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Global Warming & Vegetation Monitoring Using Satellite Images
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Global warming is significantly altering climatic patterns and impacting terrestrial ecosystems, with vegetation being a primary indicator of these changes. This paper presents a methodology for monitoring vegetation dynamics in response to global warming using satellite remote sensing. The study utilizes multi-spectral satellite imagery from platforms such as Landsat, Sentinel-2, and MODIS to compute vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). These indices are analyzed over time to assess changes in vegetation health, density, and phenology across different regions. The proposed system integrates Geographic Information Systems (GIS) and cloud-based processing tools (e.g., Google Earth Engine) for scalable, real-time monitoring. Results indicate measurable shifts in vegetation patterns correlated with rising temperatures and altered precipitation regimes. The paper concludes that satellite-based vegetation monitoring is a cost-effective, accurate, and scalable approach for tracking ecological impacts of climate change, supporting sustainable land management and policy-making.
Keywords
Global Warming, Vegetation Monitoring, Remote Sensing, NDVI, Satellite Imagery, Climate Change, GIS, Google Earth Engine
References
[1] Tucker, C.J., "Red and Photographic Infrared Linear Combinations for Monitoring Vegetation," Remote Sensing of Environment, 1979.
[2] Huete, A., et al., "Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices," Remote Sensing of Environment, 2002.
[3] IPCC, "Climate Change and Land: An IPCC Special Report," 2019.
[4] Gorelick, N., et al., "Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone," Remote Sensing of Environment, 2017.
[5] Zhu, Z., et al., "Global Data Sets of Vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) from MODIS," Remote Sensing of Environment, 2013.
How to cite this paper
@article{1712676,
author = {Md Faizan, Syed Irfan, Chetan Rajole, Afroz B, Abdul Rehaman},
title = {Global Warming & Vegetation Monitoring Using Satellite Images},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {418-421},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712676.pdf},
abstract = {Global warming is significantly altering climatic patterns and impacting terrestrial ecosystems, with vegetation being a primary indicator of these changes. This paper presents a methodology for monitoring vegetation dynamics in response to global warming using satellite remote sensing. The study utilizes multi-spectral satellite imagery from platforms such as Landsat, Sentinel-2, and MODIS to compute vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). These indices are analyzed over time to assess changes in vegetation health, density, and phenology across different regions. The proposed system integrates Geographic Information Systems (GIS) and cloud-based processing tools (e.g., Google Earth Engine) for scalable, real-time monitoring. Results indicate measurable shifts in vegetation patterns correlated with rising temperatures and altered precipitation regimes. The paper concludes that satellite-based vegetation monitoring is a cost-effective, accurate, and scalable approach for tracking ecological impacts of climate change, supporting sustainable land management and policy-making.},
keywords = {Global Warming, Vegetation Monitoring, Remote Sensing, NDVI, Satellite Imagery, Climate Change, GIS, Google Earth Engine},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712676}
}