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    Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

    Published 2024-10-01
    “…This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. …”
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  5. 85

    Landslide susceptibility evaluation and determination of critical influencing factors in eastern Sichuan mountainous area, China by Lin Zhang, Zhengxi Guo, Shi Qi, Tianheng Zhao, Bingchen Wu, Peng Li

    Published 2024-12-01
    “…The results shown that Random Forest Model proves to be the most accurate (95.1 %) in assessing the spatial distribution of shallow landslides susceptibility, followed by the Artificial Neural Network model (78.6 %), the Support Vector Machine model (69.8 %), the Generalized additive model (68.1 %) and the Logistic Regression model (67.6 %).The area with high susceptible landslide possibility was 25.3 km2 occupying 14.8 % of the study region, it is mainly distributed in the west of Tianchi Lake, southeast of Huaying City and west of the study area, along with Xiangyu Railway. …”
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    Performance of invariants of gravity gradient tensor in matching navigation: A case study in South China Sea by Xiaoyun Wan, Ming Li, Panpan Chen, Faisal Hussain

    Published 2025-05-01
    “…However, using gravity gradient invariants in existing research is seldom a concern. The gravity gradient tensor has three invariants, named as I1, I2 and I3. …”
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    A study on life assessment of metro cowcatcher based on multi-axis vibration environment reconstruction by ZHENG Yuhao, WU Xingwen, LIU Yang, CHI Maoru, LIANG Shulin

    Published 2023-11-01
    “…It was found that the resonance fatigue of the structure was mainly caused by the rail corrugation with the frequency of 93 Hz on the line. …”
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    Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data by Wenwu Zhou, Wenwu Zhou, Qingtai Shu, Cuifen Xia, Li Xu, Qin Xiang, Lianjin Fu, Zhengdao Yang, Shuwei Wang

    Published 2025-08-01
    “…Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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