CHANGE DETECTION IN BRAZILIAN SAVANNAS USING SEMIVARIOGRAMS DERIVED FROM NDVI IMAGES

Abstract

The Normalized Difference Vegetation Index (NDVI) is often used to extract information from vegetated areas since it is directly related to vegetation parameters such as percent of ground cover, photosynthetic activity of the plant and leaf area index. The aim of this paper was to analyze the potencial of semivariograms generated from NDVI values to detect changes in vegetated areas, analyzing their behavior (shape) and derived metrics (range, sill and nugget). Semivariograms were generated from NDVI values derived from Landsat TM images of May 2010, June 2010 and July 2011. The study area is located in the northern state of Minas Gerais, Brazil, and is covered by Brazilian savannas vegetation, totalizing 1,596 ha. Semivariograms were generated after the exploratory data analysis. Models were fitted, validated and their metrics analyzed. The results showed a very clear trend where the shape of semivariograms, sill and range were different when deforestation occurred and were similar when the area had not been changed. The model that generated best fit was the Gaussian, however, the three models tested showed behavior that makes it possible to detect changes in vegetation. It suggests that further researches should explore the degree to which the semivariogram can be used to quantify this spatial variability as well as to analyze the influence of sazonality for changing detection in vegetated areas.