Open AccessArticle
Near Real-Time Ground-to-Ground Infrared Remote-Sensing Combination and Inexpensive Visible Camera Observations Applied to Tomographic Stack Emission Measurements
Remote Sens. 2018, 10(5), 678; doi:10.3390/rs10050678 (registering DOI) -
Abstract
Evaluation of the environmental impact of gas plumes from stack emissions at the local level requires precise knowledge of the spatial development of the cloud, its evolution over time, and quantitative analysis of each gaseous component. With extensive developments, remote-sensing ground-based technologies are
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Evaluation of the environmental impact of gas plumes from stack emissions at the local level requires precise knowledge of the spatial development of the cloud, its evolution over time, and quantitative analysis of each gaseous component. With extensive developments, remote-sensing ground-based technologies are becoming increasingly relevant to such an application. The difficulty of determining the exact 3-D thickness of the gas plume in real time has meant that the various gas components are mainly expressed using correlation coefficients of gas occurrences and path concentration (ppm.m). This paper focuses on a synchronous and non-expensive multi-angled approach combining three high-resolution visible cameras (GoPro-Hero3) and a scanning infrared (IR) gas system (SIGIS, Bruker). Measurements are performed at a NH3 emissive industrial site (NOVACARB Society, Laneuveville-devant-Nancy, France). Visible data images were processed by a first geometrical reconstruction gOcad® protocol to build a 3-D envelope of the gas plume which allows estimation of the plume’s thickness corresponding to the 2-D infrared grid measurements. NH3 concentration data could thereby be expressed in ppm and have been interpolated using a second gOcad® interpolation algorithm allowing a precise volume visualization of the NH3 distribution in the flue gas steam. Full article
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Open AccessArticle
Microrelief Associated with Gas Emission Craters: Remote-Sensing and Field-Based Study
Remote Sens. 2018, 10(5), 677; doi:10.3390/rs10050677 (registering DOI) -
Abstract
Formation of gas emission craters (GEC) is a new process in the permafrost zone, leading to considerable terrain changes. Yet their role in changing the relief is local, incomparable in the volume of the removed deposits to other destructive cryogenic processes. However, the
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Formation of gas emission craters (GEC) is a new process in the permafrost zone, leading to considerable terrain changes. Yet their role in changing the relief is local, incomparable in the volume of the removed deposits to other destructive cryogenic processes. However, the relief-forming role of GECs is not limited to the appearance of the crater itself, but also results in positive and negative microforms as well. Negative microforms are rounded hollows, surrounded by piles of ejected or extruded deposits. Hypotheses related to the origin of these forms are put forward and supported by an analysis of multi-temporal satellite images, field observations and photographs of GECs. Remote sensing data specifically was used for interpretation of landform origin, measuring distances and density of material scattering, identifying scattered material through analysis of repeated imagery. Remote-sensing and field data reliably substantiate an impact nature of the hollows around GECs. It is found that scattering of frozen blocks at a distance of up to 293 m from a GEC is capable of creating an impact hollow. These data indicate the influence of GEC on the relief through the formation of a microrelief within a radius of 15–20 times the radius of the crater itself. Our study aims at the prediction of risk zones. Full article
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Open AccessEditorial
Editorial for Special Issue “Recent Advances in GPR Imaging”
Remote Sens. 2018, 10(5), 676; doi:10.3390/rs10050676 (registering DOI) -
Open AccessArticle
BeiDou System (BDS) Triple-Frequency Ambiguity Resolution without Code Measurements
Remote Sens. 2018, 10(5), 675; doi:10.3390/rs10050675 (registering DOI) -
Abstract
Phase and code measurements achieve ambiguity resolution in medium- or long-baseline computation. However, the code multipath effect on Global Navigation Satellite Systems (GNSS) phase and code measurements is a main source of error for ambiguity resolution, in particular the code multipath. The BeiDou
