Integration of Multispectral Satellite data with Ensemble Machine Learning Models for Wetland Classification: a new Ramsar Site in Central India

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 16 | Issue : 03 | Page :
    By

    Reshu Agarwal,

  1. Associate Professor, Department of Computer Science and Engineering , Indore Institute of Science and Technology, Rau-Pithampur Road, Opposite IIM, Madhya Pradesh, India

Abstract

For biodiversity conservation, several wetlands in India have been classified as Ramsar sites, and Sirpur Lake is a recent addition to the list. The objective of this paper is to use Sentinel optical data with 10-meter resolution to prepare a robust and accurate classified map which will be crucial for further analysis. The data on thirteen spectral bands along with four essential spectral indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Wetland Index Model based on SWIR band 11 (WMIa) and Wetland Index Model based on SWIR band 12 (WMIb), are processed via principal component analysis to find a smaller set of informative features. Three powerful machine learning models, i.e., Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) have been applied to a total of 251 training data points for building an accurate and reliable classifier. Findings reveal that XGBoost provided the highest accuracy of 96.25%, whereas the others are also close with 95% and 91.25% accurate classification for RF and SVM, respectively.

Keywords: Wetland classification, Sentinel data, Random forest, XGBoost , Support Vector Machine (SVM)

[This article belongs to Journal of Remote Sensing & GIS ]

How to cite this article:
Reshu Agarwal. Integration of Multispectral Satellite data with Ensemble Machine Learning Models for Wetland Classification: a new Ramsar Site in Central India. Journal of Remote Sensing & GIS. 2025; 16(03):-.
How to cite this URL:
Reshu Agarwal. Integration of Multispectral Satellite data with Ensemble Machine Learning Models for Wetland Classification: a new Ramsar Site in Central India. Journal of Remote Sensing & GIS. 2025; 16(03):-. Available from: https://journals.stmjournals.com/jorsg/article=2025/view=233201


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Regular Issue Subscription Original Research
Volume 16
Issue 03
Received 07/10/2025
Accepted 09/10/2025
Published 27/11/2025
Publication Time 51 Days
138

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