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

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).

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

Japanese Journal of Forest Planning ◽

10.20659/jjfp.55.1_3 ◽

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

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Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification

Forests ◽

10.3390/f5051011 ◽

2014 ◽

Vol 5 (5) ◽

pp. 1011-1031 ◽

Cited By ~ 29

Author(s):

Xiaowei Yu ◽

Paula Litkey ◽

Juha Hyyppä ◽

Markus Holopainen ◽

Mikko Vastaranta

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Low Density ◽

Species Classification ◽

Individual Tree ◽

Tree Detection ◽

Tree Species Classification ◽

Full Waveform ◽

Airborne Laser

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INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

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

10.5194/isprs-archives-xliii-b3-2021-581-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 581-585

Author(s):

S. Kuikel ◽

B. Upadhyay ◽

D. Aryal ◽

S. Bista ◽

B. Awasthi ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Tree Crown ◽

Individual Tree ◽

Banana Tree ◽

Crown Delineation ◽

Uav Images

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

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Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

Remote Sensing ◽

10.3390/rs14020295 ◽

2022 ◽

Vol 14 (2) ◽

pp. 295

Author(s):

Kunyong Yu ◽

Zhenbang Hao ◽

Christopher J. Post ◽

Elena A. Mikhailova ◽

Lili Lin ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Plantation Forest ◽

Remote Sensing Images ◽

Individual Tree ◽

Tree Detection ◽

Band Combination ◽

Individual Trees ◽

Lm Algorithm ◽

Multi Band

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees.

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Geographic Generalization in Airborne RGB Deep Learning Tree Detection

10.1101/790071 ◽

2019 ◽

Author(s):

Ben. G. Weinstein ◽

Sergio Marconi ◽

Stephanie A. Bohlman ◽

Alina Zare ◽

Ethan P. White

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Forest Type ◽

Detection Algorithm ◽

Forest Types ◽

Individual Tree ◽

Tree Detection ◽

Deep Learning Algorithm ◽

Local Site ◽

Using Data

AbstractTree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated against data from multiple forest types simultaneously. This makes it difficult to determine the generalization of proposed approaches, and limits tree detection at broad scales. Using data from the National Ecological Observatory Network we extend a recently developed semi-supervised deep learning algorithm to include data from a range of forest types, determine whether information from one forest can be used for tree detection in other forests, and explore the potential for building a universal tree detection algorithm. We find that the deep learning approach works well for overstory tree detection across forest conditions, outperforming conventional LIDAR-only methods in all forest types. Performance was best in open oak woodlands and worst in alpine forests. When models were fit to one forest type and used to predict another, performance generally decreased, with better performance when forests were more similar in structure. However, when models were pretrained on data from other sites and then fine-tuned using a small amount of hand-labeled data from the evaluation site, they performed similarly to local site models. Most importantly, a universal model fit to data from all sites simultaneously performed as well or better than individual models trained for each local site. This result suggests that RGB tree detection models that can be applied to a wide array of forest types at broad scales should be possible.

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A Composite Deep Learning Access for Leaf Species Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1009.09811s19 ◽

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%.

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

Remote Sensing ◽

10.3390/rs12152426 ◽

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.

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

Sensors ◽

10.3390/s19061284 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1284 ◽

Cited By ~ 29

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.

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