Rafael Coll Delgado

Avaliação das Estimativas de Precipitação do Produto 3B43-TRMM do Estado do Amazonas

The aim of this study was to evaluate the rainfall data via satellites in Amazonas state, Brazil. To this end, the estimates from the TRMM-3B43 product (2004-2008) were compared with data from seven Conventional Weather Stations (CWS). The comparison was based on the following statistical parameters: Average Error (AE), Root Mean Square Error (RMSE), linear correlation coefficient (r), and Wilmott’s index of agreement (d). The TRMM-3B43 estimates were similar to the surface data and represent well the seasonal variability of rainfall. The data showed high linear correlation (r = 0.83), high index of agreement (d = 0.85), and satisfactory RMSE (66.6 mm/month). Therefore, rainfall estimates from the TRMM-3B43 product can be used as an alternative source of quality data.

Cenários climáticos da radiação solar global baseados no modelo regional HadRM3 para o Estado do Acre

ABSTRACT Two future climate scenarios have been proposed for the the Western Amazon region - state of Acre, Brazil - using the HadRM3 regional climate model for global solar radiation (Rg). The two climate scenarios, A2 (pessimistic) and B2 (optimistic), are based on findings of the IPCC (Intergovernmental Panel on Climate Change) report. Two distinct seasons were defined for both climate scenarios (dry and rainy): the dry season (from April to September) and the rainy season (from October to March). We chose the time period between 1961 and 1990 as baseline and carried out future simulation from 2070 to 2100 for the A2 and B2 settings. The grid point conversion of the HadRM3 model was based on the Ordinary Kriging method. The smallest values of Rg are found for the western part of Acre state during the dry season in both scenarios. Intermediate values of Rg are observed for the north to south direction, followed by the highest values of Rg for the east side of the state, with significant increase of Rg during the rainy season between 2080 and 2090 for both settings adopted in this study. Based on

Object-based image analysis supported by data mining to discriminate large areas of soybean

ABSTRACT This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Based Image Analysis (GEOBIA) and Data Mining (DM). In order to obtain the images, the segmentation algorithm implemented by Definiens Developer was used. A decision tree (DT) was created from a training set previously prepared. Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle. The time-series data were used only for the CEI index. Furthermore, to compare the results obtained from GEOBIA, the slicing technique was used at the CEI level. After the training, the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean. In accordance with the error matrix and kappa parameter analysis, tests for statistical significance were created. Results indicate that the classification achieved by Kappa coefficients is 0.76. In short, the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale.

SPATIALIZATION OF FRACTIONS OF ORGANIC MATTER IN SOIL IN AN AGROFORESTRY SYSTEM IN THE ATLANTIC FOREST, BRAZIL

This study aimed to spatialize fractions of organic matter of soil in an agroforestry system (AFS) located in the Atlantic Forest in Brazil. Thirty-one soil samples were collected at depths of 0-10, 10-20 and 20-40 cm from georeferenced collection points. We determined total organic carbon (TOC), particulate carbon (COp), carbon associated with clay and silt (COam), carbon content in the fulvic acid fraction (C-FAF), humic acid fraction (C-HAF) and humin fraction (C-HUM). Semivariogram analysis and model adjustment were carried out using ArcGIS 10.2 software. Subsequently, spatial interpolation was performed using Ordinary Kriging. We observed spatial dependence for all variables except for TOC and COp at the 0-10 cm depth, which presented a pure nugget effect. It was possible to observe modifications in the distribution of humic substances in the study area. The results from this study are similar to those of other studies conducted in naive areas in the Atlantic Forest, demonstrating the benefits of using the agroforestry system.

SPATIALIZATION OF SOIL CHEMICAL AND PHYSICAL ATTRIBUTES IN AN AGROFORESTRY SYSTEM, SEROPÉDICA, BRAZIL

Neste trabalho, objetivou-se espacializar os atributos quimicos e fisicos do solo em um sistema agroflorestal no municipio de Seropedica, Rio de Janeiro. Foram coletadas 31 amostras de terra, nas profundidades de 0-10 cm, 10-20 cm e 20-40 cm, sendo cada ponto de coleta georreferenciado. Foram determinados pH (em H2O), acidez potencial (H+Al), calcio (Ca2+), magnesio (Mg2+), aluminio (Al3+), sodio (Na+), potassio (K+), fosforo (P), carbono orgânico (C), capacidade de troca cationica do solo (Valor T), saturacao por bases (Valor V), argila total, areia total, silte e densidade de raizes finas. O software ArcGIS 10.2 foi utilizado para fazer a analise semivariografica e o ajuste dos modelos, e posteriormente, foi empregado a interpolacao espacial atraves da Krigagem Ordinaria de primeira ordem de tres modelos espaciais, esferico, exponencial e gaussiano. De acordo com os resultados, apenas os modelos exponencial e gaussiano foram ajustados para as variaveis, exceto para as variaveis Mg2+ e Valor V, pois nao apresentaram dependencia espacial, assim expressando efeito pepita puro (EPP). Foram gerados os mapas de distribuicao para as variaveis (exceto para aquelas que exibiram EPP), onde ocorreu uma correlacao entre as variaveis pH e Al3+, carbono orgânico e cations, fosforo e argila total, e silte e areia. A geoestatistica pode ser aplicada para espacializar os atributos quimicos e fisicos do solo no sistema agroflorestal, exceto no caso do Mg2+ e Valor V.

