Open AccessArticle
Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China
Remote Sens. 2018, 10(2), 320; doi:10.3390/rs10020320 (registering DOI) -
Abstract
Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine
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Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R2 and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1%–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R2 and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend. Full article
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Open AccessArticle
Comparison between AMSR2 Sea Ice Concentration Products and Pseudo-Ship Observations of the Arctic and Antarctic Sea Ice Edge on Cloud-Free Days
Remote Sens. 2018, 10(2), 317; doi:10.3390/rs10020317 (registering DOI) -
Abstract
In recent years, much attention has been paid to the behavior of passive microwave sea ice concentration (SIC) products for marginal ice zones. Based on the definition of ice edges from ship observations, we identified pseudo-ship observations (PSO) and generated PSO ice edges
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In recent years, much attention has been paid to the behavior of passive microwave sea ice concentration (SIC) products for marginal ice zones. Based on the definition of ice edges from ship observations, we identified pseudo-ship observations (PSO) and generated PSO ice edges from twelve cloud-free moderate-resolution imaging spectroradiometer (MODIS) images. Two SIC products of the advanced microwave scanning radiometer 2 (AMSR2) were compared at the PSO ice edges: ARTIST (arctic radiation and turbulence interaction study) sea ice (ASI-SIC) and bootstrap (BST-SIC). The mean values of ASI-SIC pixels located at ice edges were 10.5% and 10.3% for the Arctic and the Antarctic, respectively, and are below the commonly applied 15% threshold, whereas the mean values of corresponding BST-SIC pixels were 23.6% and 27.3%, respectively. The mean values of both ASI-SIC and BST-SIC were lower in summer than in winter. The spatial gaps among the 15% ASI-SIC ice edge, the 15% BST-SIC ice edge and the PSO ice edge were mostly within 35 km, whereas the 15% ASI-SIC ice edge matched better with the PSO ice edge. Results also show that the ice edges were located in the thin ice region, with a mean ice thickness of around 5–8 cm. We conclude that the 15% threshold well determines the ice edge from passive microwave SIC in both the Arctic and the Antarctic. Full article
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Open AccessArticle
Quantification of Typhoon-Induced Phytoplankton Blooms Using Satellite Multi-Sensor Data
Remote Sens. 2018, 10(2), 318; doi:10.3390/rs10020318 -
Abstract
Using satellite-based multi-sensor observations, this study investigates Chl-a blooms induced by typhoons in the Northwest Pacific (NWP) and the South China Sea (SCS), and quantifies the blooms via wind-induced mixing and Ekman pumping parameters, as well as pre-typhoon mixed-layer depth (MLD). In the
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Using satellite-based multi-sensor observations, this study investigates Chl-a blooms induced by typhoons in the Northwest Pacific (NWP) and the South China Sea (SCS), and quantifies the blooms via wind-induced mixing and Ekman pumping parameters, as well as pre-typhoon mixed-layer depth (MLD). In the NWP, the Chl-a bloom is more correlated with the Ekman pumping than with the other two parameters, with an R2 value of 0.56. In the SCS, the wind-induced mixing and Ekman pumping have comparable correlations with the Chl-a increase, showing R2 values of 0.4~0.6. However, the MLD exhibits a negative correlation with the Chl-a increase. A multi-parameter quantification model of the Chl-a bloom strength achieves better results than the single-parameter regressions, yielding a more significant R2 value of 0.80, and a lower regression rms of 0.18 mg•m−3 in the SCS, and the R2 value in the NWP is also improved compared with the single-parameter regressions. The multi-parameter quantification model of Chl-a blooms is more accurate in the SCS than in the NWP, due to the fact that nutrient profiles in the NWP are uniform from surface to a deep depth (300 m). Thus, the Chl-a blooms are more correlated with the upper ocean dynamical processes in the SCS where a shallower nutricline is found. Full article
Open AccessReview
The Challenges of Remotely Measuring Oil Slick Thickness
Remote Sens. 2018, 10(2), 319; doi:10.3390/rs10020319 (registering DOI) -
Abstract
The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons.
