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%.

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socioeconomic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense vegetation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain's water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9% accurate.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Estimation of soil moisture in the root-zone from remote sensing data

Field-based soil moisture measurements are cumbersome. Thus, remote sensing techniques are needed because allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. The objective of the study was to evaluate the accuracy of an empirical relationship to calculate soil moisture from remote sensing data of irrigated soils of the Apodi Plateau, in the Brazilian semiarid region. The empirical relationship had previously been tested for irrigated soils in Mexico, Egypt, and Pakistan, with promising results. In this study, the relationship was evaluated from experimental data collected from a cotton field. The experiment was carried out in an area of 5 ha with irrigated cotton. The energy balance and evaporative fraction (Λ) were measured by the Bowen ratio method. Soil moisture (θ) data were collected using a PR2 - Profile Probe (Delta-T Devices Ltd). The empirical relationship was tested using experimentally collected Λ and θ values and was applied using the Λ values obtained from the Surface Energy Balance Algorithm for Land (SEBAL) and three TM - Landsat 5 images. There was a close correlation between measured and estimated θ values (p<0.05, R² = 0.84) and there were no significant differences according to the Student t-test (p<0.01). The statistical analyses showed that the empirical relationship can be applied to estimate the root-zone soil moisture of irrigated soils, i.e. when the evaporative fraction is greater than 0.45.

Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO4(3-), NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.

Proposal for an index to classify irrigation water quality: a case study in northeastern Brazil

One way of classifying water quality is by means of indices, in which a series of parameters analyzed are joined a single value, facilitating the interpretation of extensive lists of variables or indicators, underlying the classification of water quality. The objective of this study was to develop a statistically based index to classify water according to the Irrigation Water Quality Index (IWQI), to evaluate the ionic composition of water for use in irrigation and classify it by its source. For this purpose, the database generated during the Technology Generation and Adaptation (GAT) program was used, in which, as of 1988, water samples were collected monthly from water sources in the states of Paraíba, Rio Grande do Norte and Ceará. To evaluate water quality, the electrical conductivity (EC) of irrigation water was taken as a reference, with values corresponding to 0.7 dS m-1. The chemical variables used in this study were: pH, EC, Ca, Mg, Na, K, Cl, HCO3, CO3, and SO4. The data of all characteristics evaluated were standardized and data normality was confirmed by Lilliefors test. Then the irrigation water quality index was determined by an equation that relates the standardized value of the variable with the number of characteristics evaluated. Thus, the IWQI was classified based on indices, considering normal distribution. Finally, these indices were subjected to regression analysis. The method proposed for the IWQI allowed a satisfactory classification of the irrigation water quality, being able to estimate it as a function of EC for the three water sources. Variation in the ionic composition was observed among the three sources and within a single source. Although the water quality differed, it was good in most cases, with the classification IWQI II.

Genetic diversity of irrigated barley based on molecular and quantitative data and on malting quality

The objective of this work was to quantify the genetic diversity of elite genotypes of irrigated barley in the Brazilian savanna. Thirty elite barley genotypes from Embrapa Cerrados' collection were evaluated using 160 RAPD markers, 12 agronomic traits related to yield components, and 10 malting quality parameters. The genetic dissimilarity matrices based on molecular markers, quantitative traits, and malting quality characters were calculated and a cluster analysis was performed using the unweighted pair-group method with arithmetic mean (UPGMA) as grouping criterion. High genetic diversity among accessions were observed. The estimated genetic dissimilarities were weakly correlated, showing the complementarity of the different character groups. Selection indices and graphical dispersion analysis allowed the selection of promising genotypes and the indication of suitable crosses for maximizing the heterotic effects in breeding programs for irrigated barley in the Brazilian savanna.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Effect of the application of different water depths and nitrogen and potassium doses on quality of Tanzania grass

The objective of this study was to evaluate the effects of the application of different water depths and nitrogen and potassium doses in the quality of Tanzania grass, in the southern of the state of Tocantins. The experiment was conducted on strips of traditional sprinklers, and used, as treatments, a mixture of fertilizer combinations of N and K2O always in the ratio of 1 N:0.8 K2O. This study determined throughout the experiment: plant height (PH), the crude protein (CP) and neutral detergent fiber (NDF). The highest plant height obtained was 132.4 cm, with a fertilizer dose of 691.71 kg ha-1 in the proportion of N:0.8 K2O, in other words, 384.28 kg ha-1 of N and 307.43 kg ha-1 of K2O, and water depth of 80% of the ETc. The highest crude protein content was 12.2%, with the fertilizer dose application of 700 kg ha-1 yr-1 in the proportion of 1 N to 0.8 of K2O, in other words, 388.89 kg ha-1 of N and 311.11 kg ha-1 of K2O and absence of irrigation. The lowest level of neutral detergent fiber was 60.7% with the application of the smallest dose of fertilizer and highest water depth. It was concluded in this study that there was an increase in plant height by increasing the fertilizer dose and water depth. The crude protein content increased 5.4% in the dry season, by increasing the fertilizer dose and water depth. In the dry season, there was an increase of NDF content by 4.5% by increasing the application of fertilizer and water depth.

Pedological characterization and land use and occupation: influences on water quality in a watershed located in the zona da mata (MG)

In order to verify the influence of class and use and occupation of land on water quality, it was performed the characterization and analyzes of land use and monitoring of seven springs inserted in a watershed located in the rural of Viçosa city - Minas Gerais (MG) state, in Brazil. Through soil analyzes, carried out in five different profiles, it was possible to identify three distinct pedological classes: Argisol, Cambisol and Latosol. Furthermore, the concentrations of Fe, Mn, Cu, Zn, Cr, Cd, Pb and Ni were identified in each of the horizons in the considered profiles. Water samples were collected and analyzed monthly, over eight months, twenty two parameters of water quality. Comparing the results of each survey, it was possible to identify a relation between water quality and land use in the round of the springs, considering color, BOD, DO, E. coli and Mn as the most affected parameters, influenced mainly by soil characteristics and the presence of a large percentage of pasture in the study area.