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Phase and code measurements achieve ambiguity resolution in medium- or long-baseline computation. However, the code multipath effect on Global Navigation Satellite Systems (GNSS) phase and code measurements is a main source of error for ambiguity resolution, in particular the code multipath. The BeiDou System (BDS) of China is fully operational in the Asia-Pacific region and provides triple-frequency (B1, B2 and B3) measurements. Although previous research about BDS triple-frequency baseline computation indicated that using triple-frequency measurements improves the performance of ambiguity resolution, the respective methods are still impacted by code multipath since the code measurements are incorporated in the methods. Therefore, it is of interest to further improve the ambiguity resolution of BDS triple-frequency baseline computation by excluding the code multipath. We propose a modified phase-only method that only uses triple-frequency phase measurements to achieve BDS ambiguity resolution and evaluate the performance of the method. Observations from experimental medium and long baselines were collected with Trimble NetR9 receivers. The related ambiguity-resolution performances are computed with the phase-only method and a generalized phase-code method. The results show that the phase-only ambiguity resolution is feasible and generally performs better than the phase-code ambiguity resolution, but the improvement is subject to phase noise and satellite geometry. Full article
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Open AccessFeature PaperArticle
Groundwater Storage Changes in China from Satellite Gravity: An Overview
Remote Sens. 2018, 10(5), 674; doi:10.3390/rs10050674 (registering DOI) -
Abstract
Groundwater plays a critical role in the global water cycle and is the drinking source for almost half of the world’s population. However, exact quantification of its storage change remains elusive due primarily to limited ground observations in space and time. The Gravity
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Groundwater plays a critical role in the global water cycle and is the drinking source for almost half of the world’s population. However, exact quantification of its storage change remains elusive due primarily to limited ground observations in space and time. The Gravity Recovery and Climate Experiment (GRACE) twin-satellite data have provided global observations of water storage variations at monthly sampling for over a decade and a half, and is enable to estimate changes in groundwater storage (GWS) after removing other water storage components using auxiliary datasets and models. In this paper, we present an overview of GWS changes in three main aquifers within China using GRACE data, and conduct a comprehensive accuracy assessment using in situ ground well observations and hydrological models. GRACE detects a significant GWS depletion rate of 7.2 ± 1.1 km3/yr in the North China Plain (NCP) during 2002–2014, consistent with ground well observations and model predictions. The Liaohe River Basin (LRB) experienced a pronounced GWS decline during 2005–2009, at a depletion rate of 5.0 ± 1.2 km3/yr. Since 2010, GRACE-based GWS reveal a slow recovery in the LRB, with excellent agreement with ground well observations. For the whole study period 2002–2014, no significant long-term GWS depletion is found in the LRB nor in the Tarim Basin. A case study in the Inner Tibetan Plateau highlights there still exist large uncertainties in GRACE-based GWS change estimates. Full article
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Open AccessArticle
An Assessment of Atmospheric and Meteorological Factors Regulating Red Sea Phytoplankton Growth
Remote Sens. 2018, 10(5), 673; doi:10.3390/rs10050673 -
Abstract
This study considers the various factors that regulate nutrients supply in the Red Sea. Multi-sensor observation and reanalysis datasets are used to examine the relationships among dust deposition, sea surface temperature (SST), and wind speed, as they may contribute to anomalous phytoplankton blooms,
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This study considers the various factors that regulate nutrients supply in the Red Sea. Multi-sensor observation and reanalysis datasets are used to examine the relationships among dust deposition, sea surface temperature (SST), and wind speed, as they may contribute to anomalous phytoplankton blooms, through time-series and correlation analyses. A positive correlation was found at 0–3 months lag between chlorophyll-a (Chl-a) anomalies and dust anomalies over the Red Sea regions. Dust deposition process was further examined with dust aerosols’ vertical distribution using satellite lidar data. Conversely, a negative correlation was found at 0–3 months lag between SST anomalies and Chl-a that was particularly strong in the southern Red Sea during summertime. The negative relationship between SST and phytoplankton is also evident in the continuously low levels of Chl-a during 2015 to 2016, which were the warmest years in the region on record. The overall positive correlation between wind speed and Chl-a relate to the nutritious water supply from the Gulf of Aden to the southern Red Sea and the vertical mixing encountered in the northern part. Ocean Color Climate Change Initiative (OC-CCI) dataset experience some temporal inconsistencies due to the inclusion of different datasets. We addressed those issues in our analysis with a valid interpretation of these complex relationships. Full article