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The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens, which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods. Full article
Open AccessArticle
Performance Assessment of Balloon-Borne Trace Gas Sounding with the Terahertz Channel of TELIS
Remote Sens. 2018, 10(2), 315; doi:10.3390/rs10020315 -
Abstract
Short-term variations in the atmospheric environment over polar regions are attracting increasing attention with respect to the reliable analysis of ozone loss. Balloon-borne remote sensing instruments with good vertical resolution and flexible sampling density can act as a prototype to overcome the potential
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Short-term variations in the atmospheric environment over polar regions are attracting increasing attention with respect to the reliable analysis of ozone loss. Balloon-borne remote sensing instruments with good vertical resolution and flexible sampling density can act as a prototype to overcome the potential technical challenges in the design of new spaceborne atmospheric sensors and represent a valuable tool for validating spaceborne observations. A multi-channel cryogenic heterodyne spectrometer known as the TErahertz and submillimeter LImb Sounder (TELIS) has been developed. It allows limb sounding of the upper troposphere and stratosphere (10–40 km) within the far infrared (FIR) and submillimeter spectral regimes. This paper describes and assesses the performance of the profile retrieval scheme for TELIS with a focus on the ozone (O3), hydrogen chloride (HCl), carbon monoxide (CO), and hydroxyl radical (OH) measured during three northern polar campaigns in 2009, 2010, and 2011, respectively. The corresponding inversion diagnostics reveal that some forward/instrument model parameters play important roles in the total retrieval error. The accuracy of the radiometric calibration and the spectroscopic knowledge has a significant impact on retrieval at higher altitudes, whereas the pointing accuracy dominates the total error at lower altitudes. The TELIS retrievals achieve a vertical resolution of ∼ 2–3 km through most of the stratosphere below the balloon height. Dominant water vapor (H2O) contamination and low abundances of the target species reduce the retrieval sensitivity at the lowermost altitudes measured by TELIS. An extensive comparison shows that the TELIS profiles are consistent with profiles obtained by other limb sounders. The comparison appears to be very promising, except for discrepancies in the upper troposphere due to numerical regularization. This study not only consolidates the validity of balloon-borne TELIS FIR measurements, but also demonstrates the scientific relevance and technical feasibility of terahertz limb sounding of the stratosphere. Full article
Open AccessArticle
Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model
Remote Sens. 2018, 10(2), 316; doi:10.3390/rs10020316 -
Abstract
The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution
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The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA. Full article
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Open AccessArticle
Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries
Remote Sens. 2018, 10(2), 312; doi:10.3390/rs10020312 -
Abstract
The performance of a support vector regression (SVR) model with a Gaussian radial basis kernel to predict anthocyanin concentration, pH index and sugar content in whole grape berries, using spectroscopic measurements obtained in reflectance mode, was evaluated. Each sample contained a small number
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The performance of a support vector regression (SVR) model with a Gaussian radial basis kernel to predict anthocyanin concentration, pH index and sugar content in whole grape berries, using spectroscopic measurements obtained in reflectance mode, was evaluated. Each sample contained a small number of whole berries and the spectrum of each sample was collected during ripening using hyperspectral imaging in the range of 380–1028 nm. Touriga Franca (TF) variety samples were collected for the 2012–2015 vintages, and Touriga Nacional (TN) and Tinta Barroca (TB) variety samples were collected for the 2013 vintage. These TF vintages were independently used to train, validate and test the SVR methodology; different combinations of TF vintages were used to train and test each model to assess the performance differences under wider and more variable datasets; the varieties that were not employed in the model training and validation (TB and TN) were used to test the generalization ability of the SVR approach. Each case was tested using an external independent set (with data not included in the model training or validation steps). The best R2 results obtained with varieties and vintages not employed in the model’s training step were 0.89, 0.81 and 0.90, with RMSE values of 35.6 mg·L−1, 0.25 and 3.19 °Brix, for anthocyanin concentration, pH index and sugar content, respectively. The present results indicate a good overall performance for all cases, improving the state-of-the-art results for external test sets, and suggesting that a robust model, with a generalization capacity over different varieties and harvest years may be obtainable without further training, which makes this a very competitive approach when compared to the models from other authors, since it makes the problem significantly simpler and more cost-effective. Full article
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Open AccessArticle
Evaluating Precipitation Estimates from Eta, TRMM and CHRIPS Data in the South-Southeast Region of Minas Gerais State—Brazil
Remote Sens. 2018, 10(2), 313; doi:10.3390/rs10020313 -
Abstract
Precipitation estimates derived from the Eta model and from TRMM (Tropical Rainfall Measuring Mission) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) remotely sensed data were compared to the precipitation data of the INMET (National Institute of Meteorology) meteorological stations in
