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

  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 da...

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Vydáno v:European journal of remote sensing Ročník 55; číslo 1; s. 372 - 387
Hlavní autoři: Fundisi, Emmanuel, Tesfamichael, Solomon G., Ahmed, Fethi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cagiari Taylor & Francis 31.12.2022
Taylor & Francis Ltd
Taylor & Francis Group
Témata:
Accuracy
Artificial neural networks
Biodiversity
C band
Classification
data fusion
Deep Neural Network algorithm
Environmental management
Flowers & plants
Image classification
Land cover
Neural networks
Plant species
Plants (botany)
Radar
Radar data
Rain
Remote sensing
Remote sensing systems
Savanna
Savannahs
Sentinel-1 C-band
Sentinel-2
Short wave radiation
Species diversity
Vegetation
woody plant species diversity
Woody plants
ISSN:2279-7254, 2279-7254
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Shrnutí:  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.
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ISSN:2279-7254
2279-7254
DOI:10.1080/22797254.2022.2083984