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. © 2012 IEEE.66096612Geoscience and Remote Sensing Society (GRS)Goldemberg, J., Coelho, S.T., Guardabassi, P., The sustainability of ethanol production from sugarcane (2008) Energy Policy, 36, pp. 2086-2097Gonçalves, R.R.V., Zullo Junior, J., Ferraresso, C.S., Sousa, E.P.M., Romani, L.A.S., Traina, A.J.M., Analysis of NOAA/AVHRR multitemporal images, climate conditions and cultivated land of sugarcane fields applied to agricultural monitoring (2011) Sixth International Workshop on the Analysis of Multi-temporal Remote Sensing Images (MultiTemp-2011), 6, pp. 229-232. , Trento, ItaliaRudorff, B.F.T., Adami, M., De Aguiar, D.A., Gusso, A., Da Silva, W.F., De Freitas, R.M., Temporal series of EVI/MODIS to identify land converted to sugarcane IEEE International Geoscience and Remote Sensing Symposium - IGARSS 2009, 4 (2009), pp. 252-255. , Cape Town, South AfricaGonçalves, R.R.V., Nascimento, C.R., Zullo Junior, J., Romani, L.A.S., Relationship between the spectral response of sugar cane, based on AVHRR/NOAA satellite images, and the climate condition, in the state of Sao Paulo (Brazil), from 2001 to 2008 (2009) Fifth International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp-2009), 5, pp. 315-322. , Groton, ConnecticutGonçalves, R.R.V., Zullo Jr., J., Romani, L.A.S., Nascimento, C.R., Traina, A.J.M., Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil (2012) International Journal of Remote Sensing, 33 (15), pp. 4653-4672Jiang, Z., Huete, A.R., Didan, K., Miura, T., Development of a two-band Enhanced Vegetation Index without a blue band (2008) Remote Sensing of Environment, 112 (10), pp. 3833-3845Freitas, R.M., Arai, E., Adami, M., Souza, A.F., Sato, F.Y., Shimabukuro, Y.E., Rosa, R.R., Rudorff, B.F.T., Virtual laboratory of remote sensing time series: Visualization of MODIS EVI2 data set over South America (2011) Journal of Computational Interdisciplinary Sciences, 2 (1), pp. 57-6

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. © 2012 IEEE.66576660Geoscience and Remote Sensing Society (GRS)Zullo Jr., J., Pinto, H.S., Assad, E.D., Ávila, A.M.H., Potential for growing Arabica coffee in the extreme south of Brazil in a warmer world (2011) Climatic Change, 10, pp. 535-548Assad, E.D., Pinto, H.S., Zullo Jr., J., Ávila, A.M.H., Impacto das mudanças climáticas no zoneamento agroclimático do café no Brasil (2004) Pesquisa Agropecuária Brasileira, 39 (11), pp. 1057-1064Assad, E.D., Pinto, H.S., Zullo Jr., J., Impacts of global warming in the Brazilian agroclimatic risk zoning (2007) A Contribution to Understanding the Regional Impacts of Global Change in South America, pp. 175-182. , da Silva Dias PLS, Ribeiro WC, Nunes LH (eds). Instituto de Estudos Avançados da Universidade de São PauloAssad, E.D., Pinto, H.S., Zullo Jr., J., Marin, F.R., Mudanças climáticas e agricultura: uma abordagem agroclimatológica (2007) Ciência e Ambiente, 34, pp. 169-182Jrj, Z., Pinto, H.S., Assad, E.D., Impact assessment study of climate change on agricultural zoning (2006) Meteorol Appl, 13 (S1), pp. 69-80De Jong, H.J., Forestry and agroforestry landuse systems for carbon mitigation: An analysis in Chiapas Mexico (1997) Climate-change Mitigation and European Land-use Policies, pp. 269-284. , Adger WN, Pettenella D, Whitby M (eds). CAB InternationalPeeters, L.Y.K., Soto-Pinto, L., Perales, H., Montoya, G., Ishiki M Coffee production, timber, and firewood in traditional and Ingashaded plantations in Southern Mexico (2003) Agric Ecosyst Environ, 95, pp. 481-493Dossa, E.L., Fernandes, E.C.M., Reid, W.S., Ezui, K., Above- and belowground biomass, nutrient and carbon stocks contrasting an open-grown and a shaded coffee plantation (2008) Agroforest Syst, 72, pp. 103-115Maskova, Normalized difference vegetation index (NDVI) in the management of mountain meadows (2008) Boreal Environment Research, 13, pp. 417-432McDonald, A.J., Investigation