<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.</p>
</abstract>

Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.

Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach

Fenner School of Environment & Society, College of Science

Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and IT

Earth Observation Center, Institute of Climate Change

Department of Geology and Geophysics, College of Science

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal...

Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach

Article Category: Article

Pubblicato online: 21 ott 2023

Pagine: -

Ricevuto: 19 lug 2023

DOI: https://doi.org/10.2478/ijssis-2023-0013

Parole chiave
GEE, season-based vegetation mapping, Sentinel-2, spectral indices, topographic data, texture information

© 2023 Abolfazl Abdollahi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach

Article Category: Article

Pubblicato online: 21 ott 2023

Pagine: -

Ricevuto: 19 lug 2023

DOI: https://doi.org/10.2478/ijssis-2023-0013

Parole chiaveGEE, season-based vegetation mapping, Sentinel-2, spectral indices, topographic data, texture information

© 2023 Abolfazl Abdollahi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
GEE, season-based vegetation mapping, Sentinel-2, spectral indices, topographic data, texture information