Among the many GIS based multivariate statistical methods for landslide susceptibility zonation, the so called “Conditional Analysis method” holds a special place for its conceptual simplicity. In fact, in this method landslide susceptibility is simply expressed as landslide density in correspondence with different combinations of instability-factor classes. To overcome the operational complexity connected to the long, tedious and error prone sequence of commands required by the procedure, a shell script mainly based on the GRASS GIS was created. The script, starting from a landslide inventory map and a number of factor maps, automatically carries out the whole procedure resulting in the construction of a map with five landslide susceptibility classes. A validation procedure allows to assess the reliability of the resulting model, while the simple mean deviation of the density values in the factor class combinations, helps to evaluate the goodness of landslide density distribution. The procedure was applied to a relatively small basin (167 km2) in the Italian Northern Apennines considering three landslide types, namely rotational slides, flows and complex landslides, for a total of 1,137 landslides, and five factors, namely lithology, slope angle and aspect, elevation and slope/bedding relations. The analysis of the resulting 31 different models obtained combining the five factors, confirms the role of lithology, slope angle and slope/bedding relations in influencing slope stability.

A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines)

https://www.teses.usp.br/teses/disponiveis/44/44141/tde-03022009-141229/publico/CHGC6.pdf

SRTM-based morphotectonic analysis of the Poços de Caldas Alkaline Massif, southeastern Brazil

This paper reports a bioclimatic envelope model study of the potential distribution of 19 tree species in the Iberian Peninsula during the Last Glacial Maximum (LGM; 21 000 yr BP) and the Mid-Holocene (6000 yr BP). Current patterns of tree species richness and distributions are believed to have been strongly influenced by the climate during these periods. The modelling employed novel machine learning techniques, and its accuracy was evaluated using a threshold-independent method. Two atmospheric general circulation models, UGAMP and ECHAM3 (generated by the Palaeoclimate Modelling Intercomparison Project, PMIP), were used to provide climate scenarios under which the distributions of the 19 tree species were modelled. The results obtained for these scenarios were assessed by agreement measure analysis; they differed significantly for the LGM, but were more similar for the Mid-Holocene.



The results for the LGM support the inferred importance of pines in the Iberian Peninsula at this time, and the presence of evergreen Quercus in the south. Important differences in the altitude at which the modelled species grew were also predicted. During the LGM, some normally higher mountain species potentially became re-established in the foothills of the Pyrenees. The warm Mid-Holocene climate is clearly reflected in the predicted expansion of broad-leaved forests during this period, including the colonization of the northern part of the Iberian Peninsula by evergreen Quercus species.

Predictive modelling of tree species distributions on the Iberian Peninsula during the Last Glacial Maximum and Mid‐Holocene

West Nile Virus (WNV) transmission in Italy was first reported in 1998 as an equine outbreak near the swamps of Padule di Fucecchio, Tuscany. No other cases were identified during the following decade until 2008, when horse and human outbreaks were reported in Emilia Romagna, North Italy. Since then, WNV outbreaks have occurred annually, spreading from their initial northern foci throughout the country. Following the outbreak in 1998 the Italian public health authority defined a surveillance plan to detect WNV circulation in birds, horses and mosquitoes. By applying spatial statistical analysis (spatial point pattern analysis) and models (Bayesian GLMM models) to a longitudinal dataset on the abundance of the three putative WNV vectors [Ochlerotatus caspius (Pallas 1771), Culex pipiens (Linnaeus 1758) and Culex modestus (Ficalbi 1890)] in eastern Piedmont, we quantified their abundance and distribution in space and time and generated prediction maps outlining the areas with the highest vector productivity and potential for WNV introduction and amplification. The highest abundance and significant spatial clusters of Oc. caspius and Cx. modestus were in proximity to rice fields, and for Cx. pipiens, in proximity to highly populated urban areas. The GLMM model showed the importance of weather conditions and environmental factors in predicting mosquito abundance. Distance from the preferential breeding sites and elevation were negatively associated with the number of collected mosquitoes. The Normalized Difference Vegetation Index (NDVI) was positively correlated with mosquito abundance in rice fields (Oc. caspius and Cx. modestus). Based on the best models, we developed prediction maps for the year 2010 outlining the areas where high abundance of vectors could favour the introduction and amplification of WNV. Our findings provide useful information for surveillance activities aiming to identify locations where the potential for WNV introduction and local transmission are highest. Such information can be used by vector control offices to stratify control interventions in areas prone to the invasion of WNV and other mosquito-transmitted pathogens.

/pdf/spatio-temporal-patterns-of-distribution-of-west-nile-virus-2mbishypfn.pdf

Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy

A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study

"Open Source GIS - A GRASS GIS Approach"- Markus Neteler and Helena Mitasova

Abstract. Recent advances in modelling capabilities and data processing combined with vastly improved observation tools and networks have resulted in the expansion of available weather and climate information, from historical observations to seasonal climate forecasts, as well as decadal climate predictions and multi-decadal climate change projections. However, it remains a key challenge to ensure this information reaches the intended climate-sensitive sectors (e.g. water, energy, agriculture, health), and is fit-for-purpose to guarantee the usability of climate information for these downstream users. Climate information can be produced on demand via climate resilience information systems which are existing in various forms. To optimise the efficiency and establish better information exchange between these systems, standardisation is necessary. Here, standards and deployment options are described for how scientific methods can be be deployed in climate resilience information systems, respecting the principles of being findable, accessible, interoperable and reusable. Besides the general description of OGC-API Standards and OGC-API Processes based on existing building blocks, ongoing developments in AI-enhanced services for climate services are described.


Deployment of ai-enhanced services in climate resilience information systems

Abstract. The eMOTIONAL Cities project sets out to understand how the natural and built environment can shape the feelings and emotions of those who experience it. It does so with a cross-disciplinary approach which includes urban planners, doctors, psychologists, neuroscientists and engineers. At the core of this research project, lies a Spatial Data Infrastructure (SDI) which assembles datasets that characterise the emotional landscape and built environment, in different Cities across Europe and the US. The SDI is a key tool, not only to make the research data available within the project consortium, but also to allow cross-fertilisation with other ongoing projects and later on, to reach a wider public audience. For more than twenty years SDIs have adopted the OGC W*s service interfaces, which are based on SOAP, the Simple Object Access Protocol. In recent years a new “family” of APIs has emerged within OGC, which is more aligned with modern web practices. In this project, we set out to leverage the advantages of this new approach, and compiled a stack to implement an SDI based on OGC APIs. However, we realised that we still need to support the legacy standards, either because an OGC API replacement is not mature enough, or there are no implementations available. This has led us to compile another stack based on the legacy standards. In this paper we describe our architecture, along with the challenges that we had to address. Both stacks are based on OSGeo Software, and they are available on GitHub.


J Simoes

Papers

"Open Source GIS - A GRASS GIS Approach"- Markus Neteler and Helena Mitasova

Deployment of ai-enhanced services in climate resilience information systems

Serving geospatial data using modern and legacy standards: a case study from the urban health domain