Post on 21-Aug-2020
Automated analysis of digital cover videography for measuring LAI in urban street trees
Introduc9on: Leaf Area Index (LAI) is defined as the total one-‐side area of leaf 6ssue per unit of ground surface area and it is a common parameter used to assess canopy growth and vigour of trees. Measuring LAI, however, is a 6me-‐consuming process oBen requiring expensive agroforestry sensors with specific know-‐how to use them that can limit in-‐situ con6nuous monitoring at high temporal and spa6al resolu6on within different landscapes. Some studies have analysed con6nuous s6ll photographies over long 6me periods combining it with satellite imagery (Fuentes et al. 2008; Ryu, et al. 2012). However, this analysis has not yet been applied using moving video cameras. A rela6vely new methodology has been developed to automa6cally es6mate LAI through computa6onal analysis of videography from tree canopies. Videos obtained from moving aerial and terrestrial plaQorms can be analysed automa6cally in order to accurately es6mate canopy architecture parameters in a more cost-‐effec6ve manner (Fuentes, et al. 2014). This work has evaluated the poten6al applica6on automated computa6onal LAI es6ma6on from videography of fully grown urban trees (Ulmus procera). Results obtained demonstrated the feasibility of this applica6on and the use of smartphones to es6mate the temporal and spa6al varia6ons of LAI from urban trees of the city of Melbourne in Australia.
Juan de Dios Morales 1, Pangzhen Zhang2 and Sigfredo Fuentes 2* 1 The University of Melbourne, Office of Environmental Programs -‐ Department of Science. Parkville, 3010. Victoria. Australia.
2 The University of Melbourne, Faculty of Veterinary and Agricultural Sciences. Parkville, 3010. Victoria. Australia. *Corresponding author: sfuentes@unimelb.edu.au
Orchard'water'status'variability'assessed'using'proximal'and'aerial'infrared'thermography!S. Fuentes1*, Nolan A.P.2, Park S.2, O’Connell M.3, Ryu Dongryeol2
1 The University of Melbourne, Faculty of Veterinary and Agricultural Sciences. Building 142 Royal Parade. Parkville Victoria 3010 Australia.
2 The University of Melbourne, Melbourne School of Engineering. Parkville Victoria 3010 Australia. 3 Department of Economic Development, Jobs, Transport and Resources. Agricultural Research and Development Division.
Tatura. Victoria 3616 Australia
*Corresponding author: sfuentes@unimelb.edu.au INTRODUCTION Projected climate change and its variability predicts increased temperatures, higher evaporative demand and water scarcity over a large portion of Australia. These projections have increased the pressure to obtain high quality fruit production with more efficient water use. Several studies have demonstrated that carefully imposed water stress improves stone fruit quality parameters. However, most of the conventional methods to monitor plant water status are based on manual point measurements, which have low spatial coverage, and are resource expensive and time-consuming. This seriously constraints the efficient assessment of spatial variability of water status from orchards caused by heterogeneous soil characteristics, poor irrigation distribution uniformity and/or variability in canopy structure/architecture. This work has demonstrated the capability of unmanned aerial vehicles (UAVs) in detection of water stress for a stone fruit orchard (peach and nectarine).
MATERIALS AND METHODS The experiment was carried out during the 2014-15 growing season in field-grown grapevines and peach trees from a stone fruit experimental orchard in Tatura, Victoria. Water stress treatments were imposed by restricting irrigation in selected sections for both peach and nectarine orchards. The UAV was equipped with a visible-near/thermal-infrared camera systems (Fig. 1). Water stress indices maps, produced using the UAV-borne images, were evaluated with ground-based measurements of leaf conductance, stem water potential and proximal infrared thermography. During aerial image acquisition all of the camera parameters remained constant while the flight parameters were dynamic. In order to maintain optimal forward overlap between consecutive images, the on-board intervalometer recalculated the forward distance with every new altitude estimate from the UAV’s flight controller (Fig. 2). !!RESULTS Results showed highly comparable and significant agreements between canopy temperatures obtained using the UAV, water deficits imposed to plants and ground truth thermal data (Fig.3 & 4). !!!!!!!!!!!!!!!!!!!!!!!!!!
Figure 3. A comparison between air temperature and the average canopy temperature of treatment areas (left). Comparison between ground truth surface temperature and aerial thermal sensor measurement acquired at 100 m (right).
Fig. 1. The multirotor UAV platform (S900, DJI, Shenzhen, China) carrying a thermal and multispectral payload, IMU, GPS receiver and autopilot.
CONCLUSIONS The presented orchard case study confirmed the effectiveness of the multi-sensor (thermal & multispectral) UAV payload proposed for the estimation of biophysical parameters, such as water stress. The analysis of high spatial resolution thermal imagery confirmed that plants under water stress had a higher averaged canopy temperature (2.65°C) compared to air temperature, enabling the successful detection of water deficit plants.
