Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)

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Predicting Vegetation Trends in Sylhet Division: An LSTM-Based Analysis of NDVI Dynamics

Authors
Shovon Das1, *, Niloy Biswas1, Afifa Islam Niha1, Sandip Paul1
1Urban and Rural Planning Discipline, Khulna University, Khulna, 9208, Bangladesh
*Corresponding author. Email: shovonkuurp@gmail.com
Corresponding Author
Shovon Das
Available Online 18 November 2025.
DOI
10.2991/978-94-6463-884-4_11How to use a DOI?
Keywords
Vegetation trend; NDVI; LSTM; Time-series analysis; Ecology
Abstract

Understanding how vegetation changes over time and across different areas is important for assessing ecological health and planning land use, especially in places experiencing rapid environmental shifts. This study looks at vegetation patterns in the Sylhet Division of Bangladesh using NDVI data from 2000 to 2024. It combines remote sensing with Long Short-Term Memory (LSTM) neural networks to predict vegetation trends from 2025 to 2030. The Mann-Kendall test is used to analyze historical changes from 2013 to 2024, focusing on the summer season (March–April). The LSTM model captures complex vegetation changes well, with an R2 value of 0.84, RMSE of 0.05, and MAE of 0.03. The combination of remote sensing and machine learning helps track and forecast vegetation patterns. These findings can help policymakers in Sylhet take steps to restore degraded areas and support conservation efforts where needed. This approach can play a key role in sustainable land management and planning for future environmental challenges.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
Series
Advances in Engineering Research
Publication Date
18 November 2025
ISBN
978-94-6463-884-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-884-4_11How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shovon Das
AU  - Niloy Biswas
AU  - Afifa Islam Niha
AU  - Sandip Paul
PY  - 2025
DA  - 2025/11/18
TI  - Predicting Vegetation Trends in Sylhet Division: An LSTM-Based Analysis of NDVI Dynamics
BT  - Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
PB  - Atlantis Press
SP  - 85
EP  - 93
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-884-4_11
DO  - 10.2991/978-94-6463-884-4_11
ID  - Das2025
ER  -