Systems and Means of Informatics
2024, Volume 34, Issue 1, pp pp 57-69
MAPPING OF THE KHABAROVSK REGION ARABLE LANDS BY MACHINE LEARNING USING SENTINEL-2 IMAGES
- I. O. Prokhorets
- A. S. Stepanov
Abstract
Automated classification of arable lands using machine learning methods is one of the most important tasks in the transition to digital agriculture.
The classification of arable lands in the Khabarovsk Region was carried out using random forest (RF), minimum distance (MD), and K-means clustering methods based on Sentinel-2 images for July, August, September, and October 2022. The values of spectral bands, EVI (Enhanced Vegetation Index), and NDVI (Normalized Difference Vegetation Index) were considered as input data. Based on the results of statistical processing, it was found that the RF method demonstrated the greatest stability when changing the date of shooting and the type of input data. The accuracy of recognition of arable lands in the Khabarovsk Region in 2022 was 92.5% when using NDVI values calculated from the September Sentinel-2 image in the classifier. The proposed approach can be used for automated classification and subsequent mapping with expert correction of arable lands in the southern part of the Far East.
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[+] About this article
Title
MAPPING OF THE KHABAROVSK REGION ARABLE LANDS BY MACHINE LEARNING USING SENTINEL-2 IMAGES
Journal
Systems and Means of Informatics
Volume 34, Issue 1, pp 57-69
Cover Date
2024-04-10
DOI
10.14357/08696527240105
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
mapping; machine learning; satellite monitoring; arable lands; classification; Khabarovsk Region
Authors
I. O. Prokhorets  and A. S. Stepanov 
Author Affiliations
Computing Center of the Far Eastern Branch of the Russian Academy of Sciences, 65 Kim Yu Chen Str., Khabarovsk 680000, Russian Federation
Far Eastern Research Institute of Agriculture, 13 Klubnaya Str., Vostochnoe 680521, Khabarovsk Territory, Russian Federation
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