OBJECT BASED IMAGE ANALYSIS AND TEXTURE FEATURES FOR PASTURE CLASSIFICATION IN BRAZILIAN SAVANNAH

Abstract

The classification of different types of pasture using remote sensing imagery is still a challenge. Assessing high quality geospatial information of pasture management system and productivity are key factors for establishing local public policies related to food security. In this context, we aim to investigate how texture features, allied with Object Based Image Analysis, can contribute to the automatic classification of herbaceous pastures and shrubby pastures in a region of Brazilian Savannah. We used Sentinel-2 images from dry and rainy seasons to extract several vegetation indexes, spectral unmixing components and texture features. The SLIC algorithm was used for perform image segmentation and the Random Forest for image classification. The use of texture features on pasture classification resulted in an accuracy of 87.03%. Our key finding is that features like entropy and contrast were able to detect areas with a greater concentration of shrubby-arboreal elements, which are often present on shrubby pastures and may be the first signal of a degradation process.