Priscila Pereira Coltri

Analysis of large scale climate data: how well climate change models and data from real sensor networks agree?

Research on global warming and climate changes has attracted a huge attention of the scientific community and of the media in general, mainly due to the social and economic impacts they pose over the entire planet. Climate change simulation models have been developed and improved to provide reliable data, which are employed to forecast effects of increasing emissions of greenhouse gases on a future global climate. The data generated by each model simulation amount to Terabytes of data, and demand fast and scalable methods to process them. In this context, we propose a new process of analysis aimed at discriminating between the temporal behavior of the data generated by climate models and the real climate observations gathered from ground-based meteorological station networks. Our approach combines fractal data analysis and the monitoring of real and model-generated data streams to detect deviations on the intrinsic correlation among the time series defined by different climate variables. Our measurements were made using series from a regional climate model and the corresponding real data from a network of sensors from meteorological stations existing in the analyzed region. The results show that our approach can correctly discriminate the data either as real or as simulated, even when statistical tests fail. Those results suggest that there is still room for improvement of the state-of-the-art climate change models, and that the fractal-based concepts may contribute for their improvement, besides being a fast, parallelizable, and scalable approach.

Land use temporal analysis through clustering techniques on satellite image time series

Satellite images time series have been used to study land surface, such as identification of forest, water, urban areas, as well as for meteorological applications. However, for knowledge discovery in large remote sensing databases can be use clustering techniques in multivariate time series. The clustering technique on three-dimensional time series of NDVI, albedo and surface temperature from AVHRR/NOAA satellite images was used, in this study, to map the variability of land use. This approach was suitable to accomplish the temporal analysis of land use. Additionally, this technique can be used to identify and analyze dynamics of land use and cover being useful to support researches in agriculture, even considering low spatial resolution satellite images. The possibility of extracting time series from satellite images, analyzing them through data mining techniques, such as clustering, and visualizing results in geospatial way is an important advance and support to agricultural monitoring tasks.

Collaborative Innovation in Agrometeorology: Coordination Strategies to Develop a Monitoring IT System for Brazil

This case-study article presents the results from a morphology analysis of a knowledge and information network, focusing on the coordination mechanisms employed to generate a convergent arrangement. Agritempo was the first information system to offer (in 2003) free access to a broad range of agrometeorological data comprising all the Brazilian territory, representing an important technological innovation to the agricultural sector. To study this phenomenon an analytical framework of the Techno-Economic Network (TEN) and concepts from the Innovation Sociology field was employed. Results indicate that the durability of this arrangement – from 2003 to 2014 – can be explained by the effectiveness of the coordination strategies established in the network such as: trust based relationships; institutional and individual leadership actions; contracting; software applications and shared common working procedures.

Carbon stock estimation in coffee crops using high resolution satellites

According to IPCC, the increase of greenhouse gases emissions (GHG) in atmosphere is causing global warming, and this phenomenon could increase global temperature. In tropical areas of Brazil, the air temperature is supposed to increase from 1.1°C to 6.4°C causing large impacts in agricultures areas, including coffee production regions. The main objective of this paper was quantify the biomass of Arabica coffee trees above-ground (and carbon stock) using the vegetation index NDVI based on a high resolution image (Geoeye-1) and biophysical measures of coffee trees. In addition, the study aimed to establish an empirical relationship between biophysical measures of Arabica coffee trees, remote sensing data and dry biomass. The study was conducted in the south of Minas Gerais, which is the main producing region of Arabica coffee in Brazil. It was conclude that NDVI based on images of high spatial resolution, such as from Geoeye-1 satellite, has a strong correlation with dry biomass and carbon sink, showing that it is possible to estimate the carbon stock of coffee crops using remote sensing data without destructive methods.

Evi's estimation to improve the monitoring of sugarcane using TRMM satellite data

This paper presents an analysis of relation between EVI and TRMM data to improve the monitoring of sugarcane production in south-central Brazil. As this region has a deficient network of ground-based meteorological stations, we proposed to use TRMM satellite data in order to complete lack of data. As both data from TRMM and meteorological ground-based stations presented a high correlation, as well as there are cross-correlation between precipitation and vegetation index data, we proposed a formula to estimate EVI values from TRMM precipitation series. Results indicate the potential of using medium spatial resolution satellite in agriculture specially to regional monitoring in a country of continental dimensions such as Brazil.

To be or not to be real: fractal analysis of data streams from a regional climate change model

This paper proposes a new analysis process aimed at discriminating the temporal behavior of the data generated by climate models from the real climate observations gathered from ground-based meteorological stations. Our approach combines fractal data analysis and the monitoring of the real and the model-generated data streams to detect deviations considering the intrinsic correlation among climate time series. Experimental studies showed that our approach can discriminate the data either as real or as generated by a model. Those results suggest that there are yet space to improve the climate change models, and that the fractal-based concepts may contribute in this improvement.