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Remote Sens. 2017, 9(3), 239; doi:10.3390/rs9030239

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

1
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, 20 Datun Road, Chaoyang, Beijing 100101, China
2
USDA-Agricultural Research Service, Aerial Application Technology Research Unit, 3103 F & B Road, College Station, TX 77845, USA
3
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
4
Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Guijun Yang, Zhenhong Li, James Campbell and Prasad S. Thenkabail
Received: 30 December 2016 / Revised: 19 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
View Full-Text   |   Download PDF [8033 KB, uploaded 4 March 2017]   |  

Abstract

Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively. View Full-Text
Keywords: aerial imagery; crop mapping; consumer-grade cameras; crop height; object-based classification aerial imagery; crop mapping; consumer-grade cameras; crop height; object-based classification
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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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MDPI and ACS Style

Wu, M.; Yang, C.; Song, X.; Hoffmann, W.C.; Huang, W.; Niu, Z.; Wang, C.; Li, W. Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping. Remote Sens. 2017, 9, 239.

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