Bahram Daneshfar

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Cropland productivity is impacted by climate. Knowledge on spatial-temporal patterns of the impacts at the regional scale is extremely important for improving crop management under limiting climatic factors. The aim of this study was to investigate the effects of climate variability on cropland productivity in the Canadian Prairies between 2000 and 2013(More)
Land cover and land use classifications from remote sensing are increasingly becoming institutionalized framework data sets for monitoring environmental change. As such, the need for robust statements of classification accuracy is critical. This paper describes a method to estimate confidence in classification model accuracy using a bootstrap approach.(More)
This research aims to find the best selection of imagery (optical and polarimetric radar) and methodology for very accurate and operational crop classification that could be used as a replacement for direct field observations and annual monitoring. We use RapidEye imagery as the optical source and RADARSAT-2 imagery as the Synthetic Aperture Radar (SAR)(More)
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