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

Estimating vertical vegetation density through a SPOT5 imagery at multiple radiometric correction levels
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摘要 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 normal- ized 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 LA! is positively correlated with VI (r varies from 0.303 to 0.927, p 〈 0.001). The R: values of"pure" vegetation were generally higher than those of mixed vegetation. The average R2 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 lev- els, 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. 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 normal- ized 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 LA! is positively correlated with VI (r varies from 0.303 to 0.927, p 〈 0.001). The R: values of"pure" vegetation were generally higher than those of mixed vegetation. The average R2 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 lev- els, 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.
出处 《Forestry Studies in China》 CAS 2012年第1期55-62,共8页 中国林学(英文版)
基金 funded by the National Natural Science Foundation of China(Grant No.41071281)
关键词 radiometric correction vegetation index (VI) leaf area index (LAI) model radiometric correction, vegetation index (VI), leaf area index (LAI), model
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