ABSTRACT Soil is an important carbon repository in terrestrial ecosystems, serving a fundamental role in the intricate cycling of this elemental component. Wetlands are crucial components of the global carbon cycle, playing a significant role in carbon sequestration due to their remarkable productivity and unique sedimentary structures. Our study focuses on the wetlands east of Marajó island, recognized as the largest fluvial-marine plain in South America. In this study, we applied a methodological framework to optimize SOC content prediction in the wetlands of Marajó island using readily available environmental covariates. We collected and analyzed 81 soil samples from the most representative geoenvironments on the island at a layer of 0.00-0.20 m. Our database included vegetation indices, morphometric maps, and covariates based on distance from water bodies and archaeological sites. We tested five machine learning algorithms - Cubist, Linear Model, Random Forest, K Nearest Neighbor, and Support Vector Machine - to obtain the best prediction performance. Cubist model demonstrated the highest performance for training (R2 = 0.483) and testing (R2 = 0.505) datasets, making it the optimal choice for SOC prediction in the topsoil. The most important covariates selected by Cubist using recursive feature elimination were digital elevation model, topographic heterogeneity index, vertical distance between the summit and base of the slope, and Euclidean distance from water bodies. Geoenvironments characterized by dense alluvial rainforest with palms on Plinthosols and Gleysols, mangroves with Gleysols, and coastal muddy plains exhibited the highest SOC content in the topsoil. ecosystems component cycle structures fluvialmarine fluvial marine America 8 0.000.20 000020 0.00 0.20 0 00 20 0.00-0.2 m indices maps sites Model Forest Neighbor R2 R (R 0.483 0483 483 0.505 0505 505 datasets topsoil index slope Gleysols 000 0.000.2 00002 0.0 020 0.2 2 0.00-0. 0.48 048 48 0.50 050 50 0.000. 0000 0. 02 0.00-0 0.4 04 4 0.5 05 5 0.000 0.00-

Abstract Ground temperature’s sensitivity to climate change has garnered attention. This study aimed to monitor and analyze temporal trends and estimate Active Layer Thickness from a monitoring point at Fildes Peninsula, King George Island, in Antarctica. Quality control and consistency analysis were performed on the data. Methods such as serial autocorrelation, Mann-Kendall, Sen-Slope, Pettitt, and regression analysis tests were applied. Spearman’s correlation examined the relationship between air temperature and ground depths. The active layer thickness was estimated using the maximum monthly temperature, and the permafrost lower limit used the minimum monthly temperature. Significant summer seasonal trends were observed with Mann-Kendall tau, positive Sen-Slope, and Pettitt slope at depths of 67.5 and 83.5 cm. The regression analysis was significant and positive for all ground depths and in different seasons. The highest correlation (r=0.82) between air temperature and surface ground depth was found. Freezing prevailed at all depths during 2008-2018. The average Active Layer Thickness (ALT) was 92.61 cm. Temperature is difficult to monitor, and its estimation is still complex. However, it stands out as a fundamental element for studies that refer to the impacts of climate change temperatures s attention Peninsula Island Antarctica data autocorrelation MannKendall, MannKendall Mann Kendall, Kendall SenSlope, SenSlope Sen Slope, Slope Sen-Slope applied Spearmans Spearman tau 675 67 5 67. 835 83 83. cm seasons r=0.82 r082 r 0 82 (r=0.82 found 20082018. 20082018 2008 2018. 2018 2008-2018 ALT (ALT 9261 92 61 92.6 complex However 6 8 r=0.8 r08 (r=0.8 2008201 200 201 2008-201 926 9 92. r=0. r0 (r=0. 200820 20 2008-20 r=0 (r=0 20082 2 2008-2 r= (r= 2008- (r

Abstract The Serra do Divisor National Park (SDNP) in the Westernmost Brazilian Amazonia possesses unique Mountain landscapes of sub-andean nature, with high geo-biodiversity and pristine environments, with a potential high contribution in ecosystems services. We studied and mapped the basic geo-environmental units of the main sector of the Park, evaluating soil carbon stocks as a key ecosystem service provided by the Protected Area. For the identification, characterization and mapping of the geoenvironmental units, we integrated pedological, geomorphological and vegetation data obtained by local soil survey and field campaigns, as well as secondary data. Eight geoenvironmental units were identified and mapped, distributed in three main compartments: the Serra do Divisor (SD) the upper Moa River and the medium Moa River. This region presents similar environments to the sub-Andean region, notably the Ceja Forest at the top surface of the SD. Soils at the SD have high organic carbon accumulation, with close association with the nutrient-poor, quartz-rich rocks, and shows organic matter illuviation indicating active podzolization. The SDNP encompasses important ecosystems and services linked with high geo-biodiversity, and high soil carbon stocks, representing a new frontier for scientific research in the only area of transitional sub-andean forested landscape in Brazil. (SDNP subandean sub andean nature geobiodiversity geo biodiversity environmental Area identification pedological campaigns compartments (SD subAndean Andean accumulation nutrientpoor, nutrientpoor nutrient poor, poor nutrient-poor quartzrich quartz rich rocks podzolization geobiodiversity, biodiversity, Brazil

