AUTHOR=Zhou Weifeng , Hu Huijuan , Cheng Tianfei 

TITLE=Multi-nighttime-light data comparison analysis based on image quality values and lit fishing vessel identification effect

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1174894

DOI=10.3389/fenvs.2023.1174894

ISSN=2296-665X

ABSTRACT=Remote sensing technology allows for the monitoring of light fishing vessels at sea from the air at night, which supports the sustainable management of fisheries. To investigate the potential of different nighttime light remote sensing data for light fishing vessel identification and applications, we used the fuzzy evaluation method to quantitatively assess images in terms of their radiometric and geometric quality, and Otsu’s method to compare the effects of light fishing vessel identification. Three kinds of nighttime lighting data from DMSP/OLS, VIIRS/DNB, and Luojia1-01 were analyzed, compared, and application pointers were constructed. The results are as follows. ①In the image radiation quality evaluation, the information entropy, clarity, and noise performance of the LJ1-01 image are higher than those of the DMSP/OLS and VIIRS/DNB images, where the information entropy value of the LJ1-01 image is nearly 10 times that of VIIRS/DNB and 23 times that of DMSP/OLS. The average gradient value is 14 times that of the image from VIIRS/DNB and 1600 times that of DMSP/ OLS, while its noise is only about 2/3 of the VIIRS/DNB image and 1/3 of the DMSP/OLS image. In the geometric quality assessment, the geometric positioning accuracy and ground sampling accuracy of the VIIRS/DNB image is the best among the three images, with a relative difference percentage of 100.1%, and the LJ1-01 and DMSP/OLS images are relatively lower, at 96.9% and 92.3%, respectively. ② Using Northwest Pacific squid fishing vessels as an example, DMSP/OLS identified the location of light fishing vessels, and VIIRS/DNB estimated the number of light fishing vessels with larger gaps between them relatively accurately. For the VIIRS/DNB and LJ1-01 images with a 1′40″×1′40″ span in the same spatiotemporal range using the same batch of pelagic squid fishing vessels, LJ1-01 extracted 18 fishing vessels. VIIRS/DNB extracted 15, indicating that LJ1-01 can distinguish multiple fishing vessels in the lighted overlapping area, thus accurately identifying the fishing vessel number. This study can provide a reference for sensor/image selection for nighttime light remote sensing fishery applications and a basis for more refined fishing vessel identification, extraction, and monitoring.