Prediction and Simulation for Land Use and Land Cover Change of Paddy Field Influence by Salinization in Coastal Demak Regency
Abstract
The extent of coastal rice paddy agricultural land is vulnerable to land use and land cover (LULC) changes to non-agricultural uses due to land degradation, one of which is caused by salinity. This study aims to detect and project LULC changes up to 2031, particularly in coastal rice paddy areas affected by salinity, by comparing LULC in 2017, 2019, and 2021. Sentinel-2 Imagery is used for LULC classification, with recordings selected during the generative phase of rice growth to obtain the most optimal rice paddy area. There are six LULC classifications: water, wetland, low-medium-high vegetation cover, and built-up area. To understand the impact of salinity on crops, several vegetation indices (VIs) such as NDVI, SAVI, EVI, and ARVI are used. The LULC changes classified according to VIs are compared with the MOLUSCE plugin based on artificial neural networkmultilayer perceptron (ANN-MLP) and Cellular Automata (CA). The comparison of VIs results shows that NDVI is better at describing LULC changes due to the influence of salinity, with a kappa value of 0.63 and a Correctness of 72.565. The LULC projection using CA in all VIs indicates that wetland areas are more likely to convert into water bodies, suggesting that high salinity land tends to be unproductive for rice paddies, making it prone to conversion.
References
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2. Li, J.; Yang, W.; Wang, Y.; Li, Q.; Liu, L.; Zhang, Z. Carbon Footprint and Driving Forces of Saline Agriculture in Coastally Reclaimed Areas of Eastern China: A Survey of Four Staple Crops. Sustainability 2018, 10, 928, doi:10.3390/SU10040928.
3. Hikmat, M.; Yatno, E.; Suryani, E. Salinity of Paddy Field in Main Landforms in Indramayu Regency, West Java. 2021, 648, 12036, doi:10.1088/1755-1315/648/1/012036.
4. Daris, E.; Aminudin, I.; Feriansyah, A. Determinants of Paddy Fields Conversion in Java Island, Indonesia. 2018, 95–98, doi:10.2991/ICOSAT-17.2018.22.
5. Jian-ron, C. Analysis for Driving Forces and Ecological Risk Assessment of Soil Salinization in the Yellow River Delta. Adv. Mar. Sci. 2014.
6. Erfandi, D.; Rachman, A. Identification of Soil Salinity Due to Seawater Intrusion on Rice Field in the Northern Coast of Indramayu, West Java. J. Trop. Soils 2011, 16, 115–121, doi:10.5400/JTS.15.2.95.
7. Adnan, A.M.I. Farmers’ Perspective on Converting Their Paddy Fields in West Java Province. Agriekonomika 2023, 12, 48–67, doi:10.21107/agriekonomika.v12i1.16589.
8. Karolinoerita, V.; Annisa, W. Salinisasi Lahan Dan Permasalahannya Di Indonesia. 2020, 14, 91–99, doi:10.21082/JSDL.V14N2.2020.91-99.
9. Duan, S.; Kaushal, S.S. Salinization Alters Fluxes of Bioreactive Elements from Stream Ecosystems across Land Use. Biogeosciences 2015, 12, 7331–7347, doi:10.5194/BG-12-7331-2015.
10. Gerardo, R.; Lima, I. de Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal. Agriculture 2022, 12, 1490, doi:10.3390/agriculture12091490.
11. Novitasari, N. Relationship Between Normalized Difference Vegetation Index (NDVI) and Rice Growth Phases in Danda Jaya Swamp Irrigation Area Regency Barito Kuala. IOP Conf. Ser. 2023, 1184, 12019, doi:10.1088/1755-1315/1184/1/012019.
12. Hisham, N.H.B.; Hashim, N.I.; Saraf, N.M.; Talib, N. Monitoring of Rice Growth Phases Using Multi-Temporal Sentinel-2 Satellite Image. IOP Conf. Ser. Earth Environ. Sci. 2022, 1051, 12021, doi:10.1088/1755-1315/1051/1/012021.
13. Tivianton, T.A.; Barus, B.; Purwanto, M.Y.J.; Anwar, S.; Widiatmaka; Laudiansyah, R. Temporal NDVI Analysis to Detect the Effects of Seawater Intrusion on Rice Growth in Coastal Areas. 2021, 662, 12021, doi:10.1088/1755-1315/662/1/012021.
14. Sakai, T.; Omori, K.; Oo, A.N.; Zaw, Y.N. Monitoring Saline Intrusion in the Ayeyarwady Delta, Myanmar, Using Data from the Sentinel-2 Satellite Mission. Paddy Water Environ. 2021, 19, 1–12, doi:10.1007/S10333-020-00837-0.
15. Moussa, I.; Walter, C.; Michot, D.; Boukary, I.A.; Nicolas, H.; Pichelin, P.; Guero, Y. Soil Salinity Assessment in Irrigated Paddy Fields of the Niger Valley Using a Four-Year Time Series of Sentinel-2 Satellite Images. Remote Sens. 2020, 12, 3399, doi:10.3390/RS12203399.
