Next Article in Journal
Placetelling® as a Strategic Tool for Promoting Niche Tourism to Islands: The Case of Cape Verde
Next Article in Special Issue
Stacking Rocks to Transport Water: Folk Aqueduct Bridges of Mallorca and Spanish Colonial California
Previous Article in Journal
Varying Effects of Urban Tree Canopies on Residential Property Values across Neighborhoods
Previous Article in Special Issue
Agroforestry Heritage Systems as Agrobiodiversity Hotspots. The Case of the Mountain Oases of Tunisia
Open AccessArticle

Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method

Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(10), 4332; https://doi.org/10.3390/su12104332
Received: 30 April 2020 / Revised: 22 May 2020 / Accepted: 22 May 2020 / Published: 25 May 2020
The present study used the official 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 different 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. View Full-Text
Keywords: landscape pattern; transition matrix; systematic processes; change detection; Discrete-Time Markov Chains landscape pattern; transition matrix; systematic processes; change detection; Discrete-Time Markov Chains
Show Figures

Figure 1

MDPI and ACS Style

Viana, C.M.; Rocha, J. Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method. Sustainability 2020, 12, 4332.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop