Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging

ABSTRACT Biomass is one of the most important parameters in order for the farmer to choose the best canopy management within the field and it can be estimated using plant canopy height. In combination with a non-vegetation ground model, plant height can be obtained by quantifying the height of a canopy using crop surface models (CSMs). A modified Mikrokopter Okto unmanned aerial vehicle (UAV) acquired high-resolution multispectral images (4 cm) and a processing chain was developed to construct a 3D digital surface model (DSM) for the creation of precise digital terrain models (DTMs) based on Structure from Motion (SfM) computer vision algorithms. The DTM was then subtracted from the DSM to obtain a canopy height model (CHM) of a vineyard. The results show a good separation of ground pixels from vine rows, but their elevations were not quite in accordance with the actual height of the vines due to a smoothing effect of the reconstructed CHM. A further comparison between CHM and a vigour map obtained from normalized difference vegetation index (NDVI) values showed a good correlation. A preliminary assessment of biomass volume was made using the average canopy height and vine row width for three different homogeneous classes. This is a preliminary study on how a 3D model developed by UAV images can be useful for a simple and prompt biomass evaluation.

[1]  K. Karantzalos,et al.  LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS , 2015 .

[2]  Erwin Chong Xin Ong.  Development of an Unmanned Aerial Vehicle (UAV) , 2017 .

[3]  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 .

[4]  S. M. Jong,et al.  Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography , 2014 .

[5]  Troy Jensen,et al.  Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform , 2007 .

[6]  Henri Eisenbeiss,et al.  Investigation of uav systems and flight modes for photogrammetric applications , 2011 .

[7]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..

[8]  Johanna Link,et al.  Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System , 2014, Remote. Sens..

[9]  Erle C. Ellis,et al.  Remote Sensing of Vegetation Structure Using Computer Vision , 2010, Remote. Sens..

[10]  Y. Miao,et al.  Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China , 2013 .

[11]  Craig S. T. Daughtry,et al.  NIR-Green-Blue High-Resolution Digital Images for Assessment of Winter Cover Crop Biomass , 2011 .

[12]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[13]  Adam J. Mathews,et al.  Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud , 2013, Remote. Sens..

[14]  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.

[15]  F. Neitzel,et al.  Mobile 3d Mapping with a Low-Cost Uav System , 2012 .

[16]  Jeffrey E. Herrick,et al.  Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management , 2009 .

[17]  R A Diaz-Varela,et al.  Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. , 2014, Journal of environmental management.

[18]  Rahul Sukthankar,et al.  Classification of plant structures from uncalibrated image sequences , 2012, 2012 IEEE Workshop on the Applications of Computer Vision (WACV).

[19]  Piero Toscano,et al.  Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..