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Open AccessArticle
Monitoring Surface Deformation over a Failing Rock Slope with the ESA Sentinels: Insights from Moosfluh Instability, Swiss Alps
Remote Sens. 2018, 10(5), 672; doi:10.3390/rs10050672 -
Abstract
We leverage on optical and radar remote sensing data acquired from the European Space Agency (ESA) Sentinels to monitor the surface deformation evolution on a large and very active instability located in the Swiss Alps, i.e., the Moosfluh rock slope. In the late
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We leverage on optical and radar remote sensing data acquired from the European Space Agency (ESA) Sentinels to monitor the surface deformation evolution on a large and very active instability located in the Swiss Alps, i.e., the Moosfluh rock slope. In the late summer 2016, a sudden acceleration was reported at this location, with surface velocity rates passing from maximum values of 0.2 cm/day to 80 cm/day. A dense pattern of uphill-facing scarps and tension cracks formed within the instability and rock fall activity started to become very pronounced. This evolution of the rock mass may suggest that the most active portion of the slope could fail catastrophically. Here we discuss advantages and limitations of the use of spaceborne methods for hazard analyses and early warning by using the ESA Sentinels, and show that in critical scenarios they are often not sufficient to reliably interpret the evolution of surface deformation. The insights obtained from this case study are relevant for similar scenarios in the Alps and elsewhere. Full article
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Open AccessArticle
Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning
Remote Sens. 2018, 10(5), 671; doi:10.3390/rs10050671 -
Abstract
Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB
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Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed. Full article
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Open AccessArticle
Optimizing Window Length for Turbulent Heat Flux Calculations from Airborne Eddy Covariance Measurements under Near Neutral to Unstable Atmospheric Stability Conditions
Remote Sens. 2018, 10(5), 670; doi:10.3390/rs10050670 -
Abstract
Airborne eddy covariance (EC) is one of the most effective ways to directly measure turbulent flux at a regional scale. This study aims to find the optimum spatial window length for turbulent heat fluxes calculation from airborne eddy covariance measurements under near neutral
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Airborne eddy covariance (EC) is one of the most effective ways to directly measure turbulent flux at a regional scale. This study aims to find the optimum spatial window length for turbulent heat fluxes calculation from airborne eddy covariance measurements under near neutral to unstable atmospheric stability conditions, to reduce the negative influences from mesoscale turbulence, and to estimate local meaningful turbulent heat fluxes accurately. The airborne flux measurements collected in 2008 in the Netherlands were used in this study. Firstly, the raw data was preprocessed, including de-spike, segmentation, and stationarity test. The atmospheric stability conditions were classified as near neutral, moderately unstable, or very unstable; the stable condition was excluded. Secondly, Ogive analysis for turbulent heat fluxes from all available segmentations of the airborne measurements was used to determine the possible window length range. After that, the optimum window length for turbulent heat flux calculations was defined based on the analysis of all possible window lengths and their uncertainties. The results show that the choice of the optimum window length strongly depends on the atmospheric stability conditions. Under near neutral conditions, local turbulence is mixed insufficiently and vulnerable to heterogeneous turbulence. A relatively short window length is needed to exclude the influence of mesoscale turbulence, and we found the optimum window length ranges from 2000 m to 2500 m. Under moderately unstable conditions, the typical scale of local turbulence is relative large, and the influence of mesoscale turbulence is relatively small. We found the optimum window length ranges from 3900 m to 5000 m. Under very unstable conditions, large convective eddies dominate the transmission of energy so that the window length needs to cover the large eddies with large energy transmission. We found the optimum window length ranges from 4500 m to 5000 m. This study gives a comprehensive methodology to determine the optimizing window length in order to compromise a balance between the accuracy and the surface representativeness of turbulent heat fluxes from airborne EC measurements. Full article
