Abstract
Mapping rice area can be beneficial for change detection, irrigation management, climate change impact and vegetation protection and restoration programs. Remote sensing has provided a vantage means of mapping rice area. The unique physical characteristics of rice plants is that it is grown in flooded soil, which significantly affect the spectral reflectance from the rice fields. After a period of two months, the dense rice canopy cover replaces the flooded soil. This dynamic of the rice field is captured with the help of vegetation indices and are used to identify rice fields. Multi-spectral and multi-temporal data Landsat 8 data is used in the study. An algorithm that uses Normalized difference vegetation index (NDVI) and Land-surface Water Index (LSWI) derived from Landsat 16-days 30-meter data was used to differentiate paddy field from other areas. It works on the basis of sensitivity of LSWI for surface moisture and NDVI for vegetation content. This algorithm was used to detect rice fields in twelve local levels in Sunsari and Morang districts of Nepal. The results were validated using 0.44m resolution digital globe satellite imagery with 79 well-distributed control points. The overall accuracy of the method was found to be 79.746%.
Highlights
Rice ranks first amongst the cereal crops in terms of area and production in Nepal
Paddy rice agriculture was prevalent throughout the study area except for the urbanized region in the middle
The rice area was found to be mostly concentrated in the south-western region and the region that lies under Sunsari-Morang irrigation project
Summary
Rice ranks first amongst the cereal crops in terms of area and production in Nepal. It accounts about 40% of the total caloric intake. Much of the rice cultivated are rain-fed. Main season rice is transplanted after the onset of monsoon between June to August and is harvested between September to November (MoAD, 2015). Nepal was an exporter of rice until the early 1980s. Due to population growth and increased consumption of rice, it is slowly turning to be an importer. Mapping rice area can be beneficial for change detection, irrigation management, climate change impact and vegetation protection and restoration programs (MoAD, 2015)
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