Pixel-based image classification to map vegetation communities using SPOT5 and Landsat5 Thematic Mapper data in a tropical savanna, northern Australia

Abstract

Pixel-based image classification has been used to capture various components of vegetation for a number of applications and at a range of spatial scales across the world. The few studies that have attempted to capture the floristic composition of vegetation communities in tropical savanna environments, at fine spatial scales (1:25000 or less) using these methods, have found minimal success. To address this gap, we evaluated a supervised image classification process using the Maximum Likelihood Classifier and 50% of a floristic and structural (strata, cover, height, and growth form) field dataset applied to SPOT5 and Landsat5 Thematic Mapper multispectral data. Two approaches were conducted to evaluate the influence of ancillary data on classification results: (i) “image-only” (image and field data) and (ii) “integrated” (various combinations of ancillary data with the image and field data). Multivariate analysis and intuitive classification were employed to identify 22 vegetation communities within the 530 km2 study area situated on Bullo River Station, Northern Territory, Australia. Class (vegetation community) separability averaged 1.94 and 1.42 for Landsat5 Thematic Mapper and SPOT5, respectively. A standard accuracy assessment was based on the remaining 50% of the field data. Overall accuracy ranged from 30%–53% for 1:25000 and 1:100000 spatial scale products. The inclusion of ancillary data was superior to the image and field data alone. The results of this study emphasize the need for finer spatial scale maps for property management planning (≤ 1:25000) and coarser scales for regional applications (≥ 1:100000).