Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques

Shafri, Helmi Zulhaidi Mohd; Salleh, Mohamad Amran Mohd; Ghiyamat, Azadeh

doi:10.3844/ajassp.2006.1864.1871

Cited by 45 publications

(4 citation statements)

References 42 publications

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“…El MSR, es un índice mejorado que combina el SR y RDVI, por lo que se cree que se obtiene una mayor sensibilidad a la vegetación (Chen, 1996). Shafri et al (2006) en ambas técnicas de red edge mostraron que la longitud de onda y la reflectancia en REP para plantas con mayor contenido de clorofila se desplazan hacia una longitud de onda más larga y con mayor reflectancia en comparación con plantas con menor contenido de clorofila.…”

Section: Evaluación Del Estado Sanitario De La Araucariaunclassified

“…Estas técnicas también han sido usadas para analizar diferentes tipos de vegetación y edades; Shafri et al (2006) utilizaron datos hiperespectrales adquiridos con el sensor HyMap para determinar las posiciones del posicionamiento del punto de inflexión del red edge (REP), con las técnicas de interpolación lineal y Lagrangiana, de distintas cubiertas de vegetación en una zona boscosa del Reino Unido. Por otro lado, Ángel (2012) propuso una metodología para la clasificación de imágenes de cultivos de coca (Erytroxylum coca var.…”

Section: Introductionunclassified

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Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

et al. 2018

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The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.

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Section: Evaluación Del Estado Sanitario De La Araucariaunclassified

Section: Introductionunclassified

Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

et al. 2018

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“…The steep part of the curve is called the 'red edge' (Guyot et al, 1992). Plant characteristics such as chlorophyll content, water status, age and plant health levels can be derived from the position of the red edge (Shafri et al, 2006). Based on the red edge theory, some of the indices are developed to detect the green plants vitality or health conditions.…”

Section: The Green Plant Detection Methodsmentioning

confidence: 99%

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Liu

Lee

Saunders

2014

American Journal of Agricultural and Biological Sciences

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More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the notillage background than the existing methods and could be used as a powerful tool for the weed control.

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“…The performance of the gasifier is evaluated in terms of conversion efficiency. The equivalence ratio plays a vital role on the performance of the gasifier and it is also calculated (Shafri et al, 2006;Basu, 2010). If the equivalence ratio is below 0.2, the conversion efficiency and gas production rate are reduced drastically (Sheth and Babu, 2009).…”

Section: Zone-2 (Reduction Zone)mentioning

confidence: 99%

Performance Studies on Downdraft Gasifier With Biomass Energy Sources Available in Remote Villages

Singh¹

2014

American Journal of Applied Sciences

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Increasing global concern over the environmental issues and depletion of fossil fuels, significant interest has been shown by the researchers to develop alternate energy technologies like biomass, biogas, solar to meet the future energy demand. The prediction of the performance of different biomass energy sources in gasifiers is needed for the implementation of this technology to fulfil the need of decentralized heat and power applications, relevant to remote villages. This study presents the theoretical and experimental studies conducted on a 50 kW downdraft biomass gasifier with various biomass materials such as wood, coconut shell, rubber seed kernel and coir pith which are generally available in villages. Two-zone kinetic equilibrium model approach is used to predict the composition and temperature of the producer gas. The influence of equivalence ratio on the reaction temperature, quality of producer gas and gasifier conversion efficiency are discussed. The experimental and theoretical studies show that the rubber seed kernel can be effectively used as a feedstock of the biomass gasifier to meet the rural energy demand.

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Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques

Cited by 45 publications

References 42 publications

Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Performance Studies on Downdraft Gasifier With Biomass Energy Sources Available in Remote Villages

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