Proceedings of the 6th International Conference on Social Sciences and Interdisciplinary (ICSSIS 2024)

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Integrating Remote Sensing and Geographic Information System to Assess Water Quality in the Mangrove Forest of Langsa

Authors
Rosanti Situngkir1, *, M. Taufik Rahmadi2, Darwin Parlaungan Lubis2, Eni Yuniastuti1, Rohani Rohani1, Meilinda Suriani Harefa1
1Department of Geography Education, Universitas Negeri Medan, Medan, Indonesia
2Geography Information Science Study Program, Universitas Negeri Medan, Medan, Indonesia
*Corresponding author.
Corresponding Author
Rosanti Situngkir
Available Online 28 July 2025.
DOI
10.2991/978-2-38476-448-8_11How to use a DOI?
Keywords
Mapping; Water Condition; Mangrove; Sentinel-2
Abstract

The mangroves in Langsa are a vital asset with economic and ecological value, supporting coastal communities and preserving habitats. Sustainable management is crucial due to their dual role. This research assesses water quality in Langsa’s mangrove forests using Sentinel-2 imagery focusing on TSS, temperature, salinity, brightness, and pH. Field data were collected through random sampling of 25 points, analyzed using SNAP and ArcGIS, with supervised classification using the maximum likelihood method for accuracy. Results show TSS concentrations ranged from 0–3 mg/L, temperature from 30–32.5℃ (average 30.16℃), pH from 6.7–7.8 (average 6.93), brightness from 1–9 m (average 3.34 m), and salinity from 1–2‰ (average 1.86‰). NDWI analysis revealed the highest TSS concentration was 1.7–3.3 mg/L. The lowest temperature was 30.3℃, covering 2,800 ha, and the highest was 32.4℃, covering 1,001 ha. The pH ranged from 6.71 (2,515 ha) to 7.80 (8,455 ha). Salinity ranged from 1‰ (5,698 ha) to 2‰ (42,536 ha). Brightness varied between 1 m (11,436 ha) and 9 m (171,000 ha). These findings provide essential insights for sustainable mangrove management in Langsa City, supporting both ecological preservation and economic development.

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 6th International Conference on Social Sciences and Interdisciplinary (ICSSIS 2024)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
28 July 2025
ISBN
978-2-38476-448-8
ISSN
2352-5398
DOI
10.2991/978-2-38476-448-8_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  - Rosanti Situngkir
AU  - M. Taufik Rahmadi
AU  - Darwin Parlaungan Lubis
AU  - Eni Yuniastuti
AU  - Rohani Rohani
AU  - Meilinda Suriani Harefa
PY  - 2025
DA  - 2025/07/28
TI  - Integrating Remote Sensing and Geographic Information System to Assess Water Quality in the Mangrove Forest of Langsa
BT  - Proceedings of the 6th International Conference on Social Sciences and Interdisciplinary (ICSSIS 2024)
PB  - Atlantis Press
SP  - 94
EP  - 106
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-448-8_11
DO  - 10.2991/978-2-38476-448-8_11
ID  - Situngkir2025
ER  -