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Remote Sens. 2017, 9(10), 1054; https://doi.org/10.3390/rs9101054

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

1
College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
2
USDA-Agricultural Research Service, Aerial Application Technology Research Unit, 3103 F & B Road, College Station, TX 77845, USA
3
Northwest A&F University, College of Mechanical and Electronic Engineering, 22 Xinong Road, Yangling 712100, China
4
Department of Biosystems and Agricultural Engineering, University of Nebraska, P.O. Box 830726, Lincoln, NE 68583, USA
5
Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, 111 Jiulong Road, Hefei 230601, China
6
College of Engineering, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 26 August 2017 / Revised: 25 September 2017 / Accepted: 10 October 2017 / Published: 17 October 2017
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Abstract

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation. View Full-Text
Keywords: consumer-grade camera; modified NIR camera; crop identification; leaf area index; image classification; vegetation index consumer-grade camera; modified NIR camera; crop identification; leaf area index; image classification; vegetation index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, J.; Yang, C.; Zhao, B.; Song, H.; Clint Hoffmann, W.; Shi, Y.; Zhang, D.; Zhang, G. Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras. Remote Sens. 2017, 9, 1054.

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