地理と自然災害のジャーナル
オープンアクセス
ISSN: 2167-0587
ISSN: 2167-0587
Mirzapour S, Zarkesh MK, Safaval PA and Neshaei SA
Since rice is one of the important crops not only in Iran but also in the world, it is vital to determine the paddy rice field as accurately as possible using fast and economical methods such as remote sensing and GIS. Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases missions, food and water security, and human health. In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. The unique physical features of rice paddy fields during the flooding/open-canopy period were captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. In order to prepare information about spatial distribution of rice fields, Multi-Spectral and Multi-Temporal data can be helpful because in addition to the rice, all fields could be covered by mixture of water and soil regarding the time of crop calendar. The Landsat 8 data with 11 bands has visible, near infrared, shortwave infrared as well as thermal bands; and therefore, different vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI2105) which is sensitive to leaf water and soil moisture. In this study, special mapping algorithm that uses NDVI and LSWI2105 time series data derived from LANDSAT8 16-days 30-meter vegetation indices product of LANDSAT imagery developed to identify paddy rice fields. This algorithm works based on the sensitivity of LSWI2105 to the surface moisture and NDVI to the vegetation chlorophyll content. In this research method has been developed to define the relationship between the NDVI and LSWI2105 to detect the location of paddy rice fields in North part of Iran in 2014-15. The results, a validated with ground field works data at 56 well-distributed sample points. The overall accuracy of the method was 69.0909%.