Índice de Área Foliar de Eucalyptus Estimado por Índices de Vegetação Utilizando Imagens TM - Landsat 5

The objective of the present study was to fit regression models to the measured leaf area in eucalyptus forests and vegetation indices derived from Landsat-5 TM images. The study was carried out in commercial plantations located in the basin of the Doce River, Minas Gerais state, between 2008 and 2011. Leaf area was measured in the field, non-destructively, with the LAI-2000 device. The following indices were used: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR). The best model was adjusted from the NDVI, with a correlation coefficient of 0.73 and root mean square error of 0.37 m² m–2 (19%). We conclude that the leaf area index can be estimated by the regression models fit to the vegetation indices derived from the Landsat - 5 TM images.

Incoming Longwave Radiation Evaluation for the Legal Amazon Using HadRM3 and Geostatistic Theoretical Models

Incoming longwave radiation was estimated using air temperature data from the output of the regional HadRM3 model in the Intergovernmental Panel on Climate Change’s (IPCC) A2 scenario for projections up to 2070, 2080 and 2090 and using Swinbank’s equation. Spatial distribution was done by Ordinary Kriging through three theoretical mathematical models for the IPCC A2 scenario for the whole Legal Amazon. It was found that the highest averages and outliers occurred in 2090 compared to other years evaluated. The average incoming longwave radiation for 2070, 2080 and 2090 was 394.8, 403.9 and 413.0 Wm-2year-1, respectively. The coefficients of variation (CV) were higher for 2080 (2.6%) and 2090 (2.8%), similar to the results found by standard deviation. 2070 obtained CV (2.2%) for estimated values of incoming longwave radiation with greater accuracy. Again, 2070 was the only year that could be interpolated because the average degree of spatial dependence found for all models was 12.23%. Lastly, 2080 could only be interpolated using the Gaussian model in the Legal Amazon.

Occurrence of fire foci under different land uses in the State of Amazonas during the 2005 drought

The objective of this work is to evaluate the occurrence of fire foci during the severe drought that occurred in 2005 in the State of Amazonas. The study was conducted in the State of Amazonas, which is inserted in the northern region of Brazil. The main types of vegetation are Igapó Forest, Várzea Forest and Terra Firme Forest. Kernel density was used to spatialize fire foci to quantify them in seven classes of land use and cover (forest, pasture, exposed soil, urban area, pastoral agroforestry system, agroforestry system and agriculture). Through the regression analysis, the relation among the number of fire foci and four meteorological variables was obtained: rainfall, evapotranspiration, relative humidity and average air temperature. Forest and pasture classes were those with the highest number of fire foci corresponding, respectively, to 58 and 37% of the total number of foci. This can be explained by the greater representativeness of these classes in the State and by the high degree of soil exposure in the case of pasture. The number of fire foci was higher in the dry season, covering approximately 85% of the total fire foci. The variable that had the greatest influence on the occurrence of fire foci in the dry season was evapotranspiration. The study puts on alert the vulnerability of the State of Amazonas to the occurrence of fires and may also suggest actions to mitigate carbon emissions and biomass stock. Research like this one may provide subsidies to region’s managers in an attempt to preserve forest areas and a greater controlling in priority areas considered very high.

Relationship between the environmental conditions and floristic patterns in two phytophysiognomies of the Brazilian Cerrado

Cerrado is the second-largest Brazilian biome and an important area for nature conservation. However, little is known about the distribution of forest species in anthropized areas undergoing natural regeneration. Understanding the dynamics of ecological succession is fundamental to the decision-making process regarding revegetation of anthropic areas in the Cerrado. Thus, this study aimed to evaluate the phytosociological patterns of natural regeneration in areas anthropized by agricultural uses in the Cerrado in different soil and environmental conditions. For this purpose, the study was performed in an anthropized area that has been protected from anthropic actions since 2002. A floristic survey of forest species was carried out, and soil samples were collected at depths of 0–5, 5–10, and 10–20xa0cm to determine the physical and chemical properties of the soil. The distribution of forest species with respect to the soil characteristics was determined using multivariate analysis. The distribution of the forest species was shown to be influenced by the soil properties and the degree of succession of the vegetation. Furthermore, the natural regeneration process resulted in an improvement in the chemical properties of soils in the Gleysol class. This pattern is related to the slow decomposition of organic matter, being associated with an environment that has greater water availability and, consequently, less nutrient loss from leaching during the cycling mechanisms responsible for the return of nutrients to the soil.

Probable monthly rainfall associated with distinct biomes of Mato Grosso do Sul state

The aim of this study was to determine the probable monthly rainfall for the state of Mato Grosso do Sul, considering the level of 75% probability, and study the spatial distribution associated with its different biomes. The rainfall data of 32 stations (sites) in the state of Mato Grosso do Sul were collected in the period 1954-2013. In each of the 384 series, the average monthly rainfall was calculated, for at least 30 years of observation. The Kolmogorov-Smirnov adhesion test was applied to the rainfall time series to check the fit of the data to a normal distribution. The likely fallout was estimated at 75% probability, using the normal probability distribution and, subsequently, it was adopted the method of Ordinary Kriging interpolation mathematics to spatial data. Based on the likely monthly precipitation estimated, the State of Mato Grosso do Sul possess three distinct periods, with the precipitation associated with different biomes: the rainy season (between the months November to March, where increased precipitation occurred in the Savanna biome), dry season (between the months from June to August, when the highest rainfall occurred in the Atlantic Forest) and transition period (April and May and September and October).