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Dr. Yu-Hsuan Tu
King Abdullah University of Science and Technology

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0 Horticulture
0 Precision Agriculture
0 UAV
0 drone
0 multi-spectral remote sensing

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Horticulture
drone

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Journal article
Published: 08 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Developments in computer vision, such as structure from motion and multiview stereo reconstruction, have enabled a range of photogrammetric applications using unmanned aerial vehicles (UAV)-based imagery. However, some specific cases still present reconstruction challenges, including survey areas composed of steep, overhanging, or vertical rock formations. Here, the suitability and geometric accuracy of four UAV-based image acquisition and data processing scenarios for topographic surveying applications in complex terrain are assessed and compared. The specific cases include the use of: 1) nadir imagery; 2) nadir and oblique imagery; 3) nadir and façade imagery; and 4) nadir, oblique, and façade imagery to reconstruct a topographically complex natural surface. Results illustrate that including oblique and façade imagery to supplement the more traditional nadir collections significantly improves the geometric accuracy of point cloud data reconstruction by approximately 35% when assessed against terrestrial laser scanning data of near-vertical rock walls. Most points (99.41%) had distance errors of less than 50 cm between the point clouds derived from the nadir imagery and nadir-oblique-façade imagery. Apart from delivering enhanced spatial resolution in façade details, the geometric accuracy improvements achieved from integrating nadir, oblique, and façade imagery provide value for a range of applications, including geotechnical and geohazard investigations. Such gains are particularly relevant for studies assessing rock integrity and stability, and engineering design, planning, and construction, where information on the position of rock cracks, joints, faults, shears, and bedding planes may be required.

ACS Style

Yu-Hsuan Tu; Kasper Johansen; Bruno Aragon; Bonny M. Stutsel; Yoseline Angel; Omar A. Lopez Camargo; Samir K. M. Al-Mashharawi; Jiale Jiang; Matteo G. Ziliani; Matthew F. McCabe. Combining Nadir, Oblique, and Façade Imagery Enhances Reconstruction of Rock Formations Using Unmanned Aerial Vehicles. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -13.

AMA Style

Yu-Hsuan Tu, Kasper Johansen, Bruno Aragon, Bonny M. Stutsel, Yoseline Angel, Omar A. Lopez Camargo, Samir K. M. Al-Mashharawi, Jiale Jiang, Matteo G. Ziliani, Matthew F. McCabe. Combining Nadir, Oblique, and Façade Imagery Enhances Reconstruction of Rock Formations Using Unmanned Aerial Vehicles. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-13.

Chicago/Turabian Style

Yu-Hsuan Tu; Kasper Johansen; Bruno Aragon; Bonny M. Stutsel; Yoseline Angel; Omar A. Lopez Camargo; Samir K. M. Al-Mashharawi; Jiale Jiang; Matteo G. Ziliani; Matthew F. McCabe. 2021. "Combining Nadir, Oblique, and Façade Imagery Enhances Reconstruction of Rock Formations Using Unmanned Aerial Vehicles." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-13.

Journal article
Published: 20 May 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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Australia is one of the world’s largest producers of macadamia nuts. As macadamia trees can take up to 15 years to mature and produce maximum yield, it is important to optimize tree condition. Field based assessment of macadamia tree condition is time-consuming and often inconsistent. Using remotely sensed imagery may allow for faster, more extensive, and more consistent assessment of macadamia tree condition. To identify individual macadamia tree crowns, high spatial resolution imagery is required. Hence, the objective of this work was to develop and test an approach to map the condition of individual macadamia tree crowns using both multi-spectral Unmanned Aerial Vehicle (UAV) and WorldView-3 imagery for different macadamia varieties and three different sites located near Bundaberg, Australia. A random forest classifier, based on all available spectral bands and selected vegetation indices was used to predict five condition categories, ranging from excellent (category 1) to poor (category 5). Various combinations of the developed models were tested between the three sites and over time. The results showed that the multi-spectral WorldView-3 imagery produced the lowest out of bag (OOB) classification errors in most cases. However, for both the UAV and the WorldView-3 imagery, more than 98.5% of predicted macadamia condition categories were either correctly mapped or offset by a single category out of the five condition categories (excellent, good, moderate, fair and poor) for trees of the same variety and at one point in time. Multi-temporally, the WorldView-3 imagery performed better than the UAV data for predicting the condition of the same macadamia tree variety. Applying a model from one site to another site with the same macadamia tree variety produced OOB classification between 31.20 and 42.74%, but with >98.63% of trees predicted within a single condition category. Importantly, models trained based on one type of macadamia tree variety could not be successfully applied to a site with another variety. The developed classification models may be used as a decision and management support tool for the macadamia industry to inform management practices and improve on-demand irrigation, fertilization, and pest inspection at the individual tree level.