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Precipitation estimates derived from the Eta model and from TRMM (Tropical Rainfall Measuring Mission) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) remotely sensed data were compared to the precipitation data of the INMET (National Institute of Meteorology) meteorological stations in the south-southeast region of Minas Gerais state, Brazil, in the period between July 2009 and June 2015. Then, information about evapotranspiration (ETR), water deficit (DEF), and water surplus (EXC) was obtained from the precipitation data, using the sequential water balance (SWB) separately for each type of precipitation data (INMET, TRMM, Eta, and CHIRPS). Subsequently, the components of the SWB were comparatively analyzed. The results indicate that all three products overestimate rainfall. The strongest relationships between the INMET data and the estimated data were observed for the TRMM, in terms of precipitation estimates, as well as DEF, EXC, and ETR components. The Eta precipitation estimates are overestimated relative to those from INMET, resulting in underestimation of the water deficit (DEFETA) and overestimation of evapotranspiration (ETRETA). In general, the CHIRPS data presented a pattern similar to the station data, though statistical analyses were lower than those of the TRMM data. Full article
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Open AccessArticle
Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data
Remote Sens. 2018, 10(2), 314; doi:10.3390/rs10020314 -
Abstract
Aerosols greatly influence global and regional atmospheric systems, and human life. However, a comprehensive understanding of the source regions and three-dimensional (3D) characteristics of aerosol transport over central China is yet to be achieved. Thus, we investigate the 3D macroscopic, optical, physical, and
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Aerosols greatly influence global and regional atmospheric systems, and human life. However, a comprehensive understanding of the source regions and three-dimensional (3D) characteristics of aerosol transport over central China is yet to be achieved. Thus, we investigate the 3D macroscopic, optical, physical, and transport properties of the aerosols over central China based on the March 2007 to February 2016 data obtained from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Our results showed that approximately 60% of the aerosols distributed over central China originated from local areas, whereas non-locally produced aerosols constituted approximately 40%. Anthropogenic aerosols constituted the majority of the aerosol pollutants (69%) that mainly distributed less than 2.0 km above mean sea level. Natural aerosols, which are mainly composed of dust, accounted for 31% of the total aerosols, and usually existed at an altitude higher than that of anthropogenic aerosols. Aerosol particles distributed in the near surface were smaller and more spherical than those distributed above 2.0 km. Aerosol optical depth (AOD) and the particulate depolarization ratio displayed decreasing trends, with a total decrease of 0.11 and 0.016 from March 2007 to February 2016, respectively. These phenomena indicate that during the study period, the extinction properties of aerosols decreased, and the degree of sphericity in aerosol particles increased. Moreover, the annual anthropogenic and natural AOD demonstrated decreasing trends, with a total decrease of 0.07 and 0.04, respectively. This study may benefit the evaluation of the effects of the 3D properties of aerosols on regional climates. Full article
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Open AccessArticle
Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation
Remote Sens. 2018, 10(2), 310; doi:10.3390/rs10020310 -
Abstract
The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical
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The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale. Full article
Open AccessArticle
Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
Remote Sens. 2018, 10(2), 311; doi:10.3390/rs10020311 -
Abstract
High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring
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High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring GCPs are often the most time-consuming on-site tasks in a UAV project. Safety or accessibility concerns may impede their proper placement, so either costlier techniques must be used, or a less accurate DSM is obtained. Photogrammetric blocks flown by drones with on-board receivers capable of RTK (real-time kinematic) positioning do not need GCPs, as camera stations at exposure time can be determined with cm-level accuracy, and used to georeference the block and control its deformations. This paper presents an experimental investigation on the repeatability of DSM generation from several blocks acquired with a RTK-enabled drone, where differential corrections were sent from a local master station or a network of Continuously Operating Reference Stations (CORS). Four different flights for each RTK mode were executed over a test field, according to the same flight plan. DSM generation was performed with three block control configurations: GCP only, camera stations only, and with camera stations and one GCP. The results show that irrespective of the RTK mode, the first and third configurations provide the best DSM inner consistency. The average range of the elevation discrepancies among the DSMs in such cases is about 6 cm (2.5 GSD, ground sampling density) for a 10-cm resolution DSM. Using camera stations only, the average range is almost twice as large (4.7 GSD). The average DSM accuracy, which was verified on checkpoints, turned out to be about 2.1 GSD with the first and third configurations, and 3.7 GSD with camera stations only. Full article
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Open AccessArticle
Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment
Remote Sens. 2018, 10(2), 304; doi:10.3390/rs10020304 -
Abstract
Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their
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Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situsensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils. Full article
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Open AccessArticle
