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Remote Sens., Volume 10, Issue 5 (May 2018)

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Editorial

Jump to: Research, Review, Other

Open AccessEditorial Editorial for Special Issue “Recent Advances in GPR Imaging”
Remote Sens. 2018, 10(5), 676; https://doi.org/10.3390/rs10050676
Received: 24 April 2018 / Revised: 23 April 2018 / Accepted: 25 April 2018 / Published: 26 April 2018
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(This article belongs to the Special Issue Recent Advances in GPR Imaging)
Open AccessEditorial Remote Sensing Big Data: Theory, Methods and Applications
Remote Sens. 2018, 10(5), 711; https://doi.org/10.3390/rs10050711
Received: 2 May 2018 / Revised: 2 May 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
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Abstract
Nowadays, our ability to acquire remote sensing data has been improved to an unprecedented level.[...] Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)

Research

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Open AccessArticle Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure
Remote Sens. 2018, 10(5), 659; https://doi.org/10.3390/rs10050659
Received: 10 March 2018 / Revised: 16 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
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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
[...] Read more.
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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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; https://doi.org/10.3390/rs10050660
Received: 19 March 2018 / Revised: 15 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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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
[...] Read more.
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
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Open AccessArticle Automatic Discovery and Geotagging of Objects from Street View Imagery
Remote Sens. 2018, 10(5), 661; https://doi.org/10.3390/rs10050661
Received: 9 March 2018 / Revised: 9 April 2018 / Accepted: 18 April 2018 / Published: 24 April 2018
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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
[...] Read more.
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
(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)
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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; https://doi.org/10.3390/rs10050663
Received: 23 March 2018 / Revised: 16 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
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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.
[...] Read more.
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
(This article belongs to the Section Remote Sensing Image Processing)
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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; https://doi.org/10.3390/rs10050664
Received: 28 February 2018 / Revised: 29 March 2018 / Accepted: 16 April 2018 / Published: 24 April 2018
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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
[...] Read more.
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 Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand
Remote Sens. 2018, 10(5), 665; https://doi.org/10.3390/rs10050665
Received: 23 March 2018 / Revised: 16 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
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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.
[...] Read more.
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
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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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; https://doi.org/10.3390/rs10050666
Received: 1 February 2018 / Revised: 15 April 2018 / Accepted: 17 April 2018 / Published: 25 April 2018
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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.
[...] Read more.
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 Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation
Remote Sens. 2018, 10(5), 668; https://doi.org/10.3390/rs10050668
Received: 31 March 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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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
[...] Read more.
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
(This article belongs to the Special Issue Remote Sensing of Atmospheric Conditions for Wind Energy Applications)
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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; https://doi.org/10.3390/rs10050669
Received: 20 March 2018 / Revised: 16 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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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
[...] Read more.
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 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; https://doi.org/10.3390/rs10050670
Received: 7 March 2018 / Revised: 9 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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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
[...] Read more.
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
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning
Remote Sens. 2018, 10(5), 671; https://doi.org/10.3390/rs10050671
Received: 19 March 2018 / Revised: 16 April 2018 / Accepted: 24 April 2018 / Published: 25 April 2018
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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
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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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; https://doi.org/10.3390/rs10050672
Received: 12 March 2018 / Revised: 12 April 2018 / Accepted: 23 April 2018 / Published: 25 April 2018
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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
[...] Read more.
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
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle An Assessment of Atmospheric and Meteorological Factors Regulating Red Sea Phytoplankton Growth
Remote Sens. 2018, 10(5), 673; https://doi.org/10.3390/rs10050673
Received: 5 April 2018 / Revised: 20 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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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,
[...] Read more.
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
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle BeiDou System (BDS) Triple-Frequency Ambiguity Resolution without Code Measurements
Remote Sens. 2018, 10(5), 675; https://doi.org/10.3390/rs10050675
Received: 19 March 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 26 April 2018
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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
[...] Read more.
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 AccessArticle Microrelief Associated with Gas Emission Craters: Remote-Sensing and Field-Based Study
Remote Sens. 2018, 10(5), 677; https://doi.org/10.3390/rs10050677
Received: 1 March 2018 / Revised: 19 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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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
[...] Read more.
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
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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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; https://doi.org/10.3390/rs10050678
Received: 23 March 2018 / Revised: 19 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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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
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Dynamic Inversion of Global Surface Microwave Emissivity Using a 1DVAR Approach
Remote Sens. 2018, 10(5), 679; https://doi.org/10.3390/rs10050679
Received: 1 March 2018 / Revised: 15 April 2018 / Accepted: 23 April 2018 / Published: 27 April 2018
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Abstract
A variational inversion scheme is used to extract microwave emissivity spectra from brightness temperatures over a multitude of surface types. The scheme is called the Microwave Integrated Retrieval System and has been implemented operationally since 2007 at NOAA. This study focuses on the
