Vulnerability analysis in an Early Warning System for drought

Early Warning Systems (EWS) for drought are often based on risk models that do not, or marginally, take into account the vulnerability factor. The multifaceted nature of drought (hydrological, meteorological, and agricultural) is source of coexistence for different ways to measure this phenomenon and its effects. The mentioned issue, together with the complexity of impacts generated by this hazard, causes the current underdevelopment of drought EWS compared to other hazards. In Least Developed Countries, where drought events causes the highest numbers of affected people, the importance of correct monitoring and forecasting is considered essential. Existing early warning and monitoring systems for drought, produced at different geographic levels, provide only in a few cases an actual spatial model that tries to describe the cause-effect link between where the hazard is detected and where impacts occur. Integrate vulnerability information in such systems would permit to better estimate affected zones and livelihoods, improving the effectiveness of produced hazard-related datasets and maps. In fact, the need of simplification and, in general, of a direct applicability of scientific outputs is still a matter of concern for field experts and early warning products end-users. Even if the surplus of hazard related information produced on the occasion of catastrophic events has, in some cases, led to the creation of specific data-sharing platforms, the conveyed meaning and usefulness of each product has not yet been addressed. The present work is an attempt to fill this gap which is still an open issue for the scientific community as well as for the humanitarian aid world. The present study aims at conceiving a simplified vulnerability model to embed into an existing EWS for drought, which is based on the monitoring of vegetation phenological parameters, produced using free satellite derived datasets. The proposed vulnerability model includes (i) a pure agricultural vulnerability and (ii) a systemic vulnerability. The first considers the agricultural potential of terrains, the diversity of cultivated crops and the percentage of irrigated area as main driving factors. The second vulnerability aspect consists of geographic units that model the strategy and possibilities of people to access marketplaces; these units are shaped on the basis of the physical accessibility of market locations in one case, and according to a spatial gravity model of market catchments in other two proposed cases. Results of the model applied to two national case studies and evaluated with food insecurity data are presented

Urban detection using Decision Tree classifier: a case study

This work constitutes a first step towards the definition of a methodology for automatic urban extraction from medium spatial resolution Landsat data. Decision Tree is investigated as classification technique due to its ability in establishing which is the most relevant information to be used for the classification process and its capability of extracting rules that can be further ap-plied to other inputs. The attention was focused on the evaluation of parameters that better define the training set to be used for the learning phase of the classifier since its definition affects all the next steps of the process. Different training sets were created by combining different features, such as different level of radiometric pre-processing applied to the input images, the number of classes considered to train the classifier, the temporal extent of the training set and the use of different at-tributes (bands or spectral indexes). Different post-processing techniques were also evaluated. Classifiers, obtained by the generated training sets, were evaluated in two different areas of Pied-mont Region, where the official regional cartography at scale 1:10000 was used for validation. Accuracies round 81% in the Torino case study and around 96%-97% in Asti case study were reached, thanks to the use of indexes such as NDVI and NDBBBI and the use of post-processing such as majority filtering that allowed enhancing classifier performance

Utilizzo di Alberi Decisionali per la classificazione di aree urbanizzate

The paper describes a preliminary study on the urban classification accuracies obtained by means of the Decision Tree classifier. The study was conducted over the area of Turin (Italy), with Landsat ETM+ imagery and with an official regional map (Cartografia Tecnica Regionale) used as ground truth. In particular the variation of the accuracies was evaluated, depending on the changing of the algorithm input attributes such as the level of applied radiometric pre-processing, the considered number of classes, the temporal extent of the training set and the use of spectral indexes. Results show that overall accuracies of 80% can be achieved and that spectral indexes are the type of attribute that affect most these accuracies

Reducing the impact of soil erosion and reservoir siltation on agricultural production and water availability: the case study of the Laaba catchment (Burkina Faso)

Material complementari del cas estudi "Reducing the impact of soil erosion and reservoir siltation on agricultural production and water availability: the case study of the Laaba catchment (Burkina Faso)", part component del llibre "Case studies for developing globally responsible engineers"Peer ReviewedPostprint (author's final draft

Utilizzo di Alberi Decisionali per la classificazione di aree urbanizzate

The paper describes a preliminary study on the urban classification accuracies obtained by means of the Decision Tree classifier. The study was conducted over the area of Turin (Italy), with Landsat ETM+ imagery and with an official regional map (Cartografia Tecnica Regionale) used as ground truth. In particular the variation of the accuracies was evaluated, depending on the changing of the algorithm input attributes such as the level of applied radiometric pre-processing, the considered number of classes, the temporal extent of the training set and the use of spectral indexes. Results show that overall accuracies of 80% can be achieved and that spectral indexes are the type of attribute that affect most these accuracie

Base cartography for land and water management in Sub-Saharan Africa

Base cartography at proper scale for land and water management is rarely available in Least Developed Countries (LDCs). Despite the massive presence of international cooperation programs and projects carried out in various LDCs, a low budget is usually allocated for base data retrieval, which could be helpful for a wide range of on-site actions. A food security project in Burkina Faso, aiming at increasing the agricultural production through supporting farmers' unions, is herein used as a case study. In this framework update cartography at large scale was needed in order to plan Soil and Water Conservation (SWC) interventions at catchment scale. However, best existing official maps, dated 1984, were at 1:50.000 scale, which is a highly coarse detail level to intervene at large scales. Data at higher resolution were available at the national cartographic institute, obtained from aerial surveys performed in the last decade. Aerial imagery allowed then to perform feature extraction over the areas of interest, thus updating the existing cartography and making it suitable for land and water management plannin