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Abstract ID: 5260
Abstract
In the past years, remote sensing has been used by scientists to estimate vegetation greenness due to advancements that have reduced accessibility and cost constraints. The Normalized Difference Vegetation Index (NDVI), a popular metric derived from satellite imagery's reflectance in the red and near-infrared spectral bands, has been widely used in the estimation of the vegetation greenness. However, the accuracy of NDVI can be affected by various environmental factors, including wind speed, wind direction, precipitation, humidity, sea level pressure, and cloud cover. To address these influences, analytical techniques are essential for predicting NDVI based on multi-dimensional environmental data, which enhances forecast precision and provides a deeper understanding of vegetation health. The objective of this study is to compare the accuracy in predicting NDVI using various approaches with multidimensional data, including multiple linear regression, support vector regression, random forest, and long short-term memory. A dataset spanning eight years and seven months (January 2016 to July 2024) of NDVI satellite data with high spatial resolution was used. This research provides valuable insights into NDVI estimation, with findings revealing that long short-term memory models incorporating time-lag analysis on NDVI data significantly outperform traditional regression methods. The use of time-lag, particularly a 1-month delay in NDVI data, proved critical in capturing temporal dependencies and long-term patterns in greenness of areas. These insights offer valuable guidance for researchers and practitioners in coastal ecosystem management, emphasizing the role of time-lag in improving decision-making and enabling more effective conservation strategies.
Keywords
Machine Learning, Normalized Difference Vegetation Index (NDVI), Comparative Analytical Methods.
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1. Introduction
The Normalized Difference Vegetation Index (NDVI) is an essential metric in remote sensing, it provides critical insights into vegetation cover changes over time and across different regions, influenced by surrounding environmental factors [1]. NDVI has broad applications in fields such as coastal studies, forestry, ecology, and climate science. Accurate NDVI predictions are vital for informed decision-making in these areas. NDVI accuracy is influenced by a range of environmental factors, and these factors introduce complexities in modeling and forecasting NDVI, requiring the development of robust analytical methods. Predicting NDVI using climate variables is particularly challenging due to the dynamic interactions between environmental factors and vegetation health [2]. While various analytical...