Gilberto Ribeiro de Queiroz

TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications

This chapter describes TerraLib, an open source GIS software library. The design goal for TerraLib is to support large-scale applications using socio-economic and environmental data. TerraLib supports coding of geographical applications using spatial databases, and stores data in different database management systems including MySQL and PostgreSQL. Its vector data model is upwards compliant with Open Geospatial Consortium (OGC) standards. It handles spatio-temporal data types (events, moving objects, cell spaces, modifiable objects) and allows spatial, temporal, and attribute queries on the database. TerraLib supports dynamic modeling in generalized cell spaces, has a direct runtime link with the R programming language for statistical analysis, and handles large image data sets. The library is developed in C++, and has programming interfaces in Java and Visual Basic. Using TerraLib, the Brazilian National Institute for Space Research (INPE) developed the TerraView open source GIS, which provides functions for data conversion, display, exploratory spatial data analysis, and spatial and non-spatial queries. Another noteworthy application is TerraAmazon, Brazil’s national database for monitoring deforestation in the Amazon rainforest, which manages more than 2 million complex polygons and 60 gigabytes of remote sensing images.

A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.

Fields as a Generic Data Type for Big Spatial Data

This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.

Clustering Methods to Asses Land Cover Samples of MODIS Vegetation Indexes Time Series

MODIS vegetation indexes time series have been widely used to build land cover change maps on large scales. In this scope, to obtain good quality maps using supervised classification methods, it is crucial to select representative training samples of land cover change classes. In this paper, we evaluate two clustering methods, Hierarchical and Self-Organizing Map (SOM), to assess land cover samples of MODIS vegetation indexes time series. As we show, these techniques are suitable tools for assisting users to select representative land cover change samples from MODIS vegetation indexes time series. We present the accuracy of both methods for a case study in Ipiranga do Norte municipality in Mato Grosso state, Brazil.

A More Efficient Method for Map Overlay in Terralib

Geographical Information Systems (SIGs) are used to store, analyze and manipulate geographical data, that is, data representing objects and phenomena that have a geographical position associated to them which is essential to process and analyze them [2, 10, 13]. These systems involve problems from many areas such as computational geometry, computer graphics, database, software engineering, etc.