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 Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification

2020 ◽  
Vol 12 (22) ◽  
pp. 3710 ◽  
Author(s):  
Veerle Plakman ◽  
Thomas Janssen ◽  
Nienke Brouwer ◽  
Sander Veraverbeke
Keyword(s):  
Random Forest ◽  
Pseudotsuga Menziesii ◽  
Tree Species ◽  
Temperate Forest ◽  
Laser Scanning ◽  
Individual Tree ◽  
Tree Crowns ◽  
Airborne Laser ◽  
Kappa Value ◽  
Sentinel 2

Detailed information about tree species composition is critical to forest managers and ecologists. In this study, we used Sentinel-2 imagery in combination with a canopy height model (CHM) derived from airborne laser scanning (ALS) to map individual tree crowns and identify them to species level. Our study area covered 140 km2 of a mainly mixed temperate forest in the Veluwe area in The Netherlands. Ground truth data on tree species were acquired for 2460 trees. Tree crowns were automatically delineated from the CHM model. We identified the delineated tree crowns to species and phylum level (angiosperm vs. gymnosperm) using a random forest (RF) classification. The RF model used multitemporal spectral variables from Sentinel-2 and crown structural variables from the CHM and was validated using an independent dataset. Different combinations of variables were tested. After feature reduction from 25 to 15 features, the RF model identified tree crowns with an overall accuracy of 78.5% (Kappa value 0.75) for tree species and 84.5% (Kappa value 0.73) for tree phyla whilst using the combination of all variables. Adding crown structural and multitemporal spectral information improved the RF classification compared to using only a Sentinel image from one season as input data. The producer’s accuracies varied between 43.8% for Norway spruce (Picea abies) to 95.3% for Douglas fir (Pseudotsuga menziesii). The RF model was extrapolated to generate a tree species map over a study area (140 km2). The map showed high abundances of common oak (Quercus robur; 35.5%) and Scots pine (Pinus sylvestris; 22.8%) and low abundances of Norway spruce (Picea abies; 1.7%) and Douglas fir (Pseudotsuga menziesii; 2.8%). Our results indicate a high potential for individual tree classification based on Sentinel-2 imagery and automatically derived tree crowns from canopy height models.

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

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.

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

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6227 ◽  
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.

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

2019 ◽  
Vol 11 (18) ◽  
pp. 2078 ◽  
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.

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

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 605 ◽  
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.

scholarly journals Object-oriented classification of Ikonos satellite data for the identification of tree species composition

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 181-187 ◽  
Author(s):  
F. Hájek
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Object Oriented ◽  
Image Data ◽  
Rule Base ◽  
Automated Classification ◽  
Individual Tree ◽  
Tree Species Composition ◽  
Automated Technique

This paper describes the automated classification of tree species composition from Ikonos 4-meter imagery using an object-oriented approach. The image was acquired over a man-planted forest area with the proportion of various forest types (conifers, broadleaved, mixed) in the Krušné hory Mts., Czech Republic. In order to enlarge the class signature space, additional channels were calculated by low-pass filtering, IHS transformation and Haralick texture measures. Employing these layers, image segmentation and classification were conducted on several levels to create a hierarchical image object network. The higher level separated the image into smaller parts regarding the stand maturity and structure, the lower (detailed) level assigned individual tree clusters into classes for the main forest species. The classification accuracy was assessed by comparing the automated technique with the field inventory using Kappa coefficient. The study aimed to create a rule-base transferable to other datasets. Moreover, the appropriate scale of common image data and utilisation in forestry management are evaluated.

scholarly journals Investigation of the random forest framework for classification of hyperspectral data

2005 ◽  
Vol 43 (3) ◽  
pp. 492-501 ◽  
Author(s):  
J. Ham ◽  
Yangchi Chen ◽  
M.M. Crawford ◽  
J. Ghosh
Keyword(s):  
Random Forest ◽  
Hyperspectral Data

scholarly journals Automated detection of individual juniper tree location and forest cover changes using Google Earth Engine

2021 ◽  
Vol 64 (1) ◽  
pp. 61-72
Author(s):  
Sudeera Wickramarathna ◽  
Jamon Van Den Hoek ◽  
Bogdan Strimbu
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Forest Cover ◽  
Near Infrared ◽  
Google Earth ◽  
Random Forest Algorithm ◽  
Individual Tree ◽  
Western Juniper ◽  
Effective Manner ◽  
Google Earth Engine

Tree detection is the first step in the appraisal of a forest, especially when the focus is monitoring the growth of tree canopy. The acquisition of annual very high-resolution aerial images by the National Agriculture Imagery Program (NAIP) and their accessibility through Google Earth Engine (GEE) supports the delineation of tree canopies and change over time in a cost and time-effective manner. The objectives of this study are to develop an automated method to detect the crowns of individual western Juniper (Juniperus occidentalis) trees and to assess the change of forest cover from multispectral 1-meter resolution NAIP images collected from 2009 to 2016 in Oregon, USA. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Vegetation Index (RVI) were calculated from the NAIP images, in addition to the red-green-blue-near infrared bands. To identify the most suitable approach for individual tree crown identification, we created two training datasets: one considering yearly images separately and one merging all images, irrespective of the year. We segmented individual tree crowns using a random forest algorithm implemented in GEE and seven rasters, namely the reflectance of four spectral bands as recorded by the NAIP images (i.e., the red-green-blue-near infrared) and three calculated indices (i.e., NDVI, NDWI, and RVI). We compared the estimated location of the trees, computed as the centroid of the crown, with the visually identified treetops, which were considered as validation locations. We found that tree location errors were smaller when years were analyzed individually than by merging the years. Measurements of completeness (74%), correctness (94%), and mean accuracy detection (82 %) show promising performance of the random forest algorithm in crown delineation, considering that only four original input bands were used for crown segmentation. The change in the calculated crown area for western juniper follows a sinusoidal curve, with a decrease from 2011 to 2012 and an increase from 2012 to 2014. The proposed approach has the potential to estimate individual tree locations and forest cover area dynamics at broad spatial scales using regularly collected airborne imagery with easy-to-implement methods.

scholarly journals CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

2020 ◽  
Vol V-2-2020 ◽  
pp. 203-210
Author(s):  
S. Briechle ◽  
P. Krzystek ◽  
G. Vosselman
Keyword(s):  
Neural Network ◽  
Tree Species ◽  
Deep Neural Network ◽  
Lidar Data ◽  
Individual Tree ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Multiple Tree

Abstract. Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice.

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