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Remote Sens., Volume 11, Issue 4 (February-2 2019)

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Cover Story (view full-size image) As the cost of satellite missions grows, government agencies are working to increase the relevance [...] Read more.
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Open AccessArticle Sub-Nyquist SAR via Quadrature Compressive Sampling with Independent Measurements
Remote Sens. 2019, 11(4), 472; https://doi.org/10.3390/rs11040472
Received: 26 January 2019 / Revised: 18 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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Abstract
This paper presents an efficient sampling system for the acquisition of synthetic aperture radar (SAR) data at sub-Nyquist rate. The system adopts a quadrature compressive sampling architecture, which uses modulation, filtering, sampling and digital quadrature demodulation to produce sub-Nyquist or compressive measurements. In [...] Read more.
This paper presents an efficient sampling system for the acquisition of synthetic aperture radar (SAR) data at sub-Nyquist rate. The system adopts a quadrature compressive sampling architecture, which uses modulation, filtering, sampling and digital quadrature demodulation to produce sub-Nyquist or compressive measurements. In the sequential transmit-receive procedure of SAR, the analog echoes are modulated by random binary chipping sequences to inject randomness into the measurement projection, and the chipping sequences are independent from one observation to another. As a result, the system generates a sequence of independent structured measurement matrices, and then the resulting sensing matrix has better restricted isometry property, as proved by theoretical analysis. As a standard recovery problem in compressive sensing, image formation from the sub-Nyquist measurements has significantly improved performance, which in turn promotes low sampling/data rate. Moreover, the resulting sensing matrix has structures suitable for fast matrix-vector products, based on which we provide a first-order fast image formation algorithm. The performance of the proposed sampling system is assessed by synthetic and real data sets. Simulation results suggest that the proposed system is a valid candidate for sub-Nyquist SAR. Full article
(This article belongs to the Special Issue SAR in Big Data Era)
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Open AccessArticle Photon-Counting Lidar: An Adaptive Signal Detection Method for Different Land Cover Types in Coastal Areas
Remote Sens. 2019, 11(4), 471; https://doi.org/10.3390/rs11040471
Received: 15 December 2018 / Revised: 10 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover [...] Read more.
Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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Open AccessArticle A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences
Remote Sens. 2019, 11(4), 470; https://doi.org/10.3390/rs11040470
Received: 15 January 2019 / Revised: 18 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy [...] Read more.
Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing
Remote Sens. 2019, 11(4), 469; https://doi.org/10.3390/rs11040469
Received: 28 January 2019 / Revised: 15 February 2019 / Accepted: 18 February 2019 / Published: 25 February 2019
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Abstract
Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as [...] Read more.
Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr−1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Spatially Explicit Mapping of Soil Conservation Service in Monetary Units Due to Land Use/Cover Change for the Three Gorges Reservoir Area, China
Remote Sens. 2019, 11(4), 468; https://doi.org/10.3390/rs11040468
Received: 14 February 2019 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
Studies of land use/cover change (LUCC) and its impact on ecosystem service (ES) in monetary units can provide information that governments can use to identify where protection and restoration is economically most important. Translating ES in monetary units into decision making strongly depends [...] Read more.
Studies of land use/cover change (LUCC) and its impact on ecosystem service (ES) in monetary units can provide information that governments can use to identify where protection and restoration is economically most important. Translating ES in monetary units into decision making strongly depends on the availability of spatially explicit information on LUCC and ES. Yet such datasets are unavailable for the Three Gorges Reservoir Area (TGRA) despite its perceived soil conservation service value (SCSV). The availability of remote sensing-based datasets and advanced GIS techniques has enhanced the potential of spatially explicit ES mapping exercises. Here, we first explored LUCC in the TGRA for four time periods (1995–2000, 2000–2005, 2005–2010, and 2010–2015). Then, applying a value transfer method with an equivalent value factor spatialized using the normalized difference vegetation index (NDVI), we estimated the changes of monetary SCSV in response to LUCC in a spatially explicit way. Finally, the sensitivity of SCSV changes in response to LUCC was determined. Major findings: (i) Expansion of construction land and water bodies and contraction of cropland characterized the LUCC in all periods. Their driving factors include the relocation of residents, construction of the Three Gorges Dam, urbanization, and the Grain for Green Program; (ii) The SCSV for TGRA was generally stable for 1995–2015, declining slightly (<1%), suggesting a sustainable human–environment relationship in the TGRA. The SCSV prevails in regions with elevations (slopes) of 400–1600 m (0°–10°); for Chongqing and its surrounding regions it decreased significantly during 1995–2015; (iii) SCSV’s sensitivity index was 1.04, 0.53, 0.92, and 1.25 in the four periods, respectively, which is generally low. Chongqing and its surrounding regions, with their pervasive urbanization and dense populations, had the highest sensitivity. For 1995–2015, 70.63% of the study area underwent increases in this sensitivity index. Our results provide crucial information for policymaking concerning ecological conservation and compensation. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Ecosystem Health)
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Open AccessArticle Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe
Remote Sens. 2019, 11(4), 467; https://doi.org/10.3390/rs11040467
Received: 27 December 2018 / Revised: 11 February 2019 / Accepted: 19 February 2019 / Published: 25 February 2019
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Abstract
Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to [...] Read more.
Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to the 1980s, flying onboard a series of successive National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. However, the NOAA satellites are affected by severe orbital drift that results in spurious trends in time series. We identified the impact and extent of the orbital drift in 1 km AVHRR long-term active fire data. This record contains data of European fire activity from 1985–2016 and was analyzed on a regional scale and extended across Europe. Inconsistent sampling of the diurnal active fire cycle due to orbital drift with a maximum delay of ∼5 h over NOAA-14 lifetime revealed a ∼90% decline in the number of observed fires. However, interregional results were less conclusive and other error sources as well as interannual variability were more pronounced. Solar illumination, measured by the sun zenith angle (SZA), related changes in background temperatures were significant for all regions and afternoon satellites with major changes in −0.03 to −0.09 K deg 1 for B T 34 (p 0 . 001). Based on example scenes, we simulated the influence of changing temperatures related to changes in the SZA on the detection of active fires. These simulations showed a profound influence of the active fire detection capabilities dependent on biome and land cover characteristics. The strong decrease in the relative changes in the apparent number of active fires calculated over the satellites lifetime highlights that a correction of the orbital drift effect is essential even over short time periods. Full article
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Open AccessArticle Estimation of Surface Air Specific Humidity and Air–Sea Latent Heat Flux Using FY-3C Microwave Observations
Remote Sens. 2019, 11(4), 466; https://doi.org/10.3390/rs11040466
Received: 21 January 2019 / Revised: 6 February 2019 / Accepted: 20 February 2019 / Published: 24 February 2019
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Abstract
Latent heat flux (LHF) plays an important role in the global hydrological cycle and is therefore necessary to understand global climate variability. It has been reported that the near-surface specific humidity is a major source of error for satellite-derived LHF. Here, a new [...] Read more.
Latent heat flux (LHF) plays an important role in the global hydrological cycle and is therefore necessary to understand global climate variability. It has been reported that the near-surface specific humidity is a major source of error for satellite-derived LHF. Here, a new empirical model relating multichannel brightness temperatures ( T B ) obtained from the Fengyun-3 (FY-3C) microwave radiometer and sea surface air specific humidity ( Q a ) is proposed. It is based on the relationship between T B , Q a , sea surface temperature (SST), and water vapor scale height. Compared with in situ data, the new satellite-derived Q a and LHF both exhibit better statistical results than previous estimates. For Q a , the bias, root mean square difference (RMSD), and the correlation coefficient (R2) between satellite and buoy in the mid-latitude region are 0.08 g/kg, 1.76 g/kg, and 0.92, respectively. For LHF, the bias, RMSD, and R2 are 2.40 W/m2, 34.24 W/m2, and 0.87, respectively. The satellite-derived Q a are also compared with National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute for Research in Environmental Sciences (CIRES) humidity datasets, with a bias, RMSD, and R2 of 0.02 g/kg, 1.02 g/kg, and 0.98, respectively. The proposed method can also be extended in the future to observations from other space-borne microwave radiometers. Full article
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Open AccessArticle Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands
Remote Sens. 2019, 11(4), 465; https://doi.org/10.3390/rs11040465
Received: 24 January 2019 / Revised: 18 February 2019 / Accepted: 19 February 2019 / Published: 24 February 2019
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Abstract
A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally [...] Read more.
