ABSTRACT The production chain of the macauba palm (Acrocomia aculeata) includes the cosmetics, food and bioenergy sectors, which highlights the relevance of defining bioclimatically favorable areas for the cultivation of this plant in the state of Minas Gerais, Brazil, the center of its origin. The mesoregions of this state were characterized according to the average annual temperature (AMT), annual precipitation (AP), precipitation in the driest quarter (PDQ), and aridity index (AI), following the Boolean logic. A total of 45.18% of the area of this state is bioclimatically favorable for the cultivation of A. aculeata, which facilitates the planning of farming activities, conservation, and management of this plant in other regions with conditions similar to those observed in this state. This palm can also be cultivated with irrigation in regions with adequate bioclimatic conditions, but without adequate rainfall pattern. This information can boost the rational and sustainable use of A. aculeata for the production of green vegetable oil.

RESUMO A cadeia produtiva da palmeira macaúba (Acrocomia aculeata) inclui os setores de cosméticos, alimentos e bioenergia, aumentando a importância de se definir as áreas bioclimaticamente favoráveis para o cultivo dessa planta no estado de Minas Gerais, Brasil, centro de sua origem. As mesorregiões desse estado foram caracterizadas de acordo com a temperatura média anual (TMA), precipitação anual (PA), precipitação no trimestre mais seco (PTS) e índice de aridez (IA), seguindo a lógica booleana. Um total de 45,18% da área do estado de Minas Gerais é, bioclimaticamente, favorável ao cultivo da A. aculeata, facilitando o planejamento de atividades agropecuárias, conservação e manejo dessa planta. Essa palmeira pode, também, ser cultivada com irrigação em regiões com déficit de precipitação, mas com as outras condições bioclimáticas semelhantes às definidas nesse estado para essa planta. Essas informações aumentam as possibilidades do uso racional e sustentável de A. aculeata para produção de óleo vegetal verde.

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.

Resumo A região central do Brasil é a sua atual fronteira agrícola, com rápida expansão das culturas de soja, café e algodão sob solos arenosos. Um dos grandes desafios de manejo desse tipo de solo é a redução da sua baixa fertilidade natural e manutenção da umidade. Esse trabalho teve o objetivo de avaliar a aplicabilidade do uso de Radar de Penetração do Solo (GPR) na determinação de atributos de solos arenosos da parte central do Brasil. O estudo foi realizado no sudeste do estado da Bahia, numa área de topografia plana e sob pastagem. Foram amostrados dois perfis completos, cujas amostras foram analisadas química e fisicamente. A leitura foi realizada em transectos com comprimento acima de 500m. Foi utilizado GPR equipado com antena de 400 MHz. Foram aplicadas as equações de Topp, Ledieu e Condé para determinação da umidade gravimétrica a partir de valores de constante dielétrica obtidas dos radargramas. Foi observado tratar-se de solos profundos, sem impedimentos físico e que apresenta aumento da umidade em profundidade.

Abstract The central region of Brazil is its current agricultural frontier, with rapid expansion of soy, coffee and cotton crops under sandy soils. One of the major management challenges for this type of soil is the reduction of its low natural fertility and maintenance of humidity. This work had the objective of evaluating the applicability of the use of Soil Penetration Radar (GPR) in the determination of attributes of sandy soils in the central part of Brazil. The study was carried out in the southeastern state of Bahia, in an area of flat relief and under pasture. Two complete profiles were sampled, whose samples were analyzed chemically and physically. The reading was performed in transects with lengths over 500m. GPR equipped with a 400 MHz antenna was used. The Topp, Ledieu and Condé equations were applied to determine the gravimetric humidity from the dielectric constant values obtained from the radargrams. It was observed that these are deep soils, without physical impediments and that there is an increase in humidity in depth.

