A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Fundisi, Emmanuel; Tesfamichael, Solomon G.; Ahmed, Fethi
• Is Part Of: European journal of remote sensing. Volume 55:Issue 1(2022)
• Page Start: 372
• Page End: 387
• Publication Date: 2022-12-31
• Subjects: Savanna -- woody plant species diversity -- data fusion -- Sentinel-1 C-band -- Sentinel-2 -- Deep Neural Network algorithm; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2022.2083984
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: The co-existence of diverse plant forms in densely vegetated savanna environments presents a challenge when mapping species diversity using single remotely sensed data type that carries either optical or structural information. In the present study, Sentinel-1 RADAR and Sentinel-2 multispectral data were combined to classify morphologically similar woody plant species (n =27) and three coexisting land cover types using Deep Neural Network (DNN). The fused image recorded a higher overall classification accuracy (76%) than the sole use of Sentinel-2 (72%) and Sentinel-1 RADAR data (71%). Slightly more species (15) recorded accuracies exceeding 75% using fused image compared to Sentinel-2 and Sentinel-1 data (13 species >75%). Analysis of relative band contributions resulted in high importance from Sentinel-1 C-band ratio of VH/VV polarization (8.6%) as well as a select Sentinel-2 bands (Near infrared (9.86%), Shortwave (9.5%), and Vegetation red edge (8%)). Parallel to continual efforts to improve the accuracies of fused RADAR–optical data, the services of such data for regional-scale applications should be explored to inform timely biodiversity assessments.