Abstract
Map** and characterisation of orchards are the foremost steps in the process of orchard monitoring. This study characterised mango orchards, an important cash crop of India, according to age groups using Object-Based Image Analysis (OBIA) and Sentinel-2 imagery. The performance of three vegetation indices (NDVI (Normalised Difference Vegetation Index), ReNDVI (Red Edge Normalised Difference Vegetation Index) and LSWI (Land Surface Water Index)) was evaluated individually to map the age groups of these orchards. Findings indicated that for level 2 classification (based on map** the orchard class as a whole), ReNDVI performed the best with an overall accuracy of 87%. Results of level 3 classification (based on age groups) indicate that LSWI gave the highest user and producer accuracy for medium (82.9%, 73.9%), old (65%, 74.2%) and young (51.8%, 76.3%) orchards. This study is a novel attempt to segregate various age groups (level 3 classification) using OBIA. The findings emphasise better separability at the mean class age groups with minimum overlap and intra-class mixing.
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Acknowledgements
This study was carried out under the JECAM (Joint Experiment on Crop Assessment and Monitoring) project. I would also like to thank Dr. Suresh Kumar, Dr. NR Patel and Director IIRS for supporting the project.
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Stephen, S., Haldar, D. Categorisation of mango orchard age groups using Object-Based Image Analysis. Arab J Geosci 17, 62 (2024). https://doi.org/10.1007/s12517-024-11857-z
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DOI: https://doi.org/10.1007/s12517-024-11857-z