Abstract Following a disruption on November 5, 2015, the Gualaxo do Norte and Carmo Rivers received iron tailings from the Fundão Dam in Mariana, Minas Gerais, which affected the water’s physiochemical attributes. This study aimed to better understand the impact of this event by evaluating water quality variations, comparing results from previous studies and the limits established in the National Environmental Council (Conama), Resolution 357. For six months, water samples were collected monthly at six points, three in the Gualaxo do Norte River and three in the Carmo River. Turbidity, color, and dissolved metal values were recorded for thirty-six samples, and it was determined that the Gualaxo do Norte River had higher contamination levels than those established in the Conama Resolution 357. According to the results, this study recommends the development of specific analyses and a special water quality classification system.

Resumo O solo é um recurso natural fundamental para a subsistência e desenvolvimento da humanidade, sendo principalmente explorado pela agricultura, cujas práticas inadequadas comprometem as condições edáficas, favorecendo a ocorrência de processos erosivos, que culminam principalmente na perda de solo e nutrientes. A erosão hídrica atua como principal forma de degradação do solo, em que, a partir da ação do impacto da gota de chuva sobre o solo, ocorre o desprendimento de partículas que, associadas a fatores como clima, relevo, solo vegetação e uso e ocupação, favorecem esse processo. O objetivo do estudo foi classificar quatro voçorocas com base em seus estágios evolutivos e julgar as classificações físico-químicas do solo nas faces internas e externas das voçorocas. A morfologia das voçorocas foi classificada por imagem aérea gerada por drone. As voçorocas foram mapeadas usando imagens do Google Earth a partir de 2016. A avaliação qualitativa foi realizada usando o modelo digital de curvatura de elevação de campo (MDSC) com validação de campo. Amostras deformadas foram coletadas nas profundidades de 0-10 e 10-20 cm, enquanto amostras não deformadas foram coletadas em profundidades de 0-10 cm para avaliar seus atributos químicos e físicos. De posse dos dados foi realizada análise estatística não paramétrica de Kruskal-Wallis a 5%. Os atributos químicos e físicos do solo diferiram de acordo com o estágio evolutivo nas faces (interna e externa) em profundidades de 0 a 10 cm, enquanto as faces mostraram diferenças em profundidade de 10 a 20 cm.

Abstract Soil is a fundamental natural resource for subsistence and development of humanity, exploited mainly for agriculture. Agricultural practices, though, are improperly performed, compromising edaphic conditions and favoring erosive processes, which mainly culminate in the loss of soil and nutrients. Water erosion represents the main form of soil degradation, given that the impact of raindrops on the soil results in the detachment of particles, a process favored by factors such as climate, relief, soil vegetation and use and occupation. The objective of the study was to classify four gullies based on their evolutionary turns and to judge the physical-chemical classifications of the soil at the internal and external faces of the gullies. The morphology of the gullies was classified by aerial images generated by drones. The gullies were mapped using Google Earth images from 2016. The qualitative assessment was performed using the digital field-elevation curvature (MDESC) model with field validation. Deformed samples were collected at depths of 0-10 and 10-20 cm, while non-deformed samples were collected at depths of 0.0-0.10 cm to evaluate their chemical and physical attributes. A nonparametric 5% Kruskal-Wallis statistical analysis was performed on the resulting data. The chemical and physical attributes of the soil differed according to the evolutionary stage of the surfaces (internal and external) at depths of 0 to 10 cm, while the faces showed differences in depth of 10 to 20 cm.

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.

