Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

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 (R-2 = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R-2 = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1-10 km(2) due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R-2 = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R-2 = 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 km(2)-100 km(2)). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R-2 = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R-2 = 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.

[1]  M. Herold,et al.  Data acquisition considerations for Terrestrial Laser Scanning of forest plots , 2017 .

[2]  Derek A. Schneider,et al.  A Non-Reference Temperature Histogram Method for Determining Tc from Ground-Based Thermal Imagery of Orchard Tree Canopies , 2019, Remote. Sens..

[3]  N. T. Anderson,et al.  Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery , 2018, Precision Agriculture.

[4]  M. Keller,et al.  Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+ , 2015, Carbon Balance and Management.

[5]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[6]  Lav R. Khot,et al.  High Resolution Multispectral and Thermal Remote Sensing-Based Water Stress Assessment in Subsurface Irrigated Grapevines , 2017, Remote. Sens..

[7]  Stuart R. Phinn,et al.  Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment , 2018, Remote. Sens..

[8]  Jan-Peter Mund,et al.  UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring , 2019, Remote. Sens..

[9]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[10]  Cristina Barrado,et al.  On-the-Fly Olive Tree Counting Using a UAS and Cloud Services , 2019, Remote. Sens..

[11]  Brian Machovina,et al.  UAV remote sensing of spatial variation in banana production , 2016, Crop and Pasture Science.

[12]  D. Roberts,et al.  Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape , 2004 .

[13]  C. Silva,et al.  Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest , 2017 .

[14]  P. J. C. Stassen,et al.  Effects of pruning on flowering, yield and fruit quality in mango (Mangifera indica) , 2005 .

[15]  Dan Wu,et al.  Estimating Changes in Leaf Area, Leaf Area Density, and Vertical Leaf Area Profile for Mango, Avocado, and Macadamia Tree Crowns Using Terrestrial Laser Scanning , 2018, Remote. Sens..

[16]  Juha Hyyppä,et al.  Is field-measured tree height as reliable as believed – A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[17]  David A. Coomes,et al.  Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion , 2019, Remote. Sens..

[18]  Agostino Tombesi,et al.  MECHANICAL PRUNING OF ADULT OLIVE TREES AND INFLUENCE ON YIELD AND ON EFFICIENCY OF MECHANICAL HARVESTING , 2011 .

[19]  Mathias Schardt,et al.  Advanced DTM Generation from Very High Resolution Satellite Stereo Images , 2015 .

[20]  Andrew Robson,et al.  Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia , 2017, Remote. Sens..

[21]  Christopher Searle,et al.  Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis , 2018, Remote. Sens..

[22]  R. Hill,et al.  Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data , 2003 .

[23]  D. A. Hill,et al.  Combined high-density lidar and multispectral imagery for individual tree crown analysis , 2003 .

[24]  Nicholas C. Coops,et al.  Assessment of forest structure with airborne LiDAR and the effects of platform altitude , 2006 .

[25]  Peter Surový,et al.  Mapping Forest Structure Using UAS inside Flight Capabilities , 2018, Sensors.

[26]  Seishi Ninomiya,et al.  Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle , 2018, Horticulture Research.

[27]  M. Despotovic,et al.  Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation , 2016 .

[28]  C. Schmullius,et al.  Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective , 2017 .

[29]  P. A. Magarey,et al.  Fruit tree and vine sprayer calibration based on canopy size and length of row: Unit canopy row method , 1998 .

[30]  Matthew F. McCabe,et al.  Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery , 2020 .

[31]  Juan de la Riva,et al.  Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications , 2015, Remote. Sens..

[32]  Laura Chasmer,et al.  Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar , 2006 .

[33]  Jesús A. Gil-Ribes,et al.  Towards an Optimized Method of Olive Tree Crown Volume Measurement , 2015, Sensors.

[34]  Matthew F. McCabe,et al.  Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects , 2018, Remote. Sens..

[35]  R. Sanz,et al.  A review of methods and applications of the geometric characterization of tree crops in agricultural activities , 2012 .

[36]  Alexis Achim,et al.  Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size , 2017 .

[37]  Juha Hyyppä,et al.  The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve , 2004 .

[38]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[39]  M. Pierrot-Deseilligny,et al.  A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery , 2013 .

[40]  Yangzi Cong,et al.  3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison , 2019, Remote. Sens..

[41]  Rafael R. Sola-Guirado,et al.  Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery , 2017, Sensors.

[42]  Jasmine Muir,et al.  Evaluating satellite remote sensing as a method for measuring yield variability in Avocado and Macadamia tree crops , 2017 .

[43]  Andrew Robson,et al.  Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango , 2018, Remote. Sens..

[44]  Borja Velázquez-Martí,et al.  Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data , 2014 .

[45]  Joanne C. White,et al.  Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .

[46]  P. Reinartz,et al.  Assessment of Cartosat-1 and WorldView-2 stereo imagery in combination with a LiDAR-DTM for timber volume estimation in a highly structured forest in Germany , 2013 .

[47]  Julien Sarron,et al.  Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV , 2018, Remote. Sens..

[48]  Kasper Johansen,et al.  Evaluation of terrestrial laser scanners for measuring vegetation structure , 2012 .

[49]  Juha Hyyppä,et al.  Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes , 2015, Remote. Sens..

[50]  M. Simard,et al.  High‐resolution forest canopy height estimation in an African blue carbon ecosystem , 2015, Remote sensing in ecology and conservation.

[51]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[52]  M. Pascual,et al.  An Image-based Method to Study the Fruit Tree Canopy and the Pruning Biomass Production in a Peach Orchard , 2015 .

[53]  P. Surový,et al.  Determining tree height and crown diameter from high-resolution UAV imagery , 2017 .

[54]  Kamau Ngamau,et al.  Status of macadamia production in Kenya and the potential of biotechnology in enhancing its genetic improvement , 2009 .

[55]  C. Menzel,et al.  Increasing the productivity of avocado orchards using high-density plantings: A review , 2014 .

[56]  F. López-Granados,et al.  Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling , 2017, Plant Methods.

[57]  I. Woodhouse,et al.  Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries , 2017 .

[58]  Jan van Aardt,et al.  Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser Scanner Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[59]  Feng Zhao,et al.  Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR , 2014, Remote. Sens..

[60]  J. Estornell,et al.  Accuracy of tree geometric parameters depending on the LiDAR data density , 2016 .