Paddy lands detection using Landsat-8 satellite images and object-based classification in Rasht city, Iran

Abstract

Rice is one of the most important food staples in many countries, particularly Iran. Because irrigated rice production differs from other agricultural fields, this study developed a paddy field mapping model based on phenological aspects, various satellite sensor data, and an object-based approach. This study uses the phonological features of rice plants as well as annual data on land surface temperature (LST) to produce the paddy map. The core remote sensing data consists of the yearly LST from MODIS and multi-temporal Landsat-8 satellite imagery. The detection of phenological characteristics and the selection of relevant Landsat images were made possible by analyzing the LST time series with Google Earth Engine. After that, object-based image classification and fuzzy functions are used to create flexible and comprehensible rules for discovering paddy fields in Rasht, Iran. Data such as the digital elevation model (DEM) and spectral indices including NDVI, EVI, and LSWI were employed to improve the object-based classification. Due to the unique properties of paddy lands, a DEM of 12.5 m obtained from the ALOS PALSAR sensor could help distinguish paddy lands from other vegetation. A comparison finally made between the object-based and pixel-based classification methods showed that the former is more accurate. Overall accuracy and kappa coefficient for the pixel-based classification approach were 92% and 0.89, respectively, whereas overall accuracy and kappa coefficient for the object-based classification method were 94% and 0.92, respectively. Eventually, the producer's accuracy of the paddy class has increased from 88% to 94%.

Keywords

• GIS
• Earth observation
• Confusion matrix
• eCognition