Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve
Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kumar, Pavan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media B.V. 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Natural hazards - Springer Netherlands, 1988, 90(2017), 2 vom: 24. Okt., Seite 693-704 |
|---|---|
| Übergeordnetes Werk: |
volume:90 ; year:2017 ; number:2 ; day:24 ; month:10 ; pages:693-704 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11069-017-3062-5 |
|---|
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OLC2053683449 |
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| 520 | |a Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. | ||
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10.1007/s11069-017-3062-5 doi (DE-627)OLC2053683449 (DE-He213)s11069-017-3062-5-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kumar, Pavan verfasserin aut Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2017 Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. Soil organic carbon Predictor variables Leaf area index Kriging spatial interpolation RMSE Sajjad, Haroon aut Tripathy, Bismay Ranjan aut Ahmed, Raihan aut Mandal, Vinay Prasad aut Enthalten in Natural hazards Springer Netherlands, 1988 90(2017), 2 vom: 24. Okt., Seite 693-704 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:90 year:2017 number:2 day:24 month:10 pages:693-704 https://doi.org/10.1007/s11069-017-3062-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 90 2017 2 24 10 693-704 |
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10.1007/s11069-017-3062-5 doi (DE-627)OLC2053683449 (DE-He213)s11069-017-3062-5-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kumar, Pavan verfasserin aut Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2017 Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. Soil organic carbon Predictor variables Leaf area index Kriging spatial interpolation RMSE Sajjad, Haroon aut Tripathy, Bismay Ranjan aut Ahmed, Raihan aut Mandal, Vinay Prasad aut Enthalten in Natural hazards Springer Netherlands, 1988 90(2017), 2 vom: 24. Okt., Seite 693-704 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:90 year:2017 number:2 day:24 month:10 pages:693-704 https://doi.org/10.1007/s11069-017-3062-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 90 2017 2 24 10 693-704 |
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10.1007/s11069-017-3062-5 doi (DE-627)OLC2053683449 (DE-He213)s11069-017-3062-5-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kumar, Pavan verfasserin aut Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2017 Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. Soil organic carbon Predictor variables Leaf area index Kriging spatial interpolation RMSE Sajjad, Haroon aut Tripathy, Bismay Ranjan aut Ahmed, Raihan aut Mandal, Vinay Prasad aut Enthalten in Natural hazards Springer Netherlands, 1988 90(2017), 2 vom: 24. Okt., Seite 693-704 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:90 year:2017 number:2 day:24 month:10 pages:693-704 https://doi.org/10.1007/s11069-017-3062-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 90 2017 2 24 10 693-704 |
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10.1007/s11069-017-3062-5 doi (DE-627)OLC2053683449 (DE-He213)s11069-017-3062-5-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kumar, Pavan verfasserin aut Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2017 Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. Soil organic carbon Predictor variables Leaf area index Kriging spatial interpolation RMSE Sajjad, Haroon aut Tripathy, Bismay Ranjan aut Ahmed, Raihan aut Mandal, Vinay Prasad aut Enthalten in Natural hazards Springer Netherlands, 1988 90(2017), 2 vom: 24. Okt., Seite 693-704 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:90 year:2017 number:2 day:24 month:10 pages:693-704 https://doi.org/10.1007/s11069-017-3062-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 90 2017 2 24 10 693-704 |
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10.1007/s11069-017-3062-5 doi (DE-627)OLC2053683449 (DE-He213)s11069-017-3062-5-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kumar, Pavan verfasserin aut Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media B.V. 2017 Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. Soil organic carbon Predictor variables Leaf area index Kriging spatial interpolation RMSE Sajjad, Haroon aut Tripathy, Bismay Ranjan aut Ahmed, Raihan aut Mandal, Vinay Prasad aut Enthalten in Natural hazards Springer Netherlands, 1988 90(2017), 2 vom: 24. Okt., Seite 693-704 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:90 year:2017 number:2 day:24 month:10 pages:693-704 https://doi.org/10.1007/s11069-017-3062-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 90 2017 2 24 10 693-704 |
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Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve |
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Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve |
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Kumar, Pavan |
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Natural hazards |
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Kumar, Pavan Sajjad, Haroon Tripathy, Bismay Ranjan Ahmed, Raihan Mandal, Vinay Prasad |
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prediction of spatial soil organic carbon distribution using sentinel-2a and field inventory data in sariska tiger reserve |
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Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve |
| abstract |
Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. © Springer Science+Business Media B.V. 2017 |
| abstractGer |
Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. © Springer Science+Business Media B.V. 2017 |
| abstract_unstemmed |
Abstract Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale. © Springer Science+Business Media B.V. 2017 |
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Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve |
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Sajjad, Haroon Tripathy, Bismay Ranjan Ahmed, Raihan Mandal, Vinay Prasad |
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