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RayChaudhuri, B., et S. Bhattacharyya. « Fuzzy analysis of laboratory spectroscopy of vegetation for remote sensing applications ». International Journal of Remote Sensing 27, no 1 (10 janvier 2006) : 191–201. http://dx.doi.org/10.1080/01431160500192413.
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Mattar, Cristian, Andrés Santamaría-Artigas, Flavio Ponzoni, Cibele T. Pinto, Carolina Barrientos et Glynn Hulley. « Atacama Field Campaign : laboratory and in-situ measurements for remote sensing applications ». International Journal of Digital Earth 12, no 1 (15 mars 2018) : 43–61. http://dx.doi.org/10.1080/17538947.2018.1450901.
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Zwissler, Bonnie, Thomas Oommen, Stan Vitton et Eric A. Seagren. « Thermal Remote Sensing For Moisture Content Monitoring of Mine Tailings : Laboratory Study ». Environmental and Engineering Geoscience 23, no 4 (1 novembre 2017) : 299–312. http://dx.doi.org/10.2113/gseegeosci.23.4.299.
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Abstract Mining produces massive volumes of mine tailings that are deposited into large-scale mine tailings impoundments. A key environmental objective of managing these large impoundments is mitigating fugitive dust emissions by monitoring and controlling moisture, because moisture directly affects the tailings’ strength and the ability to apply dust control measures using motorized equipment. Therefore, understanding the spatial and temporal variations in moisture content for surface tailings is critical for characterizing dust susceptibility and trafficability. Remote sensing has been proven to be a useful tool for similar applications. This study utilized laboratory testing conducted on iron mine tailings to verify that: (1) a relationship exists between moisture content and strength for the surface of mine tailings, and (2) thermal remote sensing can be used to infer spatial variations in moisture content for surface tailings. Multivariate regressions were developed to identify the critical remote sensing and climatic variables and evaluate their influence in remotely measured moisture content. For tailings samples collected from two different North American iron mines, regressions using sample temperature and ambient humidity were able to predict surface moisture content (R2> 0.9).
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Hu, Chuanmin, Yingcheng Lu, Shaojie Sun et Yongxue Liu. « Optical Remote Sensing of Oil Spills in the Ocean : What Is Really Possible ? » Journal of Remote Sensing 2021 (13 février 2021) : 1–13. http://dx.doi.org/10.34133/2021/9141902.
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Optical remote sensing (ORS) of reflected sun light has been used to assess oil spills in the ocean for several decades. While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water, recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water. However, a review of the current literature suggests that there is still confusion on whether this is possible and, if so, how. Here, based on the recent findings from numerical models, laboratory measurements, and applications to satellite or airborne imagery, we attempt to clarify this situation by summarizing (1) the optics behind oil spill remote sensing, and in turn, (2) how to interpret optical remote sensing imagery based on optical principles. In the end, we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.
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Oancea, Adriana, Olivier Grasset, Erwan Le Menn, Olivier Bollengier, Lucile Bezacier, Stéphane Le Mouélic et Gabriel Tobie. « Laboratory infrared reflection spectrum of carbon dioxide clathrate hydrates for astrophysical remote sensing applications ». Icarus 221, no 2 (novembre 2012) : 900–910. http://dx.doi.org/10.1016/j.icarus.2012.09.020.
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Weber, Mark, Victor Gorshelev et Anna Serdyuchenko. « Uncertainty budgets of major ozone absorption cross sections used in UV remote sensing applications ». Atmospheric Measurement Techniques 9, no 9 (8 septembre 2016) : 4459–70. http://dx.doi.org/10.5194/amt-9-4459-2016.
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Abstract. Detailed uncertainty budgets of three major ultraviolet (UV) ozone absorption cross-section datasets that are used in remote sensing application are provided and discussed. The datasets are Bass–Paur (BP), Brion–Daumont–Malicet (BDM), and the more recent Serdyuchenko–Gorshelev (SG). For most remote sensing application the temperature dependence of the Huggins ozone band is described by a quadratic polynomial in temperature (Bass–Paur parameterization) by applying a regression to the cross-section data measured at selected atmospherically relevant temperatures. For traceability of atmospheric ozone measurements, uncertainties from the laboratory measurements as well as from the temperature parameterization of the ozone cross-section data are needed as input for detailed uncertainty calculation of atmospheric ozone measurements. In this paper the uncertainty budgets of the three major ozone cross-section datasets are summarized from the original literature. The quadratic temperature dependence of the cross-section datasets is investigated. Combined uncertainty budgets is provided for all datasets based upon Monte Carlo simulation that includes uncertainties from the laboratory measurements as well as uncertainties from the temperature parameterization. Between 300 and 330 nm both BDM and SG have an overall uncertainty of 1.5 %, while BP has a somewhat larger uncertainty of 2.1 %. At temperatures below about 215 K, uncertainties in the BDM data increase more strongly than the others due to the lack of very low temperature laboratory measurements (lowest temperature of BDM available is 218 K).
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Park, Jaewoo, Franklyn Jumu, Justin Power, Maxime Richard, Yomna Elsahli, Mohamad Ali Jarkas, Andy Ruan, Adina Luican-Mayer et Jean-Michel Ménard. « Drone-Mountable Gas Sensing Platform Using Graphene Chemiresistors for Remote In-Field Monitoring ». Sensors 22, no 6 (19 mars 2022) : 2383. http://dx.doi.org/10.3390/s22062383.
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We present the design, fabrication, and testing of a drone-mountable gas sensing platform for environmental monitoring applications. An array of graphene-based field-effect transistors in combination with commercial humidity and temperature sensors are used to relay information by wireless communication about the presence of airborne chemicals. We show that the design, based on an ESP32 microcontroller combined with a 32-bit analog-to-digital converter, can be used to achieve an electronic response similar, within a factor of two, to state-of-the-art laboratory monitoring equipment. The sensing platform is then mounted on a drone to conduct field tests, on the ground and in flight. During these tests, we demonstrate a one order of magnitude reduction in environmental noise by reducing contributions from humidity and temperature fluctuations, which are monitored in real-time with a commercial sensor integrated to the sensing platform. The sensing device is controlled by a mobile application and uses LoRaWAN, a low-power, wide-area networking protocol, for real-time data transmission to the cloud, compatible with Internet of Things (IoT) applications.
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Giordano, Daniele, James K. Russell, Diego González-García, Danilo Bersani, Donald B. Dingwell et Ciro Del Negro. « Raman Spectroscopy from Laboratory and Proximal to Remote Sensing : A Tool for the Volcanological Sciences ». Remote Sensing 12, no 5 (2 mars 2020) : 805. http://dx.doi.org/10.3390/rs12050805.
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Here we explore and review some of the latest ideas and applications of Raman spectroscopy to the volcanological sciences. Firstly, we provide a brief overview of how Raman spectral analysis works and how spectra from silicate glasses are interpreted. We then look at specific applications of Raman spectral analysis to the volcanological sciences based on measurements on and studies of natural materials in the laboratory. We conclude by examining the potential for Raman spectral analysis to be used as a field based aid to volcano monitoring via in situ studies of proximal deposits and; perhaps; in remote sensing campaigns
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Weisbin, C., et D. Perillard. « R & ; D Profile Jet Propulsion Laboratory Robotic Facilities and Associated Research ». Robotica 9, no 1 (janvier 1991) : 7–21. http://dx.doi.org/10.1017/s0263574700015526.
