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] Drought Monitoring of Spring Maize in the Songnen Plain Using Multi-Source Remote Sensing Data
    Pei, Zhifang
    Fan, Yulong
    Wu, Bin
    ATMOSPHERE, 2023, 14 (11)
  • [22] Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data
    Jiang, Hou
    Yao, Ling
    Lu, Ning
    Qin, Jun
    Liu, Tang
    Liu, Yujun
    Zhou, Chenghu
    ENERGY AND AI, 2022, 10
  • [23] Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data
    He, Chunyang
    Gao, Bin
    Huang, Qingxu
    Ma, Qun
    Dou, Yinyin
    REMOTE SENSING OF ENVIRONMENT, 2017, 193 : 65 - 75
  • [24] Temporal dynamic analysis of a mountain ecosystem based on multi-source and multi-scale remote sensing data
    Ibarrola-Ulzurrun, Edurne
    Marcello, Javier
    Gonzalo-Martin, Consuelo
    Luis Martin-Esquivel, Jose
    ECOSPHERE, 2019, 10 (06):
  • [25] Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment
    Liu, Xinzhu
    Zheng, Change
    Wang, Guangyu
    Zhao, Fengjun
    Tian, Ye
    Li, Hongchen
    FORESTS, 2024, 15 (11):
  • [26] Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data
    Zhu, Wenbin
    Jia, Shaofeng
    Lv, Aifeng
    REMOTE SENSING, 2014, 6 (11): : 10457 - 10482
  • [27] Detecting Photovoltaic Installations in Diverse Landscapes Using Open Multi-Source Remote Sensing Data
    Wang, Jinyue
    Liu, Jing
    Li, Longhui
    REMOTE SENSING, 2022, 14 (24)
  • [28] Urban water extraction based on multi-source remote sensing images
    Fan, Yuancheng
    Zhou, Tinggang
    Li, Chengfan
    EPLWW3S 2011: 2011 INTERNATIONAL CONFERENCE ON ECOLOGICAL PROTECTION OF LAKES-WETLANDS-WATERSHED AND APPLICATION OF 3S TECHNOLOGY, VOL 3, 2011, : 312 - 315
  • [29] Evaluation of Spatiotemporal Changes in Cropland Quantity and Quality with Multi-Source Remote Sensing
    Liu, Han
    Wang, Yu
    Sang, Lingling
    Zhao, Caisheng
    Hu, Tengyun
    Liu, Hongtao
    Zhang, Zheng
    Wang, Shuyu
    Miao, Shuangxi
    Ju, Zhengshan
    LAND, 2023, 12 (09)
  • [30] Coupling coordination analysis of urbanization and eco-environment in Yanqi Basin based on multi-source remote sensing data
    Ariken, Muhadaisi
    Zhang, Fei
    Liu, Kang
    Fang, Chuangling
    Kung, Hsiang-Te
    ECOLOGICAL INDICATORS, 2020, 114