Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (5): 789-798.doi: 10.12082/dqxxkx.2019.180418

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Object-oriented Rapid Estimation of Rice Acreage from UAV Imagery

Fangming WU(), Miao ZHANG, Bingfang WU*()   

  1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2018-08-29 Revised:2019-02-20 Online:2019-05-25 Published:2019-05-25
  • Contact: Bingfang WU E-mail:wufm@radi.ac.cn;wubf@radi.ac.cn
  • Supported by:
    Science and Technology Service Network Initiative (STS), No.KFJ-STS-ZDTP-009;National Natural Science Foundation of China, No. 41561144013, 41861144019, 41701496.

Abstract:

The methodology of combining sampling-based ground survey and satellite imagery classification has been widely used in estimating crop acreage on large scales. Use of unmanned aerial vehicle (UAV) imagery has a series of merits including low cost, high efficiency, and high resolution, which make it possible to quickly monitor the agricultural conditions over a specific area. With a research focus on rice sample plots, this study used a portable UAV Mavic Pro to obtain aerial imagery. The UAV imagery were preprocessed to generate an orthophoto with a resolution of 3.95 cm/pix. By adopting the object-oriented classification philosophy, visual assessment, and the Estimation of Scale Parameter (ESP) tool, the optimal segmentation scale was determined to be 300. The support vector machine, random forest, and nearest neighborhood classifiers were employed and contrasted for imagery classification and the extraction of rice acreage; visual interpretation was used for assessing the accuracy of the classification results. The best automatic classification method turned out to be nearest neighborhood classification, with its user accuracy of rice being 95% and the area consistency accuracy 99%. The findings show that use of UAV imagery and automatic classification can quickly acquire high-resolution imagery and extract rice acreage in rice growing areas on plains. Moreover, high-resolution UAV imagery can be used as ground truth data when cropland is in shadow. The proposed approach helps provide validation samples for estimating rice acreage and production on large scales.

Key words: UAV imagery, rice, object-oriented segmentation, nearest neighbor supervised classification, planted area