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

2013 ◽  
Author(s):  
Brenner Silva ◽  
Jörg Bendix
Keyword(s):  
Remote Sensing ◽  
Tree Crown ◽  
Individual Tree ◽  
Tropical Mountain ◽  
Mountain Ecosystem

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

2019 ◽  
Vol 11 (11) ◽  
pp. 1309 ◽  
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.

scholarly journals Individual Tree Crown Methods for 3D Data from Remote Sensing

2017 ◽  
Vol 3 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Eva Lindberg ◽  
Johan Holmgren
Keyword(s):  
Remote Sensing ◽  
Tree Crown ◽  
Individual Tree ◽  
3D Data

scholarly journals Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model

2020 ◽  
Vol 12 (15) ◽  
pp. 2426
Author(s):  
Alin-Ionuț Pleșoianu ◽  
Mihai-Sorin Stupariu ◽  
Ionuț Șandric ◽  
Ileana Pătru-Stupariu ◽  
Lucian Drăguț
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Ensemble Model ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Ensemble Models ◽  
Remote Sensing Imagery ◽  
Very High

Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to overcome these shortcomings due to its superior performance and versatility, proven with reported detection rates of ~90%. However, such models still find their limits in transferability across study areas, because of different tree conditions (e.g., isolated trees vs. compact forests) and/or resolutions of the input data. This study introduces a highly replicable deep learning ensemble design for ITC detection and species classification based on the established single shot detector (SSD) model. The ensemble model design is based on varying the input data for the SSD models, coupled with a voting strategy for the output predictions. Very high-resolution unmanned aerial vehicles (UAV), aerial remote sensing imagery and elevation data are used in different combinations to test the performance of the ensemble models in three study sites with highly contrasting spatial patterns. The results show that ensemble models perform better than any single SSD model, regardless of the local tree conditions or image resolution. The detection performance and the accuracy rates improved by 3–18% with only as few as two participant single models, regardless of the study site. However, when more than two models were included, the performance of the ensemble models only improved slightly and even dropped.

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

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.

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

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.

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

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

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

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 × 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 = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79.86 %, Kc = 0.81) and spectral-metircs method (OA = 71.26, Kc = 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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