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Open AccessArticle
Full-Waveform LiDAR Pixel Analysis for Low-Growing Vegetation Mapping of Coastal Foredunes in Western France
Remote Sens. 2018, 10(5), 669; doi:10.3390/rs10050669 -
Abstract
The monitoring of coastal sand dunes requires regular high-resolution aerial photography along hundreds of kilometers of coastal strips. Light detection and ranging (LiDAR) is now the most widely used method for detailed topographic and vegetation studies. The aim of this work is to
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The monitoring of coastal sand dunes requires regular high-resolution aerial photography along hundreds of kilometers of coastal strips. Light detection and ranging (LiDAR) is now the most widely used method for detailed topographic and vegetation studies. The aim of this work is to show how the full-waveform shapes returned from single or multiple targets can carry information relating to low-vegetation cover and ground roughness of dunes. This work focuses on marram grass, widely involved in the development of mobile dunes. Low-growing plants often exhibit identical pigmentary composition and can only be distinguished by the height of their foliage, which modifies the shape of the LiDAR waveform around the main returns at the top of the foliage. We show that ray tracing of full LiDAR waveforms on the regular grid of pixels of hyperspectral images, acquired synchronously, can resolve the confusion between low-vegetation gradients and bare sand by analyzing the waveform damping induced by cumulating microdiffusion on foliage height, but also with glint effects on the surface roughness of compact materials. Analysis of successive shorelines of wet to dry sand, sand to pioneer couch grass, and couch grass to consolidating marram grass can thereby be conducted routinely. Full article
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Open AccessArticle
Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation
Remote Sens. 2018, 10(5), 668; doi:10.3390/rs10050668 -
Abstract
This study presents the setup, methodology and results from a measurement campaign dedicated to the characterization of full-scale wind turbine wakes under different inflow conditions. The measurements have been obtained from two pulsed scanning Doppler lidars mounted on the nacelle of a 2.5
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This study presents the setup, methodology and results from a measurement campaign dedicated to the characterization of full-scale wind turbine wakes under different inflow conditions. The measurements have been obtained from two pulsed scanning Doppler lidars mounted on the nacelle of a 2.5 MW wind turbine. The first lidar is upstream oriented and dedicated to the characterization of the inflow with a variety of scanning patterns, while the second one is downstream oriented and performs horizontal planar scans of the wake. The calculated velocity deficit profiles exhibit self-similarity in the far wake region and they can be fitted accurately to Gaussian functions. This allows for the study of the growth rate of the wake width and the recovery of the wind speed, as well as the extent of the near-wake region. The results show that a higher incoming turbulence intensity enhances the entrainment and flow mixing in the wake region, resulting in a shorter near-wake length, a faster growth rate of the wake width and a faster recovery of the velocity deficit. The relationships obtained are compared to analytical models for wind turbine wakes and allow to correct the parameters prescribed until now, which were obtained from wind-tunnel measurements and large-eddy simulations (LES), with new, more accurate values directly derived from full-scale experiments. Full article
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Open AccessTechnical Note
Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation
Remote Sens. 2018, 10(5), 667; doi:10.3390/rs10050667 -
Abstract
Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation
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Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures. Full article
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Open AccessArticle
Studying Ionosphere Responses to a Geomagnetic Storm in June 2015 with Multi-Constellation Observations
Remote Sens. 2018, 10(5), 666; doi:10.3390/rs10050666 -
Abstract
The Global Navigation Satellite System (GNSS) observations with global coverage and high temporal and spatial resolution, provide abundant and high-quality Earth-ionosphere observations. By calculating the total electron content (TEC), estimations from GNSS observables global and regional ionosphere TEC morphology can be further investigated.