ABSTRACT Basin hydrological and morphometric parameters are elements that must be made on a map with sufficient information. This information collected on-site has been limited by public order problems. Basin circularity index (Ci) is among the morphometric indices, which has not been calculated yet for the basins under study. Basins automatic delimitation carried out from Geographic Information Systems (GIS), has allowed its research and management due to efficiency in data processing, ease, and low cost; in addition, increase images and databases availability. The work aim was to evaluate the Digital Elevation Model (DEM) SRTM proposed methodology in the Corporación Autónoma Regional de las Cuencas de los Ríos Negro y Nare (Cornare) jurisdiction, extracting from it the numerical drainage, in order to delimit basins and sub-basins, as well as calculate indices, such as drainage density (Dd) and Ci. The declivity map showed terrain with very soft to very strong surface relief. Basin´s automatic delimitation based on the drainage order ≥6 was the one that came closest to the basin actual characteristics; regarding the sub-basins delimitation, it was the one based on the ≥3 order. Dd and Ci indices indicated that the sub-basins have moderate drainage, runoff level, and infiltration capacity and tend to be more elongated than circular.

RESUMEN Los parámetros hidrológicos y morfométricos de una cuenca constituyen estimaciones que deben realizarse sobre un mapa con suficiente información. La recopilación de esta in situ se ha visto limitada por problemas de orden público. Entre los índices morfométricos de una cuenca se encuentra el índice de circularidad (Ic), que hasta el momento no ha sido calculado para las cuencas en estudio. La delimitación automática de cuencas realizada a partir de Sistemas de Información Geográfica (SIG), ha permitido la investigación y manejo de las mismas debido a la eficiencia en el procesamiento de datos, facilidad y bajo costo, además de la creciente disponibilidad de imágenes y bases de datos. El objetivo de este trabajo fue evaluar la metodología propuesta para utilizar el Modelo Digital de Elevación (MDE) SRTM de la jurisdicción de la Corporación Autónoma Regional de las Cuencas de los Ríos Negro y Nare (Cornare) y extraer de este el drenaje numérico, para delimitar cuencas y subcuencas, así como calcular índices, tales como densidad de drenaje (Dd) e Ic. El mapa de declividad mostró terrenos con relieve muy suave a muy fuerte. La delimitación automática de cuencas con base en el orden de drenaje ≥6 fue la que más se acercó a la realidad; en cuanto a la delimitación de subcuencas, fue aquella con base en el orden ≥3. Los índices Dd e Ic, indicaron que las subcuencas tienen drenaje, nivel de escorrentía y capacidad de infiltración moderados y tienden a ser más alargadas que circulares.

Resumen El avance de los modelos predictivos mediante Machine Learning Algorithms (ML) asociados a datos ambientales permite mejorar los modelos de fragilidad ambiental, que son herramientas fundamentales para la toma de decisiones. Este estudio tuvo como objetivo derivar una predicción de la fragilidad ambiental mediante la prueba de ML asociado con covariables ambientales en el estado de Minas Gerais. Se utilizaron variables físico-ambientales (suelo, geología, clima, relieve) con peso de fragilidad para los atributos y cálculo de la media para obtener un modelo de Fragilidad Ambiental Potencial (PEF). Posteriormente, extrajimos los valores de PEF a una cuadrícula de 4800 puntos, que se utilizó para generar una nueva predicción de ML, llamada PEFML. Esta predicción se basó en la prueba de cinco algoritmos y un conjunto de 105 covariables ambientales. Los resultados indicaron que la predicción PEFML con mejor desempeño fue el modelo Random Forest (R2 0.59 y RMSE 0.47), indicando un predominio del bajo nivel de fragilidad ambiental. Los modelos PEF y PEFML muestran fuertes correlaciones (0,7 Pearson); sin embargo, PEFML tiene correlaciones más fuertes con otros datos ambientales. Por lo tanto, la predicción PEFML es un modelo robusto que captura información de covariables y tiene patrones espaciales coherentes. (ML decisiones Gerais físicoambientales físico suelo, suelo (suelo geología clima relieve PEF. . (PEF) Posteriormente 480 puntos 10 R2 R (R 059 0 59 0.5 0.47, 047 0.47 , 47 0.47) 0,7 07 7 (0, Pearson Pearson) embargo tanto coherentes (PEF 48 1 05 5 0. 04 0.4 4 0, (0 (

