Spectrally derived values of community leaf dry matter content link shifts in grassland composition with change in biomass production

Abstract

Abstract Leaf traits link environmental effects on plant species abundances to changes in ecosystem processes but are a challenge to measure regularly and over large areas. We used measurements of canopy reflectance from grassland communities to derive a regression model for one leaf trait, leaf dry matter content (LDMC). Partial least squares regression (PLSR) analysis was used to model community‐weighted (species abundance‐weighted) values of LDMC as a function of canopy reflectance in visible and near‐infrared (NIR) wavebands. The PLSR model then was applied to airborne measurements of canopy reflectance to determine how community LDMC interacts with inter‐annual variation in precipitation to influence the normalized difference vegetation index (NDVI), a surrogate of aboveground biomass production, of restored grassland during spring over 4 years. LDMC was well‐described by a PLSR model that included reflectance measurements located primarily in red edge and NIR portions of the spectrum. Community LDMC decreased as annual forb species became more abundant and was negatively correlated with maximum values of NDVI. Decreased precipitation reduced NDVI (biomass production) both by increasing community LDMC (LDMC response) and reducing the slope of the NDVI‐LDMC relationship (LDMC effect on NDVI). We find that grassland LDMC is well‐described by a regression model using canopy reflectance in red edge and NIR wavebands. Our results demonstrate the utility of spectral estimates of LDMC for discerning shifts in grassland composition and predicting consequences for production‐related ecosystem functions.

Keywords

• Aboveground biomass production
• grassland species composition
• hyperspectral remote sensing
• leaf traits
• NDVI
• plant functional groups