Mapping the geochemistry of the northern Rub' Al Khali using multispectral remote sensing techniques

In 2016, we launched the “Promotion Project for Next Generation Volcano Research B2 (Theme B: Development of Cutting-edge Volcano Observation Technology, subtheme 2: Development of Remote Sensing Techniques for Volcano Observation), subtopic 2-2 (Development of Remote Sensing Techniques for Surface Phenomena of Volcano)” under the “Integrated Program for Next Generation Volcano Research and Human Resources Development” [1], aiming at the development of an optical multispectral remote sensing system for measuring volcanic surface phenomena. With subtopic 2-2, we are planning to develop a new observation device called a surface phenomena imaging camera (SPIC), which is technically superior to current remote sensing techniques, i.e., optical remote observation techniques used to observe volcanic surface phenomena from aircrafts or ground. We are also aiming at applying the developed observation system to quantify volcanic activities and determine volcanic eruption potentials (degrees of urgency) or branching of event trees for volcanic crises with high accuracy, contributing to better predictions of volcanic eruption transitions. To achieve the above-mentioned aims, we started the development of the SPIC by equipping it with camera-type sensors, based on preliminary analyses of the experimental observations made with the airborne spectral imaging system ARTS-SE, which consists of a pushbroom scanner and a camera system, developed by the National Research Institute for Earth Science and Disaster Resilience in FY 2015. We have already developed its components, such as the prototype filter-type multiband cameras SPIC-UC, a prototype uncooled infrared camera, SPIC-C, a cooled camera, and SPIC-SS, a visible-light camera. The SPIC-UC is a two-band camera with the function of visualizing temperature and SO2 gas concentration distributions. The SPIC-C has the function of measuring temperatures between 2 and 1075◦C with high accuracy (noise equivalent temperature difference, NETD: 16 mK); it is equipped with a sensor and a filter wheel that work in the middle wave infrared region (MWIR). The SPIC-SS is a six-lens multiband camera system that estimates the measured images from multiband spectra (6 bands) to hyper spectra (300 bands). Further, we studied a method to estimate digital surface model with a ∼30-m error. As our plan has progressed as scheduled, we intend to complete the prototype SPIC by 2020.

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A Survey Of Multispectral Remote Sensing Techniques For Earth Resources Applications

10.1117/12.953441 ◽

1970 ◽

Cited By ~ 1

Author(s):

Herbert G. Pascalar

Keyword(s):

Remote Sensing ◽

Multispectral Remote Sensing ◽

Remote Sensing Techniques

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Sensoriamento remoto como ferramenta aos estudos de doenças de plantas agrícolas: uma revisão (Remote Sensing as a Toll for the Study of Plant Diseases on Agriculture: a Revision)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v3i3.232675 ◽

2011 ◽

Vol 3 (3) ◽

pp. 190 ◽

Cited By ~ 1

Author(s):

Carine Rosa Naue ◽

Marilia W. Marques ◽

Nelson Bernardi Lima ◽

Josiclêda Domiciano Galvíncio

Keyword(s):

Remote Sensing ◽

Plant Pathogens ◽

Infected Plant ◽

Control Plant ◽

Direct Methods ◽

Epidemiological Studies ◽

Plant Diseases ◽

Indirect Methods ◽

Multispectral Remote Sensing ◽

Remote Sensing Techniques

Para que estudos epidemiológicos e medidas de controle de doenças de plantas fossem realizados foi necessário o desenvolvimento de métodos de quantificação de doenças. As doenças podem ser avaliadas por métodos diretos ou indiretos e dentre os métodos diretos encontram-se a estimativa dos parâmetros de incidência e severidade e as técnicas de sensoriamento remoto. Em estudos de doenças de plantas, o sensoriamento remoto, além de ser utilizado para quantificação, também poderá servir para a detecção de plantas infectadas. A detecção de doenças de plantas ou até mesmo sua quantificação, através do sensoriamento remoto, baseia-se na radiação refletida das folhagens. As diferenças de reflectância podem ser obtidas pelo sensoriamento remoto multispectral, que tem sido utilizado de forma eficiente para controlar a incidência de um número de patógenos de plantas e atualmente por medidas hiperespectrais. O objetivo deste artigo é apresentar uma revisão sobre o uso do sensoriamento remoto na detecção e análise de doenças de plantas. Os estudos apresentados aqui mostram que o sensoriamento remoto é uma ferramenta que pode ser utilizada para detectar plantas doentes de forma rápida e eficiente em pequenas e grandes áreas geográficas. Além disso, pode detectar plantas infectadas, dispensar a coleta e o processamento de amostras em laboratório permitindo levantamentos precisos e confiáveis, em curto espaço de tempo, independente do tamanho da área em questão. Além disso, pode proporcionar diversos estudos na área de fitopatologia e afins.Palavras-chave: Sensoriamento remoto, doenças de plantas, hiperespectral Remote Sensing as a Toll for the Study of Plant Diseases on Agriculture: a Revision ABSTRACT For epidemiological studies and measures to control plant diseases were carried out was necessary to develop methods of quantifying disease. The diseases can be assessed by direct or indirect methods and among the direct methods are estimating the incidence and severity parameters and remote sensing techniques. In studies of plant diseases, remote sensing is used to quantify, can also serve for detection of infected plants. The detection of plant diseases or even to quantify, through remote sensing, based on reflected radiation from foliage. The differences in reflectance can be obtained by multispectral remote sensing, which has been used effectively to control incidence a number of plant pathogens and recently, hyperspectral measurements. The aim of this paper is present a review on use of remote sensing in detection and analysis of plant diseases. The studies presented here show that remote sensing is a tool that can be used to detect diseased plants quickly and efficiently in large and small geographic areas. Moreover, it can detect infected plant, waive the collection and processing of lab samples allowing accurate and reliable surveys in a short space of time, regardless of size of the area. In addition, several studies can provide in the area of plant pathology and related areas. Key-words: remote sensing, diseased plants, hyperspectral.

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