UNSUPERVISED ZONING OF CULTIVATION AREAS WITH SIMILAR CULTIVATION PATTERN IN GOLESTAN PROVINCE BASED ON THE VEGETATION PRODUCTS OF MODIS SENSOR

Abstract. The estimation of cultivation area and categorizing the agricultural product types is one of the prerequisites for achieving sustainable development in the agricultural studies. In this study, an unsupervised zoning the cultivation areas with the same cultivation pattern in Golestan province is on the agenda. Therefore, due to wide spatial range, high temporal resolution and easy access of 16-day products of the vegetation of the MODIS sensor which acquired in a year (From November 2017 to October 2018), these images are used in this research. In the proposed method, after the generating of NDVI vegetation time series as a hyper-cube and separating farmlands’ boundaries in Golestan province using the land-use map; the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm and the maximum number of product variation using the statistical information of the region (Obtained from the statistics centre of Iran) are used to extract endmembers of the hyper-cube. In the following, the timing responses of the NDVI, identified as endmembers, will be refined in the second phase. In this process, identifying and eliminating noise signals (unrelated to cultivating patterns) and integrating the same cultivating patterns will be on the agenda. At the last stage of the proposed method and after refinement of the endmembers, the hyper-cube is clustered by Spectral Angle Mapper (SAM) algorithm and the mapping of regions with the same cultivation pattern is produced. In the proposed method, the zoning of agricultural land is based solely on the statistical knowledge of the variety of cultivation and the results have led to the production of interconnected spatial parts. This is consistent with the reality of the spatial occurrence of similar cultivating patterns in a geographic area. On the other hand, the visual comparison of results with large scale satellite images illustrates that there is a significant relationship between clustering results and ground truth in terms of cultivating pattern. Obviously, such products can be used as initial layers of information to produce the results of a supervised classification with the aim of applying the cultivation area of a variety of agricultural products.