Modeling the Probability of Surface Artificialization in Zêzere Watershed (Portugal) Using Environmental Data

Abstract

Abstract:The land use and land cover (LUC) of the Zêzere watershed (Portugal) have undergone major changes in recent decades, with the increase of artificial surfaces. This trend is quantified in some studies, but the probability of the increase of this type of LUC, nor the places where the next transitions or land use/cover changes (LUCC) for artificial surfaces will have high probability of occurrence has not yet been assessed. This research presents an evaluation of these two aspects, by means of bivariate statistical models (fuzzy logic and information value) and environmental data. The artificialization probability by sectors within the same watershed is also evaluated, to further understand which areas will require greater attention, taking into account the environmental conditions favorable to the occurrence of this process and bearing in mind the conditions under which this process took place in the past. The results obtained using these models were assessed independently, through curves of success, noting that the modeling through the fuzzy gamma presents slightly better efficiency in determining the probability of artificialization surfaces in the study area. The area with the highest probability of artificialization is mostly located in the SW of this watershed, but high probabilities are also present in the upstream sector, being those areas that require further preventive measures once they have influence on the water quality and quantity in the main reservoirs of this watershed.Keywords: LUC; LUCC; artificial surfaces; fuzzy logic; information value; spatial analysis; water quality The Artificialization of Surfaces and Their AssessmentUrban growth has been evaluated in different territories around the world, considering the spatiotemporal aspect, as well as the factors that induced it [1][2][3][4][5][6]. Other studies have emerged in the context of land use and land cover changes (LUCC) to assess where the soils are being converted into artificial surfaces [7][8][9][10][11], especially when there is loss of soils essential for the development of agricultural practices, or the conversion of forest areas. These processes have multiple socio-economic and environmental impacts [12][13][14][15][16]. In this sense, the determination of driving forces (socio-economic, environmental, or other) that are the cause of LUCC is fundamental to understand the factors that induced them and for the creation of measures aimed at the sustainability of land use [15,17]. Some studies demonstrating the links between driving forces and LUCC have appeared recently, also quantifying how much each factor represents to the observed changes [13,[18][19][20][21][22].

Understanding Driving Forces and Implications Associated with the Land Use and Land Cover Changes in Portugal

“…The latter is marked essentially by the urban growth, that characterizes the complex urban system of the Iberian Peninsula [86,87]. These new land cover types exert great pressures on the territory's ecosystems and they can cause problems in their sustainability, in particular in the consequent degradation of more fertile soils and other natural resources (e.g., drinking water) [36,57].…”

Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

“…The importance of each independent variable in the landslide susceptibility assessment was also determined, so that the spatial influence of each predisposition factor in the models can be understood. The accountability (A I ) and reliability (R I ) indexes have been used in different contexts to assess the importance of each independent variable in the bivariate statistical methods (e.g., Blahut et al, 2010;Meneses et al, 2016). A I explains how different classes of predisposition factors are relevant in the analysis because they contain the landslide area, while R I depends on the average density of the landslide area in the predisposing factor classes that are more relevant to the development of this process.…”

“…All the predisposing factors and landslide inventory were converted to raster (resolution 10 m) to assess the landslide susceptibility. The selection of the predisposing factors' cell size was based on several geoinformation conversion tests in the Zêzere watershed previously performed by Meneses et al (2016Meneses et al ( , 2018b.…”

An integrated approach for the assessment of the Aedes aegypti and Aedes albopictus global spatial distribution, and determination of the zones susceptible to the development of Zika virus

“…using the Fuzzy Logic or Information Value method. The results are validated at the end of the process through the intersection of the cases used to model it or with a part of the dependent variable (Meneses et al, 2016). Other methodologies use some of the dependent variable for modeling and the remaining to validate the results obtained (random partition).…”