Renormalized Difference Vegetation Index Research Articles

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.

Determining Biophysical Parameters for Olive Trees Using CASI‐Airborne and Quickbird‐Satellite Imagery

This study reports on the development of remote sensing methods for estimation of biophysical parameters in olive orchards. Field and airborne campaigns were conducted in 2003 and 2004 in two orchards located in southern Spain. Ground measurements of crown transmittance and leaf area index (LAI) of individual olive trees (Olea europaea L.) were done using the LAI‐2000 Plant Canopy Analyzer. Hyperspectral images were acquired with a compact airborne spectrographic imager (CASI) at 1‐m spatial and with QuickBird satellite sensor at 2.5‐m spatial resolution. The panchromatic 0.6‐m spatial resolution image was also acquired with QuickBird. These images enabled the application of automatic algorithms for olive crown identification and delineation, to determine tree crown size and LAI. These methods proved successful for determining projected olive crown area, obtaining determination coefficients (r 2) in the range of 0.82 to 0.65, and root mean square errors (RMSE) of 4.8 and 6.2 m2 for CASI hyperspectral and QuickBird panchromatic images, respectively. The olive crown volume was estimated using image‐estimated projected crown area values, yielding r 2 ranging between 0.87 and 0.70, with RMSE of 8.4 and 11.3 m3 for CASI hyperspectral and QuickBird panchromatic images, respectively. Olive crown transmittance and LAI of individual olive trees were evaluated using spectral vegetation indices (normalized difference vegetation index [NDVI], renormalized difference vegetation index [RDVI], simple ratio index [SR], modified simple ratio [MSR]) yielding better correlations with CASI images, r 2 in the range 0.71 to 0.75 (P < 0.0001) and 0.57 to 0.62 (P < 0.0001) for crown transmittance and LAI, respectively. These methods enable obtaining maps of biophysical parameters in olive trees at farm scale in an operational way demonstrating the validity of the methodology used.

The independent detection of drought stress and leaf density using hyperspectral resolution data

Measurement of vegetation drought stress or leaf density is essential in ecosystem and agronomic studies. The normalized differential vegetation index (NDVI), a widely used vegetation index in remote sensing, seems to have some limitations as it is known to be affected by both drought stress and leaf density. A field experiment was conducted, using two-year-old potted Quercus serrata (a deciduous tree) and Q. glauca (an evergreen tree), to determine the optimal indices of vegetation drought stress or leaf density that have the least a simultaneous effect, and to test if the existing vegetation indices are useful for independently detecting drought stress or leaf density. The results showed that NDVI and similar indices, which utilize the difference or ratio between the reflectance of red and near infrared bands, such as the ratio vegetation index (RVI), the difference vegetation index (DVI), the atmospherically resistant vegetation index (ARVI), the renormalized difference vegetation index (RDVI), the enhanced vegetation index (EVI), the perpendicular vegetation index (PVI), soil-adjusted vegetation index (SAVI) and the improved variants of SAVI, were effective for the independent detection of leaf density but relatively ineffective for drought stress because they were significantly affected by leaf area index (LAI). Similarly, vegetation indices developed as detectors of vegetation stress, such as the water index (WI), the stress index (SI) and the derivative chlorophyll index (DCI), showed weak correlation (r) and partial correlation (r p) with leaf water content (LWC). The optimal hyperspectral indices were proposed as (F 502.8 − F 852.0)/(F 502.8 + F 852.0) for LWC (r = 0.847, r p = 0.849) and R 750/R 550 (R750R550; Lichtenthaler et al. in J Plant Physiol 148:483–493, 1996) for LAI (r = 0.926, r p = 0.940) where R λ and F λ represent reflectance and first derivatives at wavelength λ nm, respectively. A simulation of lower spectral sampling intervals (ca. 3-nm intervals of original to 10-nm intervals) indicated that it will be necessary to check the appropriateness of the derivative indices approximate to the proposed indices before application because derivative spectra are less smooth as a function of wavelength than reflectance spectra.

Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices

Hyperspectral remote sensing data with a greater number of bands and narrower bandwidths can be effectively exploited for the study of ecosystem patterns and processes. Hyperspectral remote sensing of semiarid mixed grassland faces the following two challenges, however: (i) providing a good understanding of the performance of different vegetation indices (VIs) in estimating biophysical properties of grassland with a small amount of green vegetation, a large amount of dead material on the ground, and variable soil–ground conditions; and (ii) examining the spatial characterization of hyperspectral remotely sensed data to optimize sampling procedures and address scaling issues. Using ground-based hyperspectral and biophysical data, this study has compared the predictive capability of VIs for estimation of grassland leaf area index (LAI) (this paper) and examined the spatial variation of grassland LAI (the companion paper). The results in this paper indicate that the relationships between grassland LAI and VIs are significant. The performance of the renormalized difference vegetation index (RDVI), adjusted transformed soil-adjusted vegetation index (ATSAVI), and modified chlorophyll absorption ratio index 2 (MCARI2) was slightly better than that of the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating the cellulose absorption index (CAI) as a litter factor in ATSAVI, a new VI was computed (L-ATSAVI), and it improved the LAI estimation capability in our study area by about 10%.

Temporal and Spatial Relationships between Within‐Field Yield Variability in Cotton and High‐Spatial Hyperspectral Remote Sensing Imagery

Traditional remote sensing methods for yield estimation rely on broadband vegetation indices, such as the Normalized Difference Vegetation Index, NDVI. Despite demonstrated relationships between such traditional indices and yield, NDVI saturates at larger leaf area index (LAI) values, and it is affected by soil background. We present results obtained with several new narrow‐band hyperspectral indices calculated from the Airborne Visible and Near Infrared (AVNIR) hyperspectral sensor flown over a cotton (Gossypium hirsutum L.) field in California (USA) collected over an entire growing season at 1–m spatial resolution. Within‐field variability of yield monitor spatial data collected during harvest was correlated with hyperspectral indices related to crop growth and canopy structure, chlorophyll concentration, and water content. The time‐series of indices calculated from the imagery were assessed to understand within‐field yield variability in cotton at different growth stages. A K means clustering method was used to perform field segmentation on hyperspectral indices in classes of low, medium, and high yield, and confusion matrices were used to calculate the kappa (κ) coefficient and overall accuracy. Structural indices related to LAI [Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Optimized Soil‐Adjusted Vegetation Index (OSAVI)] obtained the best relationships with yield and field segmentation at early growth stages. Hyperspectral indices related to crop physiological status [Modified Chlorophyll Absorption Index (MCARI) and Transformed Chlorophyll Absorption Index (TCARI)] were superior at later growth stages, close to harvest. From confusion matrices and class analyses, the overall accuracy (and kappa) of RDVI at early stages was 61% (κ = 0.39), dropping to 39% (κ = 0.08) before harvest. The MCARI chlorophyll index remained sensitive to within‐field yield variability at late preharvest stage, obtaining overall accuracy of 51% (κ = 0.22).

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination ( r 2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.