An Airborne Multispectral Imaging System Based on Two Consumer-Grade Cameras for Agricultural Remote Sensing

This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One camera captures normal color images, while the other is modified to obtain near-infrared (NIR) images. The color camera is also equipped with a GPS receiver to allow geotagged images. A remote control is used to trigger both cameras simultaneously. Images are stored in 14-bit RAW and 8-bit JPEG files in CompactFlash cards. The second-order transformation was used to align the color and NIR images to achieve subpixel alignment in four-band images. The imaging system was tested under various flight and land cover conditions and optimal camera settings were determined for airborne image acquisition. Images were captured at altitudes of 305–3050 m (1000–10,000 ft) and pixel sizes of 0.1–1.0 m were achieved. Four practical application examples are presented to illustrate how the imaging system was used to estimate cotton canopy cover, detect cotton root rot, and map henbit and giant reed infestations. Preliminary analysis of example images has shown that this system has potential for crop condition assessment, pest detection, and other agricultural applications.

[1]  Agnès Bégué,et al.  Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test , 2008, Sensors.

[2]  Won Suk Lee,et al.  Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques , 2012 .

[3]  N. Coops,et al.  Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras , 2014 .

[4]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[5]  Ryan R. Jensen,et al.  Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities , 2011 .

[6]  Joanne N. Halls,et al.  Habitat Mapping and Change Assessment of Coastal Environments: An Examination of WorldView-2, QuickBird, and IKONOS Satellite Imagery and Airborne LiDAR for Mapping Barrier Island Habitats , 2014, ISPRS Int. J. Geo Inf..

[7]  A. Singer,et al.  A digital camera as a tool to measure colour indices and related properties of sandy soils in semi‐arid environments , 2005 .

[8]  Norman C. Elliott,et al.  Original paper: Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia , 2011 .

[9]  S. Labbé,et al.  Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: theoretical and practical study , 2014 .

[10]  W. Bausch,et al.  QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize , 2010, Precision Agriculture.

[11]  Diana Adler,et al.  Single Sensor Imaging Methods And Applications For Digital Cameras , 2016 .

[12]  Chenghai Yang,et al.  USING AERIAL PHOTOGRAPHY FOR MAPPING GIANT REED INFESTATIONS ALONG THE TEXAS-MEXICO PORTION OF THE RIO GRANDE , 2010 .

[13]  Patrick J. Wolfe,et al.  Spatio-Spectral Sampling and Color Filter Array Design , 2008 .

[14]  Albert Rango,et al.  Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments , 2011, Remote. Sens..

[15]  Andrew E. Suyker,et al.  An alternative method using digital cameras for continuous monitoring of crop status , 2012 .

[16]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[17]  Qian Du,et al.  Applying six classifiers to airborne hyperspectral imagery for detecting giant reed , 2012 .

[18]  Chenghai Yang,et al.  Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield , 2007, Precision Agriculture.

[19]  James H. Everitt,et al.  Airborne videography : current status and future perspectives , 1992 .

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

[21]  Chenghai Yang,et al.  AIRBORNE HYPERSPECTRAL IMAGERY AND YIELD MONITOR DATA FOR ESTIMATING GRAIN SORGHUM YIELD VARIABILITY , 2004 .

[22]  G. May,et al.  REAL-TIME AIRBORNE AGRICULTURAL MONITORING , 1994 .

[23]  James H. Everitt,et al.  A Twelve-Band Airborne Digital Video Imaging System (ADVIS) , 1998 .

[24]  Vincent G. Ambrosia,et al.  Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use , 2012, Remote. Sens..

[25]  Roger T Hanlon,et al.  Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  Paul E. Gessler,et al.  The design and the development of a hyperspectral and multispectral airborne mapping system , 2009 .

[27]  Chenghai Yang,et al.  A high-resolution airborne four-camera imaging system for agricultural remote sensing , 2012 .

[28]  James H. Everitt,et al.  USING MULTISPECTRAL VIDEO IMAGERY FOR DETECTING SOIL SURFACE CONDITIONS , 1989 .

[29]  Chenghai Yang,et al.  Mapping Giant Reed (Arundo donax) Infestations along the Texas–Mexico Portion of the Rio Grande with Aerial Photography , 2011, Invasive Plant Science and Management.

[30]  J. Alex Thomasson,et al.  ENGINERING AND GINNING Monitoring Cotton Root Rot Progression within a Growing Season Using Airborne Multispectral Imagery , 2014 .