Using an aerial system of remote sensing to detect different nutritional status in Brachiaria decumbens

The aim of this study was to use digital images acquired by cameras attached to a helium balloon to detect variation of the nutritional status in Brachiaria decumbens. The treatments consisted of five doses of nitrogen (0, 50, 100, 150 e 200kg ha-1) with six replications each, evaluated in a completely randomized statistical design. A remote sensing system composed of digital cameras and microcomputers was used for image acquisition, and a helium balloon lifted the cameras to the heights of 15, 20, 25 and 30m. A portable chlorophyll meter and analyses of leaf nitrogen content were used to make comparisons with data obtained by the remote sensing system. Data was acquired in two phases, in different climatic conditions. At the end of each phase, dry matter production was measured. Three vegetation indices were used to evaluate the detection of different nutritional status. The three indices were able to detect the effects of N doses. The indices constructed with the Green spectral band showed to be more efficient.

Water quality indicators for environmental and resistance profile of Escherichia coli strains isolated in Rio Cascavel, Paraná, Brazil

In this article it was evaluated the quality of water in the Cascavel river, in the city of Cascavel - Paraná using microbiological indicators, physical and chemical pollution and susceptibility / resistance in strains of Escherichia coli isolated antimicrobial trade. The water sampling was conducted between 2010-July and 2011-June at three points: a) near the source, b) urban area, c) rural area. The samples were analyzed for physical, chemical and microbiological variables: temperature, pH, color, turbidity, electrical conductivity, total nitrogen and total phosphorus, total coliforms (CT), fecal coliform (CTe) and Escherichia coli. Tests were also performed to nine antimicrobial commercial resistances. The variables studied indicated that the Cascavel river water was presented at disagreement with the resolution 357/2005 CONAMA (class I), ranking in the index as regular water quality. The physical, chemical and rainfall did not affect the growth of CT and CTe, with higher counts of E. coli in the urban area. The greatest resistance profiles of the strains of E. coli isolated from Cascavel river water was found in section 2, the urban area as a probable consequence of human influence on water quality.

Evaluation of maternal and neonatal hospital care: quality index of completeness

OBJECTIVE Develop an index to evaluate the maternal and neonatal hospital care of the Brazilian Unified Health System.METHODS This descriptive cross-sectional study of national scope was based on the structure-process-outcome framework proposed by Donabedian and on comprehensive health care. Data from the Hospital Information System and the National Registry of Health Establishments were used. The maternal and neonatal network of Brazilian Unified Health System consisted of 3,400 hospitals that performed at least 12 deliveries in 2009 or whose number of deliveries represented 10.0% or more of the total admissions in 2009. Relevance and reliability were defined as criteria for the selection of variables. Simple and composite indicators and the index of completeness were constructed and evaluated, and the distribution of maternal and neonatal hospital care was assessed in different regions of the country.RESULTS A total of 40 variables were selected, from which 27 single indicators, five composite indicators, and the index of completeness of care were built. Composite indicators were constructed by grouping simple indicators and included the following variables: hospital size, level of complexity, delivery care practice, recommended hospital practice, and epidemiological practice. The index of completeness of care grouped the five variables and classified them in ascending order, thereby yielding five levels of completeness of maternal and neonatal hospital care: very low, low, intermediate, high, and very high. The hospital network was predominantly of small size and low complexity, with inadequate child delivery care and poor development of recommended and epidemiological practices. The index showed that more than 80.0% hospitals had a low index of completeness of care and that most qualified heath care services were concentrated in the more developed regions of the country.CONCLUSIONS The index of completeness proved to be of great value for monitoring the maternal and neonatal hospital care of Brazilian Unified Health System and indicated that the quality of health care was unsatisfactory. However, its application does not replace specific evaluations.

Benthic macroinvertebrates as bioindicators of water quality in an Atlantic forest fragment

The objective of this study was to evaluate benthic macroinvertebrate communities as bioindicators of water quality in five streams located in the "Reserva Particular do Patrimônio Natural" (RPPN) Mata Samuel de Paula and its surroundings, in the municipality of Nova Lima near the city of Belo Horizonte, Minas Gerais State, southeastern Brazil. This region has been strongly modified by human activities including mining and urbanization. Samples were collected in the field every three months between August 2004 and November 2005, totaling six samplings in the rainy and dry seasons. This assessment identified one area ecologically altered while the other sampling sites were found to be minimally disturbed systems, with well-preserved ecological conditions. However, according to the Biological Monitoring Work Party (BMWP) and the Average Score Per Taxon (ASPT) indices, all sampling sites had excellent water quality. A total of 14,952 organisms was collected, belonging to 155 taxa (148 Insecta, two Annelida, one Bivalvia, one Decapoda, one Planariidae, one Hydracarina, and one Entognatha). The most abundant benthic groups were Chironomidae (47.9%), Simuliidae (12.3%), Bivalvia (7.5%), Decapoda (6.1%), Oligochaeta (5.2%), Polycentropodidae (3.7%), Hydropsychidae (2.5%), Calamoceratidae (1.8%), Ceratopogonidae (1.7%), and Libellulidae (1.2%). The assessment of the benthic functional feeding groups showed that 34% of the macroinvertebrates were collector-gatherers, 29% predators, 24% collector-filterers, 8% shredders, and 5% scrapers. The RPPN Mata Samuel de Paula comprises diversified freshwater habitats that are of great importance for the conservation of many benthic taxa that are intolerant to organic pollution.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).