Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

Using different satellite imagery and classification techniques to assess the contribution of trees outside forests in the municipality of Maringá, Brazil

Forest cover of the Maringá municipality, located in northern Parana State, was mapped in this study. Mapping was carried out by using high-resolution HRC sensor imagery and medium resolution CCD sensor imagery from the CBERS satellite. Images were georeferenced and forest vegetation patches (TOFs - trees outside forests) were classified using two methods of digital classification: reflectance-based or the digital number of each pixel, and object-oriented. The areas of each polygon were calculated, which allowed each polygon to be segregated into size classes. Thematic maps were built from the resulting polygon size classes and summary statistics generated from each size class for each area. It was found that most forest fragments in Maringá were smaller than 500 m². There was also a difference of 58.44% in the amount of vegetation between the high-resolution imagery and medium resolution imagery due to the distinct spatial resolution of the sensors. It was concluded that high-resolution geotechnology is essential to provide reliable information on urban greens and forest cover under highly human-perturbed landscapes.

Spatial analysis of leprosy incidence and associated socioeconomic factors

OBJECTIVE: To identify clusters of the major occurrences of leprosy and their associated socioeconomic and demographic factors. METHODS: Cases of leprosy that occurred between 1998 and 2007 in São José do Rio Preto (southeastern Brazil) were geocodified and the incidence rates were calculated by census tract. A socioeconomic classification score was obtained using principal component analysis of socioeconomic variables. Thematic maps to visualize the spatial distribution of the incidence of leprosy with respect to socioeconomic levels and demographic density were constructed using geostatistics. RESULTS: While the incidence rate for the entire city was 10.4 cases per 100,000 inhabitants annually between 1998 and 2007, the incidence rates of individual census tracts were heterogeneous, with values that ranged from 0 to 26.9 cases per 100,000 inhabitants per year. Areas with a high leprosy incidence were associated with lower socioeconomic levels. There were identified clusters of leprosy cases, however there was no association between disease incidence and demographic density. There was a disparity between the places where the majority of ill people lived and the location of healthcare services. CONCLUSIONS: The spatial analysis techniques utilized identified the poorer neighborhoods of the city as the areas with the highest risk for the disease. These data show that health departments must prioritize politico-administrative policies to minimize the effects of social inequality and improve the standards of living, hygiene, and education of the population in order to reduce the incidence of leprosy.

Spatial analysis of new cases of leprosy in the State of Espírito Santo, Brazil, between 2004 and 2009

INTRODUCTION: Leprosy in Brazil is a public health issue, and there are many regions in the State of Espírito Santo with high endemic incidence levels of leprosy, characterizing this state as a priority for leprosy programs. The aim of this study was to determine the spatial distribution of coefficients of new cases of leprosy in the State of Espírito Santo, Brazil. METHODS: We conducted a descriptive and ecologic study based on the spatial distribution of leprosy in the State of Espírito Santo between 2004 and 2009. Data were gathered from the available records of the Espírito Santo State Health Secretary. The global and local Bayesian empirical methods were used to produce an estimate of leprosy risk, smoothing the fluctuation effects of the detection coefficients. RESULTS: The study resulted in a coefficient adjustment of new cases in 10 towns that changed their classification, among which, 2 went from low to medium, 4 from medium to high, 3 from high to very high, and 1 from very high to hyper-endemic. An average variation of 1.02, fluctuating between 0 and 12.39 cases/100,000 inhabitants, was found in a comparative calculation between the Local Ebest value and the average coefficient of new leprosy cases in the State of Espírito Santo. CONCLUSIONS: The spatial analysis of leprosy favors the establishment of control strategies with a better cost-benefit relationship since it reveals specific and priority regions, thereby enabling the development of actions that can interfere in the transmission chain.

Spatial and temporal evolution of family-farming land use in the Tapajós region of the Brazilian Amazon

Pressures on the Brazilian Amazon forest have been accentuated by agricultural activities practiced by families encouraged to settle in this region in the 1970s by the colonization program of the government. The aims of this study were to analyze the temporal and spatial evolution of land cover and land use (LCLU) in the lower Tapajós region, in the state of Pará. We contrast 11 watersheds that are generally representative of the colonization dynamics in the region. For this purpose, Landsat satellite images from three different years, 1986, 2001, and 2009, were analyzed with Geographic Information Systems. Individual images were subject to an unsupervised classification using the Maximum Likelihood Classification algorithm available on GRASS. The classes retained for the representation of LCLU in this study were: (1) slightly altered old-growth forest, (2) succession forest, (3) crop land and pasture, and (4) bare soil. The analysis and observation of general trends in eleven watersheds shows that LCLU is changing very rapidly. The average deforestation of old-growth forest in all the watersheds was estimated at more than 30% for the period of 1986 to 2009. The local-scale analysis of watersheds reveals the complexity of LCLU, notably in relation to large changes in the temporal and spatial evolution of watersheds. Proximity to the sprawling city of Itaituba is related to the highest rate of deforestation in two watersheds. The opening of roads such as the Transamazonian highway is associated to the second highest rate of deforestation in three watersheds.

