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Your search keyword '"Tang, Bo-Hui"' showing total 281 results
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281 results on '"Tang, Bo-Hui"'

23. Identification of forest fire points under clear sky conditions with Himawari-8 satellite data.

Author

Zhou, Wei, Tang, Bo-Hui, He, Zhi-Wei, Huang, Liang, and Chen, Junyi

Subjects
*FOREST fires, *FOREST fire prevention & control, *RANDOM forest algorithms, *FOREST monitoring, *WILDFIRE prevention, *NATURAL disasters, *ZENITH distance
Abstract

Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Trend Classification of InSAR Displacement Time Series Using SAE–CNN.

Author

Li, Menghua, Wu, Hanfei, Yang, Mengshi, Huang, Cheng, and Tang, Bo-Hui

Subjects
GROUND motion, SYNTHETIC aperture radar, TIME series analysis, TIME management
Abstract

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention. [ABSTRACT FROM AUTHOR]

Published
2024
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29. A Nonlinear Split-Window Algorithm for Retrieving Land Surface Temperatures From Fengyun-4B Thermal Infrared Data

Author

Zhao, Junli, Tang, Bo-Hui, and Sima, Ouyang

Abstract

This article proposes a combination method of nonlinear split-window (NSW) algorithm and temperature and emissivity separation (TES) algorithm to estimate land surface temperature (LST) from the remotely sensed data observed by the Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4B (FY-4B), China’s second-generation meteorological geostationary satellite. The atmospheric radiation transfer model MODTRAN5.2 is used to simulate the AGRI thermal infrared (TIR) channel satellite observations in ten different viewing zenith angles (VZAs) from 0° to 70°. The optimal thermal channel combination and coefficients of the NSW algorithm are determined using a statistical regression method according to the grouping of the mean emissivity, the atmospheric water vapor content (WVC), and the LST. ERA5 reanalysis data provide atmospheric profiles for atmospheric correction, and then, the land surface emissivity (LSE) could be estimated according to the TES algorithm. The combination of Channel-12 (centered at $8.55~\mu \text{m}$ ) and Channel-14 (centered at $12.00~\mu \text{m}$ ) or the combination of Channel-13 (centered at $10.80~\mu \text{m}$ ) and Channel-14 (centered at $12.00~\mu \text{m}$ ) depends on different groups and VZAs. The statistical regression analysis showed that the root-mean-square error (RMSE) between the simulated and estimated LST is less than 0.7 and 1.8 K with the determined emissivity under VZA = 0° and VZA = 60°, respectively. Compared with the MODIS LST products (MYD11A1), the retrieved LST image has a similar spatial distribution, with the RMSE of 1.71 K.

Published
2024
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33. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications

Author

Li, Zhao‐Liang, primary, Wu, Hua, additional, Duan, Si‐Bo, additional, Zhao, Wei, additional, Ren, Huazhong, additional, Liu, Xiangyang, additional, Leng, Pei, additional, Tang, Ronglin, additional, Ye, Xin, additional, Zhu, Jinshun, additional, Sun, Yingwei, additional, Si, Menglin, additional, Liu, Meng, additional, Li, Jiahao, additional, Zhang, Xia, additional, Shang, Guofei, additional, Tang, Bo‐Hui, additional, Yan, Guangjian, additional, and Zhou, Chenghu, additional

Published
2023
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40. Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

Author

Cheng, Lijia, Tang, Bo-Hui, He, Zhiwei, Fu, Zhitao, and Li, Menghua

Subjects
General Earth and Planetary Sciences, Software, Computer Science Applications
Abstract

The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation.

Published
2023
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