Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping

This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mappin...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-03, Vol.12 (5), p.847
Main Authors: Pulella, Andrea, Aragão Santos, Rodrigo, Sica, Francescopaolo, Posovszky, Philipp, Rizzoli, Paola
Format: Article
Language:English
Subjects:
Accuracy
Backscattering
Classification
Coherence
forest mapping
Histograms
interferometric coherence
Interferometry
Land cover
Learning algorithms
Machine learning
Mapping
Methods
Rainforests
random forests
Random variables
Satellite constellations
sentinel-1
short-time-series
spatial texture
Synthetic aperture radar
temporal decorrelation
Thematic mapping
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Summary:This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85 % , when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12050847