Wheat is considered as a strategic product in the world, and in Iran it is considered as a major source of food, so that for many years, one of the goals of the government has been to achieve self-sufficiency in providing this product.
The estimation of the winter wheat crop area and its spatial distribution in the country, during the plantation and growth period, has a vital role in the value assessment, storage planning, as well as import-export planning. Remote sensing classification techniques, based on one image, are widely used to this task. Remote sensing is considered as a suitable solution to overcome the problems of common and traditional inventory methods. However, drawback of these classical methods is that the winter wheat crop’s spectral signature is very similar to spectral signature of some other crops growing simultaneously (e.g. barley, alfalfa), which limits the classification performance. To improve the classification results’ accuracy, multi-temporal imagery acquired during the growth period are instead used. Classification algorithms based on ensemble classifiers are suitable tools to decrease the classification problems of time series images. One appropriate technique for coping with multi-temporal imagery-based classification problems is random forest classifier. In researches performed to determine the area under wheat cultivation, in most cases due to high similarity between wheat and barley, these two products are considered as a same class.
In this research, ten Landsat satellite images from Marvdasht-Fars province with cloudiness less than 20% were selected. Then radiometric and atmospheric corrections were applied to all images and the time series analysis was performed as follows to increase the accuracy of wheat classification:
- Analysis and selection of optimum images in time series instead of using all images and decreasing volume of calculations and process time
- Improving and upgrading the classification results by producing new features in time series and finally surveying the improvement rate of final accuracy and increasing rate of capability of proposed method in separation of wheat from barley.
-Determining the most important input features and most suitable dates for imaging by calculation of variable importance in random forest algorithm.
-Early classification of wheat and separating it from other classes at least two months before product harvest.
The results of this research are as follows: