Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators
Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and loca...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2020-05-01
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| Series: | Journal of Landscape Ecology |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/jlecol-2020-0005 |
| _version_ | 1818598262923329536 |
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| author | Somvanshi Shivangi S. Kunwar Phool De Vries Walter Timo Kumari Maya Zubair Syed |
| author_facet | Somvanshi Shivangi S. Kunwar Phool De Vries Walter Timo Kumari Maya Zubair Syed |
| author_sort | Somvanshi Shivangi S. |
| collection | DOAJ |
| description | Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and location of the salt affected soils as it has a severe effect on the agricultural crop yield of the Gautam Buddha Nagar (GBN) district. To assess spatial variation of the salt-affected soil a simulation model integrating satellite observation data, artificial neural network (ANN) and multiple linear regression (MLR) was used. The statistical correlation amongst ground-truth data and Landsat original bands and band ratios showed that all the bands and ratios showed a non-significant correlation with SAR. While four optical bands and eleven band ratios showed high correlation with all the soil quality parameters. Combining all the remotely sensed variables into models resulted in the finest fit with the R2 value equal to 0.84, 0.69, 0.59 and 0.85 for EC, pH, ESP and TSS, respectively. The soil quality parameter maps generated using selected models revealed that most of the part of the agricultural land of the study area lies in the range of moderately saline and moderately sodic soil. Further Analytical Hierarchy Process (AHP) was applied to generate overall soil degradation probability map of the district, with respect to salt accumulation. The result revealed that the major portion of the entire agricultural field of the study area lie between low (32.74 %) to moderate (29.53 %) probability zones of salt susceptibility. |
| first_indexed | 2024-12-16T12:00:55Z |
| format | Article |
| id | doaj.art-28ef3a487e7a4ac2879a33c800364d67 |
| institution | Directory Open Access Journal |
| issn | 1805-4196 |
| language | English |
| last_indexed | 2024-12-16T12:00:55Z |
| publishDate | 2020-05-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Landscape Ecology |
| spelling | doaj.art-28ef3a487e7a4ac2879a33c800364d672022-12-21T22:32:26ZengSciendoJournal of Landscape Ecology1805-41962020-05-01131618410.2478/jlecol-2020-0005jlecol-2020-0005Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing IndicatorsSomvanshi Shivangi S.0Kunwar Phool1De Vries Walter Timo2Kumari Maya3Zubair Syed4Amity Institute of Environmental Sciences, Amity University, Sector – 125, Noida, India.Remote Sensing Application Centre – Uttar Pradesh, Lucknow, Uttar Pradesh, India.Department of Civil, Geo and Environmental Engineering, Technische Universitat Munchen, Germany.Amity School of Natural Resources and Sustainable Development, Amity University, Sector-125, Noida, India.Department of Civil Engineering, Amity School of Engineering and Technology, Amity University, Sector-125, Noida, India.Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and location of the salt affected soils as it has a severe effect on the agricultural crop yield of the Gautam Buddha Nagar (GBN) district. To assess spatial variation of the salt-affected soil a simulation model integrating satellite observation data, artificial neural network (ANN) and multiple linear regression (MLR) was used. The statistical correlation amongst ground-truth data and Landsat original bands and band ratios showed that all the bands and ratios showed a non-significant correlation with SAR. While four optical bands and eleven band ratios showed high correlation with all the soil quality parameters. Combining all the remotely sensed variables into models resulted in the finest fit with the R2 value equal to 0.84, 0.69, 0.59 and 0.85 for EC, pH, ESP and TSS, respectively. The soil quality parameter maps generated using selected models revealed that most of the part of the agricultural land of the study area lies in the range of moderately saline and moderately sodic soil. Further Analytical Hierarchy Process (AHP) was applied to generate overall soil degradation probability map of the district, with respect to salt accumulation. The result revealed that the major portion of the entire agricultural field of the study area lie between low (32.74 %) to moderate (29.53 %) probability zones of salt susceptibility.https://doi.org/10.2478/jlecol-2020-0005soil quality parameterscorrelationartificial neural network (ann)multiple linear regression (mlr)analytical hierarchy process (ahp) and weighted index overlay (wio) |
| spellingShingle | Somvanshi Shivangi S. Kunwar Phool De Vries Walter Timo Kumari Maya Zubair Syed Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators Journal of Landscape Ecology soil quality parameters correlation artificial neural network (ann) multiple linear regression (mlr) analytical hierarchy process (ahp) and weighted index overlay (wio) |
| title | Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators |
| title_full | Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators |
| title_fullStr | Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators |
| title_full_unstemmed | Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators |
| title_short | Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators |
| title_sort | unveiling spatial variation in salt affected soil of gautam buddha nagar district based on remote sensing indicators |
| topic | soil quality parameters correlation artificial neural network (ann) multiple linear regression (mlr) analytical hierarchy process (ahp) and weighted index overlay (wio) |
| url | https://doi.org/10.2478/jlecol-2020-0005 |
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