Figure 4. Thermal images from UAV showing control treatments (right) and water deficit treatments (left). Colour bar represents the temperature scale in oC.
ACKNOWLEDGEMENTS This research was funded by a Horticulture Centre for Excellence Innovation Seed Fund grant and an ARC LIEF grant (LE130100040).
Figure 2. Adaptive intervalometer algorithm used by the UAV.
Materials and Methods: Research was conducted along the Royal Parade in Melbourne, Victoria Australia. The chosen area has been mostly planted with old-‐stands of Elm trees occupying the median strip. Two smartphone devices (iPhone 5s, Apple Inc. Cuper6no, CA. USA) were mounted with the back camera in an upward-‐looking orienta6on (zero zenith angle) and 170 cm height from the ground on the roof of a small car. The car was driven in a straight-‐line for 2.5 km of distance. The iPhones were set in automa6c mode with a video resolu6on of 1080 x 1920 pixels HD at 30 fps and recorded video along the trajectory. The GPS informa6on was recorded using the View Ranger App (Augmentra Ltd, Cambridge, UK). Measurements were conducted once per month during the 2015-‐16 growing season between September (2015) un6l February (2016). The automated data analysis of videos was performed using the methodology proposed by Fuentes et al. (2008) and Fuentes et al. (2014) in order to quan6fy LAI and other canopy architecture parameters from each video frame.
Results: The results obtained clearly showed the dynamics of tree growth with maximum values corresponding to each automa6cally filtered tree (Fig. 2). Addi6onally, LAI values were consistent with spa6al and temporal varia6on expected from trees without leaves (Fig. 2 up), fully grown canopies (Fig. 2 boiom) and from the same trees at different measurement periods (Fig. 3 leB). Other parameters, such as the crown porosity was inversely propor6onal to foliage cover (see Fig. 3 right).
Conclusions: Automa6c video acquisi6on and LAI computa6on clearly demonstrates the advantages and poten6al applica6on to monitor deciduous trees in urban environments where unmanned aerial vehicles are not allowed. Addi6onally, this study demonstrated the feasibility of using more accessible and cheaper mul6use electronic devices, such as smartphones, for the assessment of tree growth. More importantly, this systema6c procedure can be highly comparable to more expensive and less efficient in situ monitoring systems. Further tes6ng needs to be performed using this methodology on public transport, rubbish trucks and council vehicles. The laier will effec6vely transform vehicles into moving robots for automa6c data collec6on and analysis helping in this way offsejng the carbon footprint of transport and other services from ci6es. Finally, this work could effec6vely contribute to the development of smart resilient ci6es that apply precision tools for decision-‐making prac6ces related to irriga6on, nutri6on and canopy management of urban trees in a more efficient and cost effec6ve manner. References: FUENTES, S., et al. 2008. An automated procedure for es6ma6ng the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its applica6on to an examina6on of the rela6onship between remotely sensed and field measurements of LAI. Func4onal Plant Biology, 35, 1070-‐1079; FUENTES, S. et al. 2014. Automated es6ma6on of leaf area index from grapevine canopies using cover photography, video and computa6onal analysis methods. Australian Journal of Grape and Wine Research, 20, 465-‐473; RYU, Y., et al. 2012. Con6nuous observa6on of tree leaf area index at ecosystem scale using upward-‐poin6ng digital cameras. Remote Sensing of Environment, 126, 116-‐125.
Figure 3: (leB) Example of the seasonal varia9on of LAI from three trees and corresponding Day of Year . (right) Canopy architecture parameters including: Frac9on of foliage, Foliage cover, Crown porosity and Clumping index from five trees.
Figure 1: Study area: Royal Parade, Melbourne, Australia. Red line denotes the trajectory for video acquisi9on and sec9on of street analysed. Source: Google Earth, 2015.
Figure 2: Automated LAI analys is f rom videography of trees before budburst in September 2015 or early Spring (Top) and f rom fu l l y g rown canopies in January or mid Summer 2016 (boWom). Red dots r e p r e s e n t s t h e automa9c filtering of the analysed data to o b t a i n L A I f r om individual trees as peak values.
0 1 2 3 4 5 6 7 8
12-‐sept-‐15 11-‐oct-‐15 24-‐dic-‐15 10-‐ene-‐16 7-‐feb-‐16
255 284 358 10 38
Day of Year
LAI
LAI
Video frames
LAI
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0.2
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1
1.2
Sep Oct Dec Jan Sep Oct Dec Jan Sep Oct Dec Jan Sep Oct Dec Jan
2015 2016 2015 2016 2015 2016 2015 2016
Frac>on of foliage (Ff)
Foliage Cover (Fc) Crown porosity Φ Clumping index Ω(0)
Tree 1
Tree 2
Tree 3
Tree 4
Tree 5