This paper focuses on the influence of Digital Elevation Models on the landslides susceptibility assessment in agricultural terraces, using Logistic Regression statistical model. This study was performed in a watershed located at Carvalhas Estate in Douro Valley, using an inventory of 109 landslides. To analyse the influence of the digital elevation model (DEM) resolution we used three DEMs, (A), (B) and (C). The DEMs (A) and (B) were directly obtained by processing aerial images and extracting different resolutions, 1 and 5 meters, respectively. The DEM (C), with 5m resolution, was processed with Topo to Raster interpolation method, using as input data contour lines of 10 m interval, elevation points and hydrography. The Logistic Regression was performed using two models which are distinguished by the independent variables alteration. At model 1 was used the slope, curvature, raiser slope, riser height, contributing areas and topographic wetness index. In scenario 2 we decide remove the independent variables related with the terrace geometry, riser slope and riser height. The results seems to indicate that there is no significant influence of different resolutions of Digital Elevation Models in susceptibility modelling at this small scale and using statistical methods. The independent variables riser slope and riser height provide information of the terraces geometry and the construction techniques that enter the modelling process with more detailed information.

The construction of earthen embankment terraces in the Douro Region raises a set of problems related to hydrological processes. The main objective of this study is the evaluation of the spatial variation of electrical resistivity in agriculture terraces at Douro valley (Portugal). To achieve this objective, two variables are analysed, the soil electrical resistivity and the flow direction algorithm. In a field survey we recorded 13 electrical resistivity profiles. The contributing area was calculated with the algorithms D∞ (Deterministic Infinity Flow) and MFD (Multiple Flow Direction) and the results are the base of the internal runoff modelling, both supported by the digital elevation model with a spatial resolution of 1m2. A correlation between the spatial variation of the soil electrical resistivity represented by the standard deviation of the electrical resistivity for each profile and the average value of the contributing area coincident with each profile was established. The electrical resistivity standard deviation seems to be moderately well correlated according to the D∞ algorithm at about 1m of depth, and it has a good correlation at 1,5m to 2m of depth with the MFD algorithm. Taken together, the results show a significant positive statistical correlation between the electrical resistivity standard deviation and the contributing areas (MFD and D∞) depending on the soil depth.

O artigo efetua a avaliação da suscetibilidade a deslizamentos, em terraços com talude em terra, no vale do Douro. São aplicados modelos matemáticos de base física: SHAllow Landslide STABility model e Stability INdex MAPping. Os modelos aplicados combinam os conceitos de talude infinito e, fluxo hidrológico em estado estacionário. Ambos os modelos, de suscetibilidade, utilizam as seguintes propriedades do solo: coesão, ângulo de atrito, peso específico do solo e espessura do solo. O SINMAP aplica ainda a coesão das raízes. Uma das principais diferenças entre os modelos refere-se à definição das áreas contributivas. O SHALSTAB utiliza o fluxo de direções múltiplas (MFD) e o SINMAP utiliza o fluxo de direções infinitas (D∞). A validação dos resultados foi realizada com base no inventário de deslizamentos, seguindo o método da matriz de contingência. Dos resultados obtidos, o SHALSTAB classifica corretamente 77% dos deslizamentos e o SINMAP 90% de deslizamentos. Contrariamente, o índice de falsos positivos do SHALSTAB é significativamente mais elevado (67%) enquanto o SINMAP apresenta (83%). No que se refere à relação entre os Índices de Verdadeiros Positivos e de Falsos Positivos o SHALSTAB apresenta um melhor balanço entre a predição dos deslizamentos e a dimensão das áreas definidas como instáveis com 1,14, relativamente a 1,09 apresentado pelo SINMAP.