Mapping heterogeneous forest-pasture mosaics in the Brazilian Amazon using a spectral vegetation variability index, band transformations and random forest classification

Abstract

ABSTRACT Amazonian tropical rainforest is being converted to other land cover types including crops and pasture. In deforested areas, secondary forest grows after pastures are abandoned, and ‘dirty pasture’ that has trees and shrubs but is actively used for grazing are also regionally important land cover types following forest conversion. This study describes a multistage process land cover classification method to map primary forest, secondary forest, pasture, pasture with trees, built and water in the Brazilian state of Rondônia. A recently developed Spectral Variability Vegetation Index (SVVI) is tested to discriminate land cover types with differing tree cover amounts. Random Forest classifier (RF) is applied to inputs from a) spectral mixture analysis (SMA), and b) tasselled cap (TC) transformation, both with and without SSVI as an additional input feature. SVVI improved the classification accuracy from 73% (TC) to 85% (TC-SVVI), and TC-SVVI yielded a land cover map with higher accuracy than that from SMA-SVVI (82%). Pasture-with-trees, secondary forest and primary forest were all distinguishable with the SVVI. Pasture-with-trees accounted for 67% of all pastures, demonstrating its importance for regional land cover. This land cover classification workflow with the SVVI index improves the accuracy of mapping heterogeneous tropical land cover types.