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A variational inversion scheme is used to extract microwave emissivity spectra from brightness temperatures over a multitude of surface types. The scheme is called the Microwave Integrated Retrieval System and has been implemented operationally since 2007 at NOAA. This study focuses on the Advance Microwave Sounding Unit (AMSU)/MHS pair onboard the NOAA-18 platform, but the algorithm is applied routinely to multiple microwave sensors, including the Advanced Technology Microwave Sounder (ATMS) on Suomi-National Polar-orbiting Partnership (SNPP), Special Sensor Microwave Imager/Sounder (SSMI/S) on the Defense Meteorological Satellite Program (DMSP) flight units, as well as to the Global Precipitation Mission (GPM) Microwave Imager (GMI), to name a few. The emissivity spectrum retrieval is entirely based on a physical approach. To optimize the use of information content from the measurements, the emissivity is extracted simultaneously with other parameters impacting the measurements, namely, the vertical profiles of temperature, moisture and cloud, as well as the skin temperature and hydrometeor parameters when rain or ice are present. The final solution is therefore a consistent set of parameters that fit the measured brightness temperatures within the instrument noise level. No ancillary data are needed to perform this dynamic emissivity inversion. By allowing the emissivity to be part of the retrieved state vector, it becomes easy to handle the pixel-to-pixel variation in the emissivity over non-oceanic surfaces. This is particularly important in highly variable surface backgrounds. The retrieved emissivity spectrum by itself is of value (as a wetness index for instance), but it is also post-processed to determine surface geophysical parameters. Among the parameters retrieved from the emissivity using this approach are snow cover, snow water equivalent and effective grain size over snow-covered surfaces, sea-ice concentration and age from ice-covered ocean surfaces and wind speed over ocean surfaces. It could also be used to retrieve soil moisture and vegetation information from land surfaces. Accounting for the surface emissivity in the state vector has the added advantage of allowing an extension of the retrieval of some parameters over non-ocean surfaces. An example shown here relates to extending the total precipitable water over non-ocean surfaces and to a certain extent, the amount of suspended cloud. The study presents the methodology and performance of the emissivity retrieval and highlights a few examples of some of the emissivity-based products. Full article
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Open AccessArticle A Geostatistical Approach for Modeling Soybean Crop Area and Yield Based on Census and Remote Sensing Data
Remote Sens. 2018, 10(5), 680; https://doi.org/10.3390/rs10050680
Received: 16 March 2018 / Revised: 13 April 2018 / Accepted: 21 April 2018 / Published: 27 April 2018
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Abstract
Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop
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Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop detection and yield prediction by linking census data, GIS, remote sensing, and geostatistics. The proposed approach combines Brazilian Institute of Geography and Statistics (IBGE) census data with an eight-day enhanced vegetation index (EVI) time series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor soybean areas and yields in Mato Grosso State, Brazil. In situ data from farms were used to validate the obtained results. Binomial areal kriging was used to generate maps of soybean occurrence over the years, and Gaussian areal kriging was used to predict soybean crop yield census data inside detected soybean areas, which had a downscaling effect on the results. The global accuracy and the Kappa index for the soybean crop detection were 92.1% and 0.84%, respectively. The yield prediction presented 95.09% accuracy considering the standard deviation and probable error. Soybean crop detection and yield monitoring can be improved by this approach. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry
Remote Sens. 2018, 10(5), 681; https://doi.org/10.3390/rs10050681
Received: 1 March 2018 / Revised: 21 April 2018 / Accepted: 24 April 2018 / Published: 27 April 2018
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Abstract
Suspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of
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Suspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of SSCs in calm waters by using the range biases of green surface points of ALB. The near water surface penetrations (NWSPs) of green lasers are calculated on the basis of the green and reference surface points. The range biases (ΔS) are calculated by using the corresponding NWSPs and beam-scanning angles. In situ measured SSCs (C) and range biases (ΔS) are used to establish an empirical CS model at SSC sampling stations. The SSCs in calm waters are retrieved by using the established CS model. The proposed method is applied to a practical ALB measurement performed by Optech Coastal Zone Mapping and Imaging LiDAR. The standard deviations of the SSCs retrieved by the 3D point-cloud method are less than 20 mg/L. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction
Remote Sens. 2018, 10(5), 682; https://doi.org/10.3390/rs10050682
Received: 9 March 2018 / Revised: 8 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
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Abstract
Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method
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Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods. Full article
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Open AccessArticle Effects of Warming Hiatuses on Vegetation Growth in the Northern Hemisphere
Remote Sens. 2018, 10(5), 683; https://doi.org/10.3390/rs10050683
Received: 21 March 2018 / Revised: 16 April 2018 / Accepted: 23 April 2018 / Published: 27 April 2018
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Abstract
There have been hiatuses in global warming since the 1990s, and their potential impacts have attracted extensive attention and discussion. Changes in temperature not only directly affect the greening of vegetation but can also indirectly alter both the growth state and the growth
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There have been hiatuses in global warming since the 1990s, and their potential impacts have attracted extensive attention and discussion. Changes in temperature not only directly affect the greening of vegetation but can also indirectly alter both the growth state and the growth tendency of vegetation by altering other climatic elements. The middle-high latitudes of the Northern Hemisphere (NH) constitute the region that has experienced the most warming in recent decades; therefore, identifying the effects of warming hiatuses on the vegetation greening in that region is of great importance. Using satellite-derived Normalized Difference Vegetation Index (NDVI) data and climatological observation data from 1982–2013, we investigated hiatuses in warming trends and their impact on vegetation greenness in the NH. Our results show that the regions with warming hiatuses in the NH accounted for 50.1% of the total area and were concentrated in Mongolia, central China, and other areas. Among these regions, 18.8% of the vegetation greenness was inhibited in the warming hiatus areas, but 31.3% of the vegetation grew faster. Because temperature was the main positive climatic factor in central China, the warming hiatuses caused the slow vegetation greening rate. However, precipitation was the main positive climatic factor affecting vegetation greenness in Mongolia; an increase in precipitation accelerated vegetation greening. The regions without a warming hiatus, which were mainly distributed in northern Russia, northern central Asia, and other areas, accounted for 49.9% of the total area. Among these regions, 21.4% of the vegetation grew faster over time, but 28.5% of the vegetation was inhibited. Temperature was the main positive factor affecting vegetation greenness in northern Russia; an increase in temperature promoted vegetation greening. However, radiation was the main positive climatic factor in northern central Asia; reductions in radiation inhibited the greenness of vegetation. Our findings suggest that warming hiatuses differentially affect vegetation greening and depend on meteorological factors, especially the main meteorological factors. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Small Magnitude Co-Seismic Deformation of the 2017 Mw 6.4 Nyingchi Earthquake Revealed by InSAR Measurements with Atmospheric Correction
