Analysis and optimization of NDVI definitions and areal fraction models in remote sensing of vegetation
Source: International Journal of Remote Sensing, Volume 30, Number 3, 2009 , pp. 721-751(31)
Publisher: Taylor and Francis Ltd
Abstract:Variations in the definition of the Normalized Difference Vegetation Index (NDVI) and inconsistencies in vegetation areal fraction models prejudice the understanding of long-term variability and change in land cover. We analysed the consequences of using NDVI definitions based on the digital number (DN), spectral radiance and spectral reflectance for six active and high spatial resolution multi- and hyperspectral satellite sensors (ALI, ASTER, ETM+, HRVIR, Hyperion and IKONOS) and optimized the NDVI definitions, and then examined the performance of three vegetation areal fraction models: the linear reflectance, linear NDVI and quadratic NDVI models. The examination was performed for three plots chosen from two biomass zones: a short and small leaf area index (LAI) creosote shrub zone, and a tall and large-LAI pinon-juniper zone. The results show that: (1) the difference in NDVI values among the NDVI definitions is sensor dependent and always significant; spectral reflectance should be used in NDVI calculations, and using radiance or DN values in calculating the NDVI should be avoided; (2) in deriving vegetation areal coverage, the linear reflectance model outperforms the other two models in the shrub biomass zone; and (3) the linear NDVI model outperforms the other two models in the pinon-juniper biomass zone. These observations are consistent with the fact that the non-linear effect is less important in shrubland than in pinon-juniper woodland and that the linear NDVI model is more capable of capturing non-linearity in the spectral analysis.
Document Type: Research Article
Affiliations: 1: Department of Geophysical Engineering, Montana Tech of the University of Montana, Butte, MT 59701, USA 2: Department of Earth and Environmental Science, New Mexico Tech, Socorro, NM 87801, USA 3: Laboratory for Remote Sensing and Geoinformatics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Publication date: 2009-01-01