of the utility of spectral vegetation indices for determining information on coniferous forests (1998) Remote Sens. Environ., 66, pp. 250-272Heute, A., Overview of the radiometric and biophysical performance of the MODIS vegetation index (2002) Remote Sens. Environ., 83, pp. 195-213Zheng, D., Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Winsconsin, USA (2004) Remote Sens. Environ., 93, pp. 402-411Lu, D., The potencial and challenge of remote sensing-based biomass estimation (2006) Int. J. Remote Sens., 27, pp. 1297-1328Zhang, Monitoring vegetation phenology using MODIS (2003) Remote Sens. Environ., 84, pp. 471-475McVicar, T.R., Jupp, D.L.B., The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: A review (1998) Agr. Syst., 57, pp. 399-468Rouse, J.W., Monitoring vegetation index in the great plains with ERTS (1974) Third ERTS-1 Symposium, 1973, pp. 309-317. , Fraden, S.C. et al (eds). 10-14 Dec, NASA SP351, Washington D.C.NASAKawamura, Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China (2005) Agric. Ecosyst. Environ., 107, pp. 83-93(2006) IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use, 4. , Japan: Institute for Global Environmental StrategiesZullo Jr., J., (1994) Correção Atmosférica de Imagens de Satélite e Aplicações, , PhD Thesis, Universidade Estadual de Campinas, CampinasBausch, W.C., Halvorson, A.D., Cipra, J., Quickbird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots (2008) Biosystems Engineering, 101, pp. 306-315Thenkabaila, P.S., Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data (2004) International Journal of Remote Sensing, 23, pp. 5447-5472Ramirez, G.M., Zullo Jr., J., Estimation of biophysical parameters of coffee fields based on high-resolution satellite images (2010) Engenharia Agricola, 30, pp. 468-479Box, E.O., Holben, E.N., Kalb, V., Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux (1989) Plant Ecology, 80 (2), pp. 71-89. , Netherlands, junSader, S.A., Waide, R.B., Lawrence, W.T., Joyce, A.T., Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data (1989) Remote Sensing of Environment, 28 (1), pp. 143-156. , Netherlands, abr/junSegura, M., Kanninen, M., Allometric models for tree volume and total aboveground biomass in a tropical humid forest in Costa Rica (2005) Biotropica, 37 (1), pp. 2-8. , United States, marCotta, M.K., Biomass quantification and emisson reduction certificates for rubber-cocoa intercropping (2008) Rev. Árvore, 32, pp. 969-978Ribeiro, S.C., Quantification of biomass and estimation of carbon stock in a capoeira in the Minas Gerais forest zone (2010) Rev. Árvore, 34, pp. 495-50

An approach based on satellite image time series mining to identify region susceptible to desertification

After extensive and devastating drought in the Sahel, Africa, in the late 60's and early 70's (1968-1973) which resulted in the deaths of thousands of people and millions of animals, the desertification issue has been considered in the international agenda being ultimately relevant for the scientific community and governments in the worldwide. In 1992, the United Nations (UN), through Agenda 21 Chapter 12, defined desertification as ”land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities”. Desertification exacerbates socio-economic problems such as poverty and migration, which mainly affects the most vulnerable people and communities, bringing risk to global food security [1]. According to the UN, the annual economic losses are close to 4 billion dollars, with a cost for recovery of 10 billion dollars per year on a global scale.847850Geoscience and Remote Sensin

An Approach Based On Satellite Image Time Series Mining To Identify Region Susceptible To Desertification