Resumo O avanço de modelos preditivos por Algoritmos de Aprendizado de Máquina (ML) associados à dados ambientais possibilita aprimoramento de modelos de fragilidade ambiental, os quais são importantes ferramentas para tomada de decisão. O objetivo desse estudo foi derivar uma predição de fragilidade ambiental, testando ML associados a covariáveis ambientais no estado de Minas Gerais. Utilizamos variáveis físico-ambientais (solo, geologia, clima, relevo) com peso de fragilidade para os atributos e cálculo da média obtendo o modelo de Fragilidade Ambiental Potencial (PEF). Posteriormente, extraímos os valores de PEF para uma grade de 4.800 pontos e usadas para gerar uma nova predição por ML, denominada PEFML. A predição foi com teste de cinco algoritmos e conjunto de 105 covariáveis ambientais. Comparamos os dois modelos de fragilidade ambiental (PEF e PEFML), inclusive com outros dados de riscos/vulnerabilidade/fragilidade. Os resultados indicaram que a predição de PEFML de melhor desempenho foi o modelo Random Forest (R2 0.59 e RMSE 0.47), indicando predomínio do nível fragilidade baixa. Os modelos de fragilidade PEF e PEFML têm forte correlação (0.7 Pearson), porém, PEFML possui correlações mais fortes com outros dados ambientais. Portanto, a predição de PEFML é um modelo robusto que capta informações de covariáveis e possui padrões espaciais coerentes (ML decisão Gerais físicoambientais físico solo, solo (solo geologia clima relevo PEF. . (PEF) Posteriormente 4800 4 800 4.80 10 PEFML, , PEFML) riscosvulnerabilidadefragilidade riscos vulnerabilidade riscos/vulnerabilidade/fragilidade R2 R (R 059 0 59 0.5 0.47, 047 0.47 47 0.47) baixa 0.7 07 7 (0. Pearson, Pearson Pearson) porém Portanto 480 80 4.8 1 05 5 0. 04 0.4 (0 48 8 4. (

Abstract The advancement of predictive models by Machine Learning Algorithms (ML) associated with environmental data enables the improvement of models of environmental fragility, which are essential tools for decision-making. This study aimed to derive a prediction of environmental fragility by testing ML associated with environmental covariates in the state of Minas Gerais. We use physical-environmental variables (soil, geology, climate, relief) with a weight of fragility for the attributes and calculation of the average to obtain a model of Potential Environmental Fragility (PEF). Subsequently, we extracted the PEF values to a 4,800-point grid, which was used to generate a new prediction by ML called PEFML. This prediction was based on testing five algorithms and a set of 105 environmental covariates. The results indicated that the best-performing PEFML prediction was the Random Forest model (R2 0.59 and RMSE 0.47), indicating a predominance of the low environmental fragility level. The PEF and PEFML models have strong correlations (0.7 Pearson); however, PEFML has stronger correlations with other environmental data. Therefore, the PEFML prediction is a robust model that captures information from covariates and has coherent spatial patterns. (ML decisionmaking. decisionmaking decision making. making decision-making Gerais physicalenvironmental physical soil, soil (soil geology climate relief PEF. . (PEF) Subsequently 4,800point 4800point point 4,800 4 800 grid 10 bestperforming best performing R2 R (R 059 0 59 0.5 0.47, 047 0.47 , 47 0.47) level 0.7 07 7 (0. Pearson Pearson) however Therefore patterns (PEF 800point 4800 4,80 80 1 05 5 0. 04 0.4 (0 480 4,8 8 ( 48 4,

Abstract Sulfurization is a pedogenic process that involves pyrite oxidation and strong soil acidification, accounting for the formation of acid sulfate soils. In Antarctica, acid sulfate soils are related to specific parent materials, such as sulfide-bearing andesites in Maritime Antarctica and pyritized sedimentary rocks in James Ross Archipelago. The hypothesis is that the acid sulfate soils of these regions vary according with a climate gradient. The reviewing of current data showed that the acid sulfate soils of warmer and wetter Maritime Antarctica have a greater weathering degree, higher acidity, leaching, phosphorus adsorption capacity, structural development, and well-crystallized iron oxides and kaolinite formation. On the other hand, the sulfurization at the drier region of James Ross Archipelago is counterbalanced by the semiaridity, resulting in lower acidity and higher base contents combined with little morphological and mineralogical evolution besides presence of weatherable minerals in the clay fraction. The sulfurization process interplays with other pedogenic processes, such as the phosphatization in Maritime Antarctica and salinization in James Ross Archipelago. Higher temperatures and soil moisture enhance the pedogenesis, showing that even the Antarctic sulfate soils, which originated from specific parent materials, have their development and characteristics controlled by a clear climatic gradient.