A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICIρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8. Full article
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Open AccessArticle Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting
Remote Sens. 2019, 11(4), 464; https://doi.org/10.3390/rs11040464
Received: 21 January 2019 / Revised: 19 February 2019 / Accepted: 19 February 2019 / Published: 24 February 2019
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Abstract
River terraces are the principal geomorphic features for unraveling tectonics, sea level, and climate conditions during the evolutionary history of a river. The increasing availability of high-resolution topography data generated by low-cost Unmanned Aerial Systems (UAS) and modern photogrammetry offer an opportunity to [...] Read more.
River terraces are the principal geomorphic features for unraveling tectonics, sea level, and climate conditions during the evolutionary history of a river. The increasing availability of high-resolution topography data generated by low-cost Unmanned Aerial Systems (UAS) and modern photogrammetry offer an opportunity to identify and characterize these features. In this paper, we assessed the capabilities of UAS-based Structure-from-Motion (SfM) photogrammetry, coupled with a river terrace detection algorithm for mapping of river terraces over a 1.9 km2 valley of complex terrain setting, with a focus on the performance of this latest technology over such complex terrains. With the proposed image acquisition approach and SfM photogrammetry, we constructed a 3.8 cm resolution orthomosaic and digital surface model (DSM). The vertical accuracy of DSM was assessed against 196 independent checkpoints measured with a real-time kinematic (RTK) GPS. The results indicated that the root mean square error (RMSE) and mean absolute error (MAE) were 3.1 cm and 2.9 cm, respectively. These encouraging results suggest that this low-cost, logistically simple method can deliver high-quality terrain datasets even in the complex terrain, competitive with those obtained using more expensive laser scanning. A simple algorithm was then employed to detect river terraces from the generated DSM. The results showed that three levels of river terraces and a high-level floodplain were identified. Most of the detected river terraces were confirmed by field observations. Despite the highly erosive nature of fluvial systems, this work obtained good results, allowing fast analysis of fluvial valleys and their comparison. Overall, our results demonstrated that the low-cost UAS-based SfM technique could yield highly accurate ultrahigh-resolution topography data over complex terrain settings, making it particularly suitable for quick and cost-effective mapping of micro to medium-sized geomorphic features under such terrains in remote or poorly accessible areas. Methods discussed in this paper can also be applied to produce highly accurate digital terrain data over large spatial extents for some other places of complex terrains. Full article
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Open AccessTechnical Note Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science
Remote Sens. 2019, 11(4), 463; https://doi.org/10.3390/rs11040463
Received: 11 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from [...] Read more.
Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessFeature PaperArticle Source Parameter Estimation of the 2009 Ms6.0 Yao’an Earthquake, Southern China, Using InSAR Observations
Remote Sens. 2019, 11(4), 462; https://doi.org/10.3390/rs11040462
Received: 23 January 2019 / Revised: 17 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
On 9 July 2009, an Ms6.0 earthquake occurred in mountainous area of Yao’an in Yunnan province of Southern China. Although the magnitude of the earthquake was moderate, it attracted the attention of many Earth scientists because of its threat to the safety of [...] Read more.
On 9 July 2009, an Ms6.0 earthquake occurred in mountainous area of Yao’an in Yunnan province of Southern China. Although the magnitude of the earthquake was moderate, it attracted the attention of many Earth scientists because of its threat to the safety of the population and its harm to the local economy. However, the source parameters remain poorly understood due to the sparse distribution of seismic and GNSS (Global Navigation Satellite System) stations in this mountainous region. Therefore, in this study, the two L-band ALOS (Advanced Land Observing Satellite-1) PALSAR (Phased Array type L-band Synthetic Aperture Radar) images from an ascending track is used to investigate the coseismic deformation field, and further determine the location, fault geometry and slip distribution of the earthquake. The results show that the Yao’an earthquake was a strike-slip event with a down-dip slip component. The slip mainly occurred at depths of 3–8 km, with a maximum slip of approximately 70 cm at a depth of 6 km, which is shallower than the reported focal depth of ~10 km. An analysis of the seismic activity and tectonics of the Yao’an area reveals that the 9 July 2009 Yao’an earthquake was the result of regional stress accumulation, which eventually led to the rupture of the northwestern most part of the Maweijing fault. Full article
(This article belongs to the Special Issue Remote Sensing of Tectonic Deformation)
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Open AccessArticle Multi-Frequency, Multi-Sonar Mapping of Shallow Habitats—Efficacy and Management Implications in the National Marine Park of Zakynthos, Greece
Remote Sens. 2019, 11(4), 461; https://doi.org/10.3390/rs11040461
Received: 22 January 2019 / Revised: 15 February 2019 / Accepted: 18 February 2019 / Published: 23 February 2019
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In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the [...] Read more.
