scholarly journals Tree Recognition on the Plantation Using UAV Images with Ultrahigh Spatial Resolution in a Complex Environment

2021 ◽  
Vol 13 (20) ◽  
pp. 4122
Author(s):  
Xuzhan Guo ◽  
Qingwang Liu ◽  
Ram P. Sharma ◽  
Qiao Chen ◽  
Qiaolin Ye ◽  
...  
Keyword(s):  
Survival Rate ◽  
Complex Terrain ◽  
Low Cost ◽  
Central China ◽  
Ground Vegetation ◽  
Tree Crown ◽  
Individual Tree ◽  
Crown Width ◽  
Aerial Vehicle ◽  
Uav Images

The survival rate of seedlings is a decisive factor of afforestation assessment. Generally, ground checking is more accurate than any other methods. However, the survival rate of seedlings can be higher in the growing season, and this can be estimated in a larger area at a relatively lower cost by extracting the tree crown from the unmanned aerial vehicle (UAV) images, which provides an opportunity for monitoring afforestation in an extensive area. At present, studies on extracting individual tree crowns under the complex ground vegetation conditions are limited. Based on the afforestation images obtained by airborne consumer-grade cameras in central China, this study proposes a method of extracting and fusing multiple radii morphological features to obtain the potential crown. A random forest (RF) was used to identify the regions extracted from the images, and then the recognized crown regions were fused selectively according to the distance. A low-cost individual crown recognition framework was constructed for rapid checking of planted trees. The method was tested in two afforestation areas of 5950 m2 and 5840 m2, with a population of 2418 trees (Koelreuteria) in total. Due to the complex terrain of the sample plot, high weed coverage, the crown width of trees, and spacing of saplings vary greatly, which increases both the difficulty and complexity of crown extraction. Nevertheless, recall and F-score of the proposed method reached 93.29%, 91.22%, and 92.24% precisions, respectively, and 2212 trees were correctly recognized and located. The results show that the proposed method is robust to the change of brightness and to splitting up of a multi-directional tree crown, and is an automatic solution for afforestation verification.

scholarly journals UNMANNED AERIAL VEHICLE APPLICATIONS OF 3D MODELLING, VISUALIZATION AND PARAMETER CALCULATIONS

2018 ◽  
Vol XLII-5 ◽  
pp. 649-652 ◽  
Author(s):  
V. Lambey ◽  
A. D. Prasad
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
3D Modelling ◽  
Aerial Photogrammetry ◽  
Average Accuracy ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Institute Of Technology ◽  
Uav Images ◽  
Economical Alternative

<p><strong>Abstract.</strong> Photogrammetric surveying with the use of Unmanned Aerial Vehicles (UAV) have gained vast popularity in short span. UAV have the potential to provide imagery at an extraordinary spatial and temporal resolution when coupled with remote sensing. Currently, UAV platforms are fastest and easiest source of data for mapping and 3D modelling. It is to be considered as a low-cost substitute to the traditional airborne photogrammetry. In the present study, UAV applications are explored in terms of 3D modelling, visualization and parameter calculations. National Institute of Technology Raipur, Raipur is chosen as study area and high resolution images are acquired from the UAV with 85% overlap. 3D model is processed through the point cloud generated for the UAV images. The results are compared with traditional methods for validation. The average accuracy obtained for elevation points and area is 97.99% and 97.75%. The study proves that UAV based surveying is an economical alternative in terms of money, time and resources, when compared to the classical aerial photogrammetry methods.</p>

scholarly journals Quality of Digital Elevation Models obtained from Unmanned Aerial Vehicles for Precision Viticulture

OENO One ◽  
2016 ◽  
Vol 50 (3) ◽  
Author(s):  
Léo Pichon ◽  
Arnaud Ducanchez ◽  
Hélène Fonta ◽  
Bruno Tisseyre
Keyword(s):  
Low Cost ◽  
Wine Industry ◽  
Image Capture ◽  
Digital Elevation ◽  
Elevation Data ◽  
Aerial Vehicle ◽  
Elevation Model ◽  
Uav Images ◽  
Data Acquisition And Processing

<p style="text-align: justify;"><strong>Aims:</strong> This work aims to study the quality of low cost Digital Surface Models (DSMs) obtained with Unmanned Aerial Vehicle (UAV) images and to test whether these DSMs meet common requirements of the wine industry.</p><p style="text-align: justify;"><strong>Methods and results: </strong>Experiments were carried out on a 4-ha vineyard located 10 km north of Beziers (France). The experimental site presents slope and aspect variations representative of mechanised commercial vineyards in Languedoc Roussillon. DSMs were provided by three UAV companies selected for the diversity of their solutions in terms of image capture altitude, type of UAV and image processing software. DSMs were obtained by photogrammetry and correspond to commercial products usually delivered by UAV companies. DSMs from UAV were compared to a reference Digital Elevation Model (DEM) acquired by a laser tachymeter. Four indicators were used to test the quality of DSMs: the mean error and its dispersion in the XY plane and in elevation Z. Results show a good georeferencing of the DSMs (MeanErrorXY&lt;10 cm) and a similar quality in elevation (MeanErrorZ&lt;10 cm) estimation. Results also show that the error in elevation is highly spatially structured. The spatial patterns observed did not depend on the elevation and could be related to algorithms used to compute the DSMs.</p><p style="text-align: justify;"><strong>Conclusion: </strong>Data acquisition and processing methods have an impact on the quality of the DSMs provided by the UAV companies. DSM qualities are good enough to meet commercial vineyard requirements. The tested DSMs fit the requirements to assess field characteristics (elevation, slope, aspects) which may be important for terroir characterisation purposes.</p><p style="text-align: justify;"><strong>Significance and impact of the study:</strong> This study proves that elevation data derived from UAV present an accuracy equivalent to the reference system used in this study. The rapidity, the low cost and the high spatial resolution of these data offer significant opportunities for the development of new services for the wine industry for field characterisation.</p>

