Identificação de fragmentos florestais visando à conectividade da paisagem: abordagem com imagens de alta-resolução, classificação orientada a objeto e métricas da paisagem.

Nesse trabalho, o objetivo foi apresentar o potencial de imagens de alta-resolução e de algumas métricas da paisagem para caracterização dos fragmentos de vegetação natural de uma área amostral na região do Vale do Paraíba, estado de São Paulo

Classificação de florestas plantadas e nativas a partir da análise orientada a objeto e técnica de mineração de dados em imagem Geoeye.

Nas discussões sobre estoque de carbono florestal, a preservação de florestas nativas e o manejo correto de florestas plantadas são algumas ações que contribuem para a redução da concentração do CO2 na atmosfera. Para esses estudos, cada vez mais tem sido empregados dados de sensoriamento remoto para apoiar mapeamentos e inventários florestais; contudo estes levantamentos apresentam, por vezes, limitações em relação a resolução espacial e espectral dos dados empregados. Alia-se a isso a dificuldade em discriminar plantios recentes de eucalipto e pastagem (Carriello e Vicens, 2011) e entre vegetação nativa e plantios adultos (Santos e Novaes Junior, 2011). Para este fim, imagens orbitais de alta-resolução podem contribuir de forma significativa para discriminar com maior precisão essas áreas florestais. Nessas abordagens vem sendo aplicada a análise orientada a objeto, onde a imagem é analisada a partir de pequenos segmentos, os objetos, gerados no processo de segmentação. Esses objetos são estruturados, formando uma rede hierárquica, em que se relacionam com os seus vizinhos e sub-objetos (Hofmann, 2001). A etapa de classificação dos objetos é baseada em regras, onde o conhecimento do usuário é usado para criar um conjunto de critérios para determinado alvo de interesse, por exemplo, que pode ser aplicado a várias imagens. Aliado a isso, a técnica de mineração de dados auxilia nessa seleção dos melhores atributos das imagens para a discriminação entre alvos

Stochastic system dynamics modelling for climate change water scarcity assessment of a reservoir in the Italian Alps

Water management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevant for mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Here stochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows in the Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulations into a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volume within the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow and volume as well as implementing a set of metrics to analyse volume extreme conditions.Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021-2050) future reductions for stored volume and turbined outflow are expected to be severe compared to the 14-year baseline (1999-2004 and 2009-2016; -24.9 % of turbined outflow and -19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041-2070; -26.2 % of turbined outflow and -20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially in the last part of the 2041-2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflow only), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021-2050. Reductions are persistently occurring for the rest of the year from April to November for turbined outflows (down to -56.3 % in August) and from May to December for stored volume (down to -44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest a general increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90th percentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volume values below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baseline conditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption of adaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods for water accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results and methodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g. hydropower, agriculture and tourism) and prepare for potential multi-risk conditions

Chapter Inventory of GIS-Based Decision Support Systems Addressing Climate Change Impacts on Coastal Waters and Related Inland Watersheds

Cosmology & the univers

Diversidade de espécies de aves em silvicultura de eucalipto.

Com a rápida e crescente expansão da silvicultura, especialmente no Estado de São Paulo, é comum o debate sobre os impactos e possíveis benefícios de tal prática, principalmente sobre a biodiversidade. Entretanto, levantamentos de indicadores de biodiversidade nessas áreas ainda são escassos e existe uma grande lacuna de conhecimento. Este trabalho apresenta resultados sobre a caracterização da avifauna encontrada em uma propriedade de silvicultura de Eucalyptus no Município de Brotas, SP. A amostragem da avifauna foi realizada usando a metodologia de contagem por pontos. Após seis campanhas de campo foram registradas 53 espécies de aves para a área de estudo, distribuídas em 23 famílias e 11 ordens. A ordem dos Passeriformes foi a que apresentou maior número de espécies (34) e de contatos (132). De acordo com a análise da frequência de ocorrência, poucas espécies mostraram-se frequentes na região e outras apareceram em um único ponto, fato que poderia ser indicativo de grande variedade de nichos ecológicos. Análises futuras avaliarão a diversidade da fauna em relação a indicadores de ecologia da paisagem e em relação à fitossociologia do sub-bosque nas áreas de cultivo de eucalipto.bitstream/item/56791/1/015-11.pd

A multi-risk methodology for the assessment of climate change impacts in coastal zones