Remote Sens. 2018, 10(5), 684; https://doi.org/10.3390/rs10050684
Received: 5 April 2018 / Revised: 24 April 2018 / Accepted: 26 April 2018 / Published: 28 April 2018
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Abstract
The Nyingchi Mw 6.4 earthquake on 17 November 2017 is the first large event since 1950 at the southeast end of the Jiali fault. This event was captured by interferometric synthetic aperture radar (InSAR) measurements from the European Space Agency (ESA) Sentinel-1A radar
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The Nyingchi Mw 6.4 earthquake on 17 November 2017 is the first large event since 1950 at the southeast end of the Jiali fault. This event was captured by interferometric synthetic aperture radar (InSAR) measurements from the European Space Agency (ESA) Sentinel-1A radar satellite, which provide the potential to determine the fault plane, as well as the co-seismic slip distribution, and understand future seismic hazards. However, due to the limited magnitude of surface displacements and the strong topography variations, InSAR-derived co-seismic signals are contaminated by strong tropospheric effects which makes it difficult (if not impossible) to determine the source parameters and co-seismic slip distribution. In this paper, we employ the Generic Atmospheric Correction Online Service for InSAR (GACOS) to generate correction maps for the co-seismic interferograms, and successfully extract co-seismic surface displacements for this large event. The phase standard deviation after correction for a seriously-contaminated interferogram reaches 0.8 cm, significantly improved from the traditional phase correlation analysis (1.13 cm) or bilinear interpolation (1.28 cm) methods. Our best model suggests that the seismogenic fault is a NW–SE striking back-thrust fault with a right-lateral strike slip component. This reflects the strain partitioning of NE shortening and eastward movement of the Eastern Tibetan plateau due to the oblique convergence between the Indian and Eurasian plates. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System
Remote Sens. 2018, 10(5), 685; https://doi.org/10.3390/rs10050685
Received: 26 March 2018 / Revised: 18 April 2018 / Accepted: 26 April 2018 / Published: 28 April 2018
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Abstract
Recently, deep learning-based methods have drawn increasing attention in hyperspectral imagery (HSI) classification, due to their strong nonlinear mapping capability. However, these methods suffer from a time-consuming training process because of many network parameters. In this paper, the concept of broad learning is
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Recently, deep learning-based methods have drawn increasing attention in hyperspectral imagery (HSI) classification, due to their strong nonlinear mapping capability. However, these methods suffer from a time-consuming training process because of many network parameters. In this paper, the concept of broad learning is introduced into HSI classification. Firstly, to make full use of abundant spectral and spatial information of hyperspectral imagery, hierarchical guidance filtering is performing on the original HSI to get its spectral-spatial representation. Then, the class-probability structure is incorporated into the broad learning model to obtain a semi-supervised broad learning version, so that limited labeled samples and many unlabeled samples can be utilized simultaneously. Finally, the connecting weights of broad structure can be easily computed through the ridge regression approximation. Experimental results on three popular hyperspectral imagery datasets demonstrate that the proposed method can achieve better performance than deep learning-based methods and conventional classifiers. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data
Remote Sens. 2018, 10(5), 686; https://doi.org/10.3390/rs10050686
Received: 12 December 2017 / Revised: 7 March 2018 / Accepted: 25 April 2018 / Published: 28 April 2018
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Abstract
Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored
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Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored or simplified because of their complexity. Therefore, we develop a voxel-based method to add leaves to a reconstructed 3D branches structure based on TLS point clouds. The location and size of each leaf depend on the spatial distribution and density of leaves points in the voxel. We reconstruct a small 3D scene with four realistic 3D trees and a virtual tree (including trunk, branches, and leaves), and validate the structure of each tree through the directional gap fractions calculated based on simulated point clouds of this reconstructed scene by the ray-tracing algorithm. The results show good coherence with those from measured point clouds data. The relative errors of the directional gap fractions are no more than 4.1%, though the method is limited by the effects of point occlusion. Therefore, this method is shown to give satisfactory consistency both visually and in the quantitative evaluation of the 3D structure. Full article
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Open AccessArticle Estimating Peatland Water Table Depth and Net Ecosystem Exchange: A Comparison between Satellite and Airborne Imagery
Remote Sens. 2018, 10(5), 687; https://doi.org/10.3390/rs10050687
Received: 23 February 2018 / Revised: 24 April 2018 / Accepted: 24 April 2018 / Published: 29 April 2018
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Abstract
Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study,
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Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, we estimated water table depth from a 6-year time sequence of airborne shortwave infrared (SWIR) hyperspectral imagery. We found that the narrowband index NDWI1240 is a strong predictor of water table position. However, we illustrate the importance of considering peatland anisotropy on SWIR imagery from the summer months when the vascular plants are in full foliage, as not all illumination conditions are suitable for retrieving water table position. We also model net ecosystem exchange (NEE) from 10 years of Landsat TM5 imagery and from 4 years of Landsat OLI 8 imagery. Our results show the transferability of the model between imagery from sensors with similar spectral and radiometric properties such as Landsat 8 and Sentinel-2. NEE modeled from airborne hyperspectral imagery more closely correlated to eddy covariance tower measurements than did models based on satellite images. With fine spectral, spatial and radiometric resolutions, new generation satellite imagery and airborne hyperspectral imagery allow for monitoring the response of peatlands to both allogenic and autogenic factors. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle Long-Term Changes in Colored Dissolved Organic Matter from Satellite Observations in the Bohai Sea and North Yellow Sea
Remote Sens. 2018, 10(5), 688; https://doi.org/10.3390/rs10050688
Received: 15 March 2018 / Revised: 24 April 2018 / Accepted: 26 April 2018 / Published: 29 April 2018
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Abstract
Spatial and temporal variations in colored dissolved organic matter (CDOM) are of great importance to understanding the dynamics of the biogeochemical properties of water bodies. This study proposed a remote sensing approach for estimating CDOM concentrations (CCDOM) based on in
