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

Authors

Jian Zhang, Huazhong Agricultural UniversityFollow
Chenghai Yang, USDA, Agricultural Research ServiceFollow
Biquan Zhao, Huazhong Agricultural UniversityFollow
Huaibo Song, USDA, Agricultural Research ServiceFollow
Wesley Clint Hoffmann, USDA-ARSFollow
Yeyin Shi, University of Nebraska-LincolnFollow
Dongyan Zhang, Anhui UniversityFollow
Guozhong Zhang, Huazhong Agricultural UniversityFollow

ORCID IDs

Jian Zhang

Document Type

Article

Date of this Version

2017

Citation

Remote Sens. 2017, 9, 1054

Comments

© 2017 by the authors.

Open access

doi:10.3390/rs9101054

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.