Assessing the vulnerability of Western Himalayan ecosystem to climate change using machine learning algorithms

Aditi Ahlawat; Arijit Roy

doi:10.1117/12.2680118

17 October 2023 Assessing the vulnerability of Western Himalayan ecosystem to climate change using machine learning algorithms

Aditi Ahlawat, Arijit Roy

Author Affiliations +

Proceedings Volume 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV; 127270E (2023) https://doi.org/10.1117/12.2680118
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

The western Himalayan region is highly vulnerable to climate change due to its fragile ecosystem, complex topography, and high dependence on natural resources, which is expected to have significant impacts on its vegetation. In this study, we investigate the vulnerability of Western Himalayan vegetation to climate change using machine learning algorithms. We analyzed remote sensing data of the region to estimate temperature, precipitation, and other variables relevant to vegetation growth. We then used GIS-based open-source software and machine learning algorithms to study the variables significant for predicting vegetation vulnerability to climate change. The study results indicate that the Western Himalayan ecosystem is highly vulnerable to climate change, and the region is likely to experience significant changes in ecosystem vulnerability and resilience in the future. The study also highlights the importance of incorporating machine learning algorithms and GIS software in assessing the vulnerability of ecosystems to climate change.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Aditi Ahlawat and Arijit Roy "Assessing the vulnerability of Western Himalayan ecosystem to climate change using machine learning algorithms", Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270E (17 October 2023); https://doi.org/10.1117/12.2680118

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