NEON NIST data science evaluation challenge: Methods and results of team Shawn

10.7287/peerj.preprints.26967 ◽

2018 ◽

Author(s):

Shawn Taylor

Keyword(s):

Remote Sensing ◽

Minimum Distance ◽

Data Science ◽

Training Data ◽

Watershed Algorithm ◽

Jaccard Coefficient ◽

Tree Crown ◽

Individual Tree ◽

Tree Crowns ◽

Ecological Information

This paper describes the methods used in the submission for team Shawn for the data science competition “Airborne Remote Sensing to Ecological Information”. I used canopy height rasters as well as NDVI rasters of the study area. I first filtered out pixels using a minimum NDVI threshold, then derived individual tree crowns using a watershed algorithm. I imposed limits on tree crown size and number using a minimum distance between two crowns and a maximum crown radius. All parameters were derived by minimizing the Jaccard coefficient. The final Jaccard coefficient on the training data was 0.117. All methods were implemented in Python are are available in code repositories.

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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.

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Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

Remote Sensing ◽

10.3390/rs11111309 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1309 ◽

Cited By ~ 38

Author(s):

Ben G. Weinstein ◽

Sergio Marconi ◽

Stephanie Bohlman ◽

Alina Zare ◽

Ethan White

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Training Data ◽

Tree Crown ◽

Individual Tree ◽

Detection Model ◽

High Resolution Imagery ◽

The Neural Network ◽

Detection Approach ◽

Crown Delineation

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.

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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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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.

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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.

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Individual Tree Crown Extraction in High Resolution Remote Sensing Image Based on Iterative H-minima Improved Watershed Algorithm

Laser & Optoelectronics Progress ◽

10.3788/lop55.122802 ◽

2018 ◽

Vol 55 (12) ◽

pp. 122802

Author(s):

滕文秀 Teng Wenxiu ◽

温小荣 Wen Xiaorong ◽

王妮 Wang Ni ◽

施慧慧 Shi Huihui

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Remote Sensing Image ◽

Watershed Algorithm ◽

Tree Crown ◽

Individual Tree

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A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

Forests ◽

10.3390/f11060605 ◽

2020 ◽

Vol 11 (6) ◽

pp. 605 ◽

Cited By ~ 1

Author(s):

Jianyu Gu ◽

Heather Grybas ◽

Russell G. Congalton

Keyword(s):

Spectral Data ◽

Spatial Resolution ◽

Forest Type ◽

Canopy Height ◽

Aerial Imagery ◽

Tree Level ◽

Tree Crown ◽

Remotely Sensed Data ◽

Individual Tree ◽

Tree Crowns

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.

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Remote sensing of vegetation in a tropical mountain ecosystem: individual tree-crown detection

10.1117/12.2029912 ◽

2013 ◽

Cited By ~ 1

Author(s):

Brenner Silva ◽

Jörg Bendix

Keyword(s):

Remote Sensing ◽

Tree Crown ◽

Individual Tree ◽

Tropical Mountain ◽

Mountain Ecosystem

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

Remote Sensing ◽

10.3390/rs10081218 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1218 ◽

Cited By ~ 46

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.

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