RESUMO A alta precisão na estimativa do volume de madeira em florestas tropicais é necessária para apoiar o manejo sustentável. Os scanners a laser terrestres (TLS) podem fornecer estimativas de alta qualidade a partir de variáveis estruturais de árvores. Comparamos estimativas de variáveis do fuste obtidas por TLS e métodos tradicionais em nível de árvore e equações de volume ajustadas usando dados para uma floresta semidecídua sazonal secundária (Mata Atlântica). Também discutimos a viabilidade do TLS em fragmentos florestais hiperdiversos e secundários. Medições tradicionais (Método I) e medidas baseadas em TLS (Método II) foram realizadas em 29 árvores pertencentes a 10 espécies. Foram geradas equações de volume baseadas nos modelos de Schumacher e Hall (SH) e Spurr. O DAP (diâmetro à altura do peito) foi igual para ambos os métodos. A altura total foi superestimada pelo Método II, e a altura comercial apresentou baixa correlação entre os dois métodos. As equações volumétricas ajustadas foram diferentes para ambos os métodos, e aquelas baseadas no modelo de volume SH apresentaram o melhor ajuste. Nossos resultados nos levam a inferir que em florestas secundárias hiperdiversas, as variáveis estruturais das árvores devem ser obtidas via TLS. No entanto, atenção deve ser dada à oclusão das árvores alvo pelo sub-bosque em regeneração e às estimativas de altura, que podem ser influenciadas pelas características da copa das espécies dominantes.

ABSTRACT High accuracy in timber volume estimation in tropical forests is required to support sustainable management. Terrestrial laser scanners (TLS) can provide high-quality estimates from tree structural variables. We compared stem variable estimations obtained by TLS and traditional methods at tree level and adjusted volume equations using data of a secondary seasonal semideciduous forest (Atlantic Forest). We also discuss the feasibility of TLS in hyperdiverse and secondary forest fragments. Traditional measurements (Method I) and TLS-based measurements (Method II) were performed on 29 trees belonging to 10 species. Volume equations based on the Schumacher and Hall (SH) and Spurr models were generated. DBH (diameter at breast height) was equal for both methods. Total height (TH) was overestimated by Method II, and commercial height (CH) showed a low correlation between the two methods. The adjusted volumetric equations were different for both methods, and those based on the SH volume model showed the best fit. Our results lead us to infer that in hyperdiverse secondary forests, tree structural variables should be obtained via TLS. However, attention should be given to the occlusion of target trees by the regenerating understory and to height estimates, which can be biased by the crown characteristics of the dominant species.

RESUMO Nos últimos anos, a redução de áreas florestais tem influenciado na fragmentação da paisagem, comprometendo funções dos ecossistemas. A floresta natural, além da manutenção da biodiversidade, é capaz de melhorar as condições hidrológicas da bacia hidrográfica. Sendo assim, o objetivo deste estudo foi caracterizar a estrutura da paisagem dos fragmentos florestais remanescentes e avaliar a sua representatividade fisiográfica e pedológica na bacia hidrográfica do ribeirão São Bartolomeu, Zona da Mata mineira. A região de estudo é responsável pela maioria do abastecimento de água do município de Viçosa, ao mesmo tempo em que está passando por um processo de urbanização, reforçando a necessidade de um processo de ordenação de uso e avaliação de áreas prioritárias para conservação. Os estudos relacionados à estrutura da paisagem foram baseados nas métricas dos fragmentos florestais. A representatividade foi analisada quanto aos aspectos da compartimentação topográfica, das faces de orientação e dos solos, por meio do teste de Qui-Quadrado. A floresta natural apresentou fragmentos de tamanhos médios elevados e boa proximidade entre os fragmentos, contudo com formas irregulares que favorecem o efeito de borda, havendo uma tendência de irregularidade à medida que se aumenta o tamanho do fragmento. Os resultados das representatividades fisiográficas e pedológica mostraram que os usos do solo são dependentes da localização na paisagem. No resultado do teste estatístico, a floresta ocorre com menor frequência que o esperado nos terraços fluviais (48,3%) e encostas convexas (2,3%), nos Argissolos Vermelho-Amarelo (53,1%) e nas faces norte (28,5%) e oeste (20,9%). Para fins de planejamento urbano e conservação da biodiversidade, recomenda-se equilibrar a área florestal nos diferentes compartimentos, dando prioridade à conexão dos fragmentos florestais.