16. Tucker, C.J. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sens. Environ. 1979, 8, 127–150, doi:10.1016/0034-4257(79)90013-0.
17. Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices. Remote Sens. Environ. 2002, 83, 195–213, doi:https://doi.org/10.1016/S0034-4257(02)00096-2.
18. Huete, A.R. A Soil-Adjusted Vegetation Index (SAVI). Remote Sens. Environ. 1988, 25, 295–309, doi:https://doi.org/10.1016/0034-4257(88)90106-X.
19. Kaufman, Y.J.; Tanre, D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 1992, 30, 261–270, doi:10.1109/36.134076.
20. Buta, M.; Paulette, L.; Man, T.; Bartha, I.; Negrusier, C.; Bordea, C. Spatial Assessment of Soil Salinity by Electromagnetic Induction Survey. Environ. Eng. Manag. J. 2019, 18, 2073–2081, doi:10.30638/EEMJ.2019.197.
21. Cassel, F.; Goorahoo, D.; Sharmasarkar, S. Salinization and Yield Potential of a Salt-Laden Californian Soil: An In Situ Geophysical Analysis. Water Air Soil Pollut. 2015, 226, 422, doi:10.1007/S11270-015-2682-1.
22. Louati, D.; Majdoub, R.; Rigane, H.; Abida, H. Effects of Irrigating with Saline Water on Soil Salinization (Eastern Tunisia). Arab. J. Sci. Eng. 2018, 43, 3793–3805, doi:10.1007/S13369-018-3215-1.
23. Yu, X.; Yang, J.; Graf, T.; Koneshloo, M.; O’Neal, M.A.; Michael, H.A. Impact of Topography on Groundwater Salinization Due to Ocean Surge Inundation. Water Resour. Res. 2016, 52, 5794–5812, doi:10.1002/2016WR018814.
24. Hasan, M.H.; Hossain, M.J.; Chowdhury, M.A.; Billah, M. Salinity Intrusion in Southwest Coastal Bangladesh: An Insight from Land Use Change. 2020, 125–140, doi:10.1007/978-3-030-47786-8_8.
25. Celleri, C. Spatial and Temporal Patterns of Soil Salinization in Shallow Groundwater Environments of the Bahía Blanca Estuary: Influence of Topography and Land Use. 2022, 33, 470–483, doi:10.1002/ldr.4162.
26. Xie, X.; Xie, X.; Pu, L.; Pu, L.; Zhu, M.; Zhu, M.; Xu, Y.; Wang, X. Linkage between Soil Salinization Indicators and Physicochemical Properties in a Long-Term Intensive Agricultural Coastal Reclamation Area, Eastern China. J. Soils Sediments 2019, 19, 3699–3707, doi:10.1007/S11368-019-02333-3.
27. Thiam, S.; Thiam, S.; Thiam, S.; Villamor, G.B.; Villamor, G.B.; Faye, L.C.; Sène, J.H.B.; Diwediga, B.; Kyei-Baffour, N. Monitoring Land Use and Soil Salinity Changes in Coastal Landscape: A Case Study from Senegal. Environ. Monit. Assess. 2021, 193, 259, doi:10.1007/S10661-021-08958-7.
28. Song, Y.; Gao, M.; Wang, Z.; Gong, T.F.; Chen, W. Spatio–Temporal Variability Characteristics of Coastal Soil Salinization and Its Driving Factors Detection. Water 2022, 14, 3326, doi:10.3390/w14203326.
29. Shalem, Y.; Yechieli, Y.; Herut, B.; Weinstein, Y. Aquifer Response to Estuarine Stream Dynamics. Water 2019, 11, 1678, doi:10.3390/W11081678.
30. Giambastiani, B.M.S.; Macciocca, V.R.; Molducci, M.; Antonellini, M. Factors Affecting Water Drainage Long-Time Series in the Salinized Low-Lying Coastal Area of Ravenna (Italy). Water 2020, 12, 256, doi:10.3390/W12010256.
31. Changshui, Y.U. Drainage Salinity Efficiency under Different Salt Discharge Treatment Conditions over Coastal Saline-Alkali Areas. Resour. Sci. 2010.
32. Pereira, C.S.; Lopes, I.; Abrantes, I.; Sousa, J.P.; Chelinho, S. Salinization Effects on Coastal Ecosystems: A Terrestrial Model Ecosystem Approach. Philos. Trans. R. Soc. B 2019, 374, 20180251, doi:10.1098/RSTB.2018.0251.
Authors
TiviantonT. A., BarusB., PurwantoM. Y. J., AnwarS. and Widiatmaka (2024) “Prediction and Simulation for Land Use and Land Cover Change of Paddy Field Influence by Salinization in Coastal Demak Regency”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 14(4), p. 811. doi: 10.29244/jpsl.14.4.811.
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