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Spatial and temporal variations in colored dissolved organic matter (CDOM) are of great importance to understanding the dynamics of the biogeochemical properties of water bodies. This study proposed a remote sensing approach for estimating CDOM concentrations (CCDOM) based on in situ observations from the Bohai Sea (BS) and the North Yellow Sea (NYS). Cross-validation demonstrated that the accuracy of the CDOM algorithm is R2 = 0.78, APD = 15.9%, RMSE = 0.92 (ppb). The CDOM algorithm was applied to estimate the 14-year (2003–2016) sea surface CCDOM in the BS and NYS areas using Moderate Resolution Imaging Spectroradiometer (MODIS) monthly products. The results showed a significant fluctuation in CDOM variations on a long-term scale. The highest values of CDOM were observed in the BS, the middle values were observed in the Bohai Strait, and the lowest values were observed in the NYS. Seasonal variations were observed with long-lasting high CDOM values from June to August in coastal waters, while relatively low values were observed in the NYS in the summer. In the spring and fall, a distinct increase appeared in the NYS. High CDOM values in the nearshore coastal waters were mostly related to terrestrial inputs, while CDOM in the offshore regions was mainly due to autochthonous production. Furthermore, ocean currents played an important role in the variations in CDOM in the BS and NYS areas, especially for variations in CDOM in the Bohai Strait. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle Rohingya Refugee Crisis and Forest Cover Change in Teknaf, Bangladesh
Remote Sens. 2018, 10(5), 689; https://doi.org/10.3390/rs10050689
Received: 6 March 2018 / Revised: 25 April 2018 / Accepted: 25 April 2018 / Published: 30 April 2018
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Abstract
Following a targeted campaign of violence by Myanmar military, police, and local militias, more than half a million Rohingya refugees have fled to neighboring Bangladesh since August 2017, joining thousands of others living in overcrowded settlement camps in Teknaf. To accommodate this mass
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Following a targeted campaign of violence by Myanmar military, police, and local militias, more than half a million Rohingya refugees have fled to neighboring Bangladesh since August 2017, joining thousands of others living in overcrowded settlement camps in Teknaf. To accommodate this mass influx of refugees, forestland is razed to build spontaneous settlements, resulting in an enormous threat to wildlife habitats, biodiversity, and entire ecosystems in the region. Although reports indicate that this rapid and vast expansion of refugee camps in Teknaf is causing large scale environmental destruction and degradation of forestlands, no study to date has quantified the camp expansion extent or forest cover loss. Using remotely sensed Sentinel-2A and -2B imagery and a random forest (RF) machine learning algorithm with ground observation data, we quantified the territorial expansion of refugee settlements and resulting degradation of the ecological resources surrounding the three largest concentrations of refugee camps—Kutupalong–Balukhali, Nayapara–Leda and Unchiprang—that developed between pre- and post-August of 2017. Employing RF as an image classification approach for this study with a cross-validation technique, we obtained a high overall classification accuracy of 94.53% and 95.14% for 2016 and 2017 land cover maps, respectively, with overall Kappa statistics of 0.93 and 0.94. The producer and user accuracy for forest cover ranged between 92.98–98.21% and 96.49–92.98%, respectively. Results derived from the thematic maps indicate a substantial expansion of refugee settlements in the three refugee camp study sites, with an increase of 175 to 1530 hectares between 2016 and 2017, and a net growth rate of 774%. The greatest camp expansion is observed in the Kutupalong–Balukhali site, growing from 146 ha to 1365 ha with a net increase of 1219 ha (total growth rate of 835%) in the same time period. While the refugee camps’ occupancy expanded at a rapid rate, this gain mostly occurred by replacing the forested land, degrading the forest cover surrounding the three camps by 2283 ha. Such rapid degradation of forested land has already triggered ecological problems and disturbed wildlife habitats in the area since many of these makeshift resettlement camps were set up in or near corridors for wild elephants, causing the death of several Rohingyas by elephant trampling. Hence, the findings of this study may motivate the Bangladesh government and international humanitarian organizations to develop better plans to protect the ecologically sensitive forested land and wildlife habitats surrounding the refugee camps, enable more informed management of the settlements, and assist in more sustainable resource mobilization for the Rohingya refugees. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Open AccessArticle Coastal Upwelling Front Detection off Central Chile (36.5–37°S) and Spatio-Temporal Variability of Frontal Characteristics
Remote Sens. 2018, 10(5), 690; https://doi.org/10.3390/rs10050690
Received: 13 March 2018 / Revised: 21 April 2018 / Accepted: 25 April 2018 / Published: 30 April 2018
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Abstract
In Eastern Boundary Upwelling Systems, cold coastal waters are separated from offshore by a strong cross-shore Sea Surface Temperature (SST) gradient zone. This upwelling front plays a major role for the coastal ecosystem. This paper proposes a method to automatically identify the front
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In Eastern Boundary Upwelling Systems, cold coastal waters are separated from offshore by a strong cross-shore Sea Surface Temperature (SST) gradient zone. This upwelling front plays a major role for the coastal ecosystem. This paper proposes a method to automatically identify the front and define its main characteristics (position, width, and intensity) from high resolution data. The spatio-temporal variability of the front characteristics is then analyzed in a region off Central Chile (37°S), from 2003 to 2016. The front is defined on daily 1 km-resolution SST maps by isotherm T0 with T0 computed from mean SST with respect to the distance from the coast. The probability of detecting a front, as well as the front width and intensity are driven by coastal wind conditions and increased over the 2007–2016 period compared to the 2003–2006 period. The front position, highly variable, is related to the coastal jet configuration and does not depend on the atmospheric forcing. This study shows an increase by 14% in the probability of detecting a front and also an intensification by 17% of the cross-front SST difference over the last 14 years. No trend was found in the front position. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessFeature PaperArticle Rainfall Variability, Wetland Persistence, and Water–Carbon Cycle Coupling in the Upper Zambezi River Basin in Southern Africa
Remote Sens. 2018, 10(5), 692; https://doi.org/10.3390/rs10050692
Received: 3 April 2018 / Revised: 25 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
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Abstract
The Upper Zambezi River Basin (UZRB) delineates a complex region of topographic, soil and rainfall gradients between the Congo rainforest and the Kalahari Desert. Satellite imagery shows permanent wetlands in low-lying convergence zones where surface–groundwater interactions are vigorous. A dynamic wetland classification based
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The Upper Zambezi River Basin (UZRB) delineates a complex region of topographic, soil and rainfall gradients between the Congo rainforest and the Kalahari Desert. Satellite imagery shows permanent wetlands in low-lying convergence zones where surface–groundwater interactions are vigorous. A dynamic wetland classification based on MODIS Nadir BRDF-Adjusted Reflectance is developed to capture the inter-annual and seasonal changes in areal extent due to groundwater redistribution and rainfall variability. Simulations of the coupled water–carbon cycles of seasonal wetlands show nearly double rates of carbon uptake as compared to dry areas, at increasingly lower water-use efficiencies as the dry season progresses. Thus, wetland extent and persistence into the dry season is key to the UZRB’s carbon sink and water budget. Whereas groundwater recharge governs the expansion of wetlands in the rainy season under large-scale forcing, wetland persistence in April–June (wet–dry transition months) is tied to daily morning fog and clouds, and by afternoon land–atmosphere interactions (isolated convection). Rainfall suppression in July–September results from colder temperatures, weaker regional circulations, and reduced instability in the lower troposphere, shutting off moisture recycling in the dry season despite high evapotranspiration rates. The co-organization of precipitation and wetlands reflects land–atmosphere interactions that determine wetland seasonal persistence, and the coupled water and carbon cycles. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Impact of Changes in Minimum Reflectance on Cloud Discrimination