ABSTRACT The reduction of forest areas in recent years has influenced landscape fragmentation, compromising ecosystem functions. The natural forest, in addition to maintaining biodiversity, is capable of improving the hydrological conditions of the watershed. This study aimed to characterize the landscape structure of the remaining forest fragments in the São Bartolomeu river basin, at the Zona da Mata of Minas Gerais State, and to assess their physiographic and pedological representativeness. The study area is responsible for most of Viçosa’s water supply, while it is undergoing an urbanization process, reinforcing the need for ordering the use, besides the evaluation of priority areas for conservation. The studies related to the landscape structure were based on the metrics of forest fragments. The representativeness was analyzed regarding the aspects of topographic compartmentation, slope faces, and soils, using the Chi-square test. The natural forest presents fragments varying from medium to large size, with satisfactory proximity between them. However, the forest patches have irregular shapes, favoring the edge effect with a tendency towards irregularity as the fragment size increases. The results of the physiographic and pedological representativeness indicate that the land uses are dependent on the location in the landscape. According to the statistical test, the forest occurs less frequently than expected on the river terraces (48.3%) and convex slopes (2.3%), as well as on the Red-Yellow Acrisols (53.1%), and on the north (28.5%) and west (20.9%) faces. For urban planning and biodiversity conservation purposes, it is recommended to balance the forest area in the different compartments of the landscape, giving priority to the connection of forest fragments.

ABSTRACT: It remains unclear whether temperature and precipitation exert independent control on tropical vegetation and soil C pools. Likewise, it is unknown whether the feedbacks of tropical C pools to climate constraints vary with nutrient availability. These aspects are critical to improving our ability to predict the response of tropical C pools to climate dynamics. This review aimed to assess climate data and the spatial distribution of vegetation and soil C pools across the Brazilian territory to investigate i) whether mean annual precipitation (MAP) and temperature (MAT) exert independent effects on tropical C pools; ii) whether vegetation and soil C pools exhibit hierarchical feedbacks to climate; and iii) how these feedbacks reflect soil nutrient availability. To account for MAP and MAT effects on tropical C cycling, we calculated Ecosystem Effective Moisture (EEM), i.e., the difference between MAP and potential evapotranspiration. We gathered substantial evidence suggesting that under high MAT and MAP controlling EEM, plants exchange more C for water and resorb more nutrients (especially P), which limitations in plant litter reduce microbial-derived C inputs into soil organic matter. Frequent soil saturation under high EEM favors denitrification rates (“open” N cycle), allowing continuous mineralization of litter and shallow soil C pools to release nutrients, sustaining high plant C pools. With decreasing MAP levels, ecosystem C pools depend on MAT controlling evapotranspiration and EEM. Accordingly, decreasing MAP under high MAT reduces EEM, with vegetation and soil C pools co-limited by low net primary productivity (NPP), frequent fire and/or nutrient losses. Otherwise, decreasing MAP and coupled to cool temperatures allow EEM to remain positive, forcing plants to increase deep-rooting and/or shed their leaves, which nutrients are immobilized with microbial-derived C into mineral-organic associations, favoring high soil C pools. Combined, the evidence gathered suggests that the sensitivity of tropical ecosystems to global increases in temperature should not be overlooked, especially if coupled to reductions in precipitation. Overall, the horizontal distribution of vegetation and soil C pools is best described by EEM rather than temperature or precipitation alone, whereas the vertical partition of C in plant-soil systems reflects biotic responses to climate-nutrient constraints.

Há evidências de que ambientes urbanos que desestimulam a caminhada contribuem para a incapacidade funcional de idosos. Vários índices foram propostos para descrever a caminhabilidade de uma área combinando aspectos do ambiente construído que promovem ou inibem a caminhada. No entanto, devido a problemas de qualidade e disponibilidade de dados no Brasil, até o momento não há um índice de caminhabilidade aplicável a todas as cidades do país e devidamente testado na população. O objetivo deste estudo foi propor um índice de caminhabilidade baseado em sistemas de informação geográfica para uma cidade de médio porte, com dados de livre acesso, bem como testar sua associação com a incapacidade funcional em idosos. Foram usados os dados da área urbana de um município de médio porte para selecionar um conjunto parcimonioso de variáveis por meio de análise fatorial. O índice obtido foi testado em relação à sua associação com a capacidade para a realização de atividades de vida diária que requerem maior movimentação, em 499 idosos utilizando equações de estimativas generalizadas. O índice de caminhabilidade resultante foi composto por densidade residencial, densidade comercial, conectividade de ruas, presença de calçadas e iluminação pública. Essas variáveis compuseram o primeiro fator da análise fatorial, excluindo-se apenas a arborização que ficou retida no segundo fator. Verificou-se que o pior escore de caminhabilidade estava associado ao maior escore de incapacidade funcional. Com base nos resultados e na validação deles, o estudo sugere um índice de caminhabilidade facilmente aplicável com grande potencial de uso em planos de ação para adequar os ambientes.