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

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.

scholarly journals The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5555 ◽  
Author(s):  
Ying Quan ◽  
Mingze Li ◽  
Zhen Zhen ◽  
Yuanshuo Hao ◽  
Bin Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Low Cost ◽  
Pearson Correlation ◽  
Point Clouds ◽  
Larix Olgensis ◽  
Individual Tree ◽  
Profile Fitting ◽  
Aerial Vehicle ◽  
Individual Trees

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.

scholarly journals Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

2019 ◽  
Vol 11 (8) ◽  
pp. 908 ◽  
Author(s):  
Xiangqian Wu ◽  
Xin Shen ◽  
Lin Cao ◽  
Guibin Wang ◽  
Fuliang Cao
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Inventory ◽  
Canopy Cover ◽  
Light Detection And Ranging ◽  
Estimation Methods ◽  
Lidar Data ◽  
Tree Crown ◽  
Polynomial Fitting ◽  
Individual Tree ◽  
Aerial Vehicle

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

scholarly journals The application of UAV images in flood detection using image segmentation techniques

2021 ◽  
Vol 23 (2) ◽  
pp. 1219
Author(s):  
N. S. Ibrahim ◽  
S. M. Sharun ◽  
M. K. Osman ◽  
S. B. Mohamed ◽  
S. H. Y. S. Abdullah
Keyword(s):  
Image Segmentation ◽  
Detection System ◽  
Low Cost ◽  
Region Growing ◽  
Ground Truth ◽  
Segmentation Accuracy ◽  
Segmented Images ◽  
Aerial Vehicle ◽  
Flood Detection ◽  
Uav Images

The application of unmanned aerial vehicle (UAV) used to capture the images of the flood areas are becoming interest of most researchers recently. This is due to its versatilities of capturing the images with low-cost and real time responses. At present, the captured images are analysed manually by human experts, which cause the task labourous, time consuming and prone toerror. This study aims to develop an UAV-based automated flood detection system. Samples of images that consist of land and river areas were capture using a camera attached to UAV to emulate flooded and non-flooded areas.The RGB and HSI colour models were utilised to represent the flood images. Two image segmentation methods were studied, which are k-mean clustering and region growing. The segmented images were validated with manually segmented (ground truth) images. Simulation results show that the RG using gray images gave better segmentation accuracy (88%) as compared to the K-mean clustering (76%). Finally, an automated flood monitoring system based on the region growing method, called flood detection structure (FDS) was developed to detect and analyse the flood severity.

Using error-in-variable regression to predict tree diameter and crown width from remotely sensed imagery

2010 ◽  
Vol 40 (6) ◽  
pp. 1095-1108 ◽  
Author(s):  
Wenhua Zhang ◽  
Yinghai Ke ◽  
Lindi J. Quackenbush ◽  
Lianjun Zhang
Keyword(s):  
Measurement Errors ◽  
Major Axis ◽  
Remotely Sensed ◽  
Ordinary Least Squares ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Diameter ◽  
Remotely Sensed Imagery ◽  
Crown Width ◽  
Response Variables

Automated individual tree detection and delineation from high spatial resolution imagery provides good opportunities for forest inventory at a large scale. However, the accuracy of delineated crown size compared with ground measurements may not be sufficient. Thus, ordinary least squares (OLS) regression is no longer an appropriate approach to estimating and predicting variables from the delineated tree crown because both response variable and regressor are subject to measurement errors. In this study, we describe the functional and structural relationships between field-measured tree variables (i.e., tree diameter and crown width) and delineated tree crown width from remotely sensed imagery. We investigated the performance of OLS and three error-in-variable regression techniques including maximum likelihood estimator (MLE), major axis (MA) regression, and reduced major axis (RMA) regression using field-measured data and simulated data under different conditions. Our results indicated that MLE was desirable for estimating unbiased model coefficients. However, the adjustment assumption of the MLE model should be checked for predicting tree variables from remotely sensed imagery. When the assumption holds, the MLE model performed better for predicting the response variables than did the OLS model. Otherwise, the MLE model produced biased predictions for the response variables.

Individual tree crown delineation from high-resolution UAV images in broadleaf forest

2021 ◽  
Vol 61 ◽  
pp. 101207
Author(s):  
Mojdeh Miraki ◽  
Hormoz Sohrabi ◽  
Parviz Fatehi ◽  
Mathias Kneubuehler
Keyword(s):  
High Resolution ◽  
Tree Crown ◽  
Individual Tree ◽  
Broadleaf Forest ◽  
Crown Delineation ◽  
Uav Images ◽  
Tree Crown Delineation
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