Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Space-temporal dynamics of limnological variables in a floodplain wetland located at Ribeira do Iguape Valley (São Paulo, Brazil) at different hydrological periods

AIM: The main goal of this research was to investigate the influence of the hydrological pulses on the space-temporal dynamics of physical and chemical variables in a wetland adjacent to Jacupiranguinha River (São Paulo, Brazil); METHODS: Eleven sampling points were distributed among the wetland, a tributary by its left side and the adjacent river. Four samplings were carried out, covering the rainy and the dry periods. Measures of pH, dissolved oxygen, electrical conductivity and redox potential were taken in regular intervals of the water column using a multiparametric probe. Water samples were collected for the nitrogen and total phosphorus analysis, as well as their dissolved fractions (dissolved inorganic phosphorus, total dissolved phosphorus, ammoniacal nitrogen and nitrate). Total alkalinity and suspended solids were also quantified; RESULTS: The Multivariate Analysis of Variance showed the influence of the seasonality on the variability of the investigated variables, while the Principal Component Analysis gave rise in two statistical significant axes, which delimited two groups representative of the rainy and dry periods. Hydrological pulses from Jacupiranguinha River, besides contributing to the inputs of nutrients and sediments during the period of connectivity, accounted for the decrease in spatial gradients in the wetland. This "homogenization effect" was evidenced by the Cluster Analysis. The research also showed an industrial raw effluent as the main point source of phosphorus to the Jacupiranguinha River and, indirectly, to the wetland; CONCLUSIONS: Therefore, considering the scarcity of information about the wetlands in the study area, this research, besides contributing to the understanding of the influence of hydrological pulses on the investigated environmental variables, showed the need for adoption of conservation policies of these ecosystems face the increase anthropic pressures that they have been submitted, which may result in lack of their ecological, social and economic functions.