Publications (5)1.12 Total impact
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ABSTRACT: The aim of this study was to compare the performance of various narrowband vegetation indices in estimating Leaf Area Index (LAI) of structurally different plant species having different soil backgrounds and leaf optical properties. The study uses a dataset collected during a controlled laboratory experiment. Leaf area indices were destructively acquired for four species with different leaf size and shape. Six widely used vegetation indices were investigated. Narrowband vegetation indices involved all possible two band combinations which were used for calculating RVI, NDVI, PVI, TSAVI and SAVI2. The red edge inflection point (REIP) was computed using three different techniques. Linear regression models as well as an exponential model were used to establish relationships. REIP determined using any of the three methods was generally not sensitive to variations in LAI (R2 < 0.1). However, LAI was estimated with reasonable accuracy from red/near-infrared based narrowband indices. We observed a significant relationship between LAI and SAVI2 (R2 = 0.77, RMSE = 0.59 (cross validated)). Our results confirmed that bands from the SWIR region contain relevant information for LAI estimation. The study verified that within the range of LAI studied (0.3 ≤ LAI ≤ 6.1), linear relationships exist between LAI and the selected narrowband indices.International Journal of Remote Sensing 12/2008; 30(23):6199-6218. · 1.12 Impact Factor
Article: Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture[show abstract] [hide abstract]
ABSTRACT: The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI¿reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ¿ RMSEcv ¿ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.International Journal of applied Earth Observation and Geoinformation 45 (2008).
Article: Hyperspectral remote sensing of vegetation parameters using statistical and physical models
Article: Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grasslandRemote Sensing of Environment 112 (2008) 5.
Article: Estimation of leaf area index and chlorophyll for a mediterranean grassland using hyperspectral data[show abstract] [hide abstract]
ABSTRACT: The study shows that leaf area index (LAI) and canopy chlorophyll content can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and chlorophyll content. We tested the utility of univariate techniques, involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, such as partial least squares regression. Among the various investigated models, canopy chlorophyll content was estimated with the highest accuracy (R 2 cv = 0.74, relative RMSE cv = 0.35) and LAI was estimated with intermediate accuracy (R 2 cv = 0.67). Compared with narrow band indices and red edge inflection point, partial least squares regression generally improved the estimation accuracies. The results of the study highlight the significance of using multivariate techniques such as partial least squares regression rather than univariate methods such as vegetation indices for providing enhanced estimates of heterogeneous grass canopy characteristics. To date, partial least squares regression has seldom been applied for studying heterogeneous grassland canopies. However, it can provide a useful exploratory and predictive tool for mapping and monitoring heterogeneous grasslands.