Delineating Agricultural and Water Surfaces in Ergani (Diyarbakır) via Maximum Likehood Classification: A Remote Sensing-Based Approach for Sustainable Agriculture

DOI

10.2991/978-94-6239-668-5_63How to use a DOI?

Keywords

Maximum Likehood Classification; Remote Sensing; Sustainable Agriculture

Abstract

The manageability of agricultural lands is linked to spatiotemporal monitoring of water–soil resources, evidence-based planning of cropping patterns, and improvements in irrigation efficiency. Within the semi-arid Tigris Basin, Ergani district (Diyarbakır, Türkiye) combines high agricultural potential with water-scarcity risk. This study integrates remote sensing with supervised classification to delineate the spatial distribution of agricultural lands and open-water surfaces. Landsat 8 OLI/TIRS scenes from January 2025 under low cloud cover were processed using reflectance-based band combinations and water- and vegetation-sensitive indices, and a Maximum Likelihood Classification (MLC) was implemented with 90 training samples. MLC models class-specific covariance structures and assigns each pixel to the class with the highest likelihood. The study area was partitioned into five classes: water, agriculture, urban/settlement, mountainous/rocky terrain, and grassland. Agricultural land accounts for 42% of the area, whereas water surfaces cover 3%, confirming the dominant role of agriculture and limited surface-water presence. In this context, groundwater emerges as a strategic buffer for drought resilience, baseflow support, and seasonal irrigation supply. Conjunctive use of surface water and aquifers, adherence to sustainable abstraction thresholds, and protection of recharge zones are critical. Integrating MLC-derived land-cover maps with piezometric head observations, baseflow metrics, and well-abstraction records can help close the water balance and guide demand management through deficit irrigation and pressurized/drip systems, providing a robust spatial basis for integrated water-resources strategies in Ergani.

Copyright

© 2026 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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TY  - CONF
AU  - Recep Çelik
AU  - Yunus Emre Çolak
PY  - 2026
DA  - 2026/05/14
TI  - Delineating Agricultural and Water Surfaces in Ergani (Diyarbakır) via Maximum Likehood Classification: A Remote Sensing-Based Approach for Sustainable Agriculture
BT  - Proceedings of the International Conference on Current Problems in Engineering and Applied Sciences (ICCPEAS 2025)
PB  - Atlantis Press
SP  - 610
EP  - 618
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-668-5_63
DO  - 10.2991/978-94-6239-668-5_63
ID  - Çelik2026
ER  -