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Academic Research Journal of Agricultural Science and Research  Vol. 2(3), pp. 47-56, July, 2014. DOI: 10.14662/ARJASR2014.017. ISSN: 2360-7874 2014 Academic Research Journals

Full Length Research

Estimation of Vegetative Characteristics by Remote Sensing

 

1John T. Murphy, 2Clenton E. Owensby, 3Jay M. Ham, and 4Patrick I. Coyne

 

 

1Professor of Biology, Southwest Baptist University, Bolivar, MO 65613.

2Professor of Range Management, Department of Agronomy , Kansas State University, Manhattan, KS, 66506.

3Professor of Environmental Physics and Micrometeorology, Department of Soil and Crop Sciences, Colorado State University, Ft. Collins, CO 80523;

4EmeritusProfessor of Physiologic Ecology, KSU Ag Research Center, Hays, 67601.

Correspondence: Clenton E. Owensby, Kansas State University, Department of Agronomy, Throckmorton Hall, Manhattan, KS 66506-5501, E-mail: owensby@ksu.edu

 

Accepted 8 July 2014

Abstract

 

Measurements of biomass, leaf area and carbon cycling rates are important components for understanding ecosystem processes. While other techniques are available to measure these components, remote sensing has the advantages of non-destructive sampling and sampling at large scales. An experiment was conducted from June through mid-October of 2006 to estimate gross canopy photosynthesis (GCP), leaf area index (LAI) and total aboveground biomass of an ungrazed tallgrass prairie with a hyperspectral radiometer and compare these estimates to flux chamber measurements. Four indices (Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), (Rnir/Rrededge)-1, and (Rnir/Rgreen)-1 were used to predict LAI and total biomass. Six indices (NDVI, SR, [(Rnir/Rrededge)-1]*photosynthetically active radiation (PAR),[(Rnir/Rgreen)-1]*PAR, Photochemical Reflectance Index (PRI) and the Water Band Index (WBI)) were used to derive an estimate of GCP. All indices had poor correlations to LAI and total aboveground biomass. However, GCP was significantly correlated to all six indices utilized in this study. While GCP measured from June-October was significantly correlated to all indices, removal of the October scans greatly increased the accuracy of all models except the PRI. This study demonstrates the strong relationship between GCP and spectral reflectance in the tallgrass prairie. These relationships may be further utilized with other methods of measuring carbon exchange to gain greater understanding of larger scale ecosystem processes.

Keywords: Hyperspectral; LAI; total biomass; gross canopy photosynthesis.



 

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