Remote Sens. 2018, 10(5), 693; https://doi.org/10.3390/rs10050693
Received: 20 March 2018 / Revised: 26 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
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Abstract
Greenhouse Gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two Earth-observing instruments: the Thermal and Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer 2 (TANSO-FTS-2) and TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 can be
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Greenhouse Gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two Earth-observing instruments: the Thermal and Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer 2 (TANSO-FTS-2) and TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 can be used to perform cloud discrimination in each band. The cloud discrimination algorithm uses minimum reflectance (Rmin) for comparisons with observed top-of-atmosphere reflectance. The creation of cloud-free Rmin requires 10 CAI or CAI-2 data. Thus, Rmin is created from CAI L1B data for a 30-day period in GOSAT, with a revisit time of 3 days. It is necessary to change the way in which 10 observations are chosen for GOSAT-2, which has a revisit time of 6 days. Additionally, Rmin processing for GOSAT CAI data was updated to version 02.00 in December 2016. Along with this change, the resolution of Rmin changed from 1/30° to 500 m. We examined the impact of changes in Rmin on cloud discrimination results using GOSAT CAI data. In particular, we performed comparisons of: (1) Rmin calculated using different methods to choose the 10 observations and (2) Rmin calculated using different generation procedures and spatial resolutions. The results were as follows: (1) The impact of using different methods to choose the 10 observations on cloud discrimination results was small, except for a few cases, e.g., snow-covered regions and sun-glint regions; (2) Cloud discrimination results using Rmin in version 02.00 were better than results obtained using Rmin in the previous version, apart from some special situations. The main causes of this were as follows: (1) The change of used band from band 2 to band 1 for Rmin calculation; (2) The change of spatial resolution of Rmin from 1/30° to 500-m. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour
Remote Sens. 2018, 10(5), 695; https://doi.org/10.3390/rs10050695
Received: 15 March 2018 / Revised: 20 April 2018 / Accepted: 27 April 2018 / Published: 2 May 2018
PDF Full-text (3380 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is
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Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle The Characteristics of the Aerosol Optical Depth within the Lowest Aerosol Layer over the Tibetan Plateau from 2007 to 2014
Remote Sens. 2018, 10(5), 696; https://doi.org/10.3390/rs10050696
Received: 12 April 2018 / Revised: 27 April 2018 / Accepted: 28 April 2018 / Published: 2 May 2018
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Abstract
The characteristics of aerosol optical depth (AOD) over the Tibetan Plateau (TP) were analyzed using 8-year (from January 2007 to December 2014) Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) level 2 aerosol layer products. Firstly, the overall feature of AOD over the
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The characteristics of aerosol optical depth (AOD) over the Tibetan Plateau (TP) were analyzed using 8-year (from January 2007 to December 2014) Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) level 2 aerosol layer products. Firstly, the overall feature of AOD over the Tibetan Plateau was investigated, including the seasonal diversities of AODS (the sum of AODs from all aerosol layers), and A (the amounts of aerosol layers). Then we deeply studied the characteristics of AOD within the lowest aerosol layer over TP, including the seasonal variations of AOD1 (The AOD of the first aerosol layer), HB1 (the height of the first aerosol layer base), TL1 (the thickness of the first aerosol layer) and PAOD1 (The AOD proportion of the first aerosol layer). The AODS was generally low (<0.2) in the main body of TP in each season. The value of A was lower (~1–1.5) than other areas around the TP, indicating that the main body of TP generally had only one aerosol layer. The HTT (height of the highest aerosol layer top) was higher in spring (~8 km) and summer (~9 km), and lower in fall (~6.5 km) and winter (~6.5 km). The PAOD1 was high in each season except spring. The high PAOD1 values (>0.9) indicated that the aerosols were mainly concentrated in the lowest layer in summer, fall, and winter in the main body of TP. In spring, the PAOD1 value was relatively low (~0.7–0.85) and the distribution exhibited obvious differences between the southern (~0.85) and the northern (~0.75) TP, which appeared to be consistent with A. Most of the aerosol loads in summer were concentrated in the lowest aerosol layer with high aerosol loads. Most of the aerosol loads in fall and winter were also concentrated in the lowest aerosol layer, but with low aerosol loads. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessCommunication Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II
Remote Sens. 2018, 10(5), 697; https://doi.org/10.3390/rs10050697
Received: 21 March 2018 / Revised: 22 April 2018 / Accepted: 27 April 2018 / Published: 2 May 2018
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Abstract
Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on
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Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 6S model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec® 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II® Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R2, values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Predicting Tropical Tree Species Richness from Normalized Difference Vegetation Index Time Series: The Devil Is Perhaps Not in the Detail
Remote Sens. 2018, 10(5), 698; https://doi.org/10.3390/rs10050698
Received: 4 February 2018 / Revised: 16 April 2018 / Accepted: 25 April 2018 / Published: 3 May 2018
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Abstract
The normalized difference vegetation index (NDVI) derived from remote sensing is a common explanatory variable inputted in correlative biodiversity models in the form of descriptive statistics summarizing complex time series. Here, we hypothesized that a single meaningful remotely-sensed scene can provide better prediction
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The normalized difference vegetation index (NDVI) derived from remote sensing is a common explanatory variable inputted in correlative biodiversity models in the form of descriptive statistics summarizing complex time series. Here, we hypothesized that a single meaningful remotely-sensed scene can provide better prediction of species richness than any usual multi-scene statistics. We tested this idea using a 15-year time series of six-day composite MODIS NDVI data combined with field measurements of tree species richness in the tropical biodiversity hotspot of New Caledonia. Although some overall, seasonal, annual and monthly statistics appeared to successfully correlate with tree species richness in New Caledonia, a range of individual scenes were found to provide significantly better predictions of both the overall tree species richness (|r| = 0.68) and the richness of large trees (|r| = 0.91). A preliminary screening of the NDVI-species richness relationship within each time step can therefore be an effective and straightforward way to maximize the accuracy of NDVI-based correlative biodiversity models. Full article
(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)
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Open AccessArticle AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products
Remote Sens. 2018, 10(5), 699; https://doi.org/10.3390/rs10050699
Received: 10 March 2018 / Revised: 25 April 2018 / Accepted: 27 April 2018 / Published: 3 May 2018
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Abstract
Himawari-8, a next-generation geostationary meteorological satellite, was successfully launched by the Japanese Meteorological Agency (JMA) on 7 October 2014 and has been in official operation since 7 July 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 channels from 0.47 to 13.3
