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Identifying key landscape pattern indices influencing the NPP: A case study of the upper and middle reaches of the Yellow River

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  • Xue, Shaobo
  • Ma, Bo
  • Wang, Chenguang
  • Li, Zhanbin

Abstract

Human activities and climate change directly affect the composition, structure, and function of ecosystems and, consequently, their net primary productivity (NPP). In this study, we explored the response relationships between landscape pattern indices and NPP changes in three major vegetation types (forest, grassland, and shrubland) in the upper and middle reaches of the Yellow River Basin using a random forest model. The results showed that landscape fragmentation increased, leading to higher landscape heterogeneity and edge effects. Patch shapes became more irregular, and spatial distribution became more dispersed. From 2000 to 2015, both vegetation types and NPP showed significant spatial heterogeneity in the study area. The number of patches (NP), largest patch index (LPI), and percent-like adjacency (PLADJ) metrics were used to determine the core landscape characteristics to assess the NPP changes in forest, shrubland, and grassland, respectively. This study provides a basis for understanding the relationships among landscape patterns, vegetation types, and NPP and serves as a reference for developing NPP predictive models in the Loess Plateau region.

Suggested Citation

  • Xue, Shaobo & Ma, Bo & Wang, Chenguang & Li, Zhanbin, 2023. "Identifying key landscape pattern indices influencing the NPP: A case study of the upper and middle reaches of the Yellow River," Ecological Modelling, Elsevier, vol. 484(C).
    • Handle: RePEc:eee:ecomod:v:484:y:2023:i:c:s0304380023001886
    DOI: 10.1016/j.ecolmodel.2023.110457
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    References listed on IDEAS

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    1. Xiaoming Feng & Bojie Fu & Shilong Piao & Shuai Wang & Philippe Ciais & Zhenzhong Zeng & Yihe Lü & Yuan Zeng & Yue Li & Xiaohui Jiang & Bingfang Wu, 2016. "Revegetation in China’s Loess Plateau is approaching sustainable water resource limits," Nature Climate Change, Nature, vol. 6(11), pages 1019-1022, November.
    2. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    3. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    4. Chi Chen & Taejin Park & Xuhui Wang & Shilong Piao & Baodong Xu & Rajiv K. Chaturvedi & Richard Fuchs & Victor Brovkin & Philippe Ciais & Rasmus Fensholt & Hans Tømmervik & Govindasamy Bala & Zaichun , 2019. "China and India lead in greening of the world through land-use management," Nature Sustainability, Nature, vol. 2(2), pages 122-129, February.
    5. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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