In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the protected Mediterranean phanerogams Posidonia oceanica and Cymodocea nodosa, as well as reefs and sandbanks. Seafloor characterization is achieved using the multi-frequency acoustic backscatter of: (a) the two simultaneous frequencies of the SSS (100 and 400 kHz) and (b) the MBES (180 kHz), as well as the MBES bathymetry. Overall, these high-resolution datasets cover an area of 84 km2 with ground coverage varying from 50% to 100%. Image texture, terrain and backscatter angular response analyses are applied to the above, to extract a range of statistical features. Those have different spatial densities and so they are combined through an object-based approach based on the full-coverage 100-kHz SSS mosaic. Supervised classification is applied to data models composed of operationally meaningful combinations between the above features, reflecting single-sonar or multi-sonar mapping scenarios. Classification results are validated against a detailed expert interpretation habitat map making use of extensive ground-truth data. The relative gain of one system or one feature extraction method or another are thoroughly examined. The frequency-dependent separation of benthic habitats showcases the potentials of multi-frequency backscatter and bathymetry from different sonars, improving evidence-based interpretations of shallow benthic habitats. Full article
(This article belongs to the Special Issue Advances in Undersea Remote Sensing)
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Open AccessArticle Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence
Remote Sens. 2019, 11(4), 460; https://doi.org/10.3390/rs11040460
Received: 8 January 2019 / Revised: 14 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM2.5 (i.e., [...] Read more.
A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM2.5 changes. PM2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m−3 m−1 s. This study highlighted the impact of meteorological conditions on PM2.5 growth, although it was much smaller than the anthropogenic emissions impact. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Detecting Square Markers in Underwater Environments
Remote Sens. 2019, 11(4), 459; https://doi.org/10.3390/rs11040459
Received: 16 January 2019 / Revised: 15 February 2019 / Accepted: 18 February 2019 / Published: 23 February 2019
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Abstract
Augmented reality can be deployed in various application domains, such as enhancing human vision, manufacturing, medicine, military, entertainment, and archeology. One of the least explored areas is the underwater environment. The main benefit of augmented reality in these environments is that it can [...] Read more.
Augmented reality can be deployed in various application domains, such as enhancing human vision, manufacturing, medicine, military, entertainment, and archeology. One of the least explored areas is the underwater environment. The main benefit of augmented reality in these environments is that it can help divers navigate to points of interest or present interesting information about archaeological and touristic sites (e.g., ruins of buildings, shipwrecks). However, the harsh sea environment affects computer vision algorithms and complicates the detection of objects, which is essential for augmented reality. This paper presents a new algorithm for the detection of fiducial markers that is tailored to underwater environments. It also proposes a method that generates synthetic images with such markers in these environments. This new detector is compared with existing solutions using synthetic images and images taken in the real world, showing that it performs better than other detectors: it finds more markers than faster algorithms and runs faster than robust algorithms that detect the same amount of markers. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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Open AccessArticle Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands
Remote Sens. 2019, 11(4), 458; https://doi.org/10.3390/rs11040458
Received: 5 February 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies [...] Read more.
Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China
Remote Sens. 2019, 11(4), 457; https://doi.org/10.3390/rs11040457
Received: 8 February 2019 / Revised: 17 February 2019 / Accepted: 19 February 2019 / Published: 22 February 2019
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Abstract
The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor [...] Read more.
The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space
Remote Sens. 2019, 11(4), 456; https://doi.org/10.3390/rs11040456
Received: 1 February 2019 / Revised: 18 February 2019 / Accepted: 18 February 2019 / Published: 22 February 2019
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Abstract
Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing [...] Read more.
Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing reflectance data at the near-infrared (NIR) and red (R) bands. Considering the distribution regularities of soil moisture in this space, we formulated the Ratio Dryness Monitoring Index (RDMI) as a new dryness monitoring indicator. We compared RDMI values with in situ soil moisture content data measured at 0–10 cm depth. Results showed that there was a strong negative correlation (R = −0.89) between the RDMI values and in situ soil moisture content. We further compared RDMI with existing remote sensing dryness indices, and the results demonstrated the advantages of the RDMI. We applied the RDMI to the Landsat-8 imagery to map dryness distribution around the Fukang area on the Northern slope of the Tianshan Mountains, and to the MODIS imagery to detect the spatial and temporal changes in dryness for the entire Xinjiang in 2013 and 2014. Overall, the RDMI index constructed, based on the NIR–Red spectral space, is simple to calculate, easy to understand, and can be applied to dryness monitoring at different scales. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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Open AccessArticle Crop Classification Based on a Novel Feature Filtering and Enhancement Method
Remote Sens. 2019, 11(4), 455; https://doi.org/10.3390/rs11040455
Received: 6 January 2019 / Revised: 19 February 2019 / Accepted: 19 February 2019 / Published: 22 February 2019
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Abstract
Vegetation indices, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from remote sensing images, are widely used for crop classification. However, vegetation index profiles for different crops with a similar phenology lead to difficulties in discerning these [...] Read more.
Vegetation indices, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from remote sensing images, are widely used for crop classification. However, vegetation index profiles for different crops with a similar phenology lead to difficulties in discerning these crops both spectrally and temporally. This paper proposes a feature filtering and enhancement (FFE) method to map soybean and maize, two major crops widely cultivated during the summer season in Northeastern China. Different vegetation indices are first calculated and the probability density functions (PDFs) of these indices for the target classes are established based on the hypothesis of normal distribution; the vegetation index images are then filtered using the PDFs to obtain enhanced index images where the pixel values of the target classes are ”enhanced”. Subsequently, the minimum Gini index of each enhanced index image is computed, generating at the same time the weight for every index. A composite enhanced feature image is produced by summing all indices with their weights. Finally, a classification is made from the composite enhanced feature image by thresholding, which is derived automatically based on the samples. The efficiency of the proposed FFE method is compared with the maximum likelihood classification (MLC), support vector machine (SVM), and random forest (RF) in a mapping operation to determine the soybean and maize distribution in a county in Northeastern China. The classification accuracies resulting from this comparison show that the FFE method outperforms MLC, and its accuracies are similar to those of SVM and RF, with an overall accuracy of 0.902 and a kappa coefficient of 0.846. This indicates that the FFE method is an appropriate method for crop classification to distinguish crops with a similar phenology. Our research also shows that when the sample size reaches a certain level (e.g., 2000), the mean and standard deviation of the sample are very close to the actual values, which leads to high classification accuracy. In a case where the condition of normal distribution is not fulfilled, the PDF of the vegetation index can be created by a lookup table. Furthermore, as the method is rather simple and explicit, and convenient in terms of computing, it can be used as the backbone for automatic crop mapping operations. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)
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Open AccessArticle Multi-GNSS Relative Positioning with Fixed Inter-System Ambiguity
Remote Sens. 2019, 11(4), 454; https://doi.org/10.3390/rs11040454
Received: 14 January 2019 / Revised: 9 February 2019 / Accepted: 16 February 2019 / Published: 22 February 2019
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Abstract
In multi-GNSS cases, two types of Double Difference (DD) ambiguity could be formed including an intra-system ambiguity and an inter-system ambiguity, which are identified as the DD ambiguity between satellites from the same and from different GNSS systems, respectively. We studied the relative [...] Read more.