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The Global Navigation Satellite System (GNSS) observations with global coverage and high temporal and spatial resolution, provide abundant and high-quality Earth-ionosphere observations. By calculating the total electron content (TEC), estimations from GNSS observables global and regional ionosphere TEC morphology can be further investigated. For the multiple constellation case, the numbers of ionosphere pierce points (IPP) has increased tremendously, and it is worth studying the features of the GNSS derived TEC under geomagnetic storms to show the benefits of multiple constellation measurements. With the Multi-GNSS Experiment (MGEX) observation data, ionosphere TEC responses to the geomagnetic storm on the 22 June 2015 were well studied. TEC perturbations were discovered, accompanied by ionosphere irregularities concentrating in high and middle latitudes. Through analysis of multi-GNSS observations, the Rate of TEC Index (ROTI) perturbations were proved to be generated by the geomagnetic storm, with simultaneous behaviors at different local times around the world, also indicating ionosphere scintillation. The ionosphere spatial gradient was also discussed with two short baseline MGEX sites; the maximum ionosphere gradient of 247.2 mm/km was found, due to ionosphere irregularity produced by the storm. This research has discussed ionosphere responses to geomagnetic storms with multi-GNSS data provided and has analyzed the availability of multi-GNSS observations to investigate ionosphere irregularity climatology. The proposed work is valuable for further investigation of GNSS performances under geomagnetic storms. Full article
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Open AccessArticle
Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand
Remote Sens. 2018, 10(5), 665; doi:10.3390/rs10050665 -
Abstract
For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand’s six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales—diurnal, seasonal, and multiyear.
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For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand’s six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales—diurnal, seasonal, and multiyear. The diurnal variation is represented by the four MODIS passages (10:00, 14:00, 22:00, and 02:00 local time) and the seasonal variation by summer and winter maps, with images covering a 14-year interval (2003–2016). Also, 126 Landsat scenes were processed to classify and map land cover changes for each city. To analyze and compare the SUHI patterns, a least-square Gaussian fitting method has been applied and the corresponding empirical metrics quantified. Such an approach represents, when applicable, an efficient quantitative tool to perform comparisons that a visual inspection of a great number of maps would not allow. Results point out that SUHI does not show significant seasonality differences, while SUHI in the daytime is a more evident phenomenon with respect to nighttime, mainly due to solar forcing and intense human activities and traffic. Across the 14 years, the biggest city, Bangkok, shows the highest SUHI maximum intensities during daytime, with values ranging between 4 °C and 6 °C; during nighttime, the intensities are rather similar for all the six cities, between 1 °C and 2 °C. However, these maximum intensities are not correlated with the urban growth over the years. For each city, the SUHI spatial extension represented by the Gaussian footprint is generally not affected by the urban area sprawl across the years, except for Bangkok and Chiang Mai, whose daytime SUHI footprints show a slight increase over the years. Orientation angle and central location of the fitted surface also provide information on the SUHI layout in relation to the land use of the urban texture. Full article
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Open AccessArticle
The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
Remote Sens. 2018, 10(5), 664; doi:10.3390/rs10050664 -
Abstract
Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and
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Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types. Full article
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Open AccessArticle
Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification
Remote Sens. 2018, 10(5), 663; doi:10.3390/rs10050663 -
Abstract
In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed.
