Assessing the effectiveness of high resolution satellite imagery for vegetation mapping in small islands protected areas

Gil A., Yu Q., Lobo A., Lourenço P., Silva L. and Calado H., 2011. Assessing the effectiveness of high resolution satellite imagery for vegetation mapping in small islands protected areas. Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium), 1663-1667. Szczecin, Poland, ISSN 0749-0208 S. Miguel Island's vascular plant flora (Archipelago of the Azores, Portugal) consists of approximately 1000 taxa and is largely dominated by non-indigenous taxa. However, existing indigenous vascular plant taxa are particularly important because they compose a very valuable ecosystem, the Azorean Laurel Forest. One of its most significant areas is the core of Pico da Vara/Ribeira do Guilherme Special Protected Area, in the former Natural Reserve of Pico da Vara, located in the mountain complex of Serra da Tronqueira. The rapid spread of some very aggressive invasive alien species, such as Pittosporum undulatum Vent. and Clethra arborea Aiton, are causing serious damages to this ecosystem. Its direct competition with native species has resulted in a significant decline in native populations and ecosystem area. This paper assessed the effectiveness of High Spatial Resolution IKONOS satellite imagery for vegetation mapping in Pico da Vara Natural Reserve using four different supervised classification techniques: Support Vector Machine, Artificial Neural Networks (nonparametric methods), Mahalanobis Distance and Maximum Likelihood (parametric methods). The overall classification results have shown that remote sensing based vegetation mapping using IKONOS image can constitute a cost-effective approach for a continuous monitoring, characterization and assessment of these insular ecosystems. Despite the poor separability (Transformed Divergence < 1700) of some vegetation categories, Maximum Likelihood, Support Vector Machine and Artificial Neural Networks classifications have achieved good overall and per class accuracies (overall accuracy > 75% and Kappa Index Agreement > 0.6). ADDITIONAL INDEX WORDS: Remote Sensing, Vegetation Mapping, High Resolution, Classification.