Geospatial Strategy for Estimation of Soil Organic Carbon in Tropical Wildlife Reserve

  • Gargi Gupta
  • Jyoti Singh
  • Prem Chandra Pandey
  • Vandana Tomar
  • Meenu Rani
  • Pavan Kumar
Chapter
Part of the Society of Earth Scientists Series book series (SESS)

Abstract

This study focuses on the estimation of soil organic carbon of Sariska Wildlife Reserve. The soil organic carbon is one of the most important issues in the research area of the global carbon cycle as it is the largest terrestrial carbon pool. Geospatial and various forest inventory approaches were used during study for statistical correlation between estimated and predicted value. Remote sensing plays a vital role in spatial data acquisition of the ecosystem carbon dynamics at local, regional, and global scale. The advantage of remote sensing is that it provides synoptic observation, periodical and continuous measurement, and availability of digital data for processing standardization. IRS P6 LISS III data (September 2012) were used to analyze the precise estimation of the percentage of the soil organic carbon associated with organic matter in soil. Statistical analysis was performed for finding the regression curve between the predicted and estimated value of soil organic carbon. The results illustrated that the determination of coefficient (r2) between the predicted and estimated SOC values is found to be 0.708.

Keywords

Bare soil index NDVI Soil organic carbon Regression analysis 

Notes

Acknowledgment

We are thankful to Haryana Space Applications Centre (Department of Science and Technology), Hisar, Haryana, India for providing satellite and ancillary data support. We are thankful to Department of Remote Sensing, Banasthali Vidyapith, Rajasthan-304022, India for providing all necessary support.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gargi Gupta
    • 1
  • Jyoti Singh
    • 1
  • Prem Chandra Pandey
    • 2
  • Vandana Tomar
    • 1
  • Meenu Rani
    • 3
  • Pavan Kumar
    • 1
  1. 1.Department of Remote SensingBanasthali UniversityNewaiIndia
  2. 2.Department of GeographyUniversity of LeicesterLeicesterUK
  3. 3.Project Directorate for Farming System ResearchMeerutIndia

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