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In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors. Full article
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Open AccessLetter
ASTER-Derived High-Resolution Ice Surface Temperature for the Arctic Coast
Remote Sens. 2018, 10(5), 662; doi:10.3390/rs10050662 -
Abstract
Ice surface temperature (IST) controls the rate of sea ice growth and the heat exchange between the atmosphere and ocean. In this study, high-resolution IST using the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) thermal infrared region (TIR) images was retrieved to
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Ice surface temperature (IST) controls the rate of sea ice growth and the heat exchange between the atmosphere and ocean. In this study, high-resolution IST using the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) thermal infrared region (TIR) images was retrieved to observe the thermal change of coastal sea ice. The regression coefficients of the multi-channel equation using ASTER brightness temperatures ( BT ) and MODIS ISTs were derived. MODIS IST products (MOD29) were used as an in situ temperature substitute. The ASTER IST using five channels from band 10 ( BT10 ) to band 14 ( BT14 ) showed an RMSE of 0.746 K for the validation images on the Alaskan coast. The uncertainty of the two-channel ( BT13 and BT14 ) ASTER IST was 0.497 K, which was better than that of the five-channel. We thus concluded that the two-channel equation using ASTER BT13 and BT14 was an optimal model for the surface temperature retrieval of coastal sea ice. The two-channel ASTER IST showed similar accuracy at higher latitudes than in Alaska. Therefore, ASTER-derived IST with 90 m spatial resolution can be used to observe small-scale thermal variations on the sea ice surface along the Arctic coast. Full article
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Open AccessArticle
Automatic Discovery and Geotagging of Objects from Street View Imagery
Remote Sens. 2018, 10(5), 661; doi:10.3390/rs10050661 -
Abstract
Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using
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Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images, while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov random field model to estimate the objects’ geolocation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with the simultaneous presence of multiple, visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and position precision of approximately 2 m, which is approaching the precision of single-frequency GPS receivers. Full article
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Open AccessArticle
Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data
Remote Sens. 2018, 10(5), 660; doi:10.3390/rs10050660 -
Abstract
A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF) is still complicated, due to fast-changing climate and topographic conditions, which modifies the forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly detectable by
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A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF) is still complicated, due to fast-changing climate and topographic conditions, which modifies the forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly detectable by small field plots, especially in natural TMF due to the high tree diversity and the inaccessibility of remote areas. Therefore, the present approach used remotely sensed data from a Light Detection and Ranging (LiDAR) sensor in combination with field measurements to estimate AGB accurately for a catchment in the Andes of south-eastern Ecuador. From the LiDAR data, information about horizontal and vertical structure of the TMF could be derived and the vegetation at tree level classified, differentiated between the prevailing forest types (ravine forest, ridge forest and Elfin Forest). Furthermore, topographical variables (Topographic Position Index, TPI; Morphometric Protection Index, MPI) were calculated by means of the high-resolution LiDAR data to analyse the AGB distribution within the catchment. The field measurements included different tree parameters of the species present in the plots, which were used to determine the local mean Wood Density (WD) as well as the specific height-diameter relationship to calculate AGB, applying regional scale modelling at tree level. The results confirmed that field plot measurements alone cannot capture completely the forest structure in TMF but in combination with high resolution LiDAR data, applying a classification at tree level, the AGB amount (Mg ha−1) and its distribution in the entire catchment could be estimated adequately (model accuracy at tree level: R2 > 0.91). It was found that the AGB distribution is strongly related to ridges and depressions (TPI) and to the protection of the site (MPI), because high AGB was also detected at higher elevations (up to 196.6 Mg ha−1, above 2700 m), if the site is situated in depressions (ravine forest) and protected by the surrounding terrain. In general, highest AGB is stored in the protected ravine TMF parts, also at higher elevations, which could only be detected by means of the remote sensed data in high resolution, because most of these areas are inaccessible. Other vegetation units, present in the study catchment (pasture and subpáramo) do not contain large AGB stocks, which underlines the importance of intact natural forest stands. Full article
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Open AccessArticle
Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure
Remote Sens. 2018, 10(5), 659; doi:10.3390/rs10050659 -
Abstract
Retrieval of forest biophysical properties using airborne LiDAR is known to differ between leaf-on and leaf-off states of deciduous trees, but much less is understood about the within-season effects of leafing phenology. Here, we compare two LiDAR surveys separated by just six weeks
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Retrieval of forest biophysical properties using airborne LiDAR is known to differ between leaf-on and leaf-off states of deciduous trees, but much less is understood about the within-season effects of leafing phenology. Here, we compare two LiDAR surveys separated by just six weeks in spring, in order to assess whether LiDAR variables were influenced by canopy changes in Mediterranean mixed-oak woodlands at this time of year. Maximum and, to a slightly lesser extent, mean heights were consistently measured, whether for the evergreen cork oak (Quercus suber) or semi-deciduous Algerian oak (Q. canariensis) woodlands. Estimates of the standard deviation and skewness of height differed more strongly, especially for Algerian oaks which experienced considerable leaf expansion in the time period covered. Our demonstration of which variables are more or less affected by spring-time leafing phenology has important implications for analyses of both canopy and sub-canopy vegetation layers from LiDAR surveys. Full article
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