Resumo Neste trabalho foram comparados diferentes modelos estatísticos para a estimativa da biomassa individual de árvores, e estimados os estoques totais de biomassa e de carbono acima do solo, nos três compartimentos (arbóreo, herbáceo-arbustivo e serapilheira) de um cerrado sensu stricto típico no oeste da Bahia. Foi realizado o inventário florestal em 20 parcelas de 1000 m2 (20 m x 50 m). Para ajuste dos modelos estatísticos foram cortadas e pesadas 90 árvores de diferentes espécies e classes diamétricas. Foram ajustados modelos para o diâmetro medido a 30 cm do solo (D30cm≥5cm) e diâmetro medido a 1,30 m do solo (DAP1,30m≥3,5 cm). A equação escolhida foi aplicada aos dados das árvores levantados no inventário florestal, e assim foram estimados os estoques por hectare e total no compartimento arbóreo. As amostragens dos compartimentos herbáceo-arbustivo e serapilheira foram realizadas por subparcelas fixas. Foram alocadas sistematicamente 20 subparcelas de 4 m2 (2 m x 2 m) para amostragem do compartimento herbáceo-arbustivo e 20 subparcelas de 1 m2 (1 m x 1 m) para amostragem da serapilheira. O estoque de carbono foi estimado por fator de conversão da biomassa. A equação proveniente do ajuste do modelo de Schumacher & Hall (logarítmica) (R2 = 93,53%; Sxy = 44,92%) foi a escolhida para estimar o estoque de biomassa das árvores do cerrado estudado. A biomassa total acima do solo, considerando os três compartimentos, foi estimada em 18,77 Mg ha-1, o que corresponde a um estoque total de 8,67 Mg ha-1 de carbono. O compartimento arbóreo foi responsável pela maior parte dos estoques (58%), seguido da serapilheira (26%) e herbáceo-arbustivo (16%).

Abstract In this work, different statistical models were compared for estimating individual tree biomass and the total biomass and carbon stocks above the soil, in the three compartments (arboreal, herbaceous-shrub and litter) of a typical Cerrado in the west of Bahia state, Brazil. The forest inventory was carried out in 20 plots of 1000 m2 (20 m x 50 m). In order to fit the statistical models, 90 trees of different species and diameter classes were cut and weighed. Models were fitted for the diameter measured at 30 cm from the soil (D30cm≥5cm) and the diameter measured at 1.30 m from the soil (DAP1,30m≥3,5 cm). The equation chosen was applied to the data of the trees collected in the forest inventory, and thus, the stocks per hectare and the total in the tree compartment were estimated. Samplings of the herbaceous-shrub and litter compartments were performed by fixed subplots. For sampling of the herbaceous-shrub and litter compartments, 20 subplots of 4 m2 (2 m x 2 m) and 20 subplots of 1 m2 (1 m x 1 m) were systematically allocated, respectively. The carbon stock was estimated by the biomass conversion factor. The equation resulting from the adjustment of the Schumacher & Hall (logarithmic) model (R2 = 93.53%; Sxy = 44.92%) was chosen to estimate the biomass stock of the studied Cerrado trees. The total biomass above the soil, considering the three compartments, was estimated at 18.77 Mg ha-1, which corresponds to a Carbon total stock of 8.67 Mg ha-1. The arboreal compartment was responsible for most of the stocks (58%), followed by litter (26%) and herbaceous-shrub (16%).

ABSTRACT The objective of this study was to map the levels of degradation of pastures and its association with chemical properties of the soil and relief. We selected a sub-basin located in the municipality of Viçosa, Minas Gerais, and we mapped the degradations through Ikonos images that were submitted for classification. Soil samples were collected and analyzed chemically; the results were transformed in spatial data using cokriging, based on variables of the digital elevation model. We associated the maps (cokriging) with data from exposure of the hillside, levels of solar radiation and levels of the pastures’ degradation. The pastures were mainly classified in moderate (44%) and very strong (30%) degradation levels. The areas with mild degradation tend to increase when there is a higher sum of bases, cationic exchange capacity and organic matter. The portions facing North and the increase of solar radiation showed more advanced stages of degradation.

