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Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method
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Orientador(es)
Resumo(s)
The present study used the o cial Portuguese land use/land cover (LULC) maps (Carta de
Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict
the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal,
which is experiencing marked landscape changes. Here, we computed the conventional transition
matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018,
providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing
on the dominant signals of change. We also investigated how the LULC is going to move in the
future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC)
model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape
changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja
district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly
di erent between periods, and explicitly revealed that arable land, pastures, and forest were the most
dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period
were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures
(2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results
showed that about 25% of the territory is predicted to experience major LULC changes from arable
land (3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.
Descrição
Palavras-chave
Landscape pattern Transition matrix Systematic processes Change detection Discrete-Time Markov Chains
Contexto Educativo
Citação
Viana, M. C., & Rocha, J. (2020). Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method. Sustainability, 12(10), 4332. https://doi.org/10.3390/su12104332.
Editora
MDPI