Base On MODIS-NDVI Information In Winter Wheat Area Extraction Research

As a large agricultural country,the food problem is a very important relationship with the development of the national economic security,economic problems,political problems and relationship between political stability,social stability.As one of the world's three largest wheat grains,grasp its planting distribution area of national significance.The traditional statistics on crop information,often consume huge manpower and material resources but the results of the investigation is no longer suitable for land use status at the time.As of twentieth Century 60's remote sensing develops integrated earth observation technology,with simultaneous observation,large area of comprehensive high timeliness,data and comparable characteristics and economy.With the rapid development of remote sensing technology,has been more and more applied to agricultural resources survey,and application in agricultural field has become an indispensable tool.This paper tightly around the extraction of winter wheat planting area remote sensing method and carried out,was to explore the extraction method to improve winter wheat area of district level of accuracy,for the future use of remote sensing data of winter wheat planting area extraction of large area to provide ideas and on the basis of the new.This paper selects Xiangyang City in Hubei province area as the research object,selected by the end of 2014 October to 2015 March at the end of the MOD11A2 products,get the winter wheat,rape and other vegetation types in NDVI time sequence curve diagram,according to the characteristics of NDVI curves of different vegetation in the study area,find out the most suitable for the extraction of Winter Wheat by remote sensing time,and design conditions for winter wheat area model,establish the model.Then through the research and improvement of Landsat-8 data,the cultivated land information extraction accuracy is improved,the accuracy of the model results to complete the upgrade.The results show that:(1)Winter wheat NDVI time curve and other vegetation types showed different characteristics.In the growth period of winter wheat,winter wheat,rape,grassland and woodland NDVI time curve have two obvious rise,winter wheat is different,no obvious decrease in the process of first rise,and rape,grassland and woodland were greatly decreased,the formation of the trough.(2)The mid to late 1 to early February this time is the best time of extraction of winter wheat planting area remote sensing method.When winter wheat NDVI is stable at a certain height,corresponding,rape,woodland and grassland NDVI value fell sharply,the difference in the maximum,so the best time for extraction of winter wheat.Why not choose the NDVI value of winter wheat reached the highest point in time,because at that time,the rape NDVI value reached the maximum,the value of NDVI is due to the recovery of grassland in the rapid rise,and the difference between NDVI values of winter wheat sharply reduced,therefore,in the peak of winter wheat NDVI,not suitable for the planting area extraction.(3)The evaluation of the accuracy of the results,the accuracy of winter wheat area extraction by method of MODIS-NDVI variation time curves of up to 91.98%.This shows that,through the MODIS data of different vegetation and crop vegetation index variation in a period of time,to establish a method of extracting area growth characteristics of crops is completely feasible.After using Landsat-8 OLI data to extract the information of cultivated land,using the MODIS-NDVI method in the study area of winter wheat planting area increased to 95.76%accuracy.That can enhance the cultivated land to some extent by using the Landsat-8 OLI data accuracy,and improve the extraction of wheat planting area based on the characteristics of NDVI time series curve precision.That Landsat-8 can not only satellite earth observation ability of the continuation of the Landsat series of long period,and also has a certain degree of optimization in the observation performance,can be used as a source of remote sensing information important help to the ecological environment monitoring.