Recent advances in remote sensing technologies for hydrocarbon exploration and environmental evaluation

2019 ◽  
Vol 38 (7) ◽  
pp. 554-555
Author(s):  
Yongyi Li ◽  
Roice Nelson ◽  
William Jeffery ◽  
Douglas Foster ◽  
Dominique Dubucq ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Sustainability ◽  
Autonomous Vehicles ◽  
Petroleum Industry ◽  
Hydrocarbon Exploration ◽  
Active Sensors ◽  
Environmental Evaluation ◽  
Satellite Systems ◽  
Unmanned Autonomous Vehicles ◽  
Surface Geology

Remote sensing detects and monitors the physical and spatial characteristics of the earth's oceans, surface, and atmosphere by measuring the reflected or scattered downwelling or emitted upwelling electromagnetic radiation or acoustic signal using passive or active sensors at a distance. It plays an important role in today's energy and environmental sustainability efforts. Remote sensing from spaceborne, airborne, terrestrial, and marine platforms has long been used in hydrocarbon exploration to map surface geology, topography, and hydrocarbon seepages, as well as to evaluate environments that relate to petroleum industry activities. Since the mid-2000s, remote sensing technologies have undergone substantial advances in data acquisition, processing, and interpretation. In the last decade, rapid advances in satellite systems, unmanned autonomous vehicles (UAVs), sensors, and scale of surveys have further expanded applications.

Introduction to this special section: Remote sensing

2021 ◽  
Vol 40 (1) ◽  
pp. 25-25
Author(s):  
Yongyi Li ◽  
Dominique Dubucq ◽  
Khalid Soofi
Keyword(s):  
Remote Sensing ◽  
Oil And Gas ◽  
Ground Deformation ◽  
Special Section ◽  
Ocean Surface ◽  
Acoustic Signals ◽  
Hydrocarbon Exploration ◽  
Active Sensors ◽  
Reservoir Conditions ◽  
Surface Geology

Remote sensing detects and monitors the physical and spatial characteristics of the earth's ocean, surface, and atmosphere by measuring the reflected or scattered electromagnetic, optical, or acoustic signals using passive or active sensors. It has long been used in hydrocarbon exploration and related environment evaluation such as mapping surface geology and topography, providing information for evaluation of well sites and oil and gas infrastructure, detecting hydrocarbon seepages and spills, and monitoring ground deformation and characterizing reservoir conditions during production.

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

scholarly journals Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture

2012 ◽  
Vol 4 (8) ◽  
pp. 2356-2372 ◽  
Author(s):  
Alejandro Egido ◽  
Marco Caparrini ◽  
Giulio Ruffini ◽  
Simonetta Paloscia ◽  
Emanuele Santi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Satellite Systems ◽  
Global Navigation ◽  
Global Navigation Satellite

scholarly journals Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4236
Author(s):  
Woosik Lee ◽  
Hyojoo Cho ◽  
Seungho Hyeong ◽  
Woojin Chung
Keyword(s):  
Motion Estimation ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Key Technology ◽  
Satellite Systems ◽  
Agricultural Robots ◽  
Global Navigation Satellite

Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.

scholarly journals Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1221
Author(s):  
Yuki Hamada ◽  
Colleen R. Zumpf ◽  
Jules F. Cacho ◽  
DoKyoung Lee ◽  
Cheng-Hsien Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Great Promise ◽  
Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

Design and Implementation of a Fuzzy Inference Model for Mapping the Sustainability of Energy Crops

2017 ◽  
pp. 657-678
Author(s):  
Fausto Cavallaro ◽  
Luigi Ciraolo
Keyword(s):  
Environmental Sustainability ◽  
Input Data ◽  
Fuzzy Inference ◽  
Renewable Energy Sources ◽  
Energy Crops ◽  
Environmental Evaluation ◽  
Uncertain Input ◽  
Inference Systems ◽  
Uncertain Input Data ◽  
Biomass For Energy

Energy crops are positioned as the most promising renewable energy sources. Over recent years, the use of biomass has been growing significantly, especially in countries that have made a strong commitment to renewable sources in their energy policies. One of the aspects of the use of biomass for energy is that it is still controversial with regard to full environmental sustainability. Unfortunately, the existing environmental evaluation tools in many cases are unable to manage uncertain input data. Fuzzy-set-based methods, instead, have proved to be able to deal with uncertainty in environmental topics. The idea of this chapter is to reproduce a solution by decoding it from the domain of knowledge with the calculus of fuzzy “if-then” rules. A methodology based on Fuzzy Inference Systems (FIS) is proposed to assess the environmental sustainability of biomass.

Active Microwave Systems for Monitoring Drought Stress

2005 ◽  
Author(s):  
Anne M. Smith
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Value Added ◽  
Agricultural Drought ◽  
Sensor System ◽  
Spatial And Temporal Scales ◽  
Satellite Systems ◽  
Radio Detection ◽  
Vegetation Water ◽  
Radar Satellite

Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.

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