A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants

European Journal of Remote Sensing

Emmanuel Fundisi¹  Solomon G. Tesfamichael¹  Fethi Ahmed² 

[1] Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Johannesburg, South Africa;Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa;

关键词: Savanna;    woody plant species diversity;    data fusion;    Sentinel-1 C-band;    Sentinel-2;    Deep Neural Network algorithm;   

DOI  :  10.1080/22797254.2022.2083984

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown