Crop type detection using an object-based classification method and multi-temporal Landsat satellite images

• Published in: Paddy and water environment 2022-07, Vol.20 (3), p.395-412
• Main Authors: Karimi, Neamat, Sheshangosht, Sara, Eftekhari, Mortaza
• Format: Article
• Language: English
• Subjects: Accuracy; Agriculture; Alfalfa; Arid regions; Arid zones; Biomedical and Life Sciences; Cereal crops; Classification; Crop yield; Crops; Decision analysis; Decision trees; Detection; Ecotoxicology; Geoecology/Natural Processes; Hydrogeology; Hydrology/Water Resources; Image analysis; Image classification; Image processing; Landsat; Landsat satellites; Life Sciences; Normalized difference vegetative index; Remote sensing; Resource allocation; Satellite imagery; Semi arid areas; Semiarid lands; Soil Science & Conservation; Spaceborne remote sensing; Support vector machines; Vegetation; Vegetation index; Water resources; Wheat; Yield forecasting
• ISSN: 1611-2490; 1611-2504
• DOI: 10.1007/s10333-022-00901-x

Crop type detection is of great importance in water resource allocation and planning mostly in arid and semi-arid regions of Iran. Landsat-OLI 16-day inter-annual images are invaluable sources obviating crop monitoring into issues of crop types detection, crop yield prediction, and crop pattern studies. Although many classification methods such as decision tree (DT), support vector machine (SVM), and maximum likelihood (ML) were implied for crop type mapping, recent researches often use an object-based classification approach. In this study, an object-based image analysis (OBIA) classifier based on rule-based decision tree (RBDT) and object-based nearest neighbor (OBNN) used to delineate five common crop types (includes Wheat and Barley together in one class, rice, multiple crop (MC), Alfalfa and Spring crops) in Isfahan city and nearby areas. The classification was applied in five scenarios using different vegetation indexes including normalized difference vegetation index (NDVI), normalized difference water index (NDWI), green normalized difference vegetation index GNDVI and their combination. All scenarios property and accuracy assessed both with by class separation distance matrix and confusion matrix. The overall accuracy of classification with using only one vegetation index was lower than other scenarios. It was the lowest for GNDVI rating 37% whereas combination of Indexes resulted better accuracy. In final map with combination of NDVI, GNDVI and NDWI, overall accuracy and kappa achieved to 88% and 0/83 successively. Comparing individual accuracy of different crops showed that MC crops with 66% has the lowest accuracy and Wheat-Barely crops with 94.8% individual accuracy has the Maximum accuracy. Other crop types accuracy alters between 66 and 94.8%.