Mineralogy of the clay fraction of alfisols in two slope curvatures: IV - spatial correlation with physical properties

Although the influence of clay mineralogy on soil physical properties has been widely studied, spatial relationships between these features in Alfisols have rarely been examined. The purpose of this work was to relate the clay minerals and physical properties of an Alfisol of sandstone origin in two slope curvatures. The crystallographic properties such as mean crystallite size (MCS) and width at half height (WHH) of hematite, goethite, kaolinite and gibbsite; contents of hematite and goethite; aluminium substitution (AS) and specific surface area (SSA) of hematite and goethite; the goethite/(goethite+hematite) and kaolinite/(kaolinite+gibbsite) ratios; and the citrate/bicarbonate/dithionite extractable Fe (Fe d) were correlated with the soil physical properties through Pearson correlation coefficients and cross-semivariograms. The correlations found between aluminium substitution in goethite and the soil physical properties suggest that the degree of crystallinity of this mineral influences soil properties used as soil quality indicators. Thus, goethite with a high aluminium substitution resulted in large aggregate sizes and a high porosity, and also in a low bulk density and soil penetration resistance. The presence of highly crystalline gibbsite resulted in a high density and micropore content, as well as in smaller aggregates. Interpretation of the cross-semivariogram and classification of landscape compartments in terms of the spatial dependence pattern for the relief-dependent physical and mineralogical properties of the soil proved an effective supplementary method for assessing Pearson correlations between the soil physical and mineralogical properties.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Spatial variability in nutritional status of arabic coffee based on dris index

The combined use of precision agriculture and the Diagnosis and Recommendation Integrated System (DRIS) allows the spatial monitoring of coffee nutrient balance to provide more balanced and cost-effective fertilizer recommendations. The objective of this work was to evaluate the spatial variability in the nutritional status of two coffee varieties using the Mean Nutritional Balance Index (NBIm) and its relationship with their respective yields. The experiment was conducted in eastern Minas Gerais in two areas, one planted with variety Catucaí and another with variety Catuaí. The NBIm of the two varieties and their yields were analyzed through geostatistics and, based on the models and parameters of the variograms, were interpolated to obtain their spatial distribution in the studied areas. Variety Catucai, with grater spatial variability, was more nutritional unbalanced than variety Catuai, and consequently produced lower yields. Excess of Fe and Mn makes these elements limiting yield factors.

Spatial variability of particle size fractions of an Oxisol cultivated with conilon coffee

Information on the spatial distribution of particle size fractions is essential for use planning and management of soils. The aim of this work to was to study the spatial variability of particle size fractions of a Typic Hapludox cultivated with conilon coffee. The soil samples were collected at depths of 0-0.20 and 0.20-0.40 m in the coffee canopy projection, totaling 109 georeferentiated points. At the depth of 0.2-0.4 m the clay fraction showed average value significantly higher, while the sand fraction showed was higher in the depth of 0-0.20 m. The silt showed no significant difference between the two depths. The particle size fractions showed medium and high spatial variability. The levels of total sand and clay have positive and negative correlation, respectively, with the altitude of the sampling points, indicating the influence of landscape configuration.

Comparison of methods for classification of the coefficient of variation in papaya

ABSTRACT The objective of this work was to study the distribution of values of the coefficient of variation (CV) in the experiments of papaya crop (Carica papaya L.) by proposing ranges to guide researchers in their evaluation for different characters in the field. The data used in this study were obtained by bibliographical review in Brazilian journals, dissertations and thesis. This study considered the following characters: diameter of the stalk, insertion height of the first fruit, plant height, number of fruits per plant, fruit biomass, fruit length, equatorial diameter of the fruit, pulp thickness, fruit firmness, soluble solids and internal cavity diameter, from which, value ranges were obtained for the CV values for each character, based on the methodology proposed by Garcia, Costa and by the standard classification of Pimentel-Gomes. The results obtained in this study indicated that ranges of CV values were different among various characters, presenting a large variation, which justifies the necessity of using specific evaluation range for each character. In addition, the use of classification ranges obtained from methodology of Costa is recommended.

