Ecological restoration evaluation of afforestation in Gudao Oilfield based on multi-source remote sensing data

被引:3
|
作者
Li, Xiuneng [1 ,2 ]
Li, Yongtao [3 ,4 ]
Wang, Hong [5 ]
Qin, Shuhong [1 ]
Wang, Xin [5 ]
Yang, Han [5 ]
Cornelis, Wim [2 ]
机构
[1] Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China
[2] Univ Ghent, Dept Environm, B-9000 Ghent, Belgium
[3] Shandong Acad Forestry Sci, Jinan 250014, Peoples R China
[4] Natl Observat & Res Stn Chinese Forest Ecosyst Yel, Dongying 257000, Peoples R China
[5] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China
基金
中国国家自然科学基金;
关键词
Ecological evaluation; Oilfield ecological restoration; Forest management; RSEI; CHINA; WATER; TEMPERATURE; INDEX; VEGETATION; DIVERSITY; INCREASES; DELTA; SOIL;
D O I
10.1016/j.ecoleng.2023.107107
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The growing petroleum industry poses significant ecological challenges, prompting an increased focus on ecological restoration projects in onshore oilfields. Current efforts focus on revegetation in deforested oilfields, while research remains limited on alternative ecological restoration strategies aimed at establishing new eco-systems in oilfields. This study employed multi-source remote sensing data from 1985 to 2022 to calculate a remote sensing-based ecological index (RSEI) and constructed an integrated forest health index (IFHI), in order to evaluate the ecological restoration effects in the Gudao shelterbelt of Shengli Oilfield in the Yellow River Delta, and investigated the impact of oil extraction by considering forest phenology. The RSEI of the shelterbelt showed an upward trend and reached a Good level of ecological environment quality from 1990 to 2003, but it declined after that, indicating the potential of RSEI to quickly assess ecological restoration effects and guide management at different stages. Comparing the restoration effects of different tree species, a Robinia pseudoacacia L. (RP) and Fraxinus velutina Torr. (FV) mixed forest demonstrated the greatest capacity to improve environ-mental quality, with the most years (25 years) of the Good and Excellent levels and the highest IFHI value (1.52). In contrast, Ulmus pumila L. (UP) and Sophora japonica L. (SJ) were unsuitable for mixed planting for ecological restoration. The study also found that monospecific RP forests within 30 m of oil wells were significantly impacted by oil extraction (P <= 0.05), necessitating tailored forest management. The research aims to serve as a reference for ecological restoration in global onshore oil production areas, particularly in delta regions and sparsely vegetated areas.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] A GRID-BASED PLATFORM FOR DISTRIBUTED MULTI-SOURCE REMOTE SENSING DATA SHARING
    Li Fan
    Zhang Xu
    Deng Guang
    Yong Shan
    Wang Hong-rong
    DCABES 2009: THE 8TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE, PROCEEDINGS, 2009, : 270 - 274
  • [22] Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data
    Tianqi Lu
    Shengbo Chen
    Yuan Tu
    Yan Yu
    Yijing Cao
    Deyang Jiang
    Chinese Geographical Science, 2019, 29 : 192 - 201
  • [23] Analysis of flood inundation in ungauged basins based on multi-source remote sensing data
    Gao, Wei
    Shen, Qiu
    Zhou, Yuehua
    Li, Xin
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2018, 190 (03)
  • [24] Analysis of flood inundation in ungauged basins based on multi-source remote sensing data
    Wei Gao
    Qiu Shen
    Yuehua Zhou
    Xin Li
    Environmental Monitoring and Assessment, 2018, 190
  • [25] Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data
    Lu Tianqi
    Chen Shengbo
    Tu Yuan
    Yu Yan
    Cao Yijing
    Jiang Deyang
    CHINESE GEOGRAPHICAL SCIENCE, 2019, 29 (02) : 192 - 201
  • [26] Formation and Hazard Analysis of Landslide Damming Based on Multi-Source Remote Sensing Data
    Shi, Wei
    Chen, Guan
    Meng, Xingmin
    Bian, Shiqiang
    Jin, Jiacheng
    Wu, Jie
    Huang, Fengchun
    Chong, Yan
    REMOTE SENSING, 2023, 15 (19)
  • [27] EFFECTIVE CLASSIFICATION OF LOCAL CLIMATE ZONES BASED ON MULTI-SOURCE REMOTE SENSING DATA
    Jing, Hao
    Feng, Yingchao
    Zhang, Wenkai
    Zhang, Yue
    Wang, Siyue
    Fu, Kun
    Chen, Kaiqiang
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 2666 - 2669
  • [28] Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data
    LU Tianqi
    CHEN Shengbo
    TU Yuan
    YU Yan
    CAO Yijing
    JIANG Deyang
    Chinese Geographical Science , 2019, (02) : 192 - 201
  • [29] Remote Sensing Monitoring of Grasslands Based on Adaptive Feature Fusion with Multi-Source Data
    Wang, Weitao
    Ma, Qin
    Huang, Jianxi
    Feng, Quanlong
    Zhao, Yuanyuan
    Guo, Hao
    Chen, Boan
    Li, Chenxi
    Zhang, Yuxin
    REMOTE SENSING, 2022, 14 (03)
  • [30] Landslide Hazard Assessment of Mountain Railways Based on Multi-source Remote Sensing Data
    Xu, Qiao
    Yu, Fei
    Yu, Shaohuai
    Journal of Railway Engineering Society, 2021, 38 (11): : 8 - 14