Forestry Studies in China

, Volume 14, Issue 1, pp 55–62 | Cite as

Estimating vertical vegetation density through a SPOT5 imagery at multiple radiometric correction levels

Research Article
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Abstract

There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces. A widely used parameter for such density, i.e., leaf area index (LAI), was measured in situ in Nanjing, China and then correlated with two vegetation indices (VI) derived from multiple radiometric correction levels of a SPOT5 imagery. The VIs were a normalized difference vegetation index (NDVI) and a ratio vegetation index (RVI), while the four radiometric correction levels were i) post atmospheric correction reflectance (PAC), ii) top of atmosphere reflectance (TOA), iii) satellite radiance (SR) and iv) digital number (DN). A total of 157 LAI-VI relationship models were established. The results showed that LAI is positively correlated with VI (r varies from 0.303 to 0.927, p < 0.001). The R 2 values of “pure” vegetation were generally higher than those of mixed vegetation. The average R 2 values of about 40 models based on DN data (0.688) were higher than that of the routinely used PAC (0.648). Independent variables of the optimal models for different vegetation quadrats included two vegetation indices at three radiometric correction levels, indicating the potential of vegetation indices at multiple radiometric correction levels in LAI inversion. The study demonstrates that taking heterogeneities of vegetation structures and uncertainties of radiometric corrections into account may help full mining of valuable information from remote sensing images, thus improving accuracies of LAI estimation.

Key words

radiometric correction vegetation index (VI) leaf area index (LAI) model 
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Copyright information

© Beijing Forestry University and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Bio-Chemical and Environmental EngineeringNanjing Xiaozhuang UniversityNanjingP. R. China

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