Effect of spatial arrangement on the production components and yield of sunflower

ABSTRACT The sunflower plant is an oilseed crop that has aroused a great interest in the Brazilian and international scenery especillay because of the possibility of using its oil for biodiesel production. The objective of this study was to evaluate productivity and yield components of Embrapa 122 sunflower according to the spatial arrangement. Treatments were arranged in 4 x 4 factorial arrangement, which are the four spacings between rows (0.30; 0.50; 0.70 and 0.90 m) and four sowing densities (30,000; 45,000; 60,000 and 75,000 plants ha-1). The experiment was carried out in a complete randomized block design with four replications. The experiments were conducted in the experimental area of the Plant Science Department in Fortaleza, State of Ceará-Brazil and on the Curu Vale Experimental Farm in Pentecoste, State of Ceará-Brazil. Productivity and the following production components were analyzed in the end of the crop cycle: harvested capitula, capitulum diameter, capitulum mass, achene mass per capitulum, mass of 100 achenes, achenes per capitulum, harvest index and oil content in the achenes. The experiments were analyzed jointly in relation to the cropping area and the data submitted to analysis of variance and quantitative factors tested by polynomial regression. The variables, spacing, density and cropping area did not interact with these variables and the spatial arrangement of the plants affected only the components. The cropping area influences the productive behavior of sunflower Embrapa 122. The spatial arrangement of the plants of sunflower of variety Embrapa 122 influences yield components but does not affect productivity.

Comparative evaluation of underlying causes of death processed by the Automated Classification of Medical Entities and the Underlying Cause of Death Selection Systems

INTRODUCTION: The correct identification of the underlying cause of death and its precise assignment to a code from the International Classification of Diseases are important issues to achieve accurate and universally comparable mortality statistics These factors, among other ones, led to the development of computer software programs in order to automatically identify the underlying cause of death. OBJECTIVE: This work was conceived to compare the underlying causes of death processed respectively by the Automated Classification of Medical Entities (ACME) and the "Sistema de Seleção de Causa Básica de Morte" (SCB) programs. MATERIAL AND METHOD: The comparative evaluation of the underlying causes of death processed respectively by ACME and SCB systems was performed using the input data file for the ACME system that included deaths which occurred in the State of S. Paulo from June to December 1993, totalling 129,104 records of the corresponding death certificates. The differences between underlying causes selected by ACME and SCB systems verified in the month of June, when considered as SCB errors, were used to correct and improve SCB processing logic and its decision tables. RESULTS: The processing of the underlying causes of death by the ACME and SCB systems resulted in 3,278 differences, that were analysed and ascribed to lack of answer to dialogue boxes during processing, to deaths due to human immunodeficiency virus [HIV] disease for which there was no specific provision in any of the systems, to coding and/or keying errors and to actual problems. The detailed analysis of these latter disclosed that the majority of the underlying causes of death processed by the SCB system were correct and that different interpretations were given to the mortality coding rules by each system, that some particular problems could not be explained with the available documentation and that a smaller proportion of problems were identified as SCB errors. CONCLUSION: These results, disclosing a very low and insignificant number of actual problems, guarantees the use of the version of the SCB system for the Ninth Revision of the International Classification of Diseases and assures the continuity of the work which is being undertaken for the Tenth Revision version.

Comorbidity adjustment index for the international classification of diseases, 10

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >1, and their mortality rate was 4.5% (66.0% of them had scores >1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.

Spatial analysis of urban violence based on emergency room data

OBJECTIVE: To estimate the spatial intensity of urban violence events using wavelet-based methods and emergency room data. METHODS: Information on victims attended at the emergency room of a public hospital in the city of São Paulo, Southeastern Brazil, from January 1, 2002 to January 11, 2003 were obtained from hospital records. The spatial distribution of 3,540 events was recorded and a uniform random procedure was used to allocate records with incomplete addresses. Point processes and wavelet analysis technique were used to estimate the spatial intensity, defined as the expected number of events by unit area. RESULTS: Of all georeferenced points, 59% were accidents and 40% were assaults. There is a non-homogeneous spatial distribution of the events with high concentration in two districts and three large avenues in the southern area of the city of São Paulo. CONCLUSIONS: Hospital records combined with methodological tools to estimate intensity of events are useful to study urban violence. The wavelet analysis is useful in the computation of the expected number of events and their respective confidence bands for any sub-region and, consequently, in the specification of risk estimates that could be used in decision-making processes for public policies.