Evidence has shown that urban environments that discourage walking contribute to functional incapacity in the elderly. Various indices have been proposed to describe an area’s walkability, combining different aspects of the built environment that promote (or inhibit) walking. However, due to problems with the quality and availability of data in Brazil, there is no walkability index to date applies to all cities of the country and that has been properly tested in the population. The current study aimed to propose a walkability index based on geographic information systems for a medium-sized city, with open-access data, and to test its association with functional incapacity in the elderly. The study used data from the urban area of a medium-sized Brazilian city to select a parsimonious set of variables through factor analysis. The resulting index was tested for its association with the capacity to perform activities of daily living that require more movement, in 499 elderly, using generalized estimating equations. The resulting walkability index consists of residential density, commercial density, street connectivity, presence of sidewalks, and public lighting. These variables comprised the first factor in the factor analysis, excluding only arborization which was retained in the second factor. The worst walkability score was associated with the highest functional incapacity score. Based on the results and their validation, the study suggests an easily applicable walkability index with great potential for use in action plans to adapt environments.

Existen evidencias de que los ambientes urbanos que desestimulan los paseos contribuyen a la incapacidad funcional de los ancianos. Se propusieron varios índices para describir la posibilidad de pasear en un área, combinando aspectos del ambiente construido que promueven o inhiben los paseos. No obstante, debido a problemas de calidad y disponibilidad de datos en Brasil, hasta el momento no existe un índice sobre la posibilidad de pasear, aplicable a todas las ciudades del país, y debidamente probado en la población. El objetivo de este estudio fue proponer un índice sobre la posibilidad de pasear, basado en sistemas de información geográfica para una ciudad de tamaño medio, con datos de libre acceso, así como probar su asociación con la incapacidad funcional en ancianos. Se utilizaron los datos del área urbana de un municipio de tamaño medio para seleccionar un conjunto parsimonioso de variables mediante análisis factorial. El índice obtenido fue probado en 499 ancianos, en lo que se refiere a su asociación con la capacidad para la realización de actividades de vida diaria, que requieren un mayor movimiento, utilizando ecuaciones de estimación generalizadas. El índice resultante sobre la posibilidad de pasear estaba compuesto por: densidad residencial, densidad comercial, conectividad de calles, presencia de aceras e iluminación pública. Estas variables formaron parte del primer factor de análisis factorial, excluyendo solamente la arborización, que quedó fijada en el segundo factor. Se verificó que la peor puntuación sobre la posibilidad de pasear se asoció a la mayor puntuación de incapacidad funcional. En base a los resultados, y a la validación de los mismos, el estudio sugiere un índice sobre la posibilidad de realizar paseos, fácilmente aplicable, con un gran potencial de uso en planes de acción para adecuar los ambientes.

Há evidências de que ambientes urbanos que desestimulam a caminhada contribuem para a incapacidade funcional de idosos. Vários índices foram propostos para descrever a caminhabilidade de uma área combinando aspectos do ambiente construído que promovem ou inibem a caminhada. No entanto, devido a problemas de qualidade e disponibilidade de dados no Brasil, até o momento não há um índice de caminhabilidade aplicável a todas as cidades do país e devidamente testado na população. O objetivo deste estudo foi propor um índice de caminhabilidade baseado em sistemas de informação geográfica para uma cidade de médio porte, com dados de livre acesso, bem como testar sua associação com a incapacidade funcional em idosos. Foram usados os dados da área urbana de um município de médio porte para selecionar um conjunto parcimonioso de variáveis por meio de análise fatorial. O índice obtido foi testado em relação à sua associação com a capacidade para a realização de atividades de vida diária que requerem maior movimentação, em 499 idosos utilizando equações de estimativas generalizadas. O índice de caminhabilidade resultante foi composto por densidade residencial, densidade comercial, conectividade de ruas, presença de calçadas e iluminação pública. Essas variáveis compuseram o primeiro fator da análise fatorial, excluindo-se apenas a arborização que ficou retida no segundo fator. Verificou-se que o pior escore de caminhabilidade estava associado ao maior escore de incapacidade funcional. Com base nos resultados e na validação deles, o estudo sugere um índice de caminhabilidade facilmente aplicável com grande potencial de uso em planos de ação para adequar os ambientes.