After extensive and devastating drought in the Sahel, Africa, in the late 60's and early 70's (1968-1973) which resulted in the deaths of thousands of people and millions of animals, the desertification issue has been considered in the international agenda being ultimately relevant for the scientific community and governments in the worldwide. In 1992, the United Nations (UN), through Agenda 21 Chapter 12, defined desertification as 'land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities'. Desertification exacerbates socio-economic problems such as poverty and migration, which mainly affects the most vulnerable people and communities, bringing risk to global food security [1]. According to the UN, the annual economic losses are close to 4 billion dollars, with a cost for recovery of 10 billion dollars per year on a global scale.847850The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (IEEE GRSS)United Nations Convention to Combat Desertification, , http://www.unccd.int, Accessed: 2013-11-05An, H., Wang, B., Zhang, Q., Tao, Z., Study on ejina oasis land cover using decision tree classification (2010) Proceedings of the International Conference on Multimedia Technology, pp. 1-4. , IEEEXu, D., Li, C., Song, X., The research of the quantitative method of desertification assessment at large scale based on modis data and decision tree model - A case study in farming-pastoral region of north China (2012) Proceedings of the 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, pp. 1-4Vogt, J., The european drought observatory (2011) GEOSS Workshop XL (GEOSS), pp. 1-16. , 2011Rangel Nunes Sousa, W., Sousa Couto, M., Felix Castro, A., Pereira Santos Silva, M., Evaluation of desertification processes in ouricuri-pe through trend estimates of times series (2013) IEEE Latin America Transactions, 11 (1), pp. 602-606Schucknecht, A., Matschullat, J., Erasmi, S., Spatial and temporal variability of vegetation status in paraba, northeastern Brazil (2012) Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 32-35Jiawei, H., Micheline, K., (2001) Data Mining: Concepts and Techniques, , Morgan Kaufmann, San Francisco, CA, USA, 2 editionBerndt, D.J., Clifford, J., Using dynamic time warping to find patterns in time series (1994) KDD Workshop, pp. 359-370. , Seattle, WASales, M.C.L., Geousp - Space and time (2003) Evolution of Desertification Studies in the Brazilian Northeast, pp. 9-19. , (in portuguese)Chino, D.Y.T., Romani, L.A.S., Traina, A.J.M., Constructing satellite image time series for climate data summarization and monitoring agricultural crops (2010) REIC, 10, pp. 1-16. , (in portuguese

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.21732176 The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (IEEE GRSS)Marengo, J.A., Chou, S.C., Kay, G., Alves, L., Pesquero, J.F., Soares, W.R., Santos, D.C., Tavares, P., Development of regional future climate change scenarios in South America using the ETA CPTEC/HadCM3 climate change projections: Climatology and regional analyses for the amazon, são francisco and and the parana river basins (2012) Climate Dynamics, 38 (9-10), pp. 1829-1848Sundaresan, J., Santosh, K.M., Déri, A., Roggema, R., Singh, R., (2014) Geospatial Technologies and Climate Change, , Springer, SwitzerlandWang, Q., Adiku, S., Tenhunen, J., Granier, A., On the relationship of ndvi with leaf area index in a deciduous forest site (2005) Remote Sensing of Environment, 94 (2), pp. 244-255Trishchenko, A.P., Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A (2009) Remote Sensing of Environment, 113, pp. 335-341Sandholt, I., Rasmussen, K., Andersen, J., A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status (2002) Remote Sensing of Environment, 79, pp. 213-224Li, J., Narayanan, R.M., Integrated spectral and spatial information mining in remote sensing imagery (2004) IEEE Transactions on Geoscience and Remote Sensing, 42 (3), pp. 673-685Romani, L.A.S., Gonçalves, R.R.V., Amaral, B.F., Chino, D.Y.T., Zullo, J., Jr., Traina, C., Jr., Sousa, E.P.M., Traina, A.J.M., Clustering analysis applied to ndvi/noaa multitemporal images to improve the monitoring process of sugarcane crops (2011) Proceedings of the The 7th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multitemp'2011), pp. 33-36. , Trento, Italy: IEEEMaimon, O., Rokach, L., (2005) The Data Mining and Knowledge Discovery Handbook, , Springer, HeidelbergChino, D.Y.T., Romani, L.A.S., Traina, A.J.M., Construindo séries temporais de imagens de satélite para sumarização de dados climáticos e monitoramento de safras agrícolas (2010) Revista Eletrônica de Iniciação Científica, 10, pp. 1-16Han, J., Kamber, M., (2001) Data Mining - Concepts and Techniques, , 1st edition, New York, NY, USA: Morgan Kaufmann PublishersBerndt, D., Clifford, J., Using dynamic time warping to find patterns in time series (1994) AAAI Workshop on Knowledge Discovery in Databases, pp. 359-370. , Seattle - Washingto

Sart: A New Association Rule Method For Mining Sequential Patterns In Time Series Of Climate Data

Technological advancement has enabled improvements in the technology of sensors and satellites used to gather climate data. The time series mining is an important tool to analyze the huge quantity of climate data. Here, we propose the Sequential Association Rules from Time series - SART method to mine association rules in time series that keeps the information of time between related events through an overlapped sliding-window approach. Also the proposed method mines association rules, while the previous ones produce frequent sequences, adding the semantic information of confidence, which was not previously defined by sequential patterns. Experiments were conducted with real data collected from climate sensors. The results showed that the proposed method increases the number of mined patterns when compared with the traditional sequential mining, revealing related events that occur over time. Also, the method adds the semantic information related to the confidence and time to the mined patterns. © 2012 Springer-Verlag.7335 LNCSPART 3743757Universidade Federal da Bahia (UFBA),Universidade Federal do Reconcavo da Bahia (UFRB),Universidade Estadual de Feira de Santana (UEFS),University of Perugia,University of Basilicata (UB)Agrawal, R., Faloutsos, C., Swami, A., Efficient similarity search in sequence databases (1993) 4th Int. CFDOA, Chicago, IL, pp. 69-84Agrawal, R., Srikant, R., Mining sequential patterns (1995) Proceedings of the 11th International Conference on Data Engineering (ICDE 1995), pp. 3-14. , Yu, P.S., Chen, A.S.P. (eds.) IEEE Press, TaipeiRibeiro, M.X., Traina, A.J.M., Traina, J.C., A new algorithm for data discretization and feature selection (2008) Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 953-954. , ACM, FortalezaSrikant, R., Agrawal, R., Mining sequential patterns: Generalizations and performance improvements (1996) ICEDT, Avignon, France, pp. 3-17. , SpringerPei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth (2001) Proceedings of the 17th International Conference on Data Engineering, pp. 215-224. , IEEE Computer Society, Washington, DCLu, H., Feng, L., Han, J., Beyond intratransaction association analysis: Mining multidimensional intertransaction association rules (2000) ACM Trans. Inf. Syst., 18, pp. 423-454Romani, L.A.S., Clearminer: A new algorithm for mining association patterns on heterogeneous time series from climate data (2010) Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 900-905. , ACM, New YorkSubramanyam, R.B.V., Goswami, A., A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns (2005) Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 13, pp. 633-652Zaki, M.J., Spade: An efficient algorithm for mining frequent sequences (2001) Mach. Learn., 42, pp. 31-60Park, J.S., Chen, M.