Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands

Abstract

Estimation of leaf area index (LAI) using spectral vegetation indices (SVIs) was studied based on data from 683 plots on two Scots pine and Norway spruce dominated sites in Finland. The SVIs studied included the normalised difference vegetation index (NDVI), the simple ratio (SR), and the reduced simple ratio (RSR), and were calculated from Landsat ETM images of the two sites. Regular grids of size 1 km 2 with gridpoints placed at 50 m intervals were established at the sites and measurements of LAI using the LAI-2000 instrument were taken at the gridpoints. SVI-LAI relationships were examined at plot scale, where the plots were defined as circular areas of radius 70 m around each gridpoint. Plotwise mean LAI was computed as a weighted average of LAI readings taken around the gridpoints belonging to the plot. Mean LAI for the plots ranged from 0.36 to 3.72 (hemisurface area). All of the studied SVIs showed fair positive correlation with LAI but RSR responded more dynamically to LAI than did SR or NDVI. Especially NDVI showed poor sensitivity to changes in LAI. RSR explained 63% of the variation in LAI when all plots were included (n = 683) and the coefficient of determination rose to 75% when data was restricted to homogeneous plots (n = 381). Maps of estimated LAI using RSR showed good agreement with maps of measured LAI for the two sites.

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

“…This type of erratic fits indicates that polynomial fitting-despite the high r 2 and low RMSE-should not be recommended for biophysical parameter retrieval. Although dating from the times where the campaigns mostly were limited to the silicon-based CCD detector range (400-1000 nm), the saturation effect is often reported and is considered as one of the major critical points when using a Red-NIR formulation (e.g., NDVI) for LAI retrieval [49,50]. A noteworthy observation is that here LAI hardly shows saturation for a linear model fit.…”

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

“…Several studies have found that SWIR modifications of the SR or NDVI improve the correlation with LAI in the coniferous forests (Nemani et al 1993, Brown et al 2000, Chen et al 2002, Stenberg et al 2004 and in the deciduous forests (Chen et al 2002). However, in the comparison of Eklundh et al (2003), RSR (which is the SWIR modification of the SR) performed poorly in the deciduous stands.…”

“…The possibility of estimating biomass and LAI by satellite remote sensing has been investigated in several studies at various spatial scales and environments (Badhwar et al 1986, Spanner et al 1990, Nemani et al 1993, Chen and Cihlar 1996, Fassnacht et al 1997, Häme et al 1997, Myneni et al 1997, Turner et al 1999, Brown et al 2000, Brown 2001, Chen et al 2002, Tomppo et al 2002, Eklundh et al 2003, Laidler and Treitz 2003, Stenberg et al 2004. The most frequently used remote sensing data continue to be from the optical moderate resolution sensors, like Landsat Enhanced Thematic Mapper Plus (ETM + ).…”

Seasonal Reflectance Courses of Forests

“…In empirical studies, shortwave spectral bands typically have a decreasing trend when canopy LAI increases (e.g. Brown et al, 2000;Nilson et al, 1999;Stenberg et al, 2004;Stenberg et al, 2008a).…”