Estimating light interception in tree crops with digital images of canopy shadow

Estimating light interception in tree crops with digital images of canopy shadow Canopy light interception (LI) is an important variable related to evapotranspiration, photosynthesis, primary productivity and yield in natural and managed vegetation, and the development of simple, reliable methods for its estimation is critical for research and practical purposes. This paper proposes a novel digital photographic technique for estimating canopy light interception based on the shadow projected by trees on the ground surface. A total of 607 pictures taken from 20 different walnut and almond orchards across California, USA, and with canopy covers ranging from 5 to 98 % were processed to derive canopy shadow fraction and compared with LI recorded at the same time and location from a mobile platform of ceptometers, the mobile light bar (MLB), which systematically collected data as it is moved under the trees. Light interception values obtained with the photographic technique were highly correlated and very similar to those of the MLB (R2 = 0.95).The contribution of MLB sampling error and other factors that lead to differences in light interception values between the two methods was analyzed and discussed. The image acquisition and processing in this new technique does not require special or expensive equipment, software or training and can be easily adopted by researchers and farmers, and the generated information can be combined with satellite imagery to extend to the orchard and regional scales. The advantages and limitations of the proposed technique are discussed along with suggestions for further improvements and automation which could lead to more accurate results and wider application for research and crop management purposes. Precision Agriculture Springer Journals

Estimating light interception in tree crops with digital images of canopy shadow

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Springer US
Copyright © 2015 by Springer Science+Business Media New York
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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