-S., Yu, P.S., An effective hash-based algorithm for mining association rules (1995) Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 175-186. , ACM, New YorkTung, A.K., Angelis, L., Vlahavas, I., Breaking the barrier of transactions: Mining intertransaction association rules (1999) Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 297-301. , ACM, New YorkFeng, L., Yu, X.J., Lu, H., Han, J., A template model for multidimensional intertransactional association rules (2002) The VLDB Journal, 11, pp. 153-175Hu, Y., Huang, T.C., Yang, H., Chen, Y., On mining multi-time-interval sequential patterns (2009) Knowledge Engineering, 68 (10), pp. 1112-1127Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C., Freespan: Frequent pattern-projected sequential pattern mining (2000) Proc. 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD 2000), Boston, MA, pp. 355-359Saputra, D., Dayang, R.A.R., Foong, O.M., Mining sequential patterns using I-prefixSpan (2008) International Journal of Computer Science and Engineering, 2, pp. 14-16Berberidis, C., Angelis, L., Vlahavas, I., Inter-transaction Association Rules Mining for Rare Events Prediction Proc. (Companion Volume) 3rd Hellenic Conference on Artificial Intelligence (SETN 2004), Samos, Greece (2004)Zhao, Q., Bhowmick, S.S., (2003) Sequential Pattern Matching: A Survey, , Technical Report, CAIS, Nanyang Technological University, SingaporeLee, A.J.T., Wang, C.-S., An efficient algorithm for mining frequent inter-transaction patterns (2007) Inf. Sci., 177 (17), pp. 3453-347

The Nina Framework Using Gesture To Improve Interaction And Collaboration In Geographical Information Systems

Nowadays, Geographical Information Systems (GIS) have expanded their functionalities including larger interactive displays exploration of spatiotemporal data with several views. These systems maintain a traditional navigation method based on keyboard and mouse, interaction devices not well suited for large screens nor for collaborative work. This paper aims at showing the applicability of new devices to fill the usability gap for the scenario of large screens presentation, interaction and collaboration. New gesture-based devices have been proposed and adopted in games and medical applications, for example. This paper presents the NInA Framework, which allows an integration of natural user interface (NUI) on GIS, with the advantage of being expandable, as new demands are posed to that systems. The validation process of our NInA Kinect-based framework was made through user experiments involving specialists and non-specialists in TerrainViewer, a geographical information system, as well as experts and non-experts in the Kinect technology. The results showed that a NUI approach demands a short learning time, with just a couple of interactions and instructions, and the user is ready to go. Moreover, the users demonstrated greater satisfaction, leading to their productivity improvement.35866Institute for Systems and Technologies of Information, Control and Communication (INSTICC)Boulos, M.N.K., Blanchard, B.J., Walker, C., Montero, J., Tripathy, A., Gutierrez-Osuna, R., Web gis in practice x: A microsoft kinect natural user interface for google earth navigation (2011) International Journal of Health Geographics, 10 (45), pp. 1-14Fitts, P.M., The information capacity of the human motor system in controlling the amplitude of movement (1954) Journal of Experimental Psychology, 47, pp. 381-391Jain, J., Lund, A., Wixon, D., The future of natural user interfaces (2011) Proceedings of the 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA '11, pp. 211-214. , New York, NY, USA, ACMMalizia, A., Bellucci, A., The artificiality of natural user interfaces (2012) Communications of the ACM, 55 (3), pp. 36-38(2013) Kinect for Windows Sdk, , http://msdn.microsoft.com/en-us/library/hh855347.aspx, Available, Accessed date January 16, 2013Norman, D.A., Natural user interfaces are not natural (2010) Interactions, 17 (3), pp. 6-10Richards-Rissetto, H., Remondino, F., Agugiaro, G., Robertsson, J., Von Schwerin, J., Girardi, G., Kinect and 3d gis in archaeology (2012) Proceedings of 18th International Conference on Virtual Systems and Multimedia (VSMM'12), pp. 331-337. , IEEE Computer SocietyRizzo, A., Kim, G.J., Yeh, S.-C., Thiebaux, M., Hwang, J., Buckwalter, J.G., Development of a benchmarking scenario for testing 3d user interface devices and interaction methods (2005) Proceedings of the 11th International Conference on Human Computer Interaction, , Las Vegas, NVStannus, S., Rolf, D., Lucieer, A., Chinthammit, W., Gestural navigation in google earth (2011) Proceedings of the 23rd Australian Computer-Human Interaction Conference, pp. 269-272. , New York, USA, AC

