Citation

Sungheetha, Akey and R., Rajesh Sharma and Aroba, Oluwasegun Julius (2026) Adaptive Multi-Spectral Remote Sensing Framework for Comprehensive Salt Lake Environmental Monitoring and Groundwater Resource Assessment. Procedia Computer Science, 282. pp. 796-809. ISSN 18770509

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Abstract

Accurate and scalable environmental monitoring of ecologically sensitive zones such as salt lake peripheries demands integration of remote sensing physics, adaptive spectral analysis, and deep learning classification. This paper presents a unified adaptive monitoring framework that combines radiometric correction, normalized spectral index computation, deep convolutional neural network (CNN) classification, statistical temporal change detection, and regression-based groundwater level estimation into a coherent operational pipeline. Radiometric correction transforms at-sensor radiance (Lsensor = 45.2W/m2/sr/µm) to surface reflectance (ρsur f ace = 0.247) using atmosphere-geometry parameters (τatm = 0.82, θs = 35◦, d = 1.012 AU). The Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) are computed per pixel to drive both classification and regression. A deep CNN trained on 14-band multispectral imagery achieves 94.7% overall accuracy (Kappa = 0.921) over four land-cover classes—water, vegetation, bare soil, and salt crust—outperforming support vector machines (78.2%) and shallow CNNs (88.1%). Temporal statistical analysis across 72 monthly observations reveals a −23.4% NDVI decline (tstat = −7.14, p < 0.001) between the reference mean (µref = 0.64) and monitoring mean (µmon = 0.49), with an annual degradation rate of −9.4% per year. A calibrated regression model (β0 = 35.2, β1 = −28.4, β2 = −15.7, RMSE = 2.8 m, R2 = 0.87) estimates groundwater depth at 12.0 ± 2.8 m for a representative pixel. All results are derived through theoretical scientific computation; no real-time devices or operational software were employed. The proposed framework offers a scientificall

Item Type:	Article
Uncontrolled Keywords:	Remote sensing framework
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 07:04
Last Modified:	02 Sep 2026 07:04
URII:	http://shdl.mmu.edu.my/id/eprint/16535

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