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SUMMARYThis paper describes the robotics facilities and associated research program of the Jet Propulsion Laboratory, lead center in telerobotics for the United States National Aeronautics and Space Administration. Emphasis is placed on evolution from teleoperation to remote System automation. Research is described in manipulator modelling and control, real-time planning and monitoring, navigation in outdoor terrain, real-time sensing and perception, human-machine interface, and overall System architectures. Applications to NASA missions emphasize robotic spacecraft for solar System exploration, satellite servicing and retrieval, assembly of structures, and surveillance. Applications to military missions include battlefield navigation, surveillance, logistics, command and control.
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Mamaghani et Salvaggio. « Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing ». Sensors 19, no 20 (14 octobre 2019) : 4453. http://dx.doi.org/10.3390/s19204453.
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This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.
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Angelopoulou, Theodora, Sabine Chabrillat, Stefano Pignatti, Robert Milewski, Konstantinos Karyotis, Maximilian Brell, Thomas Ruhtz, Dionysis Bochtis et George Zalidis. « Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation ». Remote Sensing 15, no 4 (17 février 2023) : 1106. http://dx.doi.org/10.3390/rs15041106.
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Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.
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Zhang, Yong, Chengkai Wu, Xiaoyu Liu, Li Wang, Chunyue Dai, Jianhang Cui, Yukun Li et Nicholas Kinar. « The Development of Frequency Multipliers for Terahertz Remote Sensing System ». Remote Sensing 14, no 10 (23 mai 2022) : 2486. http://dx.doi.org/10.3390/rs14102486.
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This paper summarizes the development of novel Schottky-diode-based terahertz frequency multipliers. The basic structure and manufacturing process of planar Schottky barrier diodes (SBDs) are reviewed, along with other diode structures that have been proposed in the literature. A numerical modeling method for the novel diodes in the context of terahertz frequency multipliers is presented, which includes 3D electromagnetic (EM) modeling, electro-thermal modeling and modeling of physical non-ideal effects. Furthermore, a general design methodology for developing terahertz frequency multipliers is introduced, involving a sub-division design method (SDM), a global design method (GDM) and a half-sub-division and half-global design method (HS-HGDM). These methods are summarized and compared for 110 GHz and 220 GHz frequency multipliers in the context of communication and imaging applications. Laboratory measurements of these multipliers show good agreement with numerical simulations. Finally, several classic terahertz remote sensing systems are reviewed, and a 220 GHz remote sensing system established using novel frequency multipliers for security inspection purposes is presented along with associated imaging results.
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Cui, Lu-Jun, Yan-Long Cao, Hong-Hong Guo, Shi-Rui Guo, Wenhang Zeng, Pei-Xiong Wang et Gang Chen. « Multi-gas leakage synchronous monitoring system based on the distributed optical fiber sensing technology ». Journal of Computational Methods in Sciences and Engineering 22, no 2 (28 mars 2022) : 615–21. http://dx.doi.org/10.3233/jcm-215842.
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In the process of long-distance transportation of different gas, the remote pipeline plays an irreplaceable role in energy transmission. When the pipeline is laid in remote areas for a long distance, it is easy to be influenced by geological disasters and complex working conditions, which may lead to corrosion and leakage. therefore, it is necessary to conduct pipeline gas real-time safety monitoring. An optical fiber gas leakage synchronous monitoring system was proposed and demonstrated based on distributed optical sensing technology for simultaneous multi-gas measurements. In this study, we discuss that the principle of multi-gas leakage synchronous monitoring system is investigated and then validated by the theoretical simulation experiments. Furthermore, gas concentration and leakage location discrimination tests are also conducted in laboratory. The experimental results show that the output intensity values increased obviously along with the gas concentration changes, and the response time of the sensor system is about 40 seconds, and it’s concluded that the multi-gas leakage synchronous monitoring system based on distributed optical fiber sensing technology exhibited good sensing and location discrimination performance.
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Dąbrowski, R., et A. Jenerowicz. « PORTABLE IMAGERY QUALITY ASSESSMENT TEST FIELD FOR UAV SENSORS ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (26 août 2015) : 117–22. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-117-2015.
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Nowadays the imagery data acquired from UAV sensors are the main source of all data used in various remote sensing applications, photogrammetry projects and in imagery intelligence (IMINT) as well as in other tasks as decision support. Therefore quality assessment of such imagery is an important task. The research team from Military University of Technology, Faculty of Civil Engineering and Geodesy, Geodesy Institute, Department of Remote Sensing and Photogrammetry has designed and prepared special test field- The Portable Imagery Quality Assessment Test Field (PIQuAT) that provides quality assessment in field conditions of images obtained with sensors mounted on UAVs. The PIQuAT consists of 6 individual segments, when combined allow for determine radiometric, spectral and spatial resolution of images acquired from UAVs. All segments of the PIQuAT can be used together in various configurations or independently. All elements of The Portable Imagery Quality Assessment Test Field were tested in laboratory conditions in terms of their radiometry and spectral reflectance characteristics.
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Rogic, Nikola, Annalisa Cappello et Fabrizio Ferrucci. « Role of Emissivity in Lava Flow ‘Distance-to-Run’ Estimates from Satellite-Based Volcano Monitoring ». Remote Sensing 11, no 6 (19 mars 2019) : 662. http://dx.doi.org/10.3390/rs11060662.
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Remote sensing is an established technological solution for bridging critical gaps in volcanic hazard assessment and risk mitigation. The enormous amount of remote sensing data available today at a range of temporal and spatial resolutions can aid emergency management in volcanic crises by detecting and measuring high-temperature thermal anomalies and providing lava flow propagation forecasts. In such thermal estimates, an important role is played by emissivity—the efficiency with which a surface radiates its thermal energy at various wavelengths. Emissivity has a close relationship with land surface temperatures and radiant fluxes, and it impacts directly on the prediction of lava flow behavior, as mass flux estimates depend on measured radiant fluxes. Since emissivity is seldom measured and mostly assumed, we aimed to fill this gap in knowledge by carrying out a multi-stage experiment, combining laboratory-based Fourier transform infrared (FTIR) analyses, remote sensing data, and numerical modeling. We tested the capacity for reproducing emissivity from spaceborne observations using ASTER Global Emissivity Database (GED) while assessing the spatial heterogeneity of emissivity. Our laboratory-satellite emissivity values were used to establish a realistic land surface temperature from a high-resolution spaceborne payload (ETM+) to obtain an instant temperature–radiant flux and eruption rate results for the 2001 Mount Etna (Italy) eruption. Forward-modeling tests conducted on the 2001 ‘aa’ lava flow by means of the MAGFLOW Cellular Automata code produced differences of up to ~600 m in the simulated lava flow ‘distance-to-run’ for a range of emissivity values. Given the density and proximity of urban settlements on and around Mount Etna, these results may have significant implications for civil protection and urban planning applications.