In multi-GNSS cases, two types of Double Difference (DD) ambiguity could be formed including an intra-system ambiguity and an inter-system ambiguity, which are identified as the DD ambiguity between satellites from the same and from different GNSS systems, respectively. We studied the relative positioning methods using intra-system DD observations and using Un-Difference (UD) observations, and developed a frequency-free approach for fixing inter-system ambiguity based on UD observations for multi-GNSS positioning, where the inter-system phase bias is calculated with the help of a fixed Single-Difference (SD) ambiguity. The consistency between the receiver-end uncalibrated phase delays (RUPD) and the SD ambiguity were investigated and the positioning performance of this new approach was assessed. The results show that RUPD could be modeled as a constant if the receiver were tracking satellites continuously. Furthermore, compared to the method using DD observations with only an intra-system DD ambiguity fixed, the new ambiguity fixing approach has a better performance, especially in hard environments with a large cut-off angle or serve signal obstructions. Full article
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Open AccessArticle A Set of Satellite-Based Near Real-Time Meteorological Drought Monitoring Data over China
Remote Sens. 2019, 11(4), 453; https://doi.org/10.3390/rs11040453
Received: 18 January 2019 / Revised: 15 February 2019 / Accepted: 17 February 2019 / Published: 22 February 2019
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Abstract
A high-resolution and near real-time drought monitoring dataset has not been made readily available in drought-prone China, except for the low-resolution global product. Here we developed a set of near real-time meteorological drought data at a 0.25° spatial resolution over China, by seamlessly [...] Read more.
A high-resolution and near real-time drought monitoring dataset has not been made readily available in drought-prone China, except for the low-resolution global product. Here we developed a set of near real-time meteorological drought data at a 0.25° spatial resolution over China, by seamlessly merging the satellite-based near real-time (RT) precipitation (3B42RTv7) into the high-quality gauge-based retrospective product (CN05.1) using the quantile-mapping (QM) bias-adjustment method. Comparing the standard precipitation index (SPI) from the satellite-gauge merged product (SGMP) with that from the retrospective ground product CN05.1 (OBS) shows that the SGMP reproduces well the observed spatial distribution of SPI and the pattern of meteorological drought across China, at both the 6-month and 12-month time scales. In contrast, the UN-SGMP generated by merging the unadjusted raw satellite precipitation into the gauging data shows systematical overestimation of the SPI, leaving less meteorological droughts to be identified. Furthermore, the SGMP is found to be able to capture the inter-annual variation of percentage area in meteorological droughts. These validation results suggest that the newly developed drought dataset is reliable for monitoring meteorological drought dynamics in near real-time. This dataset will be routinely updated as the satellite RT precipitation is made available, thus facilitating near real-time drought diagnosis in China. Full article
(This article belongs to the Special Issue Observations, Modeling, and Impacts of Climate Extremes)
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Open AccessArticle Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
Remote Sens. 2019, 11(4), 452; https://doi.org/10.3390/rs11040452
Received: 21 December 2018 / Revised: 11 February 2019 / Accepted: 18 February 2019 / Published: 22 February 2019
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Abstract
Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide [...] Read more.
Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide the boundary of clean ice and debris-covered glacier facies, while debris-covered glacier facies play a key role in mass balance research. The aim of this study was to develop an automatic algorithm to distinguish ice cover types based on multi-temporal satellite data, and the algorithm was implemented in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. The classification method was built upon an automated machine learning approach: Random Forest in combination with the analysis of topographic and textural features based on Landsat-8 imagery and multiple digital elevation model (DEM) data. Very high spatial resolution Gao Fen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) imagery was used to select training samples and validate the classification results. In this study, all of the land cover types were classified with overall good performance using the proposed method. The results indicated that fully debris-covered glaciers accounted for approximately 20.7% of the total glacier area in this region and were mainly distributed at elevations between 4600 m and 4800 m above sea level (a.s.l.). Additionally, an analysis of the results clearly revealed that the proportion of small size glaciers (<1 km2) were 88.3% distributed at lower elevations compared to larger size glaciers (≥1 km2). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20° and 30°, and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers at Global and Regional Scales)
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Open AccessArticle Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter
Remote Sens. 2019, 11(4), 451; https://doi.org/10.3390/rs11040451
Received: 31 December 2018 / Revised: 17 February 2019 / Accepted: 18 February 2019 / Published: 22 February 2019
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Abstract
Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, [...] Read more.
Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°. Full article
(This article belongs to the Special Issue SAR in Big Data Era)
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Open AccessArticle The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy
Remote Sens. 2019, 11(4), 450; https://doi.org/10.3390/rs11040450
Received: 21 January 2019 / Revised: 16 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
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Abstract
In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis–NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples [...] Read more.
