scholarly journals Large Scale Mapping of Forests and Land Cover with Synthetic Aperture Radar Data

10.5772/58220 ◽  
2014 ◽  
Author(s):  
Josef Kellndorfer ◽  
Oliver Cartus ◽  
Jesse Bishop ◽  
Wayne Walker ◽  
Francesco Holecz
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Aperture Radar

scholarly journals Characteristics of Synthetic Aperture Radar Data for Various Land Cover in a Watershed

1994 ◽  
Vol 38 ◽  
pp. 759-764
Author(s):  
Yasuto TACHIKAWA ◽  
Seiji SUHARA ◽  
Michiharu SHIIBA ◽  
Takuma TAKASAO ◽  
Kaoru TAKARA
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Aperture Radar

scholarly journals Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yara Mohajerani ◽  
Seongsu Jeong ◽  
Bernd Scheuchl ◽  
Isabella Velicogna ◽  
Eric Rignot ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Numerical Models ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Data ◽  
Uncertainty Estimates ◽  
Grounding Line ◽  
Aperture Radar

AbstractDelineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also find value in the machine learning approach in situations that even challenge human experts. We use this approach to map the tidal-induced variability in grounding line position around Antarctica in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West Antarctica, we demonstrate that grounding zones are one order magnitude (13.3 ± 3.9) wider than expected from hydrostatic equilibrium, which justifies the need to map grounding lines repeatedly and comprehensively to inform numerical models.

scholarly journals Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

2019 ◽  
Vol 11 (13) ◽  
pp. 1518 ◽  
Author(s):  
Rubén Valcarce-Diñeiro ◽  
Benjamín Arias-Pérez ◽  
Juan M. Lopez-Sanchez ◽  
Nilda Sánchez
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Environmental Sustainability ◽  
Agricultural Land ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Agricultural Policies ◽  
Synthetic Aperture Radar Data ◽  
Monitoring Effort ◽  
Aperture Radar

Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).

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