ABSTRACT Some technical issues that are inherent to the mapping of permanent preservation areas (PPAs) on hilltops in Brazil have not yet been properly regulated. This results in disparities in mapping an area depending on the technique used. This paper presents a case study that exposes the influence of the topographic database, mapping scale, and method of measuring the average slope on the results obtained. The total PPAs mapped on hilltops vary significantly as a result of these factors. Subjectivity in applying legislation leads to legal uncertainty, which can be resolved by establishing precise, complementary regulations on the issue. Accordingly, it is proposed that the method of measuring the mean slope and mapping scale should be defined by regulation.

RESUMO O sensoriamento remoto permite a identificação das regularidades e irregularidades do uso e cobertura do solo em relação à legislação ambiental e pode ser decisivo em tomadas de decisões para intervenção. O objetivo do trabalho foi delimitar cenários em áreas de preservação permanentes com e sem usos consolidados na bacia hidrográfica do Rio Capivari, Estado do Rio de Janeiro-Brasil, no bioma da Mata Atlântica. Foram realizados no mapeamento e a análise do uso e ocupação das áreas de preservação permanente, através dos seguintes dados: cenas do satélite RapidEye-REIS de 2012, classificador Bhattacharya Distance e hidrografia da bacia e modelo digital de elevação (1:25.000), e os softwares Spring 5.2.7 e ARCGis 10.3.1. Os cenários adotados foram: I) Lei Federal n.º 4.771/1965 e Resolução do Conselho Nacional de Meio Ambiente (CONAMA) n.° 303/2002; II) Lei Federal n.º 12.651/20120; e III) Leis Federais n.º 12.651/2012 e n.º 12.727/2012. A classificação apresentou excelente acurácia de 91,15% para exatidão global e o índice Kappa de 0,86 em relação as amostras de treinamento das seis classes de usos múltiplos, tendo os usos antrópicos de Agropecuária, Queimadas, Solo Exposto e Urbanização, os quais estiveram presentes com usos conflitivos para Cenários I, II e III. A nova legislação florestal para as APPs do Cenário III impactou na redução em 68% em relação ao Cenário I, onde corrobora a preocupação na conservação dos recursos hídricos e edáficos, e o cumprimento da legislação.

ABSTRACT Remote sensing allows for identification of regularities and irregularities in land use and land coverage (LULC) change in relation to environmental legislation. The aim of this study was to delimit scenarios in the permanent preservation areas (PPAs) according to the Brazilian forestry law, with or without consolidated uses in the basin of Capivari River and the State of Rio de Janeiro in the Atlantic Forest biome. Mapping and analysis were performed on LULC in areas of permanent preservation using the following data: RapidEye-REIS satellite scenes acquired in 2012 and Bhattacharyya distance classifier and hydrography of the basin and digital elevation model (1:25.000) using Spring and ArcGIS software. The legal scenarios adopted were as follows: I) Federal Law N°. 4,771/1965 and the National Council for the Environment (CONAMA) N°. 303/2002; II) Federal Law N°. 12,651/2012; and III) Federal Law N°. 12,651/2012 and N°. 12,727/2012. The classification presented an excellent overall accuracy of 91.15% and a Kappa Index of 0.86 in relation to the samples of the six multipurpose classes having the anthropic uses of agriculture, burned pasture, exposed soil and urbanization, which were present with conflicting uses for Scenarios I, II and III. The new forest legislation for the PPAs of Scenario III impacted the reduction of 68% compared to Scenario I, which corroborates with the concerns on the conservation of water and soil resources.

