Sky-ground integrated monitoring method for monitoring pine wood nematode disease
A technology for pine wood nematode disease and sky-ground, which is applied in the direction of instruments, character and pattern recognition, scene recognition, etc., can solve the problem that no application technology system has been formed, the advantages of satellite remote sensing and UAV remote sensing have not been fully utilized, and single technology utilization Level and other issues, to achieve the effect of taking into account efficiency and cost, high efficiency, and fast access to information
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Embodiment 1
[0063] Such as Figure 1-9 Described pine wood nematode disease monitoring air-ground integrated monitoring method, comprises the following steps:
[0064] S1. Select the location of the area. First, extract the diseased and dead trees through drone images, extract the diseased and dead tree patches through satellite images, and conduct on-the-spot marking, positioning and measurement of the dead trees through ground surveys;
[0065] S2. Obtain high-spatial resolution satellite remote sensing data and UAV remote sensing data as data sources, combine ground survey data and forest resources second-class survey data to jointly form a data set for the monitoring area;
[0066] High spatial resolution satellite remote sensing data include 50cm spatial resolution panchromatic band (470-830nm) data and 2m spatial resolution blue, green, red, near-infrared four-band multispectral data.
[0067] The relevant technical parameters of high spatial resolution satellite remote sensing dat...
Embodiment 2
[0158]In this embodiment two, in the pine forest subcompartment, check the forest coverage situation from the satellite remote sensing image, and can find that there are some areas not covered by forest trees in the subcompartment area, such as open space in the forest, wasteland and forest edge bare land. The spectral characteristics are very similar to those of the diseased wood. In order to reduce the interference of these ground features on the extraction of the diseased wood, the second embodiment will further eliminate these non-forest covered areas. Object-oriented and random forest classification methods are used to classify the images of the pine forest subcompartment, and the forest coverage area is distinguished from wasteland, forest edge bare land, etc., so as to eliminate the non-forest areas in the pine forest subcompartment, which is for the accurate extraction of diseased trees. Interference factors; the random forest model is a new type of machine learning alg...
Embodiment 3
[0160] The experimental results of the 2020 general survey of pine wood nematodes in Jindong District of Jinhua City and Yongkang City of Jinhua City were selected using this method.
[0161] Firstly, Jindong District of Jinhua City covers an area of 658.19Km2, and Yongkang City of Jinhua City covers an area of 1049Km2. Both areas use aerial photography to obtain remote sensing images. The video was taken on October 31, 2020. The spatial resolution of aerial images is 0.5 meters, including four bands of red, green, blue and near-infrared. The results of the census are shown in the table below.
[0162]
[0163]
[0164] The second step is to verify the identification results of Jindong District.
[0165] Jindong District selected Jiangdong, Lingxia, Yuandong, and Chisong townships as samples, and verified the census data based on the air-space-ground integration method with the actual number of infected trees harvested in the process of eradicating the region, and ...
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