Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network

Abstract

For estimating forest biomass and mapping of forest carbon content, an accurate classification of tropical forest stages is an important step. The successional stage of regenerating forests in a forest age map was a viable surrogate for forest biomass in an area north of Manaus City, Brazil. The forest stages considered were: (i) mature forest, (ii) regenerating forest <3 years, (iii) regenerating forest 3–5 years, (iv) regenerating forest 6–10 years and (v) regenerating forest 11–18 years. Areas of pasture were mapped also. Synthetic Aperture Radar (SAR) bands and optical TM (Thematic Mapper) bands were used to classify those classes, using the forest age map as the reference. The remotely sensed data comprised 20 bands (SAR bands from JERS-1, SIR-C and XSAR and optical bands from Landsat/TM) upon which discriminant analysis (DA) were used. DA results pointed to increased class discrimination when using SAR and TM data in relation to SAR data only. The bands selected were used as input to a neural network based classifier. Classification accuracy using SAR bands alone was around 30% for the six land cover classes. When regenerating forest stage classes were merged into a single class, the classification accuracy increased to around 80%. SAR data alone was unable to discriminate regenerating forest stages, having limited ability to discriminate between the subtle tonal/textural characteristics of each stage. A data set comprising TM and SAR bands showed increased classification accuracy in relation to SAR data alone, although some confusion between regenerating forest stages was still present. Following merging of regenerating forest stages into young (0–5 years) and intermediate (6–18 years), the overall accuracy was around 87%. The combination of SAR and TM bands were essential for the discrimination between regenerating forest stages. Pasture and mature forest were discriminated accurately in both SAR data alone and in the combined SAR and TM data.