Leveraging Multispectral and Hyperspectral Imaging for Climate Change Detection in Urban and Rural Environments
DOI:
https://doi.org/10.54228/mjaret0624004Keywords:
Multispectral Imaging; Hyperspectral Imaging; Climate Change Detection; Urban and Rural Environments; Convolutional Neural Networks (CNNs)Abstract
This paper presents the use of multispectral and hyperspectral imaging to detect climate
changes in both rural and urban environments. Multispectral and hyperspectral sensors can provide
important spatial indicators for climate change, such as vegetation health, temperature variations, and
changes in urban developments. The proposed method uses advanced image analysis techniques,
such as convolutional neural networks (CNNs) and pixel-based change detection. It can be used to
monitor climate-induced changes. The proposed system was tested against Sentinel-2 multispectral
satellite data and hyperspectral data obtained from different sensors over rural and urban areas. It
obtained high detection accuracy, of 92% and 90% in rural and urban areas, respectively. The
accuracy of the proposed system was compared to existing methods, and it showed to be 25% better,
making it an important tool for the monitoring of climate changes and urbanization scenarios.
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