Detection of woody species Schinopsis haenkeana using phenological spectral differences and NDVI texture measures in subtropical forests

Abstract

Schinopsis haenkeana is a native tree species of great importance of South America. Different tree species diverge in their vegetation phenology, providing the opportunity to map their presence based on the seasonal dynamics of vegetation indices. Currently, spatially explicit information on tree species composition provides valuable insights for biodiversity conservation. The objective of this study was to detect the presence of S. haenkeana and its forest status in subtropical forests in central Argentina. We used a combination of RGB-NIR bands and indices (NDVI, EVI, GLI, RGR) derived from Sentinel-2 images. The analyses were processed using the Earth Engine platform and the random forest algorithm was used to discriminate S. haenkeana from other plant species. NDVI texture indices were also used to discriminate different forest states where the species is present. A fruiting period and a leaf color change were detected in July, and spectral differences between fruiting and preceding (May) or subsequent (October) months proved to be highly suitable for discriminating S. haenkeana. The final species presence map achieved an overall accuracy of 91%. Only 0.76% of the total area corresponds to S. haenkeana dense forests. This analysis demonstrated the value of the proposed approach for regularly detecting and mapping S. haenkeana using RGB-NIR spectral information, vegetation indices, and phenological spectral differences. Additionally, it highlighted the importance of using texture indices to differentiate between forests and scrublands, providing suitable data for forest management.