ABSTRACT Digital soil mapping (DSM) has been increasingly used to provide quick and accurate spatial information to support decision-makers in agricultural and environmental planning programs. In this study, we used a DSM approach to map soils in western Haiti and compare the performance of the Multinomial Logistic Regression (MLR) with Random Forest (RF) to classify the soils. The study area of 4,300 km2 is mostly composed of diverse limestone rocks, alluvial deposits, and, to a lesser extent, basalt. A soil survey was conducted whereby soils were described and classified at 258 sites. Soil samples were collected and subjected to physical and chemical analyses. Recursive Feature Elimination (RFE) was used to select the most important covariates from auxiliary data, such as climate, lithology, and morphometric properties to describe the soil-landscape relationship. Mapping performance was assessed by the Kappa index and overall accuracy derived from a confusion matrix generated using a 5-fold cross validation process. In addition, an external mapping validation was carried out using an independent soil dataset. Accordingly, the soil dataset was split into 80 % and 20 % for training and validation of the models, respectively. No significant statistical difference (Z = 0.56< |1.96|) was found between maps generated with both classifiers (Kappa index 0.45 for MLR and 0.42 for RF). Based on the Kappa values, the classification performance can be characterized as moderate for both algorithms. Surprisingly, the RF classifier outperformed MLR in the validation process (Kappa values of 0.55 and 0.33, respectively). These results suggest a higher generalization ability of RF. However, no significant statistical difference (Z = 1.83< |1.96|) was observed. The soil map derived from RF indicated the occurrence of Leptosols (48.5 %), Gleysols (19.6 %), Chernozems (8 %), and Fluvisols (6.6 %) in most of the study area. The DSM approaches proved suitable for mapping soils in western Haiti and could be used in other parts of the country, thereby closing information gaps with regard to Haitian soils.

ABSTRACT: Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Morphometric maps generated from a digital elevation model, together with Landsat-8 satellite imagery, and climatic maps, were among the set of covariates to be selected by the Recursive Feature Elimination algorithm to predict soil types using machine learning algorithms. Mapping performance was assessed using the confusion matrix, and the Z-test among the Kappa indexes of the matrices. In a conventional soil survey, the soils described and classified in the Brazilian System of Soil Classification [Argissolos Vermelho-Amarelos Distróficos – PVAd (Acrisols), Cambissolos Háplicos Tb Distróficos - CXbd (Cambisols), Gleissolos Háplicos Háplicos Tb Distróficos - GXbd (Gleysols), Latossolos Amarelos Distróficos - LAd (Xanthic Ferralsos), Latossolos Vermelho-Amarelos Distróficos - LVAd (Rhodic Ferralsols), and Neossolos Litólicos Distróficos - RLd (Neossols)] were grouped into composite mapping units (MU) using the conventional method. The eight algorithms showed similar performance without statistical difference (Kappa 0.42-0.48). The mapping of soils with varying slopes (LAd, LVAd, CXbd) showed lower accuracy, whereas soils on hydromorphic lowlands (GXbd) were classified more accurately. In map algebra, the result was rather satisfactory, with 63-67 % agreement between the conventional soil map and maps produced by machine learning. The areas with the largest disagreement in the DSM occurred in the LAd unit due to subtle color variation in the Latossolos mantle without a clear relation to any environmental variable, highlighting difficulties in DSM regarding hill slope landforms. Model performance was satisfactory, and good agreement with the conventional soil map demonstrates the importance of the DSM as a potential complementary tool for assisting soil mapping in mountainous areas in Brazil for the purpose of land use planning.

ABSTRACT Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly necessary as a viable way to estimate a wide range of soil properties. The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus (P), from the spectral responses of soil samples in the visible and near infrared (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR using different preprocessing methods combined with five prediction models. Soil samples were collected in Iconha, Espírito Santo State, Brazil, in the Ribeirão Inhaúma basin. A total of 184 samples were collected from 92 sites at two depths (0.00-0.10 and 0.10-0.30 m). Physical, chemical, and spectral analyses were performed according to routine soil laboratory methods. Random selection was made of 70 % of total samples for training and 30 % for validation of the models. The coefficient of determination (R2) and root mean square error (RMSE) were calculated in order to assess model performance. The standardized indexes of prediction error RPD and RPIQ were also calculated. For clay and OC, the best R2 was found in the MIR spectrum, at 0.69 and 0.65, respectively, and for P, it was 0.57 in Vis-NIR. The MSC (Multiplicative Scatter Correction), CR (Continuum removal), and SNV (Standard Normal Variate) preprocesses were most efficient for predicting clay, OC, and P, respectively, while the PLSR - Partial Least Squares Regression (OC and P) and SVM - Support Vector Machine (clay) gave the best predictions and are therefore recommended for modeling these properties in the study area. The models identified in this study can be used to discriminate soils according to a critical test value for clay, OC, and P.

RESUMO Objetivou-se determinar a capacidade máxima de adsorção de As(V) na forma de HAsO42- (CMAAs) pela fração argila de solos em diferentes ambientes nas Penínsulas Keller e Barton, Ilha Rei George, Antártica Marítima. Foram realizadas análises química e granulométrica na terra fina seca ao ar, e na fração argila quantificou-se o material amorfo e a CMAAs. Os dados das análises químicas, granulometrica e da fração argila foram correlacionados com a CMAAs. A CMAAs foi baixa (3.554 mg kg-1), sendo o perfil com influência ornitogênica de pinguins o que apresentou os maiores valores. O pH básico das soluções de saturação não favoreceu a adsorção da espécie desprotonada HAsO42-. A quantidade de argila dos solos foi menos importante que a composição mineralógica na dinâmica de retenção de HAsO42-, sendo que os óxidos de Al e Fe amorfos foram os principais responsáveis pela adsorção desse poluente.

ABSTRACT The aim of this study was to determine the maximum capacity for the adsorption of As(V) in the form of HAsO42- (MACAs) by the clay fraction of soils in different environments of the Keller and Barton Peninsulas, King George Island, Maritime Antarctic. Chemical and particle size analyses were carried out on air-dried fine earth. The amorphous material and MACAs in the clay fraction were quantified. Data from the chemical analysis, particle size analysis and clay fraction were correlated with the MACAs. The MACAs was low (3,554 mg kg-1), with the profile under the ornithological influence of penguins displaying the highest values. The basic pH of the saturated solutions did not favour the adsorption of the deprotonated HAsO42- species. The amount of clay in the soil was less important than the mineralogical composition in the dynamics of HAsO42- retention, with the amorphous oxides of Al and Fe being mainly responsible for the adsorption of the pollutant.

RESUMO A contínua substituição da cobertura florestal pelo cultivo agrícola e a urbanização causaram alterações na maior parte do bioma Mata Atlântica no Brasil. O entendimento da interação entre os fatores físicos, antrópicos e da cobertura vegetal em uma paisagem é de fundamental importância para oferecer subsídios ao planejamento territorial de forma sustentável. Este estudo investigou a influência da altitude, declividade e orientação das vertentes na cobertura florestal em três diferentes paisagens no município de Vassouras, RJ. No geral, as áreas analisadas apresentaram a cobertura florestal fragmentada, porém foi observada uma paisagem com maior cobertura florestal do que pastagens. Entre as variáveis analisadas, a altitude e a declividade apresentaram uma relação positiva com a cobertura florestal. As paisagens estavam inseridas em diferentes contextos socioeconômicos que podem também determinar a distribuição da cobertura florestal atual. A região estudada oferece atributos socioambientais específicos de cada paisagem, cuja compreensão deve ser útil ao planejamento ambiental.

ABSTRACT The continuous replacement of forest covers for crops and urban areas changed most of the Atlantic Forest biome in Brazil. Hence, understanding the interaction between the forest cover, physical and anthropic factors in a landscape is essential in order to provide subsidies for sustainable land use. The present study aimed at investigating the influence of the altitude, declivity, and slope orientation on the forest cover of three landscapes in the municipality of Vassouras. In general, Vassouras showed fragmented forest covers, but we observed a landscape covered more by forests than pastures. Among the variables analyzed, altitude and declivity showed a positive relationship with forest cover. The analyzed landscapes were inserted in different socio-economical contexts, which can also affect the current forest cover distribution. The socio-environmental attributes of those landscapes are specific and their understanding can be strategic for environmental planning.