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Himawari-8, a next-generation geostationary meteorological satellite, was successfully launched by the Japanese Meteorological Agency (JMA) on 7 October 2014 and has been in official operation since 7 July 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 channels from 0.47 to 13.3 μm and performs full-disk observations every 10 min. This study describes AHI aerosol optical property (AOP) retrieval based on a multi-channel algorithm using three visible and one near-infrared channels (470, 510, 640, and 860 nm). AOPs were retrieved by obtaining the visible surface reflectance using shortwave infrared (SWIR) data along with normalized difference vegetation index shortwave infrared (NDVISWIR) categories and the minimum reflectance method (MRM). Estimated surface reflectance from SWIR (ESR) tends to be overestimated in urban and cropland areas. Thus, the visible surface reflectance was improved by considering urbanization effects. Ocean surface reflectance is obtained using MRM, while it is from the Cox and Munk method in ESR with the consideration of chlorophyll-a concentration. Based on validation with ground-based sun-photometer measurements from Aerosol Robotic Network (AERONET) data, the error pattern tends to the opposition between MRMver (using MRM reflectance) AOD and ESRver (Using ESR reflectance) AOD over land. To estimate optimal AOD products, two methods were used to merge the data. The final aerosol products and the two surface reflectances were merged, which resulted in higher accuracy AOD values than those retrieved by either individual method. All four AODs shown in this study show accurate diurnal variation compared with AERONET, but the optimum AOD changes depending on observation time. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Coastal Improvements for Tide Models: The Impact of ALES Retracker
Remote Sens. 2018, 10(5), 700; https://doi.org/10.3390/rs10050700
Received: 9 April 2018 / Revised: 26 April 2018 / Accepted: 3 May 2018 / Published: 3 May 2018
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Abstract
Since the launch of the first altimetry satellites, ocean tide models have been improved dramatically for deep and shallow waters. However, issues are still found for areas of great interest for climate change investigations: the coastal regions. The purpose of this study is
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Since the launch of the first altimetry satellites, ocean tide models have been improved dramatically for deep and shallow waters. However, issues are still found for areas of great interest for climate change investigations: the coastal regions. The purpose of this study is to analyze the influence of the ALES coastal retracker on tide modeling in these regions with respect to a standard open ocean retracker. The approach used to compute the tidal constituents is an updated and along-track version of the Empirical Ocean Tide model developed at DGFI-TUM. The major constituents are derived from a least-square harmonic analysis of sea level residuals based on the FES2014 tide model. The results obtained with ALES are compared with the ones estimated with the standard product. A lower fitting error is found for the ALES solution, especially for distances closer than 20 km from the coast. In comparison with in situ data, the root mean squared error computed with ALES can reach an improvement larger than 2 cm at single locations, with an average impact of over 10% for tidal constituents K 2 , O 1 , and P 1 . For Q 1 , the improvement is over 25%. It was observed that improvements to the root-sum squares are larger for distances closer than 10 km to the coast, independently on the sea state. Finally, the performance of the solutions changes according to the satellite’s flight direction: for tracks approaching land from open ocean root mean square differences larger than 1 cm are found in comparison to tracks going from land to ocean. Full article
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
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Open AccessArticle The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data
Remote Sens. 2018, 10(5), 701; https://doi.org/10.3390/rs10050701
Received: 5 March 2018 / Revised: 14 April 2018 / Accepted: 25 April 2018 / Published: 3 May 2018
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Abstract
An airborne Ku-band frequency-modulated continuous waveform (FM-CW) profiling radar, Tomoradar, records the backscatter signal from the canopy surface and the underlying ground in the southern boreal forest zone of Finland. The recorded waveforms are transformed into canopy height profiles (CHP) with a similar
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An airborne Ku-band frequency-modulated continuous waveform (FM-CW) profiling radar, Tomoradar, records the backscatter signal from the canopy surface and the underlying ground in the southern boreal forest zone of Finland. The recorded waveforms are transformed into canopy height profiles (CHP) with a similar methodology utilized in large-footprint light detection and ranging (LiDAR). The point cloud data simultaneously collected by a Velodyne® VLP-16 LiDAR on-board the same platform represent the frequency of discrete returns, which are also applied to the extraction of the CHP by calculating the gap probability and incremental distribution. To thoroughly explore the relationships of the CHP derived from Tomoradar waveforms and LiDAR data we utilized the effective waveforms of one-stripe field measurements and comparison them with four indicators, including the correlation coefficient, the root-mean-square error (RMSE) of the difference, and the coefficient of determination and the RMSE of residuals of linear regression. By setting the Tomoradar footprint as 20 degrees to contain over 95% of the transmitting energy of the main lobe, the results show that 88.17% of the CHPs derived from Tomoradar waveforms correlated well with those from the LiDAR data; 98% of the RMSEs of the difference ranged between 0.002 and 0.01; 79.89% of the coefficients of determination were larger than 0.5; and 98.89% of the RMSEs of the residuals ranged from 0.001 to 0.01. Based on the investigations, we discovered that the locations of the greatest CHP derived from the Tomoradar were obviously deeper than those from the LiDAR, which indicated that the Tomoradar microwave signal had a stronger penetration capability than the LiDAR signal. Meanwhile, there are smaller differences (the average RMSEs of differences is only 0.0042 when the total canopy closure is less than 0.5) and better linear regression results in an area with a relatively open canopy than with a denser canopy. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Hydrological Regime Monitoring and Mapping of the Zhalong Wetland through Integrating Time Series Radarsat-2 and Landsat Imagery
Remote Sens. 2018, 10(5), 702; https://doi.org/10.3390/rs10050702
Received: 2 April 2018 / Revised: 24 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
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Abstract
Zhalong wetland is a globally important breeding habitat for many rare migratory bird species. Prompted by the high demand for temporal and spatial information about the wetland’s hydrological regimes and landscape patterns, eight time series Radarsat-2 images were utilized to detect the flooding
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Zhalong wetland is a globally important breeding habitat for many rare migratory bird species. Prompted by the high demand for temporal and spatial information about the wetland’s hydrological regimes and landscape patterns, eight time series Radarsat-2 images were utilized to detect the flooding characteristics of the Zhalong wetland. Subsequently, a random forest model was built to discriminate wetlands from other land cover types, combining with optical, radar, and hydrological regime data derived from multitemporal synthetic aperture radar (SAR) images. The results showed that hydrological regimes variables, including flooding extent and flooding frequency, derived from multitemporal SAR images, improve the land cover classification accuracy in the natural wetlands distribution area. The permutation importance scores derived from the random forest classifier indicate that normalized difference vegetation index (NDVI) calculated from optical imagery and the flooding frequency derived from multitemporal SAR imagery were found to be the most important variables for land cover mapping. Accuracy testing indicate that the addition of hydrological regime features effectively depressed the omission error rates (from 52.14% to 2.88%) of marsh and the commission error (from 77.34% to 51.27%) of meadow, thereby improving the overall classification accuracy (from 76.49% to 91.73%). The hydrological regimes and land cover monitoring in the typical wetlands are important for eco-hydrological modeling, biodiversity conservation, and regional ecology and water security. Full article
