Geo-referencing remote images for precision agriculture using artificial terrestrial targets

The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2.0® software. This works as an add-on of ENVI® for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5–1.5 m, and the UAV images showed a mean accuracy of 1.0–4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., <1.5 m pixel−1), accurately geo-referenced (errors <1–2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using geo-referenced TT and AUGEO, mainly in areas where no notable hard points are available.

[1]  Soren W. Henriksen,et al.  Manual of photogrammetry , 1980 .

[2]  M. J. Kropff,et al.  Crop-weed interactions and weed population dynamics: current knowledge and new research targets. , 1997 .

[3]  R. Ryan,et al.  Measurement Sets and Sites Commonly used for Characterizations , 2002 .

[4]  W. Kornus,et al.  STRIP ADJUSTMENT OF LIDAR DATA , 2003 .

[5]  Francisca López-Granados,et al.  Assessing land-use in olive groves from aerial photographs , 2004 .

[6]  Stefan Kienzle,et al.  The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives , 2004, Trans. GIS.

[7]  Using remote sensing for identification of late-season grass weed patches in wheat , 2006 .

[8]  W. Andrew Marcus,et al.  Accuracy assessment of georectified aerial photographs: Implications for measuring lateral channel movement in a GIS , 2006 .

[9]  Francisca López-Granados,et al.  Mapping Ridolfia segetum patches in sunflower crop using remote sensing , 2007 .

[10]  Mats Söderström,et al.  Field specific overview of crops using UAV (Unmanned Aerial Vehicle) , 2007 .

[11]  Francisca López-Granados,et al.  Automatic assessment of agro-environmental indicators from remotely sensed images of tree orchards and its evaluation using olive plantations , 2008 .

[12]  Jérôme Théau,et al.  Effect of coregistration error on patchy target detection using high-resolution imagery , 2008 .

[13]  J. Alex Thomasson,et al.  Ground-based sensing system for weed mapping in cotton , 2008 .

[14]  Francisca López-Granados,et al.  Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation , 2010, Precision Agriculture.

[15]  Shih-Yuan Lin,et al.  Strip adjustment of airborne full-waveform LiDAR data , 2011 .

[16]  M. Jurado-Expósito,et al.  Discriminating cropping systems and agro-environmental measures by remote sensing , 2008, Agronomy for Sustainable Development.