scholarly journals Individual tree canopy detection and species classification of conifers by deep learning

2021 ◽  
Vol 55 (1) ◽  
pp. 3-22
Author(s):  
Yusuke Hayashi ◽  
Songqiu Deng ◽  
Masato Katoh ◽  
Ryosuke Nakamura
Keyword(s):  
Deep Learning ◽  
Tree Canopy ◽  
Species Classification ◽  
Individual Tree

Individual tree detection and species classification of Amazonian palms using UAV images and deep learning

2020 ◽  
Vol 475 ◽  
pp. 118397 ◽  
Author(s):  
Matheus Pinheiro Ferreira ◽  
Danilo Roberti Alves de Almeida ◽  
Daniel de Almeida Papa ◽  
Juliano Baldez Silva Minervino ◽  
Hudson Franklin Pessoa Veras ◽  
...  
Keyword(s):  
Deep Learning ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Detection ◽  
Uav Images

scholarly journals Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

2021 ◽  
Vol 13 (14) ◽  
pp. 2837
Author(s):  
Yago Diez ◽  
Sarah Kentsch ◽  
Motohisa Fukuda ◽  
Maximo Larry Lopez Caceres ◽  
Koma Moritake ◽  
...  
Keyword(s):  
Deep Learning ◽  
Forest Fires ◽  
Autonomous Vehicles ◽  
Cost Effective ◽  
Data Interpretation ◽  
Species Classification ◽  
Individual Tree ◽  
High Resolution Data ◽  
Tree Detection ◽  
Public Data

Forests are the planet’s main CO2 filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about forests has been collected using expensive and work-intensive field inventories, but in recent years unoccupied autonomous vehicles (UAVs) have become very popular as they represent a simple and inexpensive way to gather high resolution data of large forested areas. In addition to this trend, deep learning (DL) has also been gaining much attention in the field of forestry as a way to include the knowledge of forestry experts into automatic software pipelines tackling problems such as tree detection or tree health/species classification. Among the many sensors that UAVs can carry, RGB cameras are fast, cost-effective and allow for straightforward data interpretation. This has resulted in a large increase in the amount of UAV-acquired RGB data available for forest studies. In this review, we focus on studies that use DL and RGB images gathered by UAVs to solve practical forestry research problems. We summarize the existing studies, provide a detailed analysis of their strengths paired with a critical assessment on common methodological problems and include other information, such as available public data and code resources that we believe can be useful for researchers that want to start working in this area. We structure our discussion using three main families of forestry problems: (1) individual Tree Detection, (2) tree Species Classification, and (3) forest Anomaly Detection (forest fires and insect Infestation).

scholarly journals A Composite Deep Learning Access for Leaf Species Classification

2019 ◽  
Vol 8 (11S) ◽  
pp. 42-45
Keyword(s):  
Deep Learning ◽  
Human Life ◽  
Real Life ◽  
Recognition System ◽  
Raw Material ◽  
Species Classification ◽  
Classification Management ◽  
Almost All ◽  
Deep Learning Model

Plants are an integral part of the human life one way or the other. They have multi-dimensional use as food, medicine, clothing, art, industrial raw material and are vital for sustaining the ecological balance of our planet. All these real life applications make the identification of plants intensely important and useful. This dictates to design an accurate recognition system of plants. It will be useful to facilitate faster classification, management and apprehension. Almost all the plants are accompanied by unique leaves. In this paper, we have used this property of leaf identification for the identification of plants. In this study, we have applied a composite deep learning model, where Inception-v3 model is used for feature engineering and Stacking Ensemble model is used for the detection and classification of leaves from images. We have used a modified Flavia dataset of 1287 leaf images divided amongst 21 distinct plant species to test the proposed approach. On comparing our proposed work with other pre-existing algorithms (RF, SVM, kNN and Tree), it is found that it surpassed them, obtaining an accuracy of 99.5%.

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 Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 415 ◽  
Author(s):  
Mohammad Imangholiloo ◽  
Ninni Saarinen ◽  
Lauri Markelin ◽  
Tomi Rosnell ◽  
Roope Näsi ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Hyperspectral Imagery ◽  
Digital Terrain Model ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Density ◽  
Tree Canopy ◽  
Tree Level ◽  
Species Classification ◽  
Individual Tree

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.

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

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1284 ◽  
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.

Tree Species Classification of LiDAR Data based on 3D Deep Learning

Measurement ◽  
2021 ◽  
pp. 109301
Author(s):  
Maohua Liu ◽  
Ziwei Han ◽  
Yiming Chen ◽  
Zhengjun Liu ◽  
Yanshun Han
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Lidar Data ◽  
Species Classification ◽  
Tree Species Classification

scholarly journals Overview of Recent Deep Learning Methods Applied in Fruit Counting for Yield Estimation

2020 ◽  
Vol 65 (2) ◽  
pp. 50
Author(s):  
H.B. Mureșan ◽  
A.D. Călin ◽  
A.M. Coroiu
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Research Field ◽  
Cutting Edge ◽  
Tree Canopy ◽  
Complex Task ◽  
Yield Estimation ◽  
Learning Methods ◽  
Post Harvesting

This paper is an overview of the latest advancements of image recognition for fruit counting and yield estimation. Considering this domain is developing rapidly, we have considered the cutting-edge literature in the field, for the last 5 years, focused on the task of yield estimation by detecting and counting fruit in the tree canopy. This is a much more complex task than the classification of fruit post-harvesting, which has been more widely reviewed. Moreover, we identify the major challenges and propose the next steps for advancing this research field.

Forest Species Classification of UAV Hyperspectral Image Using Deep Learning

2020 ◽  
Author(s):  
Jing Liang ◽  
Pengshuai Li ◽  
Hui Zhao ◽  
Lu Han ◽  
Mingliang Qu
Keyword(s):  
Deep Learning ◽  
Hyperspectral Image ◽  
Forest Species ◽  
Species Classification

scholarly journals Classification of Typical Tree Species in Laser Point Cloud Based on Deep Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4750
Author(s):  
Jianchang Chen ◽  
Yiming Chen ◽  
Zhengjun Liu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Network Mapping

We propose the Point Cloud Tree Species Classification Network (PCTSCN) to overcome challenges in classifying tree species from laser data with deep learning methods. The network is mainly composed of two parts: a sampling component in the early stage and a feature extraction component in the later stage. We used geometric sampling to extract regions with local features from the tree contours since these tend to be species-specific. Then we used an improved Farthest Point Sampling method to extract the features from a global perspective. We input the intensity of the tree point cloud as a dimensional feature and spatial information into the neural network and mapped it to higher dimensions for feature extraction. We used the data obtained by Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAVLS) to conduct tree species classification experiments of white birch and larch. The experimental results showed that in both the TLS and UAVLS datasets, the input tree point cloud density and the highest feature dimensionality of the mapping had an impact on the classification accuracy of the tree species. When the single tree sample obtained by TLS consisted of 1024 points and the highest dimension of the network mapping was 512, the classification accuracy of the trained model reached 96%. For the individual tree samples obtained by UAVLS, which consisted of 2048 points and had the highest dimension of the network mapping of 1024, the classification accuracy of the trained model reached 92%. TLS data tree species classification accuracy of PCTSCN was improved by 2–9% compared with other models using the same point density, amount of data and highest feature dimension. The classification accuracy of tree species obtained by UAVLS was up to 8% higher. We propose PCTSCN to provide a new strategy for the intelligent classification of forest tree species.

Sign in / Sign up

Export Citation Format

Share Document