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Schreiner, Simon, Dubravko Culibrk, Michele Bandecchi, Wolfgang Gross et Wolfgang Middelmann. « Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors ». at - Automatisierungstechnik 69, no 4 (1 avril 2021) : 325–35. http://dx.doi.org/10.1515/auto-2020-0042.
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Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.
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Guimarães, Tainá T., Maurício R. Veronez, Emilie C. Koste, Eniuce M. Souza, Diego Brum, Luiz Gonzaga et Frederico F. Mauad. « Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images ». Sustainability 11, no 9 (5 mai 2019) : 2580. http://dx.doi.org/10.3390/su11092580.
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The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.
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Jiang, Yonghua, Yingrui Shi, Litao Li, Miaozhong Xu, Wenzhi Zeng, Yang Jiang et Zhen Li. « On-Orbit Radiance Calibration of Nighttime Sensor of LuoJia1-01 Satellite Based on Lunar Observations ». Remote Sensing 11, no 18 (19 septembre 2019) : 2183. http://dx.doi.org/10.3390/rs11182183.
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The high-resolution nighttime light (NTL) data of the LuoJia1-01 NTL remote sensing satellite has enriched the available data of NTL remote sensing applications. The radiance calibration used as a reference to convert the digital number (DN) recorded by the nighttime sensor into the radiance of the corresponding ground object is the basic premise to the effective application of the NTL data. Owing to the lack of on-board calibration equipment and the absence of an absolute radiometric calibration light source at night, it is difficult for LuoJia1-01 to carry out on-orbit radiance calibration. The moon, as an exoatmospheric stable radiation source, is widely used for the radiometric calibration of remote sensing satellite sensors and to monitor the stability of the visible and near-infrared sensors. This study, based on lunar observation of the LuoJia1-01 NTL sensor, focused on on-orbit radiometric calibration and included monitoring changes in the nighttime sensor radiometric response for nearly a year by using the Robotic Lunar Observatory (ROLO) lunar irradiance model (Version 311 g). The results showed that: (1) the consistency of the radiometric calibration results based on the ROLO model and the laboratory calibration results of LuoJia1-01 exceeded 90%; (2) the nighttime sensor of LuoJia1-01 radiometric response underwent approximately 6% degradation during the observation period of nearly one year (353 days).
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Tagarakis, Aristotelis C., Dimitrios Kateris, Remigio Berruto et Dionysis Bochtis. « Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards ». Applied Sciences 11, no 13 (24 juin 2021) : 5858. http://dx.doi.org/10.3390/app11135858.
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Wireless sensor networks (WSNs) can be reliable tools in agricultural management. In this work, a low cost, low power consumption, and simple wireless sensing system dedicated for agricultural environments is presented. The system is applicable to small to medium sized fields, located anywhere with cellular network coverage, even in isolated rural areas. The novelty of the developed system lies in the fact that it uses a dummy device as Coordinator which through simple but advanced programming can receive, process, and send data packets from all End-nodes to the cloud via a 4G cellular network. Furthermore, it is energy independent, using solar energy harvesting panels, making it feasible to operate in remote, isolated fields. A star topology was followed for the sake of simplification, low energy demands and increased network reliability. The developed system was tested and evaluated in laboratory and real field environment with satisfactory operation in terms of independence, and operational reliability concerning packet losses, communication range (>250 m covering fields up to 36 ha), energy autonomy, and uninterrupted operation. The network can support up to seven nodes in a 30 min data acquisition cycle. These results confirmed the potential of this system to serve as a viable option for monitoring environmental, soil, and crop parameters.
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Yu, Jian, Yu Zhang, Biao Qi, Xiaotian Bai, Wei Wu et Hongxing Liu. « Analysis of the Slanted-Edge Measurement Method for the Modulation Transfer Function of Remote Sensing Cameras ». Applied Sciences 13, no 24 (12 décembre 2023) : 13191. http://dx.doi.org/10.3390/app132413191.
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The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The factors influencing the slanted-edge method’s effectiveness are broadly classified into two categories: algorithmic factors and image factors. This paper innovatively comprehensively analyzes the influencing factors of the slanted-edge method and proposes an improved slanted-edge method to calculate the MTF testing method of remote sensing cameras, which is applied to the MTF testing of remote sensing cameras. Since the traditional algorithm can only be applied in the small angle situation, this paper proposes a new method of slanted-edge method test calculation based on the optimal oversampling rate (OSR) adaptive model of the slanted edge and uses simulation experiments to verify the reliability of the algorithm model through the deviation of the slanted-edge angle calculation and MTF measurement, and the results show that the algorithm improves the accuracy of the MTF measurement compared with the ISO-cos and OMINI-sine methods. Then, the effects of the slanted-edge angle, image region of interest (ROI), as well as image contrast and signal-to-noise ratio (SNR) on the accuracy of the MTF calculation by the slanted-edge method were quantitatively analyzed as the constraints of the slanted-edge method test. Based on the laboratory target experiment, the algorithm flow and various influencing factors obtained in the simulation stage are verified, and the experimental results are more consistent with the various test results obtained in the simulation stage. Consequently, the slanted-edge method introduced in this paper is applicable for future remote sensing camera MTF testing. This approach offers a valuable reference for on-orbit focusing, satellite operational condition monitoring, lifespan estimation, and image restoration.
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Fedotov, Yu V., D. A. Kravtsov, A. A. Cherpakova, M. L. Belov et V. A. Gorodnichev. « An Experimentally Studied Laser Fluorescence Method for Sensing Stress Situations of Oil-polluted Plants ». Mechanical Engineering and Computer Science, no 8 (11 septembre 2017) : 1–11. http://dx.doi.org/10.24108/0817.0001299.
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Nowadays, one of the promising applications for the laser fluorescent analysis in remote sensing is to monitor oil pollution both on the water surface and on the earth one.A task to provide laser fluorescence remote sensing of oil pollution on the earth surface is much more difficult than that of to do the same on the water surface. The monitoring oil pollution laser fluorescence results can have no large reliability (lead to the great number of false alarms) because of the great number of disrupters (for example, plant fluorescence).However, plants available in the pipeline corridor may be not only disrupter, but also an oil pollution mark. Oil pollutions lead to developmental disorder of plants and induce their stress. The laser fluorescence methods can detect such stress situations.The paper concentrates on the experimental studies of laser fluorescence remote sensing method to detect the plant oil pollution stress situations for the eye-safe fluorescence excitation wavelength of 355 nm.A laboratory setup was designed to study spectra of laser-induced fluorescence of plants. In the laboratory setup the third harmonic of the Nd-YAG laser at the eye-safe wavelength of 355 nm was used as a fluorescence-exciting source. The laser-induced fluorescence spectra of plants were measured within 380 – 780 nm spectrum range.The experimental study results of laser-induced fluorescence spectra of plants in normal and stress situations caused by oil pollution are given for the eye-safe fluorescence- exciting wavelength ofThe paper shows that the analysis of recorded laser-induced fluorescence spectra allows us to detect stress situations caused by oil pollution. An identifiable factor to characterise a profile deformation of the laser-induced fluorescence spectrum for stress situations may be a fluorescence intensity ratio in the spectral ranges of 680…690 nm and 730…740 nm.