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Open AccessArticle A Comparison of ECV and SMOS Soil Moisture Products Based on OzNet Monitoring Network
Remote Sens. 2018, 10(5), 703; https://doi.org/10.3390/rs10050703
Received: 8 April 2018 / Revised: 26 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
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Abstract
Soil moisture is an essential variable in many hydrological and meteorological models. Spatially continuous soil moisture datasets are important for understanding water cycle and climate change. Currently, satellite-based microwave sensors have been the main resources for obtaining global soil moisture data. This paper
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Soil moisture is an essential variable in many hydrological and meteorological models. Spatially continuous soil moisture datasets are important for understanding water cycle and climate change. Currently, satellite-based microwave sensors have been the main resources for obtaining global soil moisture data. This paper evaluates the performance of different soil moisture products from the combined Essential Climate Variable (ECV) and Soil Moisture and Ocean Salinity (SMOS) satellite against the stations within the OzNet soil moisture networks over southeastern Australia. SMOS soil moisture products obtained from two versions (ascending and descending) were included. The evaluations were carried out at both network and site scales. According to the validation results, the ECV products outperformed the SMOS products at both scales. Comparing the two versions of the SMOS products, the SMOS ascending product generally performed better than the SMOS descending product and obtained comparable accuracy to the ECV product at Kyeamba and Yanco sites. However, the SMOS ascending performed poorly at the Adelong sites. Moreover, the ECV product has less data gaps than the SMOS products, because the ECV products were developed by combining passive and active microwave products. Consequently, the results in this study show that the combined ECV product is recommended, as both accuracy and integrity of the soil moisture product are important. The SMOS ascending product is recommended between the two overpass versions of SMOS products. Full article
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Open AccessArticle Compressive Sound Speed Profile Inversion Using Beamforming Results
Remote Sens. 2018, 10(5), 704; https://doi.org/10.3390/rs10050704
Received: 9 April 2018 / Revised: 24 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
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Abstract
Sound speed profile (SSP) significantly affects acoustic propagation in the ocean. In this work, the SSP is inverted using compressive sensing (CS) combined with beamforming to indicate the direction of arrivals (DOAs). The travel times and the positions of the arrivals can be
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Sound speed profile (SSP) significantly affects acoustic propagation in the ocean. In this work, the SSP is inverted using compressive sensing (CS) combined with beamforming to indicate the direction of arrivals (DOAs). The travel times and the positions of the arrivals can be approximately linearized using their Taylor expansion with the shape function coefficients that parameterize the SSP. The linear relation between the travel times/positions and the shape function coefficients enables CS to reconstruct the SSP. The conventional objective function in CS is modified to simultaneously exploit the information from the travel times and positions. The SSP is estimated using CS with beamforming of ray arrivals in the SWellEx-96 experimental environment, and the performance is evaluated using the correlation coefficient and mean squared error (MSE) between the true and recovered SSPs, respectively. Five hundred synthetic SSPs were generated by randomly choosing the SSP dictionary components, and more than 80 percent of all the cases have correlation coefficients over 0.7 and MSE along depth is insignificant except near the sea surface, which shows the validity of the proposed method. Full article
(This article belongs to the Special Issue Advances in Undersea Remote Sensing)
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Open AccessArticle Global Ionosphere Mapping and Differential Code Bias Estimation during Low and High Solar Activity Periods with GIMAS Software
Remote Sens. 2018, 10(5), 705; https://doi.org/10.3390/rs10050705
Received: 16 March 2018 / Revised: 23 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
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Abstract
Ionosphere research using the Global Navigation Satellite Systems (GNSS) techniques is a hot topic, with their unprecedented high temporal and spatial sampling rate. We introduced a new GNSS Ionosphere Monitoring and Analysis Software (GIMAS) in order to model the global ionosphere vertical total
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Ionosphere research using the Global Navigation Satellite Systems (GNSS) techniques is a hot topic, with their unprecedented high temporal and spatial sampling rate. We introduced a new GNSS Ionosphere Monitoring and Analysis Software (GIMAS) in order to model the global ionosphere vertical total electron content (VTEC) maps and to estimate the GPS and GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) satellite and receiver differential code biases (DCBs). The GIMAS-based Global Ionosphere Map (GIM) products during low (day of year from 202 to 231, in 2008) and high (day of year from 050 to 079, in 2014) solar activity periods were investigated and assessed. The results showed that the biases of the GIMAS-based VTEC maps relative to the International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) VTEC maps ranged from −3.0 to 1.0 TECU (TEC unit) (1 TECU = 1 × 1016 electrons/m2). The standard deviations (STDs) ranged from 0.7 to 1.9 TECU in 2008, and from 2.0 to 8.0 TECU in 2014. The STDs at a low latitude were significantly larger than those at middle and high latitudes, as a result of the ionospheric latitudinal gradients. When compared with the Jason-2 VTEC measurements, the GIMAS-based VTEC maps showed a negative systematic bias of about −1.8 TECU in 2008, and a positive systematic bias of about +2.2 TECU in 2014. The STDs were about 2.0 TECU in 2008, and ranged from 2.2 to 8.5 TECU in 2014. Furthermore, the aforementioned characteristics were strongly related to the conditions of the ionosphere variation and the geographic latitude. The GPS and GLONASS satellite and receiver P1-P2 DCBs were compared with the IAACs DCBs. The root mean squares (RMSs) were 0.16–0.20 ns in 2008 and 0.13–0.25 ns in 2014 for the GPS satellites and 0.26–0.31 ns in 2014 for the GLONASS satellites. The RMSs of receiver DCBs were 0.21–0.42 ns in 2008 and 0.33–1.47 ns in 2014 for GPS and 0.67–0.96 ns in 2014 for GLONASS. The monthly stability of the GPS satellite DCBs was about 0.04 ns (0.07 ns) in 2008 (2014) and that for the GLONASS satellite DCBs was about 0.09 ns in 2014. The receiver DCBs were less stable than the satellite DCBs, with a mean value of about 0.16 ns (0.47 ns) in 2008 (2014) for GPS, and 0.48 ns in 2014 for GLONASS. It can be demonstrated that the GIMAS software had a high accuracy and reliability for the global ionosphere monitoring and analysis. Full article
(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))
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Open AccessArticle Sewer Inlet Localization in UAV Image Clouds: Improving Performance with Multiview Detection
Remote Sens. 2018, 10(5), 706; https://doi.org/10.3390/rs10050706
Received: 15 March 2018 / Revised: 17 April 2018 / Accepted: 30 April 2018 / Published: 4 May 2018
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Abstract
Sewer and drainage infrastructure are often not as well catalogued as they should be, considering the immense investment they represent. In this work, we present a fully automatic framework for localizing sewer inlets from image clouds captured from an unmanned aerial vehicle (UAV).