Climate change threatens coastal areas, posing significant risks to natural and human systems, including coastal erosion and inundation. This paper presents a multi-risk approach integrating multiple climate-related hazards and exposure and vulnerability factors across different spatial units and temporal scales. The multi-hazard assessment employs an influence matrix to analyze the relationships among hazards (sea-level rise, coastal erosion, and storm surge) and their disjoint probability. The multi-vulnerability considers the susceptibility of the exposed receptors (wetlands, beaches, and urban areas) to different hazards based on multiple indicators (dunes, shoreline evolution, and urbanization rate). The methodology was applied in the North Adriatic coast, producing a ranking of multi-hazard risks by means of GIS maps and statistics. The results highlight that the higher multi-hazard score (meaning presence of all investigated hazards) is near the coastline while multi-vulnerability is relatively high in the whole case study, especially for beaches, wetlands, protected areas, and river mouths. The overall multi-risk score presents a trend similar to multi-hazard and shows that beaches is the receptor most affected by multiple risks (60% of surface in the higher multi-risk classes). Risk statistics were developed for coastal municipalities and local stakeholders to support the setting of adaptation priorities and coastal zone management plans

On the Application of GIS-based Decision Support Systems to study climate change impacts on coastal systems and associated ecosystems

One of the most remarkable achievements by scientists in the field of global change in recent years is the improvedunderstanding of climate change issues. Its effects on human environments, particularly coastal zones and associated watersystems, are now a huge challenge to environmental resource managers and decision makers. International and regionalregulatory frameworks have been established to guide the implementation of interdisciplinary methodologies, useful toanalyse water-related systems issues and support the definition of management strategies against the effects of climatechange. As a response to these concerns, several decision support systems (DSS) have been developed and applied toaddress climate change through geographical information systems (GIS) and multi-criteria decision analysis (MCDA)techniques; linking the DSS objectives with specific functionalities leading to key outcomes, and aspects of the decisionmaking process involving coastal and waters resources. An analysis of existing DSS focusing on climate change impacts oncoastal and related ecosystems was conducted by surveying the open literature. Consequently, twenty DSS were identifiedand are comparatively discussed according to their specific objectives and functionalities, including a set of criteria (generaltechnical, specific technical and applicability) in order to better inform potential users and concerned stakeholders throughthe evaluation of a DSS’ actual application.Key words: Climate change, Decision support, GIS, regulations, Environmen

Inventory of GIS-Based Decision Support Systems Addressing Climate Change Impacts on Coastal Waters and Related Inland Watersheds

A Decision Support System (DSS) is a computer-based software that can assist decision makers in their decision process, supporting rather than replacing their judgment and, at length, improving effectiveness over efficiency. Environmental DSS are models based tools that cope with environmental issues and support decision makers in the sustainable management of natural resources and in the definition of possible adaptation and mitigation measures [2]. DSS have been developed and used to address complex decision-based problems in varying fields of research. For instance, in environmental resource management, DSS are generally classified into two main categories: Spatial Decision Support Systems (SDSS) and Environmental Decision Supports Systems (EDSS) [3-5]. SDSS provide the necessary platform for decision makers to analyse geographical information in a flexible manner, while EDSS integrate the relevant environmental models, database and assessment tools – coupled within a Graphic User Interface (GUI) – for functionality within a Geographical Information System (GIS) [1,4-6]. In some detail, GIS is a set of computer tools that can capture, manipulate, process and display spatial or geo-referenced data in which the enhancement of spatial data integration, analysis and visualization can be conducted [8-9]. These functionalities make GIS-tools useful for efficient development and effective implementation of DSS within the management process. For this purpose they are used either as data managers (i.e. as a spatial geo-database tool) or as an end in itself (i.e. media to communicate information to decision makers)

Base de dados espaciais para avaliação da sustentabilidade e planejamento ambiental de paisagens agrossilviculturais.

No presente documento, é descrita a metodologia adotada para a estruturação do banco de dados geográficos, a qual é uma etapa prévia para a geração de cartas temáticas de dinâmica de uso e cobertura das terras, as quais darão suporte às próximas atividades do projeto.bitstream/item/84405/1/036-12.pd

Caracterização física do solo sob pastagem em diferentes níveis de degradação no município de Guararapes, SP.

A estrutura física do solo é um dos principais indicativos relacionados à degradação das pastagens. Este trabalho teve o objetivo de avaliar os níveis de degradação de pastagens com as condições físicas do solo em pastagens de Brachiaria localizadas no município de Guararapes, SP, a extremo oeste do Estado de São Paulo. Os atributos físicos do solo (densidade, porosidade, textura e resistência a penetração foram avaliados nos níveis de degradação de pastagem: N1 (não degradado), N2 (degradação baixa), N3 (degradação média) e N4 (degradado). Houve aumento da densidade do solo e diminuição da porosidade total e microporosidade, no nível 1 (não degradado) em relação ao nível 4 (degradado). A camada compactada foi detectada de 20-30 cm. Portanto, os atributos físicos avaliados foram indicativos de degradação das pastagens classificadas quanto aos quatro níveis de degradação