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Chen, Yupeng, Jinguang Lv, Wei Yue, Yuanhang Zhao, Yuxin Qin, Jin Tao, Cheng Chen, Weibiao Wang et Jingqiu Liang. « A Snapshot Infrared Imaging Fourier Transform Spectrometer for Dynamic Target Detection ». Remote Sensing 14, no 7 (23 mars 2022) : 1543. http://dx.doi.org/10.3390/rs14071543.
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Infrared imaging spectrometry is utilized to detect and identify targets by collecting spectral images. In some cases, the infrared spectral images of dynamic targets need to be detected accurately, such as during remote sensing target tracking and engine tail flame detection applications. However, it is difficult to obtain reliable measurement results when using a traditional infrared imaging spectrometer with a scanning structure because of motion artifacts. This work proposes a snapshot infrared imaging Fourier transform spectrometer (SIIFTS) based on stepped micromirrors and a lens array. Two micromirrors sample the spectral information, and the lens array can realize multi-aperture snapshot imaging. The spectrometer is capable of collecting three-dimensional (3D) datasets during a single measurement period, and its absence of motion artifacts and its ability to work without moving parts is very important for dynamic target detection. The achromatic optical design of the SIIFTS is completed, and two front imaging systems for remote sensing and tail flame detection applications are designed for selection. A SIIFTS prototype was built, and flame detection tests were conducted in a laboratory environment. The experimental results show that the SIIFTS developed here can accurately and stably obtain real-time image and spectral information from dynamic targets.
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Singh, Daljit. « Automatic Extraction of Glacial Outlines, Contours and Volume of Ice from Satellite Imageries (Use of Open Source GIS Software - QGIS) ». International Journal of Emerging Research in Management and Technology 6, no 9 (24 juin 2018) : 138. http://dx.doi.org/10.23956/ijermt.v6i9.98.
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Remote Sensing and GIS are the two Geo-Spatial Technologies increasingly used in Geographic Research. The high cost of the software’s used to apply these technologies to geographical data did not allow expansion of its use in India except in the projects which are financed by some funding agencies. Moreover, its use for individual research by teachers and students in absence of license access to these technologies was highly restricted. Even if a license is purchased by the educational institution, its maintenance cost further inhibits is use. The present paper highlights how QGIS, open source GIS software can be used to extract glaciers from satellite imageries. By adopting UBUNTU, an open source operating system and QGIS, an open source GIS software any department of Geography can establish a GIS laboratory where not only teachers but students may learn its applications and start using it in a big way. This will enable geography teachers, researchers, and students to use remote sensing and geographical information system in understanding, analyzing and highlighting the importance of various indicators of development.
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Sayl, Khamis Naba, Sadeq Oleiwi Sulaiman, Ammar Hatem Kamel et Nadhir Al Ansari. « Towards the Generation of a Spatial Hydrological Soil Group Map Based on the Radial Basis Network Model and Spectral Reflectance Band Recognition ». International Journal of Design & ; Nature and Ecodynamics 17, no 5 (31 octobre 2022) : 761–66. http://dx.doi.org/10.18280/ijdne.170514.
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Hydrological soil group is essential to soil information for several fields of modeling and applications. This information can affect suitable environmental, agricultural, and hydrological development. Laboratory analysis for soil sampling cannot efficiently provide the needed information because these analyses are commonly costly, time-consuming, and limited in retrieving the temporal and spatial variability. In this context, remote sensing is now solid to offer meaningful spatial data for studying soil characteristics on various spatial scales utilizing the different spectral reflectance. For this study, the integration of Geographic Information System (GIS) remote sensing data and survey data with the Artificial Neural Network (ANN) were used to generate a hydrological soil group map and to infer spatial patterns of soils across complete area converges for Alghadaf Wadi in the Western Desert of Iraq. The generated soil information was tested based on the sand, silt, and clay content. The testing result indicated that the differences between actual and predicted values to determine soil classes are agreed well. Therefore, this method is vital for mapping and monitoring soil texture by providing timely, fast repetitive data and relatively cheap.
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Arciniegas-Ortega, Susana, Iñigo Molina et Cesar Garcia-Aranda. « Soil Order-Land Use Index Using Field-Satellite Spectroradiometry in the Ecuadorian Andean Territory for Modeling Soil Quality ». Sustainability 14, no 12 (17 juin 2022) : 7426. http://dx.doi.org/10.3390/su14127426.
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Land use conversion is the main cause for soil degradation, influencing the sustainability of agricultural activities in the Ecuadorian Andean region. The possibility to identify the quality based on the spectral properties allows remote sensing methods to offer an alternative form of monitoring the environment. This study used laboratory spectroscopy and multi-spectral images (Sentinel 2) with environmental covariates (physicochemical parameters) to find an affordable method that can be used to present spatial prediction models as a tool for the evaluation of the quality of Andean soils. The models were developed using statistical techniques of logistic regression and linear discriminant analysis to generate an index based on soil order and three indexes based on the combination of soil order and land use. This combined approach offers an effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil constituents, such as electrical conductivity (CE), organic matter (OM), pH, and soil moisture (HU). For Mollisol index.3 with Páramo land use, a value of organic matter (OM) ≥8.6% was obtained, whereas for Mollisol index.4 with Shrub land use, OM was ≥6.1%. These results reveal good predictive (estimation) capabilities for these soil order–land use groups. This provides a new way to monitor soil quality using remote sensing techniques, opening promising prospects for operational applications in land use planning.
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Li, Tong, Anquan Xia, Timothy I. McLaren, Rajiv Pandey, Zhihong Xu, Hongdou Liu, Sean Manning et al. « Preliminary Results in Innovative Solutions for Soil Carbon Estimation : Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy ». Remote Sensing 15, no 23 (30 novembre 2023) : 5571. http://dx.doi.org/10.3390/rs15235571.
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This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.
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Mannino, Anna Maria, Flavio Borfecchia et Carla Micheli. « Tracking Marine Alien Macroalgae in the Mediterranean Sea : The Contribution of Citizen Science and Remote Sensing ». Journal of Marine Science and Engineering 9, no 3 (6 mars 2021) : 288. http://dx.doi.org/10.3390/jmse9030288.
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The accelerating rate of the introduction of non-indigenous species (NIS) and the magnitude of shipping traffic make the Mediterranean Sea a hotspot of biological invasions. For the effective management of NIS, early detection and intensive monitoring over time and space are essential. Here, we present an overview of possible applications of citizen science and remote sensing in monitoring alien seaweeds in the Mediterranean Sea. Citizen science activities, involving the public (e.g., tourists, fishermen, divers) in the collection of data, have great potential for monitoring NIS. The innovative methodologies, based on remote sensing techniques coupled with in situ/laboratory advanced sampling/analysis methods for tracking such species, may be useful and effective tools for easily assessing NIS distribution patterns and monitoring the space/time changes in habitats in order to support the sustainable management of the ecosystems. The reported case studies highlight how these cost-effective systems can be useful complementary tools for monitoring NIS, especially in marine protected areas, which, despite their fundamental role in the conservation of marine biodiversity, are not immune to the introduction of NIS. To ensure effective and long-lasting management strategies, collaborations between researchers, policy makers and citizens are essential.
