| IDENTIFICATION AND SEPARATION OF RICE FIELDS USING REMOTE SENSING |
| Paper ID : 1147-GEOSPATIAL (R6) |
| Authors: |
|
Salar Mirzapour1, Payam Alemi Safaval *2, Sharmin Karimi3, Saeed Behzadi4, Mir Masoud Kheirkhah Zarkesh5, Hossein Zavar6, Seyyed Hassan Hashemi Ashka7 1PhD Student of Remote Sensing and GIS, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran 2MSc Remote Sensing and GIS, Geological Survey & Mineral Exploration of Iran 3MSc Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran 4Surveying Engineering Department, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University 5Associate professor of soil conservation and watershed management research institute, Agriculture research education and extension organization AREEO, Iran 6MSc photogrammetry, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran 7MSc In Surveying Engineering-Geospatial Information Systems, Director of the Mapping and GIS Department at Management and Planning Organization of Guilan, Iran |
| Abstract: |
| Rice is one of the main crops that plays an important role in world food security. Due to the suitable climate in the north of Iran, rice is the dominant crop of farmers in this region. In this study, rice paddies were identified and separated by using the relationship between NDVI and LSWI2105 from the OLI sensor in year 2014. In order to identify suitable fields, an algorithm was presented to identify the paddy fields of waterlogged soils during the stages transplanting. According to the climate of the region, the factors of evergreen covers, water bodies, and altitude classes were identified. Finally, a spatial distribution map of rice paddies was created. 56 control points were used in rice and non-rice fields to evaluate the work. The results showed that the spatial distribution map of rice paddy fields for Amol city has an overall accuracy of 83.6364% and a kappa coefficient of 0.8108. |
| Keywords: |
| Paddy rice, Landsat 8, LSWI2105, NDVI, Caspian Sea |
| Status : Paper Accepted (Poster Presentation) |