Evidence has shown that urban environments that discourage walking contribute to functional incapacity in the elderly. Various indices have been proposed to describe an area’s walkability, combining different aspects of the built environment that promote (or inhibit) walking. However, due to problems with the quality and availability of data in Brazil, there is no walkability index to date applies to all cities of the country and that has been properly tested in the population. The current study aimed to propose a walkability index based on geographic information systems for a medium-sized city, with open-access data, and to test its association with functional incapacity in the elderly. The study used data from the urban area of a medium-sized Brazilian city to select a parsimonious set of variables through factor analysis. The resulting index was tested for its association with the capacity to perform activities of daily living that require more movement, in 499 elderly, using generalized estimating equations. The resulting walkability index consists of residential density, commercial density, street connectivity, presence of sidewalks, and public lighting. These variables comprised the first factor in the factor analysis, excluding only arborization which was retained in the second factor. The worst walkability score was associated with the highest functional incapacity score. Based on the results and their validation, the study suggests an easily applicable walkability index with great potential for use in action plans to adapt environments.

Existen evidencias de que los ambientes urbanos que desestimulan los paseos contribuyen a la incapacidad funcional de los ancianos. Se propusieron varios índices para describir la posibilidad de pasear en un área, combinando aspectos del ambiente construido que promueven o inhiben los paseos. No obstante, debido a problemas de calidad y disponibilidad de datos en Brasil, hasta el momento no existe un índice sobre la posibilidad de pasear, aplicable a todas las ciudades del país, y debidamente probado en la población. El objetivo de este estudio fue proponer un índice sobre la posibilidad de pasear, basado en sistemas de información geográfica para una ciudad de tamaño medio, con datos de libre acceso, así como probar su asociación con la incapacidad funcional en ancianos. Se utilizaron los datos del área urbana de un municipio de tamaño medio para seleccionar un conjunto parsimonioso de variables mediante análisis factorial. El índice obtenido fue probado en 499 ancianos, en lo que se refiere a su asociación con la capacidad para la realización de actividades de vida diaria, que requieren un mayor movimiento, utilizando ecuaciones de estimación generalizadas. El índice resultante sobre la posibilidad de pasear estaba compuesto por: densidad residencial, densidad comercial, conectividad de calles, presencia de aceras e iluminación pública. Estas variables formaron parte del primer factor de análisis factorial, excluyendo solamente la arborización, que quedó fijada en el segundo factor. Se verificó que la peor puntuación sobre la posibilidad de pasear se asoció a la mayor puntuación de incapacidad funcional. En base a los resultados, y a la validación de los mismos, el estudio sugiere un índice sobre la posibilidad de realizar paseos, fácilmente aplicable, con un gran potencial de uso en planes de acción para adecuar los ambientes.

Abstract Fire behavior prediction models can assist environmental agencies with fire prevention and control. This study aimed to adjust a fire prediction model for the state of Minas Gerais, Brazil. Using the R program and hotspots provided by the National Institute for Space Research (INPE) for 2010, prediction of the probability of fires through the Random Forest algorithm was conducted using the Bootstrapping method. The model generated a prediction map with global kappa value of 0.65. External validation was performed with hotspots in 2015. Results showed that 58% of the hotspots are in areas with ignition probability > 50%, being 24% of them in areas with 25-50% probability, and 17% in areas with < 25% probability. These results were considered satisfactory, demonstrating that the model is suitable for predicting fires.

ABSTRACT The northwestern part of the Acre State (Brazil) possesses singular soils in Brazilian Amazonia, but have been very little studied. This study aimed to discuss the genesis and some micropedological aspects of the soils from Serra do Divisor and adjacent floodplain soils of the Moa river, to enhance the knowledge on their formation. A toposequence of soils ranging from the uppermost part of sub-Andean Serra do Divisor to the Alluvial soils of Moa river floodplain was studied, regarding chemical, physical, mineralogical, and micromorphological attributes. The parent material of the Serra do Divisor is basically quartzose sandstone, and the soils along the toposequence were classified as Typic Haplorthods (P1), Spodic Quartzipsamment (P2), Lithic Quartzipsamment (P3), and Lithic Quartzipsamment (P4). Along the Moa river floodplain, we also identified and collected, Typic Udifluvent (P5), Typic Kandiudult (P6), Typic Kandiudalf (P7), and Arenic Plinthic Kandiudult (P8). The Serra do Divisor soils have very low fertility, high acidity, and low cation exchange capacities, presenting a coarse sandy texture, even shallow pedons. The X-ray diffraction analysis of these soils indicates the predominance of kaolinite, with traces of quartz and gibbsite. The shallow mountain Podzols on sandstone have an expressive accumulation of organic material in surface horizons, with evidence of ferrihydrite and imogolite in the subsurface. At the Moa river floodplain, all soils are originated from recent sediments (Cenozoic), which have a geological source upstream. Varying sedimentary layers are key aspects influencing soil genesis. Those soils have evidence of 2:1 clays with hydroxyl-Al interlayers in subsurface horizons. The Serra do Divisor steep landforms and the coarse texture of the soils promote good drainage and favor leaching and chemical impoverishment. Kaolinite and gibbsite were formed by severe leaching and there are evidences of in situ neoformation of gibbsite by extreme Si losses. All studied soils have some peculiarities such as high accumulation of organic material or 2:1 clay minerals. Most investigated soils were affected by colluvial, reworking, mass movements or a strong variation on sedimentation.

