Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite—advanced synthetic aperture radar data

2011 ◽  
Vol 5 (1) ◽  
pp. 059501 ◽  
Author(s):  
Vyjayanthi Nizalapur
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Central India ◽  
Land Cover Classification ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Forested Areas ◽  
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 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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