Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed GrasslandsReport as inadecuate

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Alberta Terrestrial Imaging Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada


Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK S7N 5C8, Canada


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Abstract Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead materials, and shadowing of vegetation canopies. Dead materials refer to the dead component of vegetation, including fallen litter and standing dead grasses accumulated from previous years. The abundant dead materials have been presenting challenges to accurately estimate green vegetation using spectral vegetation indices VIs derived from remote sensing data in mixed grasslands. Therefore, a close investigation of the relationship between VIs and dead materials is needed. The identified relationships could provide better insight into not only using remote sensing data for quantitative estimation of dead materials, but also the improvement of green vegetation estimation in the mixed grassland that has a high proportion of dead materials. In this article, the spectral reflectance of dead materials and green vegetation mixtures and dead material cover were measured in mixed grasslands located in Grassland National Park GNP, Saskatchewan, Canada. Nine VIs were derived from the measured spectral reflectance. The relationship between dead material cover and VIs was quantified using the regression model and sensitivity analysis. Results indicated that the relationship between dead material cover and VIs is a function of the amount of dead material cover. Weak positive relationship was found between VIs and dead materials where the cover was less than 50%, and a significant high negative relationship was evident when cover was greater than 50%. When the combined exponential and linear model was applied to fit the negative relationships, more than 90% variation in dead material cover could be explained by VIs. Sensitivity analysis was further applied to the developed models, indicating that sensitivities of all VIs were significant over the entire range of dead material cover except for the triangular vegetation index TVI, which has insignificant sensitivity when dead material cover was greater than 94%. Among all VIs, the weighted difference vegetation index WDVI had the highest sensitivity to changes in dead material cover higher than 50%. The results from this study indicated that vegetation indices based on combination of reflectance in red and NIR bands can be used to estimate dead material cover that is greater than 50%. View Full-Text

Keywords: material cover; sensitivity analysis; regression model; remote sensing material cover; sensitivity analysis; regression model; remote sensing

Author: Xiaohui Yang 1 and Xulin Guo 2,*

Source: http://mdpi.com/


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