Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach-Reference-Cited by-同舟云学术

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

Published:2023-01-01 Issue:1 Volume:16 Page:
ISSN:1178-5608
Container-title:International Journal on Smart Sensing and Intelligent Systems
language:en
Short-container-title:

Author:

Abdollahi Abolfazl¹²,Pradhan Biswajeet²³,Alamri Abdullah⁴

Affiliation:

1. Fenner School of Environment & Society, College of Science , The Australian National University , Canberra , ACT , Australia

2. Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and IT , University of Technology Sydney , Ultimo NSW , Australia

3. Earth Observation Center, Institute of Climate Change , Universiti Kebangsaan Malaysia , UKM , Bangi , Selangor , Malaysia

4. Department of Geology and Geophysics, College of Science , King Saud University , P.O. Box 2455 , Riyadh , Saudi Arabia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

https://www.sciendo.com/pdf/10.2478/ijssis-2023-0013

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