Land Use and Land Cover Maps for Stream Water Quality Assessment in Spatial Buffers: A Systematic Review of Recent Trends (2020–2024)

Giancarlo Alciaturi and Artur Gil ()
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Giancarlo Alciaturi: Programa de Doctorado en Geografía, Facultad de Geografía e Historia, Universidad Complutense de Madrid, 28040 Madrid, Spain
Artur Gil: IVAR—Research Institute for Volcanology and Risk Assessment, University of the Azores, 9500-321 Ponta Delgada, Portugal

Land, 2025, vol. 14, issue 9, 1-33

Abstract: Assessing the impact of land use and land cover (LULC) on water quality (WQ) is central to land-based environmental research. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study analyses recent trends using LULC maps to assess stream WQ within buffers, focusing on papers published between 2020 and 2024. It identifies relevant remote sensing practices for LULC mapping, landscape metrics, WQ physicochemical parameters, statistical techniques for correlating LULC and WQ, and conventions for configuring buffers. Materials include Scopus, Web of Science, and Atlas.ti, which support both qualitative data analysis and Conversational Artificial Intelligence (CAI) tasks via its integration with OpenAI’s large language models. The methodology highlights creating a bibliographic database, coding, CAI, and validating prompts. Official maps and visual or digital interpretations of optical imagery provided inputs for LULC. Classifiers from earlier generations have shaped LULC cartography. The most employed WQ parameters were phosphorus, total nitrogen, and pH. The three most referenced landscape metrics were the Largest Patch Index, Patch Density, and Landscape Shape Index. The literature mainly relied on Redundancy Analysis, Principal Component Analysis, and alternative correlation approaches. Buffer configurations varied in size. CAI facilitated an agile systematic review; however, it encountered challenges related to a phenomenon known as hallucination, which hampers its optimal performance.

Keywords: conversational artificial intelligence; geographic information technologies; landscape ecology; natural language processing; watershed management (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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