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Development of a Knowledge-based Hierarchical Decision Tree Method for Classification of Croplands based on Cropping System in Google Earth Engine (case study: Sharekurd)
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Alireza Taheri Dehkordi *   , Rohollah Goodarzi , Nohammad Javad Valadan Zoej |
| K.N.Toosi University of Technology |
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Abstract: (1354 Views) |
| Agriculture, as one of the main factors in ensuring food security of the society, is of special importance in decision making, especially in making policies related to the import and export of certain agricultural products. Hence, determining crop acreage is essential for each agricultural year. The division of croplands based on the agricultural system during the cropping year can provide us with more accurate area estimation for autumn and spring cultivation . Because the area of lands with double crops (autumn and spring cultivation) is also talking into consideration in in two times. This study uses the time series of sentinel2 vegetation index (NDVI) and a knowledge-based decision tree method for classifying agricultural lands into four classes (autumn, spring, alfalfa cultivation, and double-crop fields). All parts of the method have been implemented in the Google earth engine (GEE) programming interface. The performance of the proposed method is evaluated in a study area in Shahrekord city using ground truth data gathered by extensive field surveys and eventually, the proposed method with an overall accuracy of 97.27 % has outperformed the Nearest Neighbor (overall accuracy = 93.76%) and the Decision Tree (overall accuracy = 94.32%) classifiers. The final result also shows a high similarity of the map produced by the proposed method and the Support Vector Machine (SVM) classifier. Although, the SVM with an overall accuracy of 97.84% is slightly more accurate than the proposed method, the simplicity, understandablity and the direct use of the crop phenology features in the various crop years without the need for retraining, are the unique advantages of the proposed method. |
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| Keywords: Classification, Google Earth Engine, Croplands, Cropping system, Remote Sensing. |
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Full-Text [PDF 2103 kb]
(288 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2021/04/7 | Accepted: 2021/09/26 | Published: 2024/06/20
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