Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

Eduarda Martiniano de Oliveira Silveira; Michele Duarte de Menezes; Fausto Weimar Acerbi Júnior; Marcela Castro Nunes Santos Terra; José Márcio de Mello

doi:10.1117/1.JRS.11.036004

21 July 2017 Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

Eduarda Martiniano de Oliveira Silveira, Michele Duarte de Menezes, Fausto Weimar Acerbi Júnior, Marcela Castro Nunes Santos Terra, José Márcio de Mello

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 3, 036004 (July 2017). https://doi.org/10.1117/1.JRS.11.036004

Abstract

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

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Eduarda Martiniano de Oliveira Silveira, Michele Duarte de Menezes, Fausto Weimar Acerbi Júnior, Marcela Castro Nunes Santos Terra, and José Márcio de Mello "Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover," Journal of Applied Remote Sensing 11(3), 036004 (21 July 2017). https://doi.org/10.1117/1.JRS.11.036004

Received: 21 April 2017; Accepted: 28 June 2017; Published: 21 July 2017

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