Showing 481 - 499 results of 499 for search '"Panzhihua"', query time: 0.12s Refine Results
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    Developing a system for comprehensive regional Eco-environmental quality assessment in mountainous areas—A case study of Western Sichuan, China by Xiaojie Chen, Xiaojie Chen, Xiaojie Chen, Shengbin Chen, Zhengwei He, Zhengwei He, Dongjian Xue, Dongjian Xue, Guozheng Fang, Kaiwen Pan, Kun Fang

    Published 2022-09-01
    “…After applying this model to the region, the results show that: 1) The ecological environmental quality in the Western Sichuan mountains has improved over the past 10 years. 2) The eco-environment in the study area is generally Good, with small areas receiving a rating of Moderate. 3) The areas considered Better are mainly distributed on Longmen Mountain, Daliang mountain and Qionglai mountain, while the Moderate areas are mainly distributed in the western Chengdu Plain, the Panzhihua urban area, and Shaluli mountain. 4) The areas rated Better earn their rating due to high vegetation coverage, high habitat quality, and low degree of land degradation, such as land desertification and soil erosion. …”
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  4. 484

    Cropping system optimization for drought prevention and disaster reduction with a risk assessment model in Sichuan Province by Yufang Zhang, Huihui Qu, Xiaoguang Yang, Mingtian Wang, Ningsheng Qin, Yujia Zou

    Published 2020-09-01
    “…The results showed that winter wheat-maize-sweet potato (soybean) triple cropping pattern in upland is the best cropping system for the wide valley area of Southwest Sichuan (such as Panzhihua), the mountain area of Southwest Sichuan (such as Xichang), the basin-edged mountain area (such as Guangyuan) and the parallel ridge-valley area of eastern basin (such as Dazhou). …”
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    Geochemical Characteristics and Re-Os isotopic dating of Tongde Graphite Deposit, Sichuan Province by Chao Chen, Decai Kong, Xiaolin Tian, Zhicheng Liu, Yuheng Guo, Deqiang Wu, Zhenzhen Wen, Bo Long, Yi Zheng

    Published 2023-02-01
    “…The Tongde Graphite Deposit in Panzhihua is located in the accretionary zone on the western margin of the Yangtze plate. …”
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  11. 491

    First identification and molecular subtyping of Blastocystis sp. in zoo animals in southwestern China by Lei Deng, Jingxin Yao, Shanyu Chen, Tingmei He, Yijun Chai, Ziyao Zhou, Xiaogang Shi, Haifeng Liu, Zhijun Zhong, Hualin Fu, Guangneng Peng

    Published 2021-01-01
    “…The highest prevalence of Blastocystis sp. was found in Panzhihua Zoo (24.3%), which was significantly higher than that in Chengdu Zoo (6.9%), and Xichang Zoo (2.9%) (P < 0.05). …”
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  12. 492

    Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data by Xiangxiang Zheng, Guojin He, Shanshan Wang, Yi Wang, Guizhou Wang, Zhaoying Yang, Junchuan Yu, Ning Wang

    Published 2021-04-01
    “…The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. …”
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  13. 493

    Research on the spatiotemporal characteristics of RECC in resource-based cities based on the EWM-CPM: A case study of Sichuan Province, China by Xinyue Fan, Bin Liu, Kai Wang, Tingting Feng, Zhongli Zhou

    Published 2023-03-01
    “…From the calculation results of the dynamic evaluation measure model, the level of RECC in PZH (Panzhihua) was stable, and the level of RECC in GA (Guang'an) increased with time, whereas the level of RECC in NC (Nanchong) was poor. (2) In terms of space, the eight resource-based cities showed uneven development in regard to RECC from 2010 to 2019, and the urban advantageous industries were related to the types of resources in the NEES. (3) The path analysis method results indicated that the directional impact of natural resources on RECC was the highest (0.658), followed by the effect of social resources, environmental resources and economic resources. …”
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  14. 494

    Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China by Wanyu Peng, Yugui Wei, Guangsheng Chen, Guofan Lu, Qing Ye, Runping Ding, Peng Hu, Zhenyu Cheng

    Published 2023-11-01
    “…The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region.…”
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