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Ecological Quality Response to Multi-Scenario Land-Use Changes in the Heihe River Basin

Author

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  • Shengtang Wang

    (Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yingchun Ge

    (Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China)

Abstract

To investigate the spatial-temporal effects of land-use changes on ecological quality and future trends, an integrated framework combining the Dyna-CLUE model and the remote sensing ecological index (RSEI) was developed. Land-use changes from 2000 to 2035 were simulated and projected under the current trend scenario (CTS), economic development scenario (EDS) and ecological protection scenario (EPS) in the Heihe River Basin, while the RSEI was predicted using the elastic net regression (machine learning method); finally, the predicted results were synthesized and analyzed. The results showed that forest, grassland and water were positively correlated with ecological quality, with the green space coverage under the CTS, EPS and EDS accounting for 34.15%, 70.65% and 34.72% of the total transferred land area, respectively. The increase in the area of build-up land and unutilized land was detrimental to ecological quality, with the area of building land in the EDS being 1.75 times larger than in the year 2000. The EDS contributes to the sustainable development of the upstream area and the EPS is more conducive to the midstream and downstream areas by limiting the expansion of build-up land and by developing unutilized land in a limited way to increase the area of green space after reconciling economic conditions. Projection results promote the rational allocation of various land-use types in the future (semi) arid region, such as artificial forestation, unutilized land development and restriction of urban expansion, and also lay the foundation for the formulation of policies such as water allocation and ecological protection to facilitate the sustainable development of regional society, economy and ecology.

Suggested Citation

  • Shengtang Wang & Yingchun Ge, 2022. "Ecological Quality Response to Multi-Scenario Land-Use Changes in the Heihe River Basin," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2716-:d:758743
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    References listed on IDEAS

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    1. Mansour, Shawky & Al-Belushi, Mohammed & Al-Awadhi, Talal, 2020. "Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques," Land Use Policy, Elsevier, vol. 91(C).
    2. Mauro Raposo & Ricardo Quinto-Canas & Ana Cano-Ortiz & Giovanni Spampinato & Carlos Pinto Gomes, 2020. "Originalities of Willow of Salix atrocinerea Brot. in Mediterranean Europe," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    3. Karina Winkler & Richard Fuchs & Mark Rounsevell & Martin Herold, 2021. "Global land use changes are four times greater than previously estimated," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    6. Shirmohammadi, Bagher & Malekian, Arash & Salajegheh, Ali & Taheri, Bahram & Azarnivand, Hossein & Malek, Ziga & Verburg, Peter H., 2020. "Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran," Land Use Policy, Elsevier, vol. 90(C).
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