Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Spatial Analysis of Soil Organic Carbon in the Thuckalay Block of the Kanyakumari District
The soil has organic and inorganic matter, which helps the plants to grow. The main component of organic matter is soil organic carbon (SOC). SOC is studied in the Thuckalay block of the Kanyakumari district with Sentinel-2 data. The following indices, Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), and Soil Adjusted Vegetation Index (SAVI), are analyzed using linear regression, and R2 values were compared. The linear regression values for NDVI are R2 = 0.5146; for BSI, R2 = 0.7608; and for SAVI, R2 = 0.7493. Then 15 soil samples were collected, and the Walkley-Black technique was used for the analysis of soil organic carbon for validation. The relationship between SOC and BSI is high, while the relationship between SOC and NDVI is very low, and the relationship between SOC and SAVI is medium. When BSI is high, the SOC will also be increased, and vice versa, and the SOC has a low impact due to the vegetation content. Land degradation causes nutrient depletion and soil erosion which can be reduced by analyzing the relationship between LULC and land degradation. The identification of SOC helps in precision farming and good yield for agriculture. Increasing SOC levels in degraded soils helps to restore soil health, improve soil fertility, and enhance the capacity of the soil to support healthy ecosystems.
Spatial Analysis of Soil Organic Carbon in the Thuckalay Block of the Kanyakumari District
The soil has organic and inorganic matter, which helps the plants to grow. The main component of organic matter is soil organic carbon (SOC). SOC is studied in the Thuckalay block of the Kanyakumari district with Sentinel-2 data. The following indices, Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), and Soil Adjusted Vegetation Index (SAVI), are analyzed using linear regression, and R2 values were compared. The linear regression values for NDVI are R2 = 0.5146; for BSI, R2 = 0.7608; and for SAVI, R2 = 0.7493. Then 15 soil samples were collected, and the Walkley-Black technique was used for the analysis of soil organic carbon for validation. The relationship between SOC and BSI is high, while the relationship between SOC and NDVI is very low, and the relationship between SOC and SAVI is medium. When BSI is high, the SOC will also be increased, and vice versa, and the SOC has a low impact due to the vegetation content. Land degradation causes nutrient depletion and soil erosion which can be reduced by analyzing the relationship between LULC and land degradation. The identification of SOC helps in precision farming and good yield for agriculture. Increasing SOC levels in degraded soils helps to restore soil health, improve soil fertility, and enhance the capacity of the soil to support healthy ecosystems.
Spatial Analysis of Soil Organic Carbon in the Thuckalay Block of the Kanyakumari District
Lecture Notes in Civil Engineering
Reddy, Krishna R. (Herausgeber:in) / Ravichandran, P. T. (Herausgeber:in) / Ayothiraman, R. (Herausgeber:in) / Joseph, Anil (Herausgeber:in) / Arthi, A. P. (Autor:in) / Kumar, J. Satish (Autor:in)
International Conference on Civil Engineering Innovative Development in Engineering Advances ; 2023 ; Kattankulathur, India
31.01.2024
13 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| DOAJ | 2022
Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China
| DOAJ | 2016