A review of machine learning and big data applications in addressing ecosystem service research gaps
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DOI: 10.1016/j.ecoser.2022.101478
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Cited by:
- Behnoosh Abbasnezhad & Jesse B. Abrams & Jeffrey Hepinstall-Cymerman, 2023. "Incorporating Social and Policy Drivers into Land-Use and Land-Cover Projection," Sustainability, MDPI, vol. 15(19), pages 1-18, September.
- Schirpke, Uta & Ghermandi, Andrea & Sinclair, Michael & Van Berkel, Derek & Fox, Nathan & Vargas, Leonardo & Willemen, Louise, 2023. "Emerging technologies for assessing ecosystem services: A synthesis of opportunities and challenges," Ecosystem Services, Elsevier, vol. 63(C).
- Linlin Wu & Fenglei Fan, 2024. "A Parameter Optimized Method for InVEST Model in Sub-Pixel Scale Integrating Machine Learning Algorithm and Vegetation–Impervious Surface–Soil Model," Land, MDPI, vol. 13(11), pages 1-21, November.
- Nápoles-Vértiz, Sonia & Caro-Borrero, Angela, 2024. "Conceptual diversity and application of ecosystem services and disservices: A systematic review," Ecosystem Services, Elsevier, vol. 67(C).
- Daniel Rozas-Vásquez & Marcin Spyra & Felipe Jorquera & Sebastián Molina & Nica Claudia Caló, 2022. "Ecosystem Services Supply from Peri-Urban Landscapes and Their Contribution to the Sustainable Development Goals: A Global Perspective," Land, MDPI, vol. 11(11), pages 1-18, November.
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Keywords
Ecosystem services; Machine learning; Big data; Nature’s contribution to people; Modeling; Socio-ecological systems; Uncertainty;All these keywords.
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