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Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data

Ramachandra, TV and Mondal, T and Setturu, B (2023) Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data. In: SN Applied Sciences, 5 (10).

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Official URL: https://doi.org/10.1007/s42452-023-05496-4


Analyses of spatial and temporal patterns of land use and land cover through multi-resolution remote sensing data provide valuable insights into landscape dynamics. Land use changes leading to land degradation and deforestation have been a prime mover for changes in the climate. This necessitates accurately assessing land use dynamics using a machine-learning algorithm’s temporal remote sensing data. The current study investigates land use using the temporal Landsat data from 1973 to 2021 in Chikamagaluru district, Karnataka. The land cover analysis showed 2.77% decrease in vegetation cover. The performance of three supervised learning techniques, namely Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood classifier (MLC) were assessed, and results reveal that RF has performed better with an overall accuracy of 90.22% and a kappa value of 0.85. Land use classification has been performed with supervised machine learning classifier Random Forest (RF), which showed a decrease in the forest cover (48.91%) with an increase of agriculture (6.13%), horticulture (43.14%) and built-up cover (2.10%). Forests have been shrinking due to anthropogenic forces, especially forest encroachment for agriculture and industrial development, resulting in forest fragmentation and habitat loss. The fragmentation analysis provided the structural change in the forest cover, where interior forest cover was lost by 27.67% from 1973 to 2021, which highlights intense anthropogenic pressure even in the core Western Ghats regions with dense forests. Temporal details of the extent and condition of land use form an information base for decision-makers. © 2023, The Author(s).

Item Type: Journal Article
Publication: SN Applied Sciences
Publisher: Springer Nature
Additional Information: The copyright for this article belongs to Authors.
Keywords: Classification (of information); Decision making; Deforestation; Horticulture; Learning algorithms; Learning systems; Maximum likelihood estimation; Remote sensing; Support vector machines, Forest cover; Forest fragmentations; Land use land cover; Land use/land cover; Landuse classifications; Learning techniques; Machine learning algorithms; Machine-learning; Random forests; Supervised learning technique, Land use
Department/Centre: Division of Biological Sciences > Centre for Ecological Sciences
Division of Interdisciplinary Sciences > Center for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP)
Division of Mechanical Sciences > Centre for Sustainable Technologies (formerly ASTRA)
Date Deposited: 14 Dec 2023 04:24
Last Modified: 14 Dec 2023 04:24
URI: https://eprints.iisc.ac.in/id/eprint/83402

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