ACS Style

Kasper Johansen; Qibin Duan; Yu-Hsuan Tu; Chris Searle; Dan Wu; Stuart Phinn; Andrew Robson; Matthew F. McCabe. Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 165, 28 -40.

AMA Style

Kasper Johansen, Qibin Duan, Yu-Hsuan Tu, Chris Searle, Dan Wu, Stuart Phinn, Andrew Robson, Matthew F. McCabe. Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 165 ():28-40.

Chicago/Turabian Style

Kasper Johansen; Qibin Duan; Yu-Hsuan Tu; Chris Searle; Dan Wu; Stuart Phinn; Andrew Robson; Matthew F. McCabe. 2020. "Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery." ISPRS Journal of Photogrammetry and Remote Sensing 165, no. : 28-40.

Journal article
Published: 29 February 2020 in International Journal of Applied Earth Observation and Geoinformation
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To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R2 = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R2 = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km2 due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R2 = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R2 = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km2–100 km2). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R2 = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R2 = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.

ACS Style

Dan Wu; Kasper Johansen; Stuart Phinn; Andrew Robson; Yu-Hsuan Tu. Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns. International Journal of Applied Earth Observation and Geoinformation 2020, 89, 102091 .

AMA Style

Dan Wu, Kasper Johansen, Stuart Phinn, Andrew Robson, Yu-Hsuan Tu. Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns. International Journal of Applied Earth Observation and Geoinformation. 2020; 89 ():102091.

Chicago/Turabian Style

Dan Wu; Kasper Johansen; Stuart Phinn; Andrew Robson; Yu-Hsuan Tu. 2020. "Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns." International Journal of Applied Earth Observation and Geoinformation 89, no. : 102091.

Dissertation
Published: 31 January 2020
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The University of Queensland's institutional repository, UQ eSpace, aims to create global visibility and accessibility of UQ’s scholarly research.

ACS Style

Yu-Hsuan Tu. Optimising drone image acquisition and analysis for mapping horticultural tree crops. 2020, 1 .

AMA Style

Yu-Hsuan Tu. Optimising drone image acquisition and analysis for mapping horticultural tree crops. . 2020; ():1.

Chicago/Turabian Style

Yu-Hsuan Tu. 2020. "Optimising drone image acquisition and analysis for mapping horticultural tree crops." , no. : 1.

Journal article
Published: 18 December 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
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In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties.

ACS Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson; Dan Wu. Optimising drone flight planning for measuring horticultural tree crop structure. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 160, 83 -96.

AMA Style

Yu-Hsuan Tu, Stuart Phinn, Kasper Johansen, Andrew Robson, Dan Wu. Optimising drone flight planning for measuring horticultural tree crop structure. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 160 ():83-96.

Chicago/Turabian Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson; Dan Wu. 2019. "Optimising drone flight planning for measuring horticultural tree crop structure." ISPRS Journal of Photogrammetry and Remote Sensing 160, no. : 83-96.

Technical note
Published: 30 January 2019 in Remote Sensing
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Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

ACS Style

Yu-Hsuan Tu; Kasper Johansen; Stuart Phinn; Andrew Robson. Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sensing 2019, 11, 269 .

AMA Style

Yu-Hsuan Tu, Kasper Johansen, Stuart Phinn, Andrew Robson. Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sensing. 2019; 11 (3):269.

Chicago/Turabian Style

Yu-Hsuan Tu; Kasper Johansen; Stuart Phinn; Andrew Robson. 2019. "Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment." Remote Sensing 11, no. 3: 269.

Journal article
Published: 25 October 2018 in Remote Sensing
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Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

ACS Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson. Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications. Remote Sensing 2018, 10, 1684 .

AMA Style

Yu-Hsuan Tu, Stuart Phinn, Kasper Johansen, Andrew Robson. Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications. Remote Sensing. 2018; 10 (11):1684.

