Data Mining Using NDVI Time Series Applied to Change Detection

Abstract

Quantifying and monitoring woody cover distribution in semiarid regions is challenging, due to their scattered distribution. Data mining has been widely used with remote sensing data for the information extraction of spectral and temporal data in the analysis of change detection. The main objective of this study was to characterize the land cover and use over the 2000-2010 time period for the Brazilian Caatinga seasonal biome using a temporal Normalized Difference Vegetation Index (NDVI) series and Geographic Object-Based Image Analysis (GEOBIA). For each of the target years NDVI images were derived from a Moderate Resolution Imaging Spectroradiometer (MODIS, MOD13Q1, at a 250 m spatial and 16-day temporal scale) sensor during the dry season to predict wood cover in the municipality of Buriti dos Montes, in the state of Piaui in the northeast region of Brazil (H13V09 tile). The images were automatically pre-processed and the GEOBIA approach was performed for image segmentation, spatial and spectral attribute extraction and labelling according to the following legend, tree cover (TC) and cropland/grass (CG), to obtain a classification using the decision tree supervised algorithm. Our results showed that the approach using GEOBIA presented a Kappa index of 0.58 and global accuracy (GA) of 0.81% and showed better accuracy for the tree cover. Finally, we recommend new studies adding others parameters strongly related to the vegetation of semiarid regions.