On 20 February 2010, an extreme rainfall episode occurred on Madeira Island, which caused an exceptionally strong flash flood and several soil slip-debris flows, producing 45 confirmed deaths and 6 persons declared missing, as well as extensive material damages. In order to understand and quantify the importance of landsliding in routing sediment through mountainous drainage, such as Madeira Island's landscape, it was essential to perform extensive landslide analysis. This study describes the methodology used to semi-automatically detect the landslides, produce the landslide inventory maps and estimate the sediment volume produced during this particular event which ranged from 217 000 m³ to 344 000 m³ and 605 000 m³ to 984 000 m³ for the Funchal and Ribeira Brava basins, respectively. These results contributed to the design and implementation of measures to prevent damages caused by landslides in Madeira Island

Abstract.A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km 2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

The extreme rainfall event of February 20 th , 2010 triggered a series of landslides and alluvium episodes with extensive life and material damages. The use of automatic detection of the landslides over satellite imagery allowed the identification and characterization of the affected areas, the mapping of the landslide features and the calculation of the displaced sediment volume. The study shows that the occurred landslides were shallow, being the basins of Ribeira Brava the most affected areas, with almost two thirds of the total identified landslide polygons.

Irrigation can be responsible for salt accumulation in the root zone of grapevines when late autumn and winter precipitation is not enough to leach salts from the soil upper horizons, turning the soil unsuitable for grape production. The aim of this work is to present a novel methodology to outline areas, within a drip-irrigated vineyard, with a low soil moisture content (SMC) during, and after, an 11-month agricultural drought. Soil moisture (SM) field measurements were performed in two plots at the vineyard, followed by a geostatistical method (indicator kriging) to estimate the SM class probabilities according to a threshold value, enlarging the training set for the classification algorithms. The logistic regression (LR) and Random Forest (RF) methods used the features of the Sentinel-1 and Sentinel-2 images and terrain parameters to classify the SMC probabilities at the vineyard. Both methods classified the highest SMC probabilities above 14% that is located close to the stream at the lower altitudes. The RF method performed very well in classifying the topsoil zones with a lower SMC during the autumn-winter period. This delineation allows the prevention of the occurrence of areas affected by salinisation, indicating which areas will need irrigation management strategies to control the salinity, especially under climate change, and the expected increase in droughts.

No abstract