Shortwave IR Adaption of the Mid-Infrared Radiance Method of Fire Radiative Power (FRP) Retrieval for Assessing Industrial Gas Flaring Output
Remote Sens. 2018, 10(2), 305; doi:10.3390/rs10020305 -
Abstract
The radiative power (MW) output of a gas flare is a useful metric from which the rate of methane combustion and carbon dioxide emission can be inferred for inventorying purposes and regular global surveys based on such assessments are now being used to
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The radiative power (MW) output of a gas flare is a useful metric from which the rate of methane combustion and carbon dioxide emission can be inferred for inventorying purposes and regular global surveys based on such assessments are now being used to keep track of global gas flare reduction efforts. Several multispectral remote sensing techniques to estimate gas flare radiative power output have been developed for use in such surveys and single band approaches similar to those long used for the estimation of landscape fire radiative power output (FRP) can also be applied. The MIR-Radiance method, now used for FRP retrieval within the MODIS active fire products, is one such single band approach—but its applicability to gas flare targets (which are significantly hotter than vegetation fires) has not yet been assessed. Here we show that the MIR-Radiance approach is in fact not immediately suitable for retrieval of gas flare FRP due to their higher combustion temperatures but that switching to use data from a SWIR (rather than MWIR) spectral channel once again enables the method to deliver unbiased FRP retrievals. Over an assumed flaring temperature range of 1600–2200 K we find a maximum FRP error of ±13.6% when using SWIR observations at 1.6 µm and ±6.3% when using observations made at 2.2 µm. Comparing these retrievals to those made by the multispectral VIIRS ‘NightFire’ algorithm (based on Planck Function fits to the multispectral signals) we find excellent agreement (bias = 0.5 MW, scatter = 1.6 MW). An important implication of the availability of this new SWIR radiance method for gas flare analysis is the potential to apply it to long time-series from older and/or more spectrally limited instruments, unsuited to the use of multispectral algorithms. This includes the ATSR series of sensors operating between 1991–2012 on the ERS-1, ERS-2 and ENVISAT satellites and such long-term data can be used with the SWIR-Radiance method to identify key trends in global gas flaring that have occurred over the last few decades. Full article
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Open AccessArticle
Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes
Remote Sens. 2018, 10(2), 306; doi:10.3390/rs10020306 -
Abstract
Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in
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Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km2; 39% of total area), as well as total forest conversion into agricultural plantations (3214 km2). Gross forest loss (5133 km2) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5471 km2) and rubber (4025 km2) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes. Full article
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Open AccessArticle
Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada
Remote Sens. 2018, 10(2), 307; doi:10.3390/rs10020307 -
Abstract
Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are
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Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN) to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other), accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found between till veneer and till blanket as well as glaciofluvial subclasses (esker, terraced, and hummocky ice-contact). Merging these classes respectively increased accuracy for independent test area to 68% on average. Relative to the more widely used Random Forest machine learning algorithm, this represents an improvement in accuracy of 4%. Furthermore, the CNN produced better results for less frequent classes with distinct spatial structure. Full article
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Open AccessArticle
Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia
Remote Sens. 2018, 10(2), 308; doi:10.3390/rs10020308 -
Abstract
This paper presents a case study for the analysis of endangered lowland native grassland communities in the Tasmanian Midlands region using field spectroscopy and spectral convolution techniques. The aim of the study was to determine whether there was significant improvement in classification accuracy
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This paper presents a case study for the analysis of endangered lowland native grassland communities in the Tasmanian Midlands region using field spectroscopy and spectral convolution techniques. The aim of the study was to determine whether there was significant improvement in classification accuracy for lowland native grasslands and other vegetation communities based on hyperspectral resolution datasets over multispectral equivalents. A spectral dataset was collected using an ASD Handheld-2 spectroradiometer at Tunbridge Township Lagoon. The study then employed a k-fold cross-validation approach for repeated classification of a full hyperspectral dataset, a reduced hyperspectral dataset, and two convoluted multispectral datasets. Classification was performed on each of the four datasets a total of 30 times, based on two different class configurations. The classes analysed were Themedatriandragrassland, Danthonia/Poagrassland, Wilsoniarotundifolia/Sellieraradicans,saltpan, and a simplified C3 vegetation class. The results of the classifications were then tested for statistically significant differences using ANOVA and Tukey’s post-hoc comparisons. The results of the study indicated that hyperspectral resolution provides small but statistically significant increases in classification accuracy for Themedaand Danthoniagrasslands. For other classes, differences in classification accuracy for all datasets were not statistically significant. The results obtained here indicate that there is some potential for enhanced detection of major lowland native grassland community types using hyperspectral resolution datasets, and that future analysis should prioritise good performance in these classes over others. This study presents a method for identification of optimal spectral resolution across multiple datasets, and constitutes an important case study for lowland native grassland mapping in Tasmania. Full article
