Near-infrared fiber optics gas sensor for remote sensing of CH 4 gas in coal mines

2006 ◽  
Author(s):  
Sanguo Li ◽  
Yan Zhang ◽  
Thomas Koscica ◽  
Hong-Liang Cui
Keyword(s):  
Remote Sensing ◽  
Fiber Optics ◽  
Gas Sensor ◽  
Near Infrared ◽  
Coal Mines

Near-Infrared Off-Axis Integrated Cavity Output Spectroscopic Gas Sensor for Real-Time, In Situ Atmospheric Methane Monitoring

2020 ◽  
pp. 1-1
Author(s):  
Kaiyuan Zheng ◽  
Chuantao Zheng ◽  
Haipeng Zhang ◽  
Junhao Li ◽  
Zidi Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Gas Sensor ◽  
Near Infrared ◽  
Atmospheric Methane ◽  
Cavity Output

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

scholarly journals Lidar measurement of snow depth: a review

2013 ◽  
Vol 59 (215) ◽  
pp. 467-479 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Thomas H. Painter ◽  
David C. Finnegan
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Near Infrared ◽  
Ground Surface ◽  
Lidar Measurement ◽  
Snow Hydrology ◽  
Sensing Technology ◽  
Transfer Error ◽  
Scale Range ◽  
Visible Wavelengths

AbstractLaser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1 m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1 m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.

scholarly journals iHEART: a miniaturized near-infrared in-line gas sensor using heart-shaped substrate-integrated hollow waveguides

The Analyst ◽  
2016 ◽  
Vol 141 (18) ◽  
pp. 5298-5303 ◽  
Author(s):  
Rafael L. Ribessi ◽  
Thiago de A. Neves ◽  
Jarbas J. R. Rohwedder ◽  
Celio Pasquini ◽  
Ivo M. Raimundo ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Near Infrared ◽  
Gas Sensing ◽  
Hollow Waveguide ◽  
Hollow Waveguides ◽  
Sensing System

Integration of a heart-shaped substrate-integrated hollow waveguide with a micro-spectrometer results in an ultra-compact gas sensing system: iHEART.

Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Weed Science ◽  
2004 ◽  
Vol 52 (4) ◽  
pp. 492-497 ◽  
Author(s):  
E. Raymond Hunt ◽  
James E. McMurtrey ◽  
Amy E. Parker Williams ◽  
Lawrence A. Corp
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Leafy Spurge ◽  
Hyperspectral Remote Sensing ◽  
Total Carotenoids ◽  
Physiological Basis ◽  
Infrared Wavelengths ◽  
Pigment Concentrations

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

scholarly journals Snow dunes and glazed surfaces in Antarctica: new field and remote-sensing data

2002 ◽  
Vol 34 ◽  
pp. 81-88 ◽  
Author(s):  
Massimo Frezzotti ◽  
Stefano Gandolfi ◽  
Floriana La Marca ◽  
Stefano Urbini
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accumulation Rate ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Remotely Sensed Data ◽  
Surface Mass ◽  
Glazed Surface ◽  
Transverse Dunes

AbstractAs part of the International Trans-Antarctic Scientific Expedition project, the Italian Antarctic Programme undertook two traverses from the Terra Nova station to Talos Dome and to Dome C. Along the traverses, the party carried out several tasks (drilling, glaciological and geophysical exploration). The difference in spectral response between glazed surfaces and snow makes it simple to identify these areas on visible/near-infrared satellite images. Integration of field observation and remotely sensed data allows the description of different mega-morphologic features: wide glazed surfaces, sastrugi glazed surface fields, transverse dunes and megadunes. Topography global positioning system, ground penetrating radar and detailed snow-surface surveys have been carried out, providing new information about the formation and evolution of mega-morphologic features. The extensive presence, (up to 30%) of glazed surface caused by a long hiatus in accumulation, with an accumulation rate of nil or slightly negative, has a significant impact on the surface mass balance of a wide area of the interior part of East Antarctica. The aeolian processes creating these features have important implications for the selection of optimum sites for ice coring, because orographic variations of even a few metres per kilometre have a significant impact on the snow-accumulation process. Remote-sensing surveys of aeolian macro-morphology provide a proven, high-quality method for detailed mapping of the interior of the ice sheet’s prevalent wind direction and could provide a relative indication of wind intensity.

The Risk of Coal Fires And Land Subsidence in Jharia Coalfields, India, Analysed Using Remote Sensing Techniques

2021 ◽  
Author(s):  
Vamshi Karanam ◽  
Shagun Garg ◽  
Mahdi Motagh ◽  
Kamal Jain
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Risk ◽  
Land Subsidence ◽  
Coal Mines ◽  
Geographical Information ◽  
Residential Areas ◽  
Persistent Scatterer Interferometry ◽  
Coal Fire ◽  
Coal Fires

<p>Coal fires, land subsidence, roof collapse, and other life-threatening risks are a predictable phenomenon for the mineworkers and the neighbourhood population in coalfields. Jharia Coalfields in India are suffered heavily from land subsidence and coal fires for over a century. In addition to the loss of precious coal reserves, this has led to severe damage to the environment, livelihood, transportation, and precious lives.</p><p>Such incidents highlight the dire need for a well-defined methodology for risk analysis for the coalfield. In this study, we regenerated a Land Use Land Cover map prepared using Indian Remote Sensing satellite imagery and ground survey. Persistent Scatterer Interferometry analysis using Sentinel -1 images was carried out to study the land subsidence phenomenon between Nov 2018 and Apr 2019. For the same study period, coal fire zones were identified with Landsat – 8 thermal band imagery. Integration of coal fire maps, subsidence velocity maps, and land use maps was further implemented in a geographical information background environment to extract the high-risk zones. These high-risk areas include residential areas, railways, and mining sites, requiring immediate attention.</p><p>The results show that the coal mines are affected by subsidence of up to 20 cm/yr and a temperature anomaly of nearly 20<sup>o</sup>C is noticed. A high-risk zone of almost 18 sq. km. was demarcated with Kusunda, Gaslitand, and West Mudidih collieries being the most critically affected zones in the Coal mines. The study demonstrates the potential to combine data from multiple satellite sensors to build a safer ecosystem around the coal mines.  </p>

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

Support Irrigation Water Management of Cereals Using Optical Remote Sensing and Modeling in a Semi-Arid Region

2019 ◽  
pp. 124-145 ◽  
Author(s):  
Tarik Benabdelouahab ◽  
Hayat Lionboui ◽  
Rachid Hadria ◽  
Riad Balaghi ◽  
Abdelghani Boudhar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Near Infrared ◽  
Irrigated Agriculture ◽  
Optical Remote Sensing ◽  
Crop Modeling ◽  
Management Tools ◽  
Irrigated Perimeter ◽  
Semi Arid Region ◽  
Semi Arid

Irrigated agriculture is an important strategic sector for Morocco, contributing to food security and employment. Nowadays, irrigation scheme managers shall ensure that water is optimally used. The main objective was to support the irrigation monitoring and management of wheat in the irrigated perimeter using optical remote sensing and crop modeling. The potential of spectral indices derived from SPOT-5 images was explored for quantifying and mapping surface water content changes at large scale. Indices were computed using the reflectance in red, near infrared, and shortwave infrared bands. A field crop model (AquaCrop) was adjusted and tested to simulate the grain yield and the temporal evolution of soil moisture status. This research aimed at providing a scientific and technical approach to assist policymakers and stakeholders to improve monitoring irrigation and mitigating wheat water stress at field and irrigation perimeter levels in semi-arid areas. The approach could lead to operational management tools for an efficient irrigation at field and regional levels.

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