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 groundbased 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 fractalbased concepts may contribute for their improvement, besides being a fast, parallelizable, and scalable approach.517526Comite Gestor da Internet no Brazil (CGI.BR),Nucleo de Informatcao e Coordenacao do Ponto BR (NIC.BR),BR PETROBRAS,Banco do Brasil,MicrosoftAhlgren, P., Jarneving, B., Rousseau, R., Requirements for a cocitation similarity measure, with special reference to pearson's correlation coefficient (2003) Journal of the American Society for Information Science and Technology, 54 (6), pp. 550-560Alves, L.M., Marengo, J.A., Assessment of regional seasonal predictability using the PRECIS regional climate modeling system over south america (2010) Theoretical and Applied Climatology, 100, pp. 337-350Ambrizzi, T.E.A., Cenarios regionalizados de clima no brasil para o seculo xxi: Projecoes de clima usando tres modelos regionais: Relatorio 3 (2007) Technical Report, MMA, , BrasiliaAssad, E.D., Pinto, H.S., Zullo, J.J., Impacts of global warming in the brazilian agroclimatic risk zoning (2007) A Contribution to Understanding the Regional Impacts of Global Change in South America, pp. 175-182. , Sao Paulo, Brazil, Instituto de Estudos Avancados da USPBaioco, G.B., Traina, A.J.M., Traina, C., Mamcost: Global and local estimates leading to robust cost estimation of similarity queries (2007) SSDBM 2007, pp. 6-16. , Ban, Canada, ACM PressBarbara, D., Chen, P., Fractal mining - self similarity-based clustering and its applications (2010) Data Mining and Knowledge Discovery Handbook, pp. 573-589. , O. Maimon and L. Rokach, editors, SpringerBarbara, D., Chen, P., Using the fractal dimension to cluster datasets (2000) ACM SIGKDD, pp. 260-264. , Boston, MABlack, T., The new nmc mesoscale eta/cptec model: Description and forecast examples (1994) Forecasting, 9, pp. 265-278Bohm, C., A cost model for query processing in high dimensional data spaces (2000) ACM TODS, 25 (2), pp. 129-178Chakrabarti, D., Faloutsos, C., F4: Large-scale automated forecasting using fractals (2002) CIKM, 1, pp. 2-9. , McLean, VA - EUA, ACM PressChou, S.C., Marengo, J.A., Lyra, A.A., Sueiro, G., Pesquero, J.F., Alves, L.M., Kay, G., Tavares, P., Downscaling of south america present climate driven by 4-member hadcm3 runs (2007) Springer - ClimDyn, 25, pp. 33-59Cordeiro, R.L.F., Traina, A.J.M., Faloutsos, C., Traina, C., Finding clusters in subspaces of very large, multi-dimensional datasets (2010) Proceedings of the 26th International Conference on Data Engineering (ICDE 2010), pp. 625-636. , Long Beach, California, USA, IEEECordeiro, R.L.F., Traina, A.J.M., Faloutsos, C., Traina, C., Halite: Fast and scalable multiresolution local-correlation clustering (2013) IEEE Trans. Knowl. Data Eng., 25 (2), pp. 387-401Djuric, Dusan, (1994) Weather Analysis - Chapter I, , Prentice-Hall IncFaloutsos, C., Kamel, I., Beyond uniformity and independence: Analysis of r-trees using the concept of fractal dimension (1994) ACM PODS, pp. 4-13. , Minneapolis, MNFaloutsos, C., Seeger, B., Traina, A.J.M., Traina, C., Spatial join selectivity using power laws (2000) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD'00), pp. 177-188. , Dallas, USA, MayField, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Midgley, P.M., Managing the risks of extreme events and disasters to advance climate change adaptation (2012) A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, , editors. Cambridge University PressForster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Dorland, R.V., Changes in atmospheric constituents and in radioactive forcing (2007) Climate Change 2007: The Physical Science Basis, , Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 2007(2007) Climate Change 2007: Fourth Assessment Report (AR4), , Intergovernmental Panel on Climate Change { IPCC. Cambridge University Press, Cambridge, UK(2007) Climate Change 2007: Summary for Policymakers, , Intergovernmental Panel on Climate Change { IPCC. Cambridge Univ. Press., Formally agreed statement of the IPCC concerning key findings and uncertainties contained in the Working Group contributions to the Fourth Assessment Report(2007) Intergovernmental Panel on Climate Change, , http://www.ipcc.ch/ipccreports/index.htm, accessed: March, 2009Legates, D.R., Mccabe, G.J., Evaluating the use of goodness-of-t measures in hydrologic and hydroclimatic model validation (1999) Water Resources Res., 35 (1), pp. 233-241Marengo, J.A., Jones, R., Alves, L.M., Valverde, M.C., Future change of temperature and precipitation extremes in south america as derived from the PRECIS regional climate modeling system (2009) International Journal of Climatology, 29 (15), pp. 2241-2255Moriasi, D.N., Arnold, J.G., Liew, M.W.V., Bingner, R.L., Harmel, R.D., Veith, T.L., Model evaluation guidelines for systematic quanti-cation of accuracy in watershed simulations (2007) Transactions of the ASABE, 50 (3), pp. 885-900(2012) Nasa Research Finds 2010 Tied for Warmest Year on Record, , http://www.nasa.gov/topics/earth/features/2010-warmest-year.html, Online]. Available at:, Day of access: September 1Petersen, (1956) Weather Analysis and ForecastingPinto, H.S., Assad, E.D., (2008) Global Warming and the New Geography of Agricultural Production in Brazil, p. 42. , Brasilia, Brazil, The British EmbassyPinto, H.S., Assad, E.D., Impacts of climate change on brazilian agriculture (2012) Brazil: Assessment of the Vulnerability and Impacts of Climate Change on Brazilian Agriculture, , Development report for World Bank Project P118037Schroeder, M., (1991) Fractals, , Chaos, Power Laws. W. H. Freeman and CompanySousa, E.P.M., Traina, C., Traina, A.J.M., Faloutsos, C., Measuring evolving data streams' behavior through their intrinsic dimension (2007) New Generation Computing Journal, 25, pp. 33-59Sousa, E.P.M., Traina, C., Traina, A.J.M., Wu, L., Faloutsos, C., A fast and effective method to find correlations among attributes in databases (2007) DMKD, 14 (3), pp. 367-407(2008) Understanding and Responding to Climate Change: Highlights of National Academies Reports, , The National Academies. The National AcademiesTraina, C., Sousa, E.P.M., Traina, A.J.M., Using fractals in data mining (2005) New Generation of Data Mining Applications, 1, pp. 599-630. , In M. M. Kantardzic and J. Zurada, editors, (Chapter 24). Wiley/IEEE PressTraina, C., Traina, A.J.M., Wu, L., Faloutsos, C., Fast feature selection using fractal dimension (2010) Journal of Information and Data Management - JIDM, 1 (1), pp. 3-16Traina, C., Traina, A.J.M., Wu, L., Faloutsos, C., Fast feature selection using fractal dimension - ten years later (2010) Journal of Information and Data Management - JIDM, 1 (1), pp. 17-20Willmott, C.J., On the validation of models (1981) Physical Geography, 2, pp. 184-194Willmott, C.J., (1984) On the Evaluation of Model Performance in Physical Geography, , Gaile and Willmott, eds. NorwellWillmott, C.J., Davis, R., Feddema, J., Klink, K., Legates, D., Rowe, C., Ackleson, S., O'Donnell, J., Statistics for the evaluation and comparison of models (1985) Journal of Geophysical Research, 90, pp. 8995-9005. , SeptWillmott, C.J., Robeson, S.M., Matsuura, K., A refined index of model performance (2012) International Journal of Climatology, 32 (13), pp. 2088-209