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Remote Sens., Volume 11, Issue 12 (June-2 2019)

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Cover Story (view full-size image) Satellite-based high spatial resolution information on benthic habitats is essential for coral reef [...] Read more.
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Open AccessArticle
Measurement of Road Surface Deformation Using Images Captured from UAVs
Remote Sens. 2019, 11(12), 1507; https://doi.org/10.3390/rs11121507
Received: 26 April 2019 / Revised: 30 May 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
This paper presents a methodology for measuring road surface deformation due to terrain instability processes. The methodology is based on ultra-high resolution images acquired from unmanned aerial vehicles (UAVs). Flights are georeferenced by means of Structure from Motion (SfM) techniques. Dense point clouds, [...] Read more.
This paper presents a methodology for measuring road surface deformation due to terrain instability processes. The methodology is based on ultra-high resolution images acquired from unmanned aerial vehicles (UAVs). Flights are georeferenced by means of Structure from Motion (SfM) techniques. Dense point clouds, obtained using the multiple-view stereo (MVS) approach, are used to generate digital surface models (DSM) and high resolution orthophotographs (0.02 m GSD). The methodology has been applied to an unstable area located in La Guardia (Jaen, Southern Spain), where an active landslide was identified. This landslide affected some roads and accesses to a highway at the landslide foot. The detailed road deformation was monitored between 2012 and 2015 by means of eleven UAV flights of ultrahigh resolution covering an area of about 260 m × 90 m. The accuracy of the analysis has been established in 0.02 ± 0.01 m in XY and 0.04 ± 0.02 m in Z. Large deformations in the order of two meters were registered in the total period analyzed that resulted in maximum average rates of 0.62 m/month in the unstable area. Some boundary conditions were considered because of the low required flying height (<50 m above ground level) in order to achieve a suitable image GSD, the fast landslide dynamic, continuous maintenance works on the affected roads and dramatic seasonal vegetation changes throughout the monitoring period. Finally, we have analyzed the relation of displacements to rainfalls in the area, finding a significant correlation between the two variables, as well as two different reactivation episodes. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Capturing Coastal Dune Natural Vegetation Types Using a Phenology-Based Mapping Approach: The Potential of Sentinel-2
Remote Sens. 2019, 11(12), 1506; https://doi.org/10.3390/rs11121506
Received: 20 May 2019 / Revised: 18 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and [...] Read more.
Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and spectral resolution is promising in this context. Thus, in a sector of the Mediterranean coast (Lazio region, Italy), we tested the strength of a phenology-based vegetation mapping approach and statistically compared results with previous studies, making use of open source products across all the processing chain. We identified five accurate land cover classes in three hierarchical levels, with good values of agreement with previous studies for the first and the second hierarchical level. The implemented procedure resulted as being effective for mapping a highly fragmented coastal dune system. This is encouraging to take advantage of the earth observation through remote sensing technology in an open source perspective, even at the fine scale of highly fragmented sