Tropical Geography ›› 2019, Vol. 39 ›› Issue (4): 482-491.doi: 10.13284/j.cnki.rddl.003153

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Identification and Monitoring of Blooming Mikania micrantha Outbreak Points Based on UAV Remote Sensing

Sun Zhongyu1,Jing Wenlong1,Qiao Xi2 and Yang Long1   

  1. (1. Guangdong Provincial Public Laboratory of Geospatial Information Technology and Application//Guangzhou Institute of Geography, Guangzhou 510070, China; 2. Shenzhen Institute of Agricultural Genomics, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China)
  • Online:2019-07-10 Published:2019-07-10

Abstract:

The identification and monitoring of outbreak points on a local scale is a challenge in the study of Mikania micrantha invasion. In this study, orthophoto images of the research area were acquired by using a red, green, and blue (RGB) camera mounted on an unmanned aerial vehicle (UAV). Three methods, including band math, image segmentation, and deep learning, were tested in order to identify the blooming point of Mikania micrantha. Results showed that high-resolution RGB mosaic images could be directly used to visually identify Mikania micrantha outbreak points. Neither the over-green index (EGI), normalized over-green index (NEGI), blue-green differential index (BGDI), green-red differential index (GRDI), normalized green-red differential index (NGRDI), nor Plant Pigment Ratio index (PPR) could separate Mikania micrantha from its host plants. Though it should be noted that PPR could provide parameter support for multi-scale segmentation. Object-oriented multi-scale segmentation was able to automatically identify the eruption point of Mikania micrantha, though the area of eruption was underestimated. The automatic recognition method based on deep learning (Deeplab V3+) was also able to accurately identify the eruption point and area of Mikania micrantha. The average intersection ratio (mIoU) of the test set was 78.46% and the pixel accuracy was 88.62%. UAV remote sensing data provides a basis for the study of the Mikania micrantha diffusion mechanism on a local scale, as well as strong support for the monitoring, early warning, and precise control of Mikania micrantha invasions. Despite our advances, challenges still exist in system integration, sensor price, aviation control, and discipline interaction for the applications of a UAV remote sensing system in invasion ecology. Recognition algorithm, data quality, and phenological period are three key factors for the automatic identification accuracy of invasive plants. As such, the integration of information from multi-sensors could enhance identification precision. Building a database that includes spectrum, temperature, and height information of invasive plants is helpful for the automatic identification and quantification of these.

Key words: machine learning, deep learning, automatic identification, ecological monitoring, UAV remote sensing, Mikania micrantha