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Data Mining Using NDVI Time Series Applied to Change Detection

National Institute for Space Research (INPE), Av. dos Astronautas, 1758, 12227-010, São José dos Campos—SP, Brazil
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Presented at the 2nd International Electronic Conference on Remote Sensing, 22 March–5 April 2018; Available online: https://sciforum.net/conference/ecrs-2.
Proceedings 2018, 2(7), 356; https://doi.org/10.3390/ecrs-2-05169
Published: 22 March 2018
(This article belongs to the Proceedings of The 2nd International Electronic Conference on Remote Sensing)
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 north-east 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.
Keywords: land cover change; deforestation; GeoDMA; semiarid; Caatinga land cover change; deforestation; GeoDMA; semiarid; Caatinga
MDPI and ACS Style

Dutra, A.C.; Shimabukuro, Y.E.; Escada, M.I.S. Data Mining Using NDVI Time Series Applied to Change Detection. Proceedings 2018, 2, 356. https://doi.org/10.3390/ecrs-2-05169

AMA Style

Dutra AC, Shimabukuro YE, Escada MIS. Data Mining Using NDVI Time Series Applied to Change Detection. Proceedings. 2018; 2(7):356. https://doi.org/10.3390/ecrs-2-05169

Chicago/Turabian Style

Dutra, Andeise Cerqueira; Shimabukuro, Yosio Edemir; Escada, Maria Isabel Sobral. 2018. "Data Mining Using NDVI Time Series Applied to Change Detection" Proceedings 2, no. 7: 356. https://doi.org/10.3390/ecrs-2-05169

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