In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis–NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples and applied six commonly used spectral pretreatments to preprocess soil spectra, namely, Savitzky–Golay (SG) smoothing, first derivative (FD), logarithmic function log(1/R), mean centering (MC), standard normal variate (SNV), and multiplicative scatter correction (MSC). Then, the Kennard–Stone (KS) strategy was used to select calibration samples based on the pretreated spectra, and the size of the calibration set varied from 10 samples to 86 samples (80% of the total samples). These calibration sets were employed to construct partial least squares regression models (PLSR) to predict SOM, and the built models were validated by a set of 21 samples (20% of the total samples). The results showed that 64−78% of the calibration sets selected by the inclusion of pretreatment demonstrated significantly better performance of SOM estimation. The average improved residual predictive deviations (ΔRPD) were 0.06, 0.13, 0.19, and 0.13 for FD, log(1/R), MSC, and SNV, respectively. Thus, we concluded that spectral pretreatment improves the sample selection strategy, and the degree of its influence varies with the size of the calibration set and the type of pretreatment. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series
Remote Sens. 2019, 11(4), 449; https://doi.org/10.3390/rs11040449
Received: 6 January 2019 / Revised: 15 February 2019 / Accepted: 16 February 2019 / Published: 21 February 2019
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Abstract
Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the [...] Read more.
Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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Open AccessArticle Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning
Remote Sens. 2019, 11(4), 448; https://doi.org/10.3390/rs11040448
Received: 16 January 2019 / Revised: 16 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
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Abstract
The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on [...] Read more.
The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN. Full article
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Open AccessArticle Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring
Remote Sens. 2019, 11(4), 447; https://doi.org/10.3390/rs11040447
Received: 28 January 2019 / Revised: 15 February 2019 / Accepted: 17 February 2019 / Published: 21 February 2019
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The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous [...] Read more.
The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous United States (CONUS) are quantified for a 36-year period (1 January 1982 to 31 December 2017). Complex patterns of ARD availability occur due to the satellite orbit and sensor geometry, cloud, sensor acquisition and health issues and because of changing relative orientation of the ARD tiles with respect to the Landsat orbit paths. Quantitative per-pixel and summary ARD tile results are reported. Within the CONUS, the average annual number of non-cloudy observations in each 150 × 150 km ARD tile varies from 0.53 to 16.80 (Landsat 4 TM), 11.08 to 22.83 (Landsat 5 TM), 9.73 to 21.72 (Landsat 7 ETM+) and 14.23 to 30.07 (all three sensors). The annual number was most frequently only 2 to 4 Landsat 4 TM observations (36% of the CONUS tiles), increasing to 14 to 16 Landsat 5 TM observations (26% of tiles), 12 to 14 Landsat 7 ETM+ observations (31% of tiles) and 18 to 20 observations (23% of tiles) when considering all three sensors. The most frequently observed ARD tiles were in the arid south-west and in certain mountain rain shadow regions and the least observed tiles were in the north-east, around the Great Lakes and along parts of the north-west coast. The quality of time series algorithm results is expected to be reduced at ARD tiles with low reported availability. The smallest annual number of cloud-free observations for the Landsat 5 TM are over ARD tile h28v04 (northern New York state), for Landsat 7 ETM+ are over tile h25v07 (Ohio and Pennsylvania) and for Landsat 4 TM are over tile h22v08 (northern Indiana). The greatest annual number of cloud-free observations for the Landsat 5 TM and 7 ETM+ ARD are over southern California ARD tile h04v11 and for the Landsat 4 TM are over southern Arizona tile h06v13. The reported results likely overestimate the number of good surface observations because shadows and cirrus clouds were not considered. Implications of the findings for terrestrial monitoring and future ARD research are discussed. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessArticle Fusing Multimodal Video Data for Detecting Moving Objects/Targets in Challenging Indoor and Outdoor Scenes
Remote Sens. 2019, 11(4), 446; https://doi.org/10.3390/rs11040446
Received: 31 December 2018 / Revised: 8 February 2019 / Accepted: 16 February 2019 / Published: 21 February 2019
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Abstract
Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tracking tasks in challenging outdoor and indoor scenes under various environmental/illumination conditions. [...] Read more.
Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tracking tasks in challenging outdoor and indoor scenes under various environmental/illumination conditions. Towards this direction, we have designed a multisensor system based on thermal, shortwave infrared, and hyperspectral video sensors and propose a processing pipeline able to perform in real-time object detection tasks despite the huge amount of the concurrently acquired video streams. In particular, in order to avoid the computationally intensive coregistration of the hyperspectral data with other imaging modalities, the initially detected targets are projected through a local coordinate system on the hypercube image plane. Regarding the object detection, a detector-agnostic procedure has been developed, integrating both unsupervised (background subtraction) and supervised (deep learning convolutional neural networks) techniques for validation purposes. The detected and verified targets are extracted through the fusion and data association steps based on temporal spectral signatures of both target and background. The quite promising experimental results in challenging indoor and outdoor scenes indicated the robust and efficient performance of the developed methodology under different conditions like fog, smoke, and illumination changes. Full article
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Open AccessArticle Spatiotemporal Patterns and Morphological Characteristics of Ulva prolifera Distribution in the Yellow Sea, China in 2016–2018
Remote Sens. 2019, 11(4), 445; https://doi.org/10.3390/rs11040445
Received: 22 December 2018 / Revised: 11 February 2019 / Accepted: 14 February 2019 / Published: 21 February 2019
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Abstract
The world’s largest macroalgal blooms, Ulva prolifera, have appeared in the Yellow Sea every summer on different scales since 2007, causing great harm to the regional marine economy. In this study, the Normalized Difference of Vegetation Index (NDVI) index was used to [...] Read more.
The world’s largest macroalgal blooms, Ulva prolifera, have appeared in the Yellow Sea every summer on different scales since 2007, causing great harm to the regional marine economy. In this study, the Normalized Difference of Vegetation Index (NDVI) index was used to extract the green tide of Ulva prolifera from MODIS images in the Yellow Sea in 2016–2018, to investigate its spatiotemporal patterns and to calculate its occurrence probability. Using the standard deviational ellipse (SDE), the morphological characteristics of the green tide, including directionality and regularity, were analyzed. The results showed that the largest distribution and coverage areas occurred in 2016, with 57,384 km2 and 2906 km2, respectively and that the total affected region during three years was 163,162 km2. The green tide drifted northward and died out near Qingdao, Shandong Province, which was found to be a high-risk region. The coast of Jiangsu Province was believed to be the source of Ulva prolifera, but it was probably not the only one. The regularity of the boundary shape of the distribution showed a change that was opposite to the variation of scale. Several sharp increases were found in the parameters of the SDE in all three years. In conclusion, the overall situation of Ulva prolifera was still severe in recent years, and the sea area near Qingdao became the worst hit area of the green tide event. It was also shown that the sea surface wind played an important part in its migration and morphological changes. Full article
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
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Open AccessArticle Shrinkage of Nepal’s Second Largest Lake (Phewa Tal) Due to Watershed Degradation and Increased Sediment Influx
Remote Sens. 2019, 11(4), 444; https://doi.org/10.3390/rs11040444
Received: 14 January 2019 / Revised: 12 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
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Abstract
Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial [...] Read more.
Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial damming and sedimentation is disputed in the literature due to the lack of a historical time series. In this paper, we present an analysis of the lake’s evolution from 1926 to 2018 and model the 50-year trajectory of shrinkage. The area of Phewa Lake expanded from 2.44 ± 1.02 km2 in 1926 to a maximum of 4.61 ± 0.07 km2 in 1961. However, the lake area change was poorly constrained prior to a 1957–1958 map. The contemporary lake area was 4.02 ± 0.07 km2 in April 2018, and expands seasonally by ~0.18 km2 due to the summer monsoon. We found no evidence to support a lake area of 10 km2 in 1956–1957, despite frequent reporting of this value in the literature. Based on the rate of areal decline and sediment influx, we estimate the lake will lose 80% of its storage capacity in the next 110–347 years, which will affect recreational use, agricultural irrigation, fishing, and a one-megawatt hydroelectric power facility. Mitigation of lake shrinkage will require addressing landslide activity and sediment transport in the watershed, as well as urban expansion along the shores. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
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Open AccessArticle A Novel Approach for the Detection of Developing Thunderstorm Cells
Remote Sens. 2019, 11(4), 443; https://doi.org/10.3390/rs11040443
Received: 16 January 2019 / Revised: 14 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
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Abstract
This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond [...] Read more.
This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network. Full article
(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)
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Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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