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Sewer and drainage infrastructure are often not as well catalogued as they should be, considering the immense investment they represent. In this work, we present a fully automatic framework for localizing sewer inlets from image clouds captured from an unmanned aerial vehicle (UAV). The framework exploits the high image overlap of UAV imaging surveys with a multiview approach to improve detection performance. The framework uses a Viola–Jones classifier trained to detect sewer inlets in aerial images with a ground sampling distance of 3–3.5 cm/pixel. The detections are then projected into three-dimensional space where they are clustered and reclassified to discard false positives. The method is evaluated by cross-validating results from an image cloud of 252 UAV images captured over a 0.57-km2 study area with 228 sewer inlets. Compared to an equivalent single-view detector, the multiview approach improves both recall and precision, increasing average precision from 0.65 to 0.73. The source code and case study data are publicly available for reuse. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data
Remote Sens. 2018, 10(5), 708; https://doi.org/10.3390/rs10050708
Received: 19 March 2018 / Revised: 28 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
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Abstract
How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at
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How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPPEC). Temperature can explain most of the seasonal variation in VIs and GPPEC fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPPVPM) and GPPEC indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPPEC fluxes in terms of patterns and magnitudes, explaining about 85% of GPPEC changes over the study years (p < 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink/source. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle Online Hashing for Scalable Remote Sensing Image Retrieval
Remote Sens. 2018, 10(5), 709; https://doi.org/10.3390/rs10050709
Received: 9 April 2018 / Revised: 27 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
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Abstract
Recently, hashing-based large-scale remote sensing (RS) image retrieval has attracted much attention. Many new hashing algorithms have been developed and successfully applied to fast RS image retrieval tasks. However, there exists an important problem rarely addressed in the research literature of RS image
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Recently, hashing-based large-scale remote sensing (RS) image retrieval has attracted much attention. Many new hashing algorithms have been developed and successfully applied to fast RS image retrieval tasks. However, there exists an important problem rarely addressed in the research literature of RS image hashing. The RS images are practically produced in a streaming manner in many real-world applications, which means the data distribution keeps changing over time. Most existing RS image hashing methods are batch-based models whose hash functions are learned once for all and kept fixed all the time. Therefore, the pre-trained hash functions might not fit the ever-growing new RS images. Moreover, the batch-based models have to load all the training images into memory for model learning, which consumes many computing and memory resources. To address the above deficiencies, we propose a new online hashing method, which learns and adapts its hashing functions with respect to the newly incoming RS images in terms of a novel online partial random learning scheme. Our hash model is updated in a sequential mode such that the representative power of the learned binary codes for RS images are improved accordingly. Moreover, benefiting from the online learning strategy, our proposed hashing approach is quite suitable for scalable real-world remote sensing image retrieval. Extensive experiments on two large-scale RS image databases under online setting demonstrated the efficacy and effectiveness of the proposed method. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function
Remote Sens. 2018, 10(5), 710; https://doi.org/10.3390/rs10050710
Received: 14 March 2018 / Revised: 18 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
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Abstract
Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images.
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Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm. Full article
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Open AccessArticle Improving the Accuracy of Remotely Sensed Irrigated Areas Using Post-Classification Enhancement Through UAV Capability
Remote Sens. 2018, 10(5), 712; https://doi.org/10.3390/rs10050712
Received: 3 April 2018 / Revised: 30 April 2018 / Accepted: 3 May 2018 / Published: 5 May 2018
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Abstract
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing
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Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data
Remote Sens. 2018, 10(5), 713; https://doi.org/10.3390/rs10050713
Received: 15 March 2018 / Revised: 25 April 2018 / Accepted: 4 May 2018 / Published: 5 May 2018
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Abstract
The Poyang Lake, the largest freshwater lake in China, is famous for its ecological and economic importance as well as frequent flood characteristics. In this study, multiple satellite remote sensing observations (e.g., GRACE, MODIS, Altimetry, and TRMM), hydrological models, and in situ data
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The Poyang Lake, the largest freshwater lake in China, is famous for its ecological and economic importance as well as frequent flood characteristics. In this study, multiple satellite remote sensing observations (e.g., GRACE, MODIS, Altimetry, and TRMM), hydrological models, and in situ data are used to characterize the flood phenomena over the Poyang Lake basin between 2003 and 2016. To improve the accuracy of the terrestrial water storage (TWS) estimates over the Poyang Lake basin, a modified forward-modeling method is introduced in the GRACE processing. The method is evaluated using the contaminated noise onboard observations for the first time. The results in both spectral and spatial domains infer a good performance of the method on the suppression of high-frequency noise while reducing the signal loss. After applying forward-modeling method, the TWS derived from the GRACE spherical harmonic coefficients presents a comparable performance with the solution derived from the newly released CSR Release05 mascon product over the Poyang Lake basin. The flood events in 2010 and 2016 are identified from the positive anomalies in non-seasonal TWSs derived by GRACE and hydrological models. The flood signatures also coincide with the largest inundated areas estimated from MODIS data, and the observed areas in 2010 and 2016 are 3370.3 km2 (30% higher than the long-term mean) and 3445.0 km2 (33% higher), respectively. The water levels in the Hukou station exceed the warning water level for 25 days in 2010 and 28 days in 2016. These continuous warning-exceeded water levels also imply the severe flood events, which are primarily driven by the local plenteous precipitation in the rainy season (1528 mm in 2010, 1522 mm in 2016). Full article
(This article belongs to the Special Issue GRACE Facing the Challenge of Extreme Spatial and Temporal Scales)
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Open AccessArticle