R. Stark

Bio: R. Stark is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Spectral bands & Wastewater. The author has an hindex of 8, co-authored 8 publications receiving 1389 citations.

Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction

Abstract: The goal of this study is to investigate the information content of reflectance spectra of crops in the visible and near infrared range of the spectrum and develop a technique for remote estimation of vegetation fraction ( VF ). For four wheat species with VF =100% in a wide range of pigment contents and compositions, a high degree of covariance was found for paired reflectances ( R ) at 550 nm versus 700 nm ( R 550 versus R 700) and 500 nm versus 670 nm ( R 500 versus R 670). Both relationships, defined as 'vegetation lines', were linear with determination coefficients r 2 >0.9 and the plotted points were tightly clustered. Using the same coordinates to plot reflectances for a variety of soils, a high degree of covariance ( r 2 >0.94) and a distinct 'soil line' were found. The vegetation and soil lines define a two-dimensional spectral construct within which canopy reflectances, regardless of VF, may be located. Based on these optical properties of vegetation and soils, an attempt was made to estimate VF...

Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices

Abstract: Early drought detection and impact assessment on the amount of pasture biomass are important in Mongolia, whose economy strongly depends on livestock production. The country's large area and a lack of information on grass availability due to the sparseness of biomass-observing and/or meteoro- logical stations make it difficult to optimize nomadic livestock output in the Mongolian dry climate. The application of a new satellite-based method for drought detection and for assessment of wild biomass in Mongolia was investigated. Measurements of biomass at an experimental station in a semi-dry steppe ecosystem during 1985-1997 were compared with the Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices. The results showed the indices can be used as proxies for biomass production estimation (biomass anomaly, BA) applying the following equation BA~ 43.201z0.881 VHI (R 2 ~0.658).

Non-destructive and remote sensing techniques for estimation of vegetation status

Abstract: The goal of this work is to develop non-destructive techniques that can conveniently, rapidly and accurately assess crop physiological status and objectively evaluate plant responses to environmental factors, both natural and anthropogenic. High spectral resolution reflectance and absorption spectra of different and unrelated plant species were analyzed to determine spectral variability and information content in the visible and near-infrared spectrum at leaf and canopy levels. Techniques were developed to quantitatively retrieve chlorophyll, carotenoid and anthocyanin content from reflectance in a wide range of pigment content and composition. Techniques for vegetation fraction retrieval those based on channels in visible range of the spectrum were developed and validated. Despite the fact that the reflectance contrast among the visible channels is much smaller than between the visible and near infrared, the sensitivity to moderate to high values of vegetation fraction is much higher than for NDVI and the error in vegetation fraction prediction did not exceed 10 per cent. Leaf pigment content estimation Optical properties of the leaves of several plant species in a wide range of chlorophyll (Chl), carotenoid (Car) and anthocyanin (Anth) contents have been investigated (Gitelson and Merzlyak, 1997; Zur et al., 2000; Gitelson et al., 2001a) in order to develop techniques for non-destructive estimation of pigment content and composition. It was found that the reflectance in spectral band near 700 nm was sensitive to Chl content only, whereas reflectance around 550 nm was sensitive to both Chl and Anth content, and near 500-520 nm was sensitive to both Car and Chl content. The technique was developed to remove Chl effect from reflectance near 550 nm and 500-520 nm. Algorithms for Chl, Car and Anth contents estimating were devised and, finally, validated by few independent data sets. Gitelson, Merzlyak, Zur, Stark & Gritz in the Proceedings of the 3rd European Conference on Precision Agriculture, Montpelier, France, 2001. Grenier & Blackmore, editors.

Significant remote sensing vegetation indices: A review of developments and applications

Abstract: Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV) Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Abstract: A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping.

Abstract: Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.