Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications

VTT Technical Research Centre of Finland has developed a Fabry-Perot Interferometer (FPI) based hyperspectral imager compatible with the light weight UAV platforms. The concept of the hyperspectral imager has been published in the SPIE Proc. 7474 and 7668. In forest and agriculture applications the recording of multispectral images at a few wavelength bands is in most cases adequate. The possibility to calculate a digital elevation model of the forest area and crop fields provides means to estimate the biomass and perform forest inventory. The full UAS multispectral imaging system will consist of a high resolution false color imager and a FPI based hyperspectral imager which can be used at resolutions from VGA (480 x 640 pixels) up to 5 Mpix at wavelength range 500 - 900 nm at user selectable spectral resolutions in the range 10...40 nm @ FWHM. The resolution is determined by the order at which the Fabry- Perot interferometer is used. The overlap between successive images of the false color camera is 70...80% which makes it possible to calculate the digital elevation model of the target area. The field of view of the false color camera is typically 80 degrees and the ground pixel size at 150 m flying altitude is around 5 cm. The field of view of the hyperspectral imager is presently is 26 x 36 degrees and ground pixel size at 150 m flying altitude is around 3.5 cm. The UAS system has been tried in summer 2011 in Southern Finland for the forest and agricultural areas. During the first test campaigns the false color camera and hyperspectral imager were flown over the target areas at separate flights. The design and calibration of the hyperspectral imager will be shortly explained. The test flight campaigns on forest and crop fields and their preliminary results are also presented in this paper.

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

[2]  Christer Holmlund,et al.  Novel hyperspectral imager for lightweight UAVs , 2010, Defense + Commercial Sensing.

[3]  Oliver Weatherbee,et al.  Compact high-resolution VIS/NIR hyperspectral sensor , 2011, Defense + Commercial Sensing.

[4]  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.

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

[6]  Sakari Tuominen,et al.  Performance of airborne laser scanning- and aerial photograph-based statistical and textural features in forest variable estimation , 2008 .

[7]  Juha Hyyppä,et al.  Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I) , 2009, Remote. Sens..

[8]  J. Everaerts,et al.  Evaluation of a lightweigth UAS-prototype for hyperspectral imaging , 2010 .

[9]  Jeffrey G. White,et al.  Aerial Color Infrared Photography to Optimize In‐Season Nitrogen Fertilizer Recommendations in Winter Wheat , 2007 .

[10]  Richard G. Oderwald,et al.  Spectral Separability among Six Southern Tree Species , 2000 .

[11]  Christian Ginzler,et al.  High‐resolution digital surface models (DSMs) for modelling fractional shrub/tree cover in a mire environment , 2008 .

[12]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[13]  Heikki Saari,et al.  Novel miniaturized hyperspectral sensor for UAV and space applications , 2009, Remote Sensing.

[14]  Stanley R. Herwitz,et al.  Collection of Ultra High Spatial and Spectral Resolution Image Data over California Vineyards with a Small UAV , 2003 .