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Brook, Anna. « Spectroscopy and Remote Sensing Techniques to Assess Active- and Post-Fire Effects ». Proceedings 30, no 1 (29 mai 2020) : 78. http://dx.doi.org/10.3390/proceedings2019030078.
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Fires were once a natural phenomenon that helped to shape species distribution, contributed to the persistence of fire-dependent species, and assisted the natural evolution of ecosystems. However, nowadays, most of the forest fires worldwide are not of natural causes. Therefore, wildfires have received significant attention over the past few decades. Major ecological and policy changes were stimulated by historical frequency, extent, and severity of fires in the dry forests. These fires are important at both local to regional scales, as it might change the maintenance of landscape structure, composition, and function. Moreover, it affects pollutants, impacts air quality and raises human health risks. Many studies suggested using remote sensing data and techniques to assess fire characteristics and post-fire effects. Due to its ability to quantify patterns of variation in space and time, the remote sensing data are especially important to detect active fire extents at local and regional scales, mapping fuel loading and identify areas with long or problematic natural recovery. In the past few decades, the advantages of multi-temporal remote sensing techniques to monitor landscape change in a rapid and cost-effective manner, are reported in the scientific literature. Many studies focused on the development of techniques to evaluate and quantify fire behavior and fuel combustion. Yet the main contribution is recorded for spectral indices, e.g. the Normalized Burn Ratio (NBR), the difference in the Normalized Burn Ratio between pre- and post-fire images (dNBR), and the Normalized Difference Vegetation Index (NDVI), which are calculated by a simple combinations of different sensor bands, rely on spectral changes of the burning or burned surfaces. Numerous papers are focused on more advanced and very detailed spectral models of fuel and post-fire ash residues, mainly using laboratory spectrometers, e.g., Fourier Transform Infrared (FTIR). However, many of the developed models are not applicable in the real world. In the current talk, we will present the most recent studies and scientific activities in the field of (1) active fire detection and characterization, using mainly hyperspectral ground and airborne technologies; (2) future space-borne applications on board of nano- and micro-satellites; (3) discuss the contribution of detailed and precise spectral models for post-fire ecological effects studies; (4) describe field assessment; (5) discuss management applications and future directions of fire-related remote sensing research.
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Mosley, R. M., et R. R. Williams. « Fourier Transform near Infrared Absorption Spectroscopy of Gases ». Journal of Near Infrared Spectroscopy 2, no 3 (juin 1994) : 119–25. http://dx.doi.org/10.1255/jnirs.38.
Texte intégralRésumé :
The application of Fourier transform near infrared spectroscopy (FT-NIR) to the analysis of gas phase molecules is presented. The use of this technique with both sealed laboratory cells and open-path measurements is demonstrated. The gas phase absorption spectra of several environmentally important organic molecules were collected for qualitative comparison. Calibration data and detection limits for several of the compounds are presented. For ammonia, the background equivalent concentration for a 10 cm path was determined to be 1.5 ppm while propane in the same pathlength was found to be 180 ppm. Other applications of this technique, including the use of fibre optics for remote sensing, are also discussed.
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Romeo, Saverio, Antonio Cosentino, Francesco Giani, Giandomenico Mastrantoni et Paolo Mazzanti. « Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring : The Poggio Baldi Landslide Natural Laboratory ». Sensors 21, no 8 (8 avril 2021) : 2632. http://dx.doi.org/10.3390/s21082632.
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Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.
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Zhao, Xingmin, Shuo Shi, Jian Yang, Wei Gong, Jia Sun, Biwu Chen, Kuanghui Guo et Bowen Chen. « Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR ». Sensors 20, no 3 (10 février 2020) : 935. http://dx.doi.org/10.3390/s20030935.
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Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system’s performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.
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Boruah, Manash J., et Gazi A. Ahmed. « Visible light scattering properties of irregularly shaped silica microparticles using laser based laboratory simulations for remote sensing and medical applications ». Laser Physics 28, no 1 (8 décembre 2017) : 015701. http://dx.doi.org/10.1088/1555-6611/aa8ed2.
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Dubois-Fernandez, Pascale, Olivier Ruault du Plessis, Aurélien Arnaubec, Sébastien Angelliaume, Rémi Baqué, Grégory Bonin, Xavier Briottet et al. « The SETHI remote sensing airborne platform and the related science activities ». Revue Française de Photogrammétrie et de Télédétection, no 200 (19 avril 2014) : 36–47. http://dx.doi.org/10.52638/rfpt.2012.60.
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L'ONERA, le laboratoire français en aérospatial, construit des systèmes radar aéroporté depuis plus de deux décennies pour des applications de défense mais aussi scientifiques. Au cours des six dernières années, l'instrument SETHI, embarqué à bord d'un avion Falcon 20, a été développé principalement pour des applications scientifiques. Dans cet article, la philosophie de son développement est mise en exergue et les instruments sont décrits en détails. L'instrument SETHI comprend trois systèmes radar en bandes P, L et X, une caméra dans le domaine visible, en visée oblique afin de suivre le champ de vue des radar, et une caméra hyperspectrale à visée au nadir. Cet article résume alors les dernières campagnes SETHI dans le cas de préparation de deux missions spatiales. Il s'agit tout d'abord de la mission de l'ESA, BIOMASS, comprenant un radar bande P pour la cartographie de la biomasse à grande échelle. La deuxième mission traite de surveillance maritime et comprend un radar en bande X. Pour chaque campagne, les principaux objectifs sont détaillés, les références aux articles correspondants sont fournies et les principaux résultats sont mis en avant. Finalement, l'article fournit un aperçu des prochains développements liés au système SETHI dans le cadre de la fusion de données optique et radar.
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Orych, A., P. Walczykowski, A. Jenerowicz et Z. Zdunek. « Impact of the cameras radiometric resolution on the accuracy of determining spectral reflectance coefficients ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (7 novembre 2014) : 347–49. http://dx.doi.org/10.5194/isprsarchives-xl-1-347-2014.
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Nowadays remote sensing plays a very important role in many different study fields, i.e. environmental studies, hydrology, mineralogy, ecosystem studies, etc. One of the key areas of remote sensing applications is water quality monitoring. Understanding and monitoring of the water quality parameters and detecting different water contaminants is an important issue in water management and protection of whole environment and especially the water ecosystem. There are many remote sensing methods to monitor water quality and detect water pollutants. One of the most widely used method for substance detection with remote sensing techniques is based on usage of spectral reflectance coefficients. They are usually acquired using discrete methods such as spectrometric measurements. These however can be very time consuming, therefore image-based methods are used more and more often. In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify the impact of cameras radiometric resolution on the accuracy of determination of them. This paper presents laboratory experiments that were conducted using two monochromatic XEVA video sensors (400–1700 nm spectral data registration) with two different radiometric resolutions (12 and 14 bits). In view of determining spectral characteristics from images, the research team used set of interferometric filters. All data collected with multispectral digital video cameras were compared with spectral reflectance coefficients obtained with spectroradiometer. The objective of this research is to find the impact of cameras radiometric resolution on reflectance values in chosen wavelength. The main topic of this study is the analysis of accuracy of spectral coefficients from sensors with different radiometric resolution. By comparing values collected from images acquired with XEVA sensors and with the curves obtained with spectroradiometer it's possible to determine accuracy of imagebased spectral reflectance coefficients and decide which sensor will be more accurate to determine them for protection of water aquatic environment purpose.
