{'fraction_non_alphanumeric': 0.013431013431013432, 'fraction_numerical': 0.002442002442002442, 'mean_word_length': 4.574829931972789, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A observation of predictor of Japanese oak wilt using high spectral sensor

The incidence of Japanese oak wilt (JOW) has been increasing in Japan since the late 1980s. JOW is caused by the ambrosia fungus Raffaelea quercivorus vectored by the ambrosia beetle Platypus quercivorus. Detection of trees killed by JOW is important to prevent new incidence of JOW but is difficult because most of JOW mortality occurs in forests in mountainous areas. Remote sensing with high spatial resolution imagery can be labor saving technology to detect individual trees killed by JOW over wide areas. The detection of JOW area in early stage is important in the prevention of JOW. In the past method, the detection of JOW area was possible in the stage after outbreak, but the detection in the early stage of JOW was impossible because the symptoms of JOW did not appear in the property on the multi-spatial sensor on the satellite. Recently the number of satellite-borne hyper-spectral sensor is increasing and the performance of the sensor is improved in the spatial and the spectral resolution. If the symptoms of JOW appear in the data from hyper-spectral sensor, the detection of JOW in early stage is realized. In this study, the artificial JOW model trees were set up and the spectral property of the model trees were measured from making of the model trees with a spectrometer. We measured the property of the model trees until the dead of model trees and analyzed the property to find the symptoms of JOW in early stage. As a result, we could find a feature on the property as the symptoms of JOW by calculation of spectral differential value.