Seasonal LAI in slash pine estimated with landsat TM

Curran, Paul J.; Dungan, Jennifer; Gholz, Henry L.

doi:10.1016/0034-4257(92)90136-8

Cited by 142 publications

(62 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most of the studies on forests used one or two ratio-based VI, such as the simple ratio (SR) or the normalised difference vegetation index (NDVI), computed from broadband remote sensing data (Curran et al, 1992;Herwitz et al, 1990;Peterson et al, 1987;Spanner et al, 1990a,b). A systematic investigation on the performance of various broadband multispectral and narrow band hyperspectral vegetation indices has been done on agricultural crops (Boegh et al, 2002;Broge & Mortensen, 2002) and very recently, also in forests.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Schlerf¹,

Atzberger²,

Hill³

2005

Remote Sensing of Environment

277

171

View full text Add to dashboard Cite

This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-linerelated broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R 2 =0.67, RMSE=0.69 m 2 m À2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2 =0.77, RMSE=0.54 m 2 m À2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R 2 =0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved. D

show abstract

Section: Introductionmentioning

confidence: 99%

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Schlerf¹,

Atzberger²,

Hill³

2005

Remote Sensing of Environment

277

171

View full text Add to dashboard Cite

show abstract

“…Curran et al 1990, Spanner et al 1990, Nemani et al 1993, Law and Waring 1994, Jakubauskas and Price 1997, White et al 1997, Chen et al 1999, Nilson et al 1999, Turner et al 1999, Eklundh et al 2001.…”

Section: Introductionmentioning

confidence: 99%

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

Stenberg¹,

Rautiainen²,

Manninen³

et al. 2004

Silva Fenn.

129

View full text Add to dashboard Cite

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.

show abstract

“…The estimated attributes may include basal area, AGB, canopy closure, diameter at breast height (DBH), tree height, stand density, and leaf area index (LAI) (Franklin, 1986;Horler and Ahern, 1986;Peterson et al, 1986 and1987;Lathrop and Pierce, 1991;Ardo, 1992;Curran et al, 1992;Cohen and Spies, 1992;Gemmell, 1995;Kimes et al, 1996;Trotter et al, 1997;Turner et al, 1999;Eklundh et al, 2001;Lu et al, 2004;Hall et al, 2006). One of the methods used to estimate attributes of forests from LANDSAT imagery is the direct correlation with radiometric values where regression relationships can be calculated between spectral bands, band ratios or vegetation indices (Franklin, 1986;Roy and Ravan, 1996;Jakubauskas and Price, 1997;Foody et al, 2003;Labrecque et al, 2006).…”

Section: Multispectral Data For Forest Biomass Estimationmentioning

confidence: 99%

Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations

Hernández¹,

Corvalán²,

Emery³

et al. 2012

Remote Sensing of Biomass - Principles and Applications

View full text Add to dashboard Cite

Seasonal LAI in slash pine estimated with landsat TM

Cited by 142 publications

References 31 publications

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

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

Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations

Contact Info

Product

Resources

About