Land use change and soil salinization in the Sundarbans: a machine-learning based analysis of long-term transformation and future projections.
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| Title: | Land use change and soil salinization in the Sundarbans: a machine-learning based analysis of long-term transformation and future projections. |
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| Authors: | Mandal UK; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Ghosh A; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Karim F; CSIRO Environment, Canberra, ACT, 2601, Australia. fazlul.karim@csiro.au., Mallick S; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India. sonalimallickiari@gmail.com., Nayak DB; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Bhutia RN; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India. rinchenacademia@gmail.com., Bhardwaj AK; ICAR-Central Soil Salinity Research Institute, Karnal, 132 001, India. ajaykbhardwaj@gmail.com., Lama TD; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Burman D; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Choudhury P; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Mahanta KK; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Raut S; ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, 743 329, India., Mandal S; Dairy Economics Statistics and Management, ICAR-National Dairy Research Institute, Karnal, 132 001, India., Mainuddin M; CSIRO Environment, Canberra, ACT, 2601, Australia. |
| Source: | Environmental monitoring and assessment [Environ Monit Assess] 2025 Nov 28; Vol. 197 (12), pp. 1380. Date of Electronic Publication: 2025 Nov 28. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981- |
| MeSH Terms: | Machine Learning* , Environmental Monitoring*/methods , Soil*/chemistry , Salinity* , Conservation of Natural Resources*, India ; Satellite Imagery ; Agriculture |
| Abstract: | Competing Interests: Declarations. Ethics approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests: The authors declare no competing interests. Quantitative data on coastal land use changes are essential for effective resource management and sustainable development. In this study, we examined land use and land cover (LULC) changes, along with erosion and accretion, in the climate-sensitive Sundarbans of India using satellite imagery from 1973 to 2022. Over this period, approximately 478 km 2 of land was lost to erosion, while 408 km 2 were gained through accretion. Although single cropping remains the predominant land use system, the areas dedicated to settlements and aquaculture grew 7 times and 2.4 times, respectively. The double-cropped area expanded by 75.8%, while the mangrove area remained unchanged. Among all land use categories, single-cropped land was found to be the most vulnerable to loss. The soil salinity area of the arable land, calculated using the canopy response salinity index (CRSI) from satellite imagery, rose from 2246.2 km 2 to 2669 km 2 in the 30 years since 1989. Using a machine learning algorithm (cellular automata-artificial neural network (CA-ANN)), which incorporated both anthropogenic factors and projected temperature and rainfall data as explanatory variables, we estimated that by 2049, the settlement area will increase by 31.6%, aquaculture will expand by 30%, and vegetation cover will decrease by 12.6%, compared to 2019 levels. The LULC change trend and coastline dynamics are expected to further exacerbate land degradation as the model predicts an increase in soil salinity by 5% over the same period. The results help farmers and policymakers to develop effective land management plans to enhance community readiness and resilience against vulnerabilities. (© 2025. The Author(s).) |
| References: | J Environ Manage. 2024 Aug;366:121787. (PMID: 38981259) Environ Monit Assess. 2017 Oct 17;189(11):565. (PMID: 29039035) Environ Sci Pollut Res Int. 2022 Dec;29(57):86337-86348. (PMID: 35112256) Sci Total Environ. 2023 Apr 1;867:161394. (PMID: 36634773) Heliyon. 2024 Sep 19;10(19):e38012. (PMID: 39386770) Heliyon. 2023 Apr 25;9(5):e15617. (PMID: 37159710) |
| Grant Information: | DST/TMD/EWO/WTI/2K19/EWFH/2019/286 Department of Science and Technology, Government of India and ICAR-NICRA (Indian Council of Agricultural Research National Innovations in Climate Resilient Agriculture) |
| Contributed Indexing: | Keywords: CA-ANN; Climate change; Erosion-accretion; MOLUSCE; Soil salinity; Vulnerability |
| Substance Nomenclature: | 0 (Soil) |
| Entry Date(s): | Date Created: 20251128 Date Completed: 20251128 Latest Revision: 20251201 |
| Update Code: | 20251201 |
| PubMed Central ID: | PMC12662945 |
| DOI: | 10.1007/s10661-025-14829-2 |
| PMID: | 41313492 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declarations. Ethics approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests: The authors declare no competing interests.<br />Quantitative data on coastal land use changes are essential for effective resource management and sustainable development. In this study, we examined land use and land cover (LULC) changes, along with erosion and accretion, in the climate-sensitive Sundarbans of India using satellite imagery from 1973 to 2022. Over this period, approximately 478 km <sup>2</sup> of land was lost to erosion, while 408 km <sup>2</sup> were gained through accretion. Although single cropping remains the predominant land use system, the areas dedicated to settlements and aquaculture grew 7 times and 2.4 times, respectively. The double-cropped area expanded by 75.8%, while the mangrove area remained unchanged. Among all land use categories, single-cropped land was found to be the most vulnerable to loss. The soil salinity area of the arable land, calculated using the canopy response salinity index (CRSI) from satellite imagery, rose from 2246.2 km <sup>2</sup> to 2669 km <sup>2</sup> in the 30 years since 1989. Using a machine learning algorithm (cellular automata-artificial neural network (CA-ANN)), which incorporated both anthropogenic factors and projected temperature and rainfall data as explanatory variables, we estimated that by 2049, the settlement area will increase by 31.6%, aquaculture will expand by 30%, and vegetation cover will decrease by 12.6%, compared to 2019 levels. The LULC change trend and coastline dynamics are expected to further exacerbate land degradation as the model predicts an increase in soil salinity by 5% over the same period. The results help farmers and policymakers to develop effective land management plans to enhance community readiness and resilience against vulnerabilities.<br /> (© 2025. The Author(s).) |
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| ISSN: | 1573-2959 |
| DOI: | 10.1007/s10661-025-14829-2 |