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Jenal, Alexander, Georg Bareth, Andreas Bolten, Caspar Kneer, Immanuel Weber et Jens Bongartz. « Development of a VNIR/SWIR Multispectral Imaging System for Vegetation Monitoring with Unmanned Aerial Vehicles ». Sensors 19, no 24 (13 décembre 2019) : 5507. http://dx.doi.org/10.3390/s19245507.
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Short-wave infrared (SWIR) imaging systems with unmanned aerial vehicles (UAVs) are rarely used for remote sensing applications, like for vegetation monitoring. The reasons are that in the past, sensor systems covering the SWIR range were too expensive, too heavy, or not performing well enough, as, in contrast, it is the case in the visible and near-infrared range (VNIR). Therefore, our main objective is the development of a novel modular two-channel multispectral imaging system with a broad spectral sensitivity from the visible to the short-wave infrared spectrum (approx. 400 nm to 1700 nm) that is compact, lightweight and energy-efficient enough for UAV-based remote sensing applications. Various established vegetation indices (VIs) for mapping vegetation traits can then be set up by selecting any suitable filter combination. The study describes the selection of the individual components, starting with suitable camera modules, the optical as well as the control and storage parts. Special bandpass filters are used to select the desired wavelengths to be captured. A unique flange system has been developed, which also allows the filters to be interchanged quickly in order to adapt the system to a new application in a short time. The characterization of the system was performed in the laboratory with an integrating sphere and a climatic chamber. Finally, the integration of the novel modular VNIR/SWIR imaging system into a UAV and a subsequent first outdoor test flight, in which the functionality was tested, are described.
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Jacq, Kévin, Maxime Debret, Bernard Fanget, Didier Coquin, Pierre Sabatier, Cécile Pignol, Fabien Arnaud et Yves Perrette. « Theoretical Principles and Perspectives of Hyperspectral Imaging Applied to Sediment Core Analysis ». Quaternary 5, no 2 (1 juin 2022) : 28. http://dx.doi.org/10.3390/quat5020028.
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Hyperspectral imaging is a recent technology that has been gaining popularity in the geosciences since the 1990s, both in remote sensing and in the field or laboratory. Indeed, it allows the rapid acquisition of a large amount of data that are spatialized on the studied object with a low-cost, compact, and automatable sensor. This practical article aims to present the current state of knowledge on the use of hyperspectral imaging for sediment core analysis (core logging). To use the full potential of this type of sensor, many points must be considered and will be discussed to obtain reliable and quality data to extract many environmental properties of sediment cores. Hyperspectral imaging is used in many fields (e.g., remote sensing, geosciences and artificial intelligence) and offers many possibilities. The applications of the literature will be reviewed under five themes: lake and water body trophic status, source-to-sink approaches, organic matter and mineralogy studies, and sedimentary deposit characterization. Afterward, discussions will be focused on a multisensor core logger, data management, integrated use of these data for the selection of sample areas, and other opportunities. Through this practical article, we emphasize that hyperspectral imaging applied to sediment cores is still an emerging tool and shows many possibilities for refining the understanding of environmental processes.
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Oliveira, R. A., R. Näsi, P. Korhonen, A. Mustonen, O. Niemeläinen, N. Koivumäki, T. Hakala, J. Suomalainen, J. Kaivosoja et E. Honkavaara. « HYPERSPECTRAL UAS IMAGERY FOR GRASS SWARDS BIOMASS AND NITROGEN ESTIMATION ». International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (14 décembre 2023) : 1861–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1861-2023.
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Abstract. Monitoring agricultural grass fields is particularly important for meat and milk production in Northern Europe, where three harvests occur during a growing season to maximize yields. Reliable data on forage, including biomass and nitrogen concentration, are essential for making informed decisions regarding seed mixtures, fertilizer rates, and harvest timing. Miniaturized hyperspectral cameras mounted on unmanned aerial systems (UAS) have become increasingly accessible and efficient. These cameras, operating in the visible to near-infrared (VNIR) range, have shown potential in estimating grass sward quantity and feeding quality. Additional advancements in hyperspectral technology have emerged the short-wave infrared (SWIR) range for UAS applications, previously utilized mainly in laboratory and aircraft-based systems. This study aims to explore the potential of VNIR and SWIR hyperspectral UAS-based remote sensing in biomass and nitrogen estimation during primary and re-growth stages. Grass fresh yield and nitrogen concentration prediction models were built after selecting the most significant features from the cameras to cope with the high dimensionality of the data. Using best features and machine learning, both fresh yield and nitrogen concentration were estimated with normalized root mean square error better than 10%. This work contributes to the development of accurate remote sensing techniques, supporting sustainable agricultural practices and climate change studies.
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Lang, Harold R., et Steven M. Baloga. « Validation airborne visible-infrared imaging spectrometer data at Ray Mine, Arizona ». Environmental and Engineering Geoscience 6, no 3 (1 août 2000) : 187–200. http://dx.doi.org/10.2113/gseegeosci.6.3.187.
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Abstract The fundamental promise of imaging spectroscopy is to provide surface mineralogy based on remotely-acquired, gridded reflectance spectra of comparable quality to those from high resolution laboratory and field spectrometers. For regulatory and environmental monitoring, validating imaging spectrometer data is a major issue with this emerging technology. In this paper we validate 1997 Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) reflectance spectra covering 0.4 mu m-2.4 mu m at a stable, flat, manmade target at Ray Mine, Arizona, for EPA/NASA assessment of the utility of remote sensing for monitoring acid drainage from an active open pit copper mine. For validation, we a) compare qualitatively, laboratory and field reflectance spectra with corresponding AVIRIS spectra; b) compare quantitatively, mineralogically diagnostic statistics from field spectra with the same statistics from field spectra with the same statistics from AVIRIS spectra; and c) demonstrate a methodology for validating imaging spectrometer data for environmental applications.
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de Moustier, C. « Approaches to Acoustic Backscattering Measurements From the Deep Seafloor ». Journal of Energy Resources Technology 110, no 2 (1 juin 1988) : 77–84. http://dx.doi.org/10.1115/1.3231369.
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Because the average ocean depth is four kilometers, seafloor investigations are mostly remote sensing operations. The primary means to determine the morphology, the structure, and the texture of the seafloor are acoustic. This paper considers the current seafloor remote sensing approaches involving acoustic backscattering. The physical constraints imposed by the ocean as a propagation medium, by the seafloor as a backscattering boundary, and by the measuring instruments are briefly reviewed. The sonar systems currently used by the oceanographic community for deep seafloor acoustic backscattering measurements deal with these constraints differently, depending on their specific application and on whether they are towed behind a ship or mounted on her hull. Towed sidescan systems such as Gloria II (U.K.), the Sea Mapping and Remote Characterization (Sea MARC) I and II, the Deep Tow system of the Marine Physical Laboratory (MPL), and hull-mounted systems, such as Swathmap all give a qualitative measure of backscattering by converting echo amplitudes to gray levels to produce a sidescan image of the seafloor. A new approach is presented which uses a Sea Beam multibeam echo-sounder to produce similar acoustic images. Quantitative measurements of backscattering have been attempted in recent experiments using the Deep Tow system and Sea Beam. Such measurements provide some insight into the geological processes responsible for the acoustic backscatter, with useful applications for geologists as well as designers and operators of bottom-interacting sonars.