ABSTRACT Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest - RF, Gradient Boosted Machine - GBM, C5.0, and multinomial log-linear model - MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by ROIC. To evaluate the random effect to split the dataset, we ran each model 50 times and observed the overall accuracy (OA) and kappa index, and uncertainty, majority and variety maps. The OA of Dataset01 to 04 was lower than to Dataset05 accuracy. However, map outputs of RF and GBM for Dataset01 and Dataset05 had the same majority prediction. It seems that RF and GBM produce consistent results in map outputs according to this methodology and pedologist expertise. To evaluate the uncertainty and the consistency of soil unit prediction, we used the majority maps process. Random Forest, similar to GBM, presented the best results. The increase in the number of covariates was not a guarantee of improvement in the OA or in the quality of the map output. Geographic position and distance raster did not improve the map output according to expert evaluation. Because the variance between the ROICs, when the training and validation datasets were split based on it, the subsets are quite different in relation to the covariates, and this is the reason for the worse results of this model, comparing with the Dataset05. On the other hand, when considering one complete dataset not based on ROICs, the variance of training and validation subsets is lower and produced more accurate parameters of quality.

RESUMO O mapeamento de atributos químicos do solo em larga escala pode acarretar em ganhos no planejamento de uso e ocupação do mesmo. Existem diferentes técnicas disponíveis para tal fim, cujos desempenhos devem ser testados para diferentes situações de paisagem. Objetivou-se neste trabalho espacializar atributos químicos do solo, comparando oito métodos para predição. Como variáveis independentes foram utilizados 40 atributos morfométricos gerados a partir do modelo digital de elevação, além de dados geofísicos, imagens do satélite Landsat 8 e o NDVI. Calculou-se todas combinações possíveis entre as bandas do satélite, gerando 28 novas variáveis. Também foram feitas combinações entre as bandas de Th, U e K obtidas dos dados geofísicos, gerando outras três variáveis. As últimas variáveis calculadas foram as distâncias entre os quatro pontos das extremidades da bacia (d1, d2, d3 e d4). As variáveis dependentes do modelo foram teores de Al, Ca, Fe, K, Mg, Na, Si, Ti, Cr, Cu, Mn, Ni, P, Pb, V, Zn, Zr, S e Cl. Foram utilizadas 200 amostras de solo, coletadas em 100 pontos em duas profundidades (0-10 e 10-30 cm), e os elementos totais foram determinados em analisador de fluorescência de raios-X. Random Forest mostrou-se superior aos demais para predizer os atributos químicos do solo nas duas profundidades, sendo indicado para predição dos atributos dos solos da região de estudo. As variavéis espaciais mostraram-se altamente prescindíveis, devendo ser consideradas nas modelagens dos elementos químicos do solo. É possível a predição dos elementos com R2 variando de 0,32 a 0,62 pelos métodos testados.

ABSTRACT Mapping the chemical attributes of the soil on a large scale can result in gains when planning the use and occupation of the land. There are different techniques available for this purpose, whose performance should be tested for different types of landscapes. The aim of this study was to spatialize chemical attributes of the soil, comparing eight methods of prediction. Forty morphometric attributes, generated from a digital elevation model, were used as independent variables, in addition to geophysical data, images from the Landsat 8 satellite and the NDVI. All possible combinations between the satellite bands were calculated, generating 28 new variables. Combinations between the Th, U and K bands obtained from the geophysical data were also calculated, generating a further three variables. The final variables to be calculated were the distances between the four points of the edges of the basin (d1, d2, d3 and d4). The dependent variables for the model were Al, Ca, Fe, K, Mg, Na, Si, Ti, Cr, Cu, Mn, Ni, P, Pb, V, Zn, Zr, S and Cl. A total of 200 soil samples were used, which were collected from 100 points at two depths (0-10 and 10-30 cm); the total elements were determined using an X-ray fluorescence analyzer. The Random Forest algorithm proved to be superior to the others in predicting the chemical attributes of the soil at both depths, and is suitable for predicting soil attributes in the study region. Spatial variables are essential, and should be considered when modelling chemical elements in the soil. Using the methods under test, it is possible to predict elements with R2 values ranging from 0.32 to 0.62.