In 2011 Brazil experienced the worst disaster in the country’s history. There were 918 deaths and thousands made homeless in the mountainous region of Rio de Janeiro State due to several landslides triggered by heavy rainfalls. This area constantly suffers high volumes of rain and episodes of landslides. Due to these experiences, we used the MaCumBa (Massive CUMulative Brisk Analyser) software to identify rainfall intensity–duration thresholds capable of triggering landslides in the most affected municipalities of this region. More than 3000 landslides and rain data from a 10-year long dataset were used to define the thresholds and one year was used to validate the results. In this work, a set of three thresholds capable of defining increasing alert levels (moderate, high and very high) has been defined for each municipality. Results show that such thresholds may be used for early alerts. In the future, the same methodology can be replicated to other Brazilian municipalities with different datasets, leading to more accurate warning systems.

Intensity-duration rainfall thresholds are commonly used in regional-scale landslide warning systems. In this manuscript, 3D thresholds are defined also considering the mean rainfall amount fallen in each alert zone (MeAR, mean areal rainfall) in Emilia Romagna region (Northern Italy). In the proposed 3D approach, thresholds are represented by a plane instead of a line, and the third dimension allows to indirectly account for the influence of complex rainfall patterns. MeAR values are calculated according to different time periods ranging from 7 to 30 days, and all threshold parameters are calibrated independently for the 8 alert zones in which the region is divided. The approach was validated and compared with classical intensity-duration thresholds, finding that the 3D threshold may be used to get better performances, especially in terms of a consistent reduction of false alarms:− 20 to − 86%, depending on the alert zone and the selected MeAR duration. These results open new encouraging perspectives for the development of the regional warning system that is operated in the study area.

Landslide susceptibility maps are widely used in landslide hazard management. Although many models have been proposed, mapping unit definition is a matter that still needs to be fully examined. In the literature, the most reported mapping units are pixels and slope units, while in this work, developed in the Rio de Janeiro region (Brazil), the use of drainage basins as a mapping unit is examined; even if their use leads to the definition of maps with a coarser spatial resolution than pixels-based maps, they convey information that can be easily and rapidly handled by civil defense organizations. However, for the morphometrical characterization of entire basins, a standardized procedure does not exist, and the susceptibility results may be sensitive to the approach used. To investigate this issue, a random forest model was used to assess landslide susceptibility, using 12 independent variables: four categorical (land use, soil type, lithology and slope orientation) and eight numerical variables (slope gradient, elevation, slope curvature, profile curvature, planar curvature, flow accumulation, topographic wetness index, stream power index). For each basin, the numerical variables were aggregated according to different approaches, which, in turn, were used to set up four different model configurations: i) maximum values, ii) mean values, iii) standard deviation values, iv) joint use of all the above. The resulting maps showed noticeable differences and a quantitative validation procedure showed that the best configurations were the ones based on mean values of independent variables, and the one based on the combination of all the values of the numerical variables. The main outcomes of this work consist of a landslide susceptibility map of the study area, to be used in operational procedures of risk management and in some insights on the best approaches to aggregate raster cell data into wider spatial units.

1RESUMO -A disponibilidade de dados hiperespectrais trouxe expectativas nos meios acadêmicos e empresariais quanto à potencialidade de sua aplicação no setor florestal. O objetivo deste trabalho foi avaliar o potencial da aplicação de dados hiperespectrais do sensor Hyperion EO-1 na quantificação da variável biofísica volume de madeira em plantios de Eucalyptus spp. e o impacto do sombreamento do relevo nessa quantificação. Para isso, estabeleceram-se correlações entre os dados espectrais e o volume de madeira, seguidos da definição INTRODUÇÃOA demanda crescente por produtos florestais fez que as empresas do setor investissem em pesquisas voltadas para o melhoramento genético, definição de tratos silviculturais como, adubação, plantio em diferentes espaçamentos e no desenvolvimento de bancos de dados (georreferenciados ou não) que municiam o processo de tomada de decisão no âmbito da produção florestal. Atualmente, o grau de complexidade das informações necessárias para a otimização desse processo de decisão tornou-se tão grande que são

Abstract. This study presents a methodology for susceptibility mapping of shallow landslides just from data and software from the public domain. The study was conducted in a mountainous region located on the southeastern Brazilian coast, in the state of São Paulo. The proposal is that the methodology can be replicated in a practical and reliable way in several other municipalities that do not have such mappings and that often suffer from landslide-related disasters. The susceptibility mapping was generated based on the following maps: geological, soils, slope, horizontal and vertical curvatures, and land use. The thematic classes of these maps were weighted according to technical and scientific criteria related to the triggering of landslides, and were crossed by the fuzzy gamma technique. The mapping was compared with the risk sector survey made by the Brazilian Geological Survey (CPRM), which is the official database used by municipalities and civil defense in risk management. The results showed positive correlations, so that the critical risk sectors had higher proportions for the more susceptible classes. To compare the approach with other studies using landslide-scar maps, correlated indices were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas.