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Potůčková, M., J. Albrechtová, K. Anders, L. Červená, J. Dvořák, K. Gryguc, B. Höfle et al. « E-TRAINEE : OPEN E-LEARNING COURSE ON TIME SERIES ANALYSIS IN REMOTE SENSING ». International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (13 décembre 2023) : 989–96. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-989-2023.
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Abstract. Time series (TS) analysis has always been an important topic in Remote Sensing (RS). Open access to archives of Earth Observation missions, periodically acquired nation-wide aerial imagery and LiDAR point clouds, and various research datasets, together with open tools for TS data processing, have brought new possibilities in RS research but also challenges in education. The topic of TS analysis in RS has become an essential part of MSc curricula in geoinformatics, geography, and related fields. Open learning materials systematically addressing the issue of TS at the master's level are rare and their development is time and resource demanding. Thus, based on previous collaboration, the four research groups from Charles University, Heidelberg University, University of Innsbruck, and University of Warsaw joint their specific expertise and developed an open E-learning course on Time Series Analysis in RS for Understanding Human-Environment Interactions (E-TRAINEE). The course consists of four Modules covering the topics of TS from general approaches (M1) to specific methods of processing TS of satellite multispectral images (M2), 3D/4D point clouds (M3), and aerial image and laboratory spectroscopy (M4). Theoretical parts are supported with exercises/tutorials and case studies based on research activities of the involved teams. The course is accessible via a web site and is published under the CC-BY SA 4.0 license. The primary target group are MSc and PhD students of geoinformatics and geography, but it is also relevant to students of environmental studies, ecology, or geology, as well as potential users from the public and private sectors dealing with applications of RS.
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Idwasi, Patrick O., Gary W. Small, Roger J. Combs, Robert B. Knapp et Robert T. Kroutil. « Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry ». Applied Spectroscopy 55, no 11 (novembre 2001) : 1544–52. http://dx.doi.org/10.1366/0003702011953784.
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Digital filtering methods are evaluated for use in the automated detection of ethanol from passive Fourier transform infrared (FT-IR) data collected during laboratory and open-air remote sensing experiments. In applications in which analyte signals are overwhelmed by the overlapping signals of an interference, the use of multiple digital filters is observed to improve the sensitivity of the analyte detection. The detection strategy is based on the application of bandpass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer. To implement the automated detection of a target analyte, the filtered interferogram segments are supplied as input to piecewise linear discriminant analysis. Through the use of a set of training data, discriminants are computed that can subsequently be applied to detect the presence of the analyte in an automated manner. This research focuses on the detection of ethanol vapor in the presence of an ammonia interference. A two-filter detection strategy based on the use of separate ethanol and ammonia filters is compared to an approach based on a single ethanol filter. Bandpass parameters of the digital filters and the interferogram segment location are optimized through the use of laboratory data in which ethanol and ammonia vapors are generated in a gas cell and viewed against various infrared background radiances. The filter and segment parameters obtained through this optimization are subsequently tested with field remote sensing data collected when the spectrometer is allowed to view ethanol and ammonia plumes generated from a heated stack. The two-filter strategy is found to outperform the single-filter approach with both the laboratory and field data in situations in which the ammonia interference dominates the ethanol signature.
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Marqués-Mateu, Á., M. Balaguer-Puig, H. Moreno-Ramón et S. Ibáñez-Asensio. « A laboratory procedure for measuring and georeferencing soil colour ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (28 avril 2015) : 57–64. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-57-2015.
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Remote sensing and geospatial applications very often require ground truth data to assess outcomes from spatial analyses or environmental models. Those data sets, however, may be difficult to collect in proper format or may even be unavailable. In the particular case of soil colour the collection of reliable ground data can be cumbersome due to measuring methods, colour communication issues, and other practical factors which lead to a lack of standard procedure for soil colour measurement and georeferencing. In this paper we present a laboratory procedure that provides colour coordinates of georeferenced soil samples which become useful in later processing stages of soil mapping and classification from digital images. The procedure requires a laboratory setup consisting of a light booth and a trichromatic colorimeter, together with a computer program that performs colour measurement, storage, and colour space transformation tasks. Measurement tasks are automated by means of specific data logging routines which allow storing recorded colour data in a spatial format. A key feature of the system is the ability of transforming between physically-based colour spaces and the Munsell system which is still the standard in soil science. The working scheme pursues the automation of routine tasks whenever possible and the avoidance of input mistakes by means of a convenient layout of the user interface. The program can readily manage colour and coordinate data sets which eventually allow creating spatial data sets. All the tasks regarding data joining between colorimeter measurements and samples locations are executed by the software in the background, allowing users to concentrate on samples processing. As a result, we obtained a robust and fully functional computer-based procedure which has proven a very useful tool for sample classification or cataloging purposes as well as for integrating soil colour data with other remote sensed and spatial data sets.
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Popovici, Ioana Elisabeta, Philippe Goloub, Thierry Podvin, Luc Blarel, Rodrigue Loisil, Florin Unga, Augustin Mortier et al. « Description and applications of a mobile system performing on-road aerosol remote sensing and in situ measurements ». Atmospheric Measurement Techniques 11, no 8 (9 août 2018) : 4671–91. http://dx.doi.org/10.5194/amt-11-4671-2018.
Texte intégralRésumé :
Abstract. The majority of ground-based aerosols observations are limited to fixed locations, narrowing the knowledge on their spatial variability. In order to overcome this issue, a compact Mobile Aerosol Monitoring System (MAMS) was developed to explore the aerosol vertical and spatial variability. This mobile laboratory is equipped with a micropulse lidar, a sun photometer and an aerosol spectrometer. It is distinguished from other transportable platforms through its ability to perform on-road measurements and its unique feature lies in the sun photometer's capacity for tracking the sun during motion. The system presents a great flexibility, being able to respond quickly in case of sudden aerosol events such as pollution episodes, dust, fire or volcano outbreaks. On-road mapping of aerosol physical parameters such as attenuated aerosol backscatter, aerosol optical depth, particle number and mass concentration and size distribution is achieved through the MAMS. The performance of remote sensing instruments on-board has been evaluated through intercomparison with instruments in reference networks (i.e. AERONET and EARLINET), showing that the system is capable of providing high quality data. This also illustrates the application of such a system for instrument intercomparison field campaigns. Applications of the mobile system have been exemplified through two case studies in northern France. MODIS AOD data was compared to ground-based mobile sun photometer data. A good correlation was observed with R2 of 0.76, showing the usefulness of the mobile system for validation of satellite-derived products. The performance of BSC-DREAM8b dust model has been tested by comparison of results from simulations for the lidar–sun-photometer derived extinction coefficient and mass concentration profiles. The comparison indicated that observations and the model are in good agreement in describing the vertical variability of dust layers. Moreover, on-road measurements of PM10 were compared with modelled PM10 concentrations and with ATMO Hauts-de-France and AIRPARIF air quality in situ measurements, presenting an excellent agreement in horizontal spatial representativity of PM10. This proves a possible application of mobile platforms for evaluating the chemistry-models performances.
