◽

Tree Species ◽

Remote Sensing Data ◽

Lidar Data ◽

Tree Crown ◽

Species Classification ◽

Individual Tree ◽

Sensing Data ◽

Tree Species Classification ◽

Shape Characteristics

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.

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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.

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