Abstract:In Brazil, plantations of exotic species such as Eucalyptus have expanded substantially in recent years, due in large part to the great demand for cellulose and wood. The combination of the steep slopes in some of these regions, such as the municipalities located close to the Serra do Mar and Serra da Mantiqueira, and the soil exposure that occurs in some stages in the Eucalyptus cultivation cycle, can cause landslides. The use of a geographic information system (GIS) assists with the identification of areas that are susceptible to landslides, and one of the GIS tools used is the spatial inference technique. In this work, the landslide susceptibility of areas occupied by Eucalyptus plantations in different stages of development in municipalities in the state of São Paulo was examined. Eight thematic maps were used, and, the fuzzy gamma technique was used for data integration and the generation of susceptibility maps, in which scenarios were created with different gamma values for the dry and rainy seasons. The results for areas planted with Eucalyptus were compared with those obtained for other land uses and covers. In the moderate and high susceptibility classes, the pasture is the land use type that presented the greatest susceptibility, followed by new Eucalyptus plantations and urban areas.

Abstract. In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil – Santos, Cubatão, Caraguatatuba and Ubatuba – in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

Resumo Quantificação de macronutrientes em Floresta Ombrófila Mista Montana utilizando dados de campo e dados obtidos a partir de imagens do satélite IKONOS II.O objetivo deste trabalho foi desenvolver uma metodologia para estimar os macronutrientes (N, P, K, S, Ca e Mg) presentes em uma floresta nativa, utilizando dados espectrais provenientes de satélite de alta resolução, IKONOS II, e dados de campo. As amostras de biomassa foram coletadas em 20 parcelas distribuídas em vários estágios sucessionais da floresta. Os teores de nutrientes em cada espécie foram obtidos em análises de laboratório, e a quantificação por parcela foi feita multiplicando-se esses teores pela biomassa seca. Por meio de análise estatística, relacionaram-se as quantidades de nutrientes nas parcelas com os dados obtidos nas imagens de satélite. Os valores de reflectância nas bandas MS-1, MS-2, MS-3, MS-4 e os índices de vegetação NDVI, SAVI e Razão de Bandas entraram no modelo como variáveis independentes, e os nutrientes, como dependentes. Foram geradas equações alométricas, o que permitiu a quantificação e o mapeamento dos nutrientes para a área. Palavras-chave: Macronutrientes; sensoriamento remoto; modelos de predição; Araucaria angustifolia.Abstract Estimate macronuntrients content in mixed ombrophylus forest using field data and data from satelite image IKONOS II. The main objective of this research work was to develop a methodology to estimate nutrient (N, P, K, S Ca and Mg) content in a pristine Araucarian forest using spectral data from high resolution IKONOS II satellite and field data. Samples for biomass estimates were collected in 20 plots distributed over several growth stages of secondary forest. Nutrient contents in each species were obtained from analysis in a laboratory and total amount in the plots was calculated by multiplying nutrient concentration by dry biomass. Statistical analysis provided the nutrient content and satellite data relation. The reflectance of MS-1, MS-2, MS-3, MS-4 bands and NDVI, SAVI and band ratio were input in the model as independent variables, while nutrients content were the dependent variables. Allometric equations were developed for estimates to the entire area. Keywords: Macro-nutrients; remote sensing; prediction models; Araucaria angustifolia. INTRODUÇÃOEstudos envolvendo quantificação de biomassa de maneira indireta, ou seja, não destrutiva, tiveram início na década de 80. Em florestas plantadas, o entendimento do ciclo do carbono, nitrogênio e outros nutrientes tem feito com que a exploração florestal inove seus princípios.Sabe-se que grande parte dos nutrientes presentes em uma árvore se concentra nas folhas, galhos e casca. A quantificação dos nutrientes nesses componentes tem contribuído para que as operações florestais sejam feitas de uma maneira mais racional, em que restos de culturas, que antes eram descartados, sejam incorporados ao solo, diminuindo assim a degradação deste e a demanda por