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Baah, Gabriel A., Igor Yu Savin et Yuri I. Vernyuk. « Pollution from Highways Detection Using Winter UAV Data ». Drones 7, no 3 (6 mars 2023) : 178. http://dx.doi.org/10.3390/drones7030178.
Texte intégralRésumé :
This study identified and evaluated the association between metal content and UAV data to monitor pollution from roadways. A total of 18 mixed snow samples were collected at the end of winter, utilizing a 1 m long and 10 cm wide snow collection tube, from either side of the Caspian Highway (Moscow-Tambo-Astrakhan) in Moscow. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to examine the chemical composition of the samples, yielding 35 chemical elements (metals). UAV data and laboratory findings were calculated and examined. Regression estimates demonstrated the possibility of using remote sensing data to identify Al, Ba, Fe, K, and Na metals in snow cover near roadways due to dust dispersal. This discovery supports the argument that UAV sensing data can be utilized to monitor air pollution from roadways.
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Verma, Tushar S. « Water Quality Analysis Through Satellite Images ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no 05 (14 mai 2024) : 1–5. http://dx.doi.org/10.55041/ijsrem33992.
Texte intégralRésumé :
This project report presents a comprehensive study on water quality analysis using satellite images. The increasing global demand for water resources and the growing impact of human activities on water ecosystems underscore the need for efficient and scalable methods for water quality assessment. Leveraging remote sensing technology, particularly satellite imagery, provides a promising solution for monitoring large water bodies at a regional and global scale. The project focuses on the development and implementation of a robust methodology for water quality analysis through the utilization of satellite images. Key objectives include the extraction of relevant water quality indicators such as turbidity, chlorophyll-a concentration, and dissolved organic matter. Image processing techniques, including spectral analysis and machine learning algorithms, are employed to derive quantitative information from satellite data. The study employs a multi-sensor approach, utilizing data from various satellite platforms, to enhance the accuracy and reliability of the water quality assessments. Additionally, ground truth data collected from in situ measurements and laboratory analysis are integrated into the analysis to validate and calibrate the remote sensing results. The project contributes to the advancement of remote sensing applications in environmental monitoring, providing a cost-effective and timely method for water quality assessment. The findings of this research have implications for water resource management, environmental conservation, and policy formulation. Furthermore, the developed methodology can be applied to monitor changes in water quality over time, supporting sustainable water resource management practices. The report concludes with recommendations for further research and the potential integration of the developed methodology into existing water quality monitoring systems. Overall, the project highlights the significance of satellite-based approaches in addressing contemporary challenges related to water quality on a global scale.
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Maguire, Mitchell S., Christopher M. U. Neale et Wayne E. Woldt. « Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications ». Remote Sensing 13, no 9 (22 avril 2021) : 1635. http://dx.doi.org/10.3390/rs13091635.
Texte intégralRésumé :
Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.
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Zhao, Yanpeng, Yuanqing Wang, Kun Liang, Yangrui Xu, Yuanxin Guo et Kassim Makame. « Underwater Temperature and Salinity Measurement by Rayleigh–Brillouin Spectroscopy Using Fizeau Interferometer and PMT Array ». Remote Sensing 16, no 12 (19 juin 2024) : 2214. http://dx.doi.org/10.3390/rs16122214.
Texte intégralRésumé :
This article presents a LiDAR system that utilizes a Fizeau interferometer and photomultiplier tube array to detect the water Rayleigh–Brillouin spectrum, utilized to obtain underwater temperature and salinity synchronizing measurements based on the Brillouin spectral linewidth and shift. Temperature and salinity measurements were conducted in the laboratory to verify the efficiency of the system. The results demonstrate that the LiDAR system can accurately obtain the Rayleigh–Brillouin spectral backscattering profiles of water. Following linear fitting and reconstruction, the retrieved temperature accuracy is ±0.13 °C and salinity accuracy is ±0.16‰. By effectively leveraging the multiparameter information contained in the Rayleigh–Brillouin spectrum, the system achieved precise temperature and salinity measurements. This study provides a reference for marine remote sensing applications
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Bhattacharjee, C., D. Deb, H. S. Das, A. K. Sen et R. Gupta. « Modelling Laboratory Data of Bidirectional Reflectance of a Regolith Surface Containing Alumina ». Publications of the Astronomical Society of Australia 28, no 3 (2011) : 261–65. http://dx.doi.org/10.1071/as10025.
Texte intégralRésumé :
AbstractBidirectional reflectance of a surface is defined as the ratio of the scattered radiation at the detector to the incident irradiance as a function of geometry. Accurate knowledge of the bidirectional reflection function for layers composed of discrete, randomly positioned scattering particles is essential for many remote sensing, engineering, and biophysical applications, as well as for different areas of astrophysics. Computations of bidirectional reflection functions for plane parallel particulate layers are usually reduced to solving the radiative transfer equation by the existing techniques. In this work we present our laboratory data on bidirectional reflectance versus phase angle for two sample sizes of alumina, 0.3 and 1 μm, for the He–Ne laser at wavelengths of 632.8 nm (red) and 543.5 nm (green). The nature of the phase curves of the asteroids depends on the parameters like particle size, composition, porosity, roughness, etc. In the present study we analyze data which are being generated using a single scattering phase function, that is, Mie theory of treating particles as a compact sphere. The well-known Hapke formula, along with different particle phase functions such as Mie and Henyey–Greenstein, will be used to model the laboratory data obtained at the asteroid laboratory of Assam University.
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Wu, Weicheng, Yalan Liu et Mingxing Hu. « Editorial on Special Issue “Geo-Information Technology and Its Applications” ». ISPRS International Journal of Geo-Information 11, no 6 (13 juin 2022) : 347. http://dx.doi.org/10.3390/ijgi11060347.
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Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.
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Hermosilla, Gabriel, Francisco Pizarro, Sebastián Fingerhuth, Francisco Lazcano, Francisco Santibanez, Nelson Baker, David Castro et Carolina Yáñez. « Real-Time Remote Sensing of the Lobesia botrana Moth Using a Wireless Acoustic Detection Sensor ». Applied Sciences 11, no 24 (14 décembre 2021) : 11889. http://dx.doi.org/10.3390/app112411889.
Texte intégralRésumé :
This article presents a wireless sensor for pest detection, specifically the Lobesia botrana moth or vineyard moth. The wireless sensor consists of an acoustic-based detection of the sound generated by a flying Lobesia botrana moth. Once a Lobesia botrana moth is detected, the information about the time, geographical location of the sensor and the number of detection events is sent to a server that gathers the detection statistics in real-time. To detect the Lobesia botrana, its acoustic signal was previously characterized in a controlled environment, obtaining its power spectral density for the acoustic filter design. The sensor is tested in a controlled laboratory environment where the detection of the flying moths is successfully achieved in the presence of all types of environmental noises. Finally, the sensor is installed on a vineyard in a region where the moth has already been detected. The device is able to detect flying Lobesia botrana moths during its flying period, giving results that agree with traditional field traps.