Document Type : Research Paper
Authors
1 Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization, AREEO, Karaj, Iran
2 Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran
3 Professor, Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran
Abstract
Introduction The use of remote sensing, due to the provision of timely data and the high capability of image analysis, as well as the possibility of studying in a wide range with acceptable accuracy, is of great help to the planners and implementers of the agricultural sector. Remote sensing is one of the effective tools for monitoring, studying and determining the level of cultivation of agricultural and horticultural products, especially in large areas. Therefore, Annual Landsat images are valuable resources that enable crop monitoring in issues related to diagnosis, crop yield prediction, and crop-cultivation pattern studies.
Materials and Methods Landsat 8 and OLI satellite images related to 2018 were used to estimate the overall level of crops in the area and to separate the agricultural area from other areas. These images were downloaded from the site http://earthexplorer.usgs.gov, which is related to row 37 and path 166. In order to collect additional information used in this research, Google Earth images and ground control points taken with a GPS device, Garmin model 64s, were used. Finally, the area under cultivation of the products was calculated. The statistics of the Ministry of Agricultural Jihad in the crop year 2016-2017 were used to evaluate the results. The classification steps are done to classify different classes.
Results and Discussion For this purpose, in this research, to estimate the monitoring of main crops in the area and to classification agricultural crops, satellite images of Landsat 8 sensors with OLI sensor and NDVI index related to the time series of one-year images were used. The area of wheat and barley cultivated land in the region was estimated to be 10639 hectares, which shows an error of about 6.8% compared to the results of Jihad Keshavarzi statistics, which is 9956.56 hectares. After classifying the satellite images, using teaching samples that were not involved in the classification process, the accuracy of the classified image was evaluated. In this research, after calculating the cultivated area in GIS, the results were validated by comparing with the available statistics of Jihad-Agriculture and regional service centers and time control points by GPS, kappa coefficient and general accuracy coefficient. Supervised classification method with support vector machine algorithm and artificial neural network was used to separate farms. The crop classification maps were divided into 5 categories, wheat and barley, summer crops, rice crops, irrigated crops and non-agricultural lands. The lowest amount of commission error in both classification methods, 8.1 %, is related to summer cultivation, and the emission error in non-agricultural lands was 0.5 % in support vector machine method and 2.5% in artificial neural network method. Sentinel-2 images with a spatial resolution of 10 meters were used to prepare the classification map.
Conclusion After classification, the maps produced by satellite images were compared with reference data and ground facts, as well as with the help of satellite images available in Google Earth software. Then the error matrix was formed. Kappa coefficient and overall accuracy were obtained in the vector machine method as 0.74 and 68%, respectively, and in the neural network method as 0.70 and 76% respectively.
Results and Discussion For this purpose, in this research, to estimate the monitoring of main crops in the area and to classification agricultural crops, satellite images of Landsat 8 sensors with OLI sensor and NDVI index related to the time series of one-year images were used. The area of wheat and barley cultivated land in the region was estimated to be 10639 hectares, which shows an error of about 6.8% compared to the results of Jihad Keshavarzi statistics, which is 9956.56 hectares. After classifying the satellite images, using teaching samples that were not involved in the classification process, the accuracy of the classified image was evaluated. In this research, after calculating the cultivated area in GIS, the results were validated by comparing with the available statistics of Jihad-Agriculture and regional service centers and time control points by GPS, kappa coefficient and general accuracy coefficient. Supervised classification method with support vector machine algorithm and artificial neural network was used to separate farms. The crop classification maps were divided into 5 categories, wheat and barley, summer crops, rice crops, irrigated crops and non-agricultural lands. The lowest amount of commission error in both classification methods, 8.1 %, is related to summer cultivation, and the emission error in non-agricultural lands was 0.5 % in support vector machine method and 2.5% in artificial neural network method. Sentinel-2 images with a spatial resolution of 10 meters were used to prepare the classification map.
Conclusion After classification, the maps produced by satellite images were compared with reference data and ground facts, as well as with the help of satellite images available in Google Earth software. Then the error matrix was formed. Kappa coefficient and overall accuracy were obtained in the vector machine method as 0.74 and 68%, respectively, and in the neural network method as 0.70 and 76% respectively.
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