Analysis of LULC Change Dynamics That Have Occurred in Tuscany (Italy) Since 2007

Lorenzo Arcidiaco and Manuela Corongiu ()
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Lorenzo Arcidiaco: Institute of BioEconomy, National Research Council (CNR-IBE), CNR Research Area—Building D, Via Madonna Del Piano, 10, 50019 Firenze, Italy
Manuela Corongiu: Environmental Monitoring and Modeling Laboratory for the Sustainable Development (Lamma Consortium), c/o CNR Research Area—Building D, Via Madonna Del Piano, 10, 50019 Firenze, Italy

Land, 2025, vol. 14, issue 3, 1-41

Abstract: The dynamics of Land Use/Land Cover changes are crucial to environmental sustainability, socio-economic development, and spatial planning. These changes stem from complex interactions between human activities, natural processes, and policies. In recent decades, LULC transformations have been linked to global challenges such as biodiversity loss, climate change, and resource degradation. Key drivers include urban sprawl, agricultural expansion and abandonment, and deforestation, emphasizing the need for effective frameworks to monitor and assess their impacts. This study investigates Land Use/Land Cover (LULC) changes in Tuscany (Italy) over the period from 2007 to 2019. To achieve this, statistical analyses were conducted to quantify variations in LULC across different classes and administrative territories represented by provincial local authorities. Specifically, data spanning five temporal intervals (2007, 2010, 2013, 2016, and 2019) enabled a comprehensive comparative analysis of spatial persistence in LULC patterns. Changes were assessed using a statistical approach based on Odds Ratios (OR). Additionally, Generalized Linear Models (GLMs) at the provincial level were employed to facilitate one-to-many provincial comparisons and to evaluate the statistical significance of observed LULC changes. The analysis revealed that certain classes exhibit a greater susceptibility to changes compared to others. Specifically, the classes categorized under ’Artificial Surfaces’ (LC_100) were, on average, 6.7 times more likely to undergo changes than those classified as ’Agricultural Areas’ (LC_200) and 11 times more likely than those under ’Forest and Semi-natural Areas’ (LC_300). Over time, the areas classified as artificial territories have exhibited a progressively decreasing probability of change. Notably, during the first update period (2007–2010), these areas were 3.5 times more susceptible to change compared to the most recent update period (2016–2019). An additional significant finding emerged from the statistical comparison of LULC changes across administrative regions governed by different authorities (Provinces). These findings underscore the potential of using administrative indicators and morphological parameters to analyze LULC change trends. The proposed approach provides a robust framework for interpreting territorial resilience and informing spatial planning strategies effectively.

Keywords: land management; contingence table; odds ratio; landscape; machine learning; likelihood; Tuscany region; land cover/land use (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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