Scientia Agricultura Sinica ›› 2017, Vol. 50 ›› Issue (15): 2983-2992.doi: 10.3864/j.issn.0578-1752.2017.15.012

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Evaluation of Maize Waterlogging Disaster Using UAV LiDAR Data

GAN PING1,2,3,4, DONG YanSheng2,3,4, SUN Lin1, YANG GuiJun2,3,4, LI ZhenHai2,3,4, YANG Fan2,3,4, WANG LiZhi2,3,4, WANG JianWen1,2,3,4   

  1. 1Geomatics College, Shandong University of Science and Technology, Qingdao 266590, Shandong; 2National Engineering Research Center for Information Technology in Agriculture, Beijing 100097; 3Key Laboratory of Agricultural Information Technology, Ministry of Agriculture, Beijing 100097; 4Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097
  • Received:2016-12-20 Online:2017-08-01 Published:2017-08-01

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Abstract: 【Objective】Unmanned aerial vehicle (UAV) remote sensing technology is a hot research topic in the remote sensing sector, which is also one of the forces driving the development of modern agriculture. The objective of this study is to quickly and precisely measure the area of maize waterlogging disaster and evaluate disaster levels by analyzing maize canopy height derived from UAV LiDAR point cloud data. Thus it can provide a guideline for disaster prevention and mitigation, high and stable yield, agricultural insurance claims, etc. The aim of this study is to expand the application of UAV LiDAR data in agriculture, and provide a guarantee for agriculture field to quickly and effectively master agricultural information.【Method】The experiment was carried out and the UAV LiDAR data were obtained in Changping District, Beijing, where suffered a heavy rainstorm which led to a large-scale maize waterlogging on July 19-20, 2016. LiDAR point cloud data were classified and extracted, and canopy height of maize was obtained by LiDAR point cloud data from canopy height model (CHM). A double threshold partition strategy based on the normal statistics theory was adopted to determine the thresholds and a remote sensing monitoring model for maize waterlogging was built by analyzing the differences of canopy heights to evaluate the disaster levels of the maize waterlogging. Finally, the accurate assessment of the model was conducted by comparing the in-field measured data with predicted results from the built model.【Result】(1) After the occurrence of waterlogging of maize, there was a significant difference of maize growth between pre and post the disaster, and maize height showed the most obvious difference after the disaster. The maize canopy heights of final severe waterlogging, medium waterlogging, and the slight waterlogging were 0.30-0.84 m, 0.84-1.70 m, and above 1.70 m, respectively. (2) The confusion matrix analysis on the results estimated using the airborne LiDAR data was performed via ground survey samples; the overall classification accuracy of waterlogging degree reached 72.15%, and the Kappa coefficient was 0.44. (3) In general, remote sensing mapping was consistent with the monitoring data from the digital images.【Conclusion】The maize canopy height inversion can be achieved by UAV LiDAR data, and the waterlogging levels can be effectively reflected by the differences in maize plant heights. UAV LiDAR data can measure the area of maize waterlogging and evaluate disaster levels at regional scale, providing a convenient and efficient way to acquire the disaster information.

Key words: maize, waterlogging, evaluation of disaster level, unmanned aerial vehicle (UAV) LiDAR, canopy height

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