RESUMOO trabalho teve objetivo estudar a variabilidade temporal da temperatura do ar, precipitação pluviométrica e umidade relativa do ar na cidade de Botucatu-SP, Brasil, utilizando técnicas geoestatísticas. Os dados de precipitação pluviométrica, temperatura do ar e umidade relativa do ar utilizados no presente estudo são provenientes da Estação Meteorológica da Fazenda Lageado, da Faculdade de Ciências Agronômicas-UNESP. As observações foram realizadas no período de 1988 a 2007, referem-se ao total mensal de precipitação pluvial expressa em altura de lâmina d'água (mm), médias mensais de temperatura em ºC e umidade relativa em %. Os dados foram avaliados por meio da estatística clássica e geoestatística. As variáveis climáticas tiveram sua dependência verificada por variogramas, apresentando dependência temporal maior que 76%. A série temporal de umidade relativa do ar foi a que apresentou maior alcance (8,67 meses) e, conseqüentemente, maior estabilidade climática. O conhecimento da distribuição temporal das variáveis climáticas é importante para o estudo e realização do zoneamento agroclimático, bem como para o dimensionamento do sistema de irrigação das culturas. UNITERMOS: geoestatística, mapeamento e krigagem SARTORI, A. A. C.; SILVA, A. F.; RAMOS, C. M. C; ZIMBACK, C. R. L. TEMPORAL VARIABILITY AND CLIMATE DATA MAPS OF BOTUCATU-SP ABSTRACTThe objective of this research to study the temporal variability of air temperature, rainfall and relative humidity at Botucatu-SP, Brazil, using geostatistics techniques. The data of rainfall, air temperature and relative humidity used in this study were obtamed from the Meteorological Station of the Agricultural Sciences College. The observations were made in the period from 1988 to 2007 and refer to the total monthly rainfall expressed in water depth (mm), average monthly of temperature in °C and relative humidity in %. The data were evaluated by means of classical statistics and geostatistics. The climatic variables were their dependence verified by variogramas, presenting temporal dependence greater than 76%. The temporal series of relative humidity presented the greatest value (8.67 months) and, consequently, more stability climate. Knowledge of the temporal distribution of climate variables is important for the study and realization of agroclimatic zoning and for design measurement of rrigation systems.
Resumo -Este trabalho foi realizado para estudar a variabilidade espacial do fósforo, estimando-a através de cokrigagem, tendo como co-variáveis as frações granulométricas do solo. O estudo foi realizado na fazenda experimental do INCAPER-ES. O solo foi amostrado na projeção da copa da cultura e na profundidade de 0-0,20 m, em uma malha irregular com 109 pontos. Os dados foram, inicialmente, submetidos a uma análise descritiva e de correlação. Por meio da geoestatística realizou-se o ajuste dos variogramas. O P apresentou correlação significativa com as frações areia e argila indicando que áreas com maiores concentrações de argila apresentam menor disponibilidade desse nutriente. Ambas as frações possuem igual eficiência como co-variável na estimativa dos níveis de P no solo.Palavras-chave -Geoestatística. Solos. Teor de Fósforo. Latossolos. Interpolação.Abstract -This study was conducted to study the spatial variability of phosphorus, estimating it through cokriging taking as covariables the size fractions of soil. The study was conducted at the experimental farm INCAPER-ES. The soil was sampled in the canopy projection of culture and depth of 0-0.20 meters in an irregular mesh with 109 points. The data were initially submitted to a descriptive analysis and correlation. Through geostatistics was made the adjustment of the variograms. The P showed significant correlation with the sand and clay fractions indicating that areas with higher concentrations of clay have lower availability of this nutrient. Both fractions have equal performance as co-variable in the estimate of the levels of P in the soil.
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%.
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