Chicago/Turabian Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson. 2018. "Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications." Remote Sensing 10, no. 11: 1684.

Preprint
Published: 29 September 2018
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UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

ACS Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson. Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications. 2018, 1 .

AMA Style

Yu-Hsuan Tu, Stuart Phinn, Kasper Johansen, Andrew Robson. Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications. . 2018; ():1.

Chicago/Turabian Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson. 2018. "Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications." , no. : 1.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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UAS-based multi-spectral imagery is becoming ubiquitous for monitoring and managing various horticultural crops. To accurately measure and monitor their structure and condition and estimate yields, appropriately corrected data must be used to drive the necessary algorithms. There are several popular radiometric correction methods commonly used for UAS-based data correction. However, their relative and absolute accuracies are not known. This study used three flight datasets, including along- and across-tree-row flight patterns in an avocado orchard. Four correction methods were applied to produce at-surface reflectance image mosaics for each flight pattern as well as the grid pattern and the results were compared to assess the reflectance consistency. Results show that no method provided consistently correct at-surface reflectance for the same features. A BRDF correction workflow was being developed to address these limitations. Preliminary application of the BRDF correction shows that it significantly improves the brightness consistency of features across different images.

ACS Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson. Assessing Radiometric Corrections for UAS Multi-Spectral Imagery in Horticultural Environments. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 5449 -5452.

AMA Style

Yu-Hsuan Tu, Stuart Phinn, Kasper Johansen, Andrew Robson. Assessing Radiometric Corrections for UAS Multi-Spectral Imagery in Horticultural Environments. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():5449-5452.

Chicago/Turabian Style

Yu-Hsuan Tu; Stuart Phinn; Kasper Johansen; Andrew Robson. 2018. "Assessing Radiometric Corrections for UAS Multi-Spectral Imagery in Horticultural Environments." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 5449-5452.

Journal article
Published: 24 March 2015 in Entropy
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High recharge areas significantly influence the groundwater quality and quantity in regional groundwater systems. Many studies have applied recharge potential analysis (RPA) to estimate groundwater recharge potential (GRP) and have delineated high recharge areas based on the estimated GRP. However, most of these studies define the RPA parameters with supposition, and this represents a major source of uncertainty for applying RPA. To objectively define the RPA parameter values without supposition, this study proposes a systematic method based on the theory of parameter identification. A surrogate variable, namely the average storage variation (ASV) index, is developed to calibrate the RPA parameters, because of the lack of direct GRP observations. The study results show that the correlations between the ASV indexes and computed GRP values improved from 0.67 before calibration to 0.85 after calibration, thus indicating that the calibrated RPA parameters represent the recharge characteristics of the study area well; these data also highlight how defining the RPA parameters with ASV indexes can help to improve the accuracy. The calibrated RPA parameters were used to estimate the GRP distribution of the study area, and the GRP values were graded into five levels. High and excellent level areas are defined as high recharge areas, which composed 7.92% of the study area. Overall, this study demonstrates that the developed approach can objectively define the RPA parameters and high recharge areas of the Choushui River alluvial fan, and the results should serve as valuable references for the Taiwanese government in their efforts to conserve the groundwater quality and quantity of the study area.

ACS Style

Jui-Pin Tsai; Yu-Wen Chen; Liang-Cheng Chang; Yi-Ming Kuo; Yu-Hsuan Tu; Chen-Che Pan. High Recharge Areas in the Choushui River Alluvial Fan (Taiwan) Assessed from Recharge Potential Analysis and Average Storage Variation Indexes. Entropy 2015, 17, 1558 -1580.

AMA Style

Jui-Pin Tsai, Yu-Wen Chen, Liang-Cheng Chang, Yi-Ming Kuo, Yu-Hsuan Tu, Chen-Che Pan. High Recharge Areas in the Choushui River Alluvial Fan (Taiwan) Assessed from Recharge Potential Analysis and Average Storage Variation Indexes. Entropy. 2015; 17 (4):1558-1580.

Chicago/Turabian Style

Jui-Pin Tsai; Yu-Wen Chen; Liang-Cheng Chang; Yi-Ming Kuo; Yu-Hsuan Tu; Chen-Che Pan. 2015. "High Recharge Areas in the Choushui River Alluvial Fan (Taiwan) Assessed from Recharge Potential Analysis and Average Storage Variation Indexes." Entropy 17, no. 4: 1558-1580.