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Open AccessArticle
Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data
Remote Sens. 2018, 10(2), 309; doi:10.3390/rs10020309 -
Abstract
The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and the Water Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and
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The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and the Water Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and available methods is indispensable for the local environment evaluation and engineering constructions planning. Most of the previous permafrost maps have employed traditional mapping method based on field surveys and borehole investigation data. However their accuracy is limited because it is extremely difficulties in obtaining mass data in the high-altitude and cold regions as the QTP; moreover, the mapping method, which would effectively integrate many factors, is still facing great challenges. With the rapid development of remote sensing technology in permafrost mapping, spatial data derived from the satellite sensors can recognize the permafrost environment features and quantitatively estimate permafrost distribution. Until now there is no map indicated permafrost presence/absence on the QTP that has been generated only by remote sensing data as yet. Therefore, this paper used permafrost-influencing factors and examined distribution features of each factor in permafrost regions and seasonally frozen ground regions. Then, using the Decision Tree method with the environmental factors, the 1 km resolution permafrost map over the QTP was obtained. The result shows higher accuracy compared to the previous published map of permafrost on the QTP and the map of the glaciers, frozen ground and deserts in China, which also demonstrates that making comprehensive use of remote sensing technology in permafrost mapping research is fast, macro and feasible. Furthermore, this result provides a simple and valid method for further permafrost research. Full article
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Open AccessArticle
Accuracy Assessment Measures for Object Extraction from Remote Sensing Images
Remote Sens. 2018, 10(2), 303; doi:10.3390/rs10020303 -
Abstract
Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm.
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Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm. Full article
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Open AccessArticle
Variational Destriping in Remote Sensing Imagery: Total Variation with L1 Fidelity
Remote Sens. 2018, 10(2), 300; doi:10.3390/rs10020300 -
Abstract
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based on the basic principles of regularization and
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This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based on the basic principles of regularization and data fidelity with certain constraints using modern methods in variational optimization, namely, total variation (TV), L1 fidelity, and the alternating direction method of multipliers (ADMM). The proposed algorithm, TV–L1, uses sparsity-promoting energy functionals to achieve two important imaging effects. The TV term maintains boundary sharpness of the content in the underlying clean image, while the L1 fidelity allows for the equitable removal of stripes without over- or under-penalization, providing a more accurate model of presumably independent sensors with an unspecified and unrestricted bias distribution. A comparison is made between the TV–L2 model and the proposed TV–L1 model to exemplify the qualitative efficacy of an L1 striping penalty. The model makes use of novel minimization splittings and proximal mapping operators, successfully yielding more realistic destriped images in very few iterations. Full article
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Open AccessArticle
Analysis of Permafrost Region Coherence Variation in the Qinghai–Tibet Plateau with a High-Resolution TerraSAR-X Image
Remote Sens. 2018, 10(2), 298; doi:10.3390/rs10020298 -
Abstract
The Qinghai–Tibet Plateau (QTP) is heavily affected by climate change and has been undergoing serious permafrost degradation due to global warming. Synthetic aperture radar interferometry (InSAR) has been a significant tool for mapping surface features or measuring physical parameters, such as soil moisture,
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The Qinghai–Tibet Plateau (QTP) is heavily affected by climate change and has been undergoing serious permafrost degradation due to global warming. Synthetic aperture radar interferometry (InSAR) has been a significant tool for mapping surface features or measuring physical parameters, such as soil moisture, active layer thickness, that can be used for permafrost modelling. This study analyzed variations of coherence in the QTP area for the first time with high-resolution SAR images acquired from June 2014 to August 2016. The coherence variation of typical ground targets was obtained and analyzed. Because of the effects of active-layer (AL) freezing and thawing, coherence maps generated in the Beiluhe permafrost area exhibits seasonal variation. Furthermore, a temporal decorrelation model determined by a linear temporal-decorrelation component plus a seasonal periodic-decorrelation component and a constant component have been proposed. Most of the typical ground targets fit this temporal model. The results clearly indicate that railways and highways can hold high coherence properties over the long term in X-band images. By contrast, mountain slopes and barren areas cannot hold high coherence after one cycle of freezing and thawing. The possible factors (vegetation, soil moisture, soil freezing and thawing, and human activity) affecting InSAR coherence are discussed. This study shows that high-resolution time series of TerraSAR-X coherence can be useful for understanding QTP environments and for other applications. Full article