**Author details**

urban areas and forest. Clustering of these areas was defined mainly by surface temperature, being higher for targets with lower canopy, such as urban areas and exposed soil, and lower for woodland (**Figure 9A** and **C**). For example, the forest areas represented by cluster 4, in **Figure 8A** and **B**, have high NDVI values (**Figure 9A**) and lower surface temperature values

However, sugarcane fields were well clustered over the crop seasons because the sugarcane has a typical behavior (long seasonal cycle) than other crops. In **Figure 8A** and **B**, it is possible to observe the dynamic of this agricultural crop, represented by cluster 3 (yellow), throughout the decade in which in the crop years 2001–2002 the acreage was low, with higher production, and planted in the northeast area of the state, and in the end of the crop years 2009–2010, there was a significant increase in the planted area toward the western of the state. This technique of clustering in three dimensional (multivariate) time series database was efficient to perform temporal analysis of land use, indicating that this methodology can be used to identify and

This chapter presented a new approach to boost the agricultural monitoring including the expansion of crops to different regions, through techniques of time series mining. We used clustering analysis associated with the Euclidean and the DTW distance functions. We demonstrated that it is possible to take advantage of off-the-shelf computational methods to support agricultural monitoring as well as to automatically determine sugarcane fields' expansion

Moreover, we also showed the potential use of time series of satellite images with low spatial resolution in agricultural monitoring although spectral mixtures can occur. The main advantage of this approach is the high temporal resolution, low cost and global coverage of the remote sensing system used (AVHRR/NOAA). The performance analysis of a simple clustering technique based on a time series of satellite images is in providing a further step in the researches on the use of renewable energy sources, such as the sugarcane ethanol. The impact of such approach becomes even stronger, and it increases the need for researching on new ways to reduce greenhouse gas emissions, mainly in the trail of the recent occurrences of

The authors thank FAPESP/AlcScens and CNPq for funding and Cepagri/Unicamp for the

(**Figure 9C**), as they are very shady and dense vegetation coverage areas.

analyze the dynamics of land use and cover.

that is a valuable contribution of this work.

extreme events in different locations of the planet.

**Acknowledgements**

database of remote sensing imagery.

**4. Conclusions**

36 Time Series Analysis and Applications

Renata Ribeiro do Valle Gonçalves1 \*, Jurandir Zullo Junior1 , Bruno Ferraz do Amaral2 , Elaine Parros Machado Sousa2 and Luciana Alvim Santos Romani3

\*Address all correspondence to: renata@cpa.unicamp.br

1 Center of Meteorological and Climate Researches Applied to Agriculture (Cepagri), University of Campinas (Unicamp), Cidade Universitária Zeferino Vaz, Campinas, SP, Brazil

