Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy.

[1]  Capability of crop canopy sensing to predict crop parameters of cut grass swards aiming at early season variable rate nitrogen top dressings , 2017 .

[2]  Richard Gloaguen,et al.  The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo - A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data , 2017, Remote. Sens..

[3]  Jan G. P. W. Clevers,et al.  Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data - potential of unmanned aerial vehicle imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Florian Hartig,et al.  Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass , 2014 .

[5]  E. Milton,et al.  The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .

[6]  Michael Pflanz,et al.  Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery , 2016, Remote. Sens..

[7]  Heikki Saari,et al.  Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications , 2011, Remote Sensing.

[8]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[9]  Johanna Link,et al.  A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation , 2016 .

[10]  Heikki Saari,et al.  Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV , 2013, Remote Sensing.

[11]  Juha Suomalainen,et al.  Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches , 2015, ISPRS Int. J. Geo Inf..

[12]  Heikki Saari,et al.  Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture , 2013, Remote. Sens..

[13]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[14]  M. Wachendorf,et al.  Combining ultrasonic sward height and spectral signatures to assess the biomass of legume-grass swards , 2013 .

[15]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[16]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[17]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[18]  P. Zarco-Tejadaa,et al.  Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle ( UAV ) , 2013 .

[19]  S. Fountas,et al.  Precision Agriculture Technologies positively contributing to GHG emissions mitigation, farm productivity and economics , 2017 .

[20]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[21]  Barbara Koch,et al.  Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment , 2010 .

[22]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[23]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[24]  Z. Niu,et al.  Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system , 2016 .

[25]  Eija Honkavaara,et al.  A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone , 2018 .

[26]  Eija Honkavaara,et al.  Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level , 2015, Remote. Sens..

[27]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[28]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Eija Honkavaara,et al.  Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes , 2017 .

[30]  Heikki Saari,et al.  Unmanned aerial vehicle (UAV) operated megapixel spectral camera , 2011, Security + Defence.

[31]  Eija Honkavaara,et al.  The Metrology of Directional, Spectral Reflectance Factor Measurements Based on Area Format Imaging by UAVs Messung von gerichteten Reflektanzen bei hyperspektralen Flächenkameras auf UAVs , 2014 .

[32]  Pablo J. Zarco-Tejada,et al.  Assessing structural effects on PRI for stress detection in conifer forests , 2011 .

[33]  E. Honkavaara,et al.  Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft , 2018 .

[34]  Eija Honkavaara,et al.  Applying Different Remote Sensing Data to Determine Relative Biomass Estimations of Cereals for Precision Fertilization Task Generation , 2017, HAICTA.

[35]  Luis A. Méndez-Barroso,et al.  Estimation of hydromorphological attributes of a small forested catchment by applying the Structure from Motion (SfM) approach , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Juliane Bendig,et al.  Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements , 2015 .

[37]  Juliane Bendig,et al.  UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .

[38]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[39]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[40]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[41]  Qiang Cao,et al.  Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice , 2014 .

[42]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[43]  Heikki Saari,et al.  Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV) , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[44]  G. Waldhoff,et al.  Spatial variability detection of crop height in a single field by terrestrial laser scanning , 2013 .

[45]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[46]  Raquel Alves de Oliveira.  Generation of hyperspectral digital surface model in forest areas using hyperspectral 2D frame camera onboard RPAS , 2017 .

[47]  Gérard Dedieu,et al.  Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas , 2016 .

[48]  Wolfram Mauser,et al.  Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy , 2017, Remote. Sens..

[49]  Heikki Saari,et al.  Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity , 2018, Remote. Sens..

[50]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[51]  Eija Honkavaara,et al.  Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization , 2018, Sensors.

[52]  Eija Honkavaara,et al.  OPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD , 2018 .

[53]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[54]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[55]  Juha Hyyppä,et al.  Terrestrial Laser Scanning of Agricultural Crops , 2008 .

[56]  Xinkai Zhu,et al.  Estimation of biomass in wheat using random forest regression algorithm and remote sensing data , 2016 .

[57]  Juha Hyyppä,et al.  Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2018, Remote. Sens..

[58]  Guijun Yang,et al.  A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy , 2018, Remote. Sens..

[59]  Yanjie Wang,et al.  Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models , 2017, Remote. Sens..

[60]  E. Honkavaara,et al.  Geometric Calibration of a Hyperspectral Frame Camera , 2016 .

[61]  M. Wachendorf,et al.  A multi-sensor approach for predicting biomass of extensively managed grassland , 2014 .

[62]  Heikki Saari,et al.  A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data , 2013, Remote Sensing.

[63]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[64]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[65]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[66]  P. Häkli.  Practical Test on Accuracy and Usability of Virtual Reference Station Method in Finland , 2004 .

[67]  Eija Honkavaara,et al.  Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment , 2018 .

[68]  L. Caturegli,et al.  Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses , 2016, PloS one.

[69]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[70]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[71]  Eija Honkavaara,et al.  Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment , 2018, Remote. Sens..

[72]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[73]  Andrew M. Cunliffe,et al.  Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry , 2016 .

[74]  G. Guyot,et al.  Utilisation de la Haute Resolution Spectrale pour Suivre L'etat des Couverts Vegetaux , 1988 .

[75]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[76]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[77]  Yong Liu,et al.  Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[78]  Juha Hyyppä,et al.  Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..

[79]  E. Honkavaara,et al.  ASSESSMENT OF VARIOUS REMOTE SENSING TECHNOLOGIES IN BIOMASS AND NITROGEN CONTENT ESTIMATION USING AN AGRICULTURAL TEST FIELD , 2017 .

[80]  Nilton Nobuhiro Imai,et al.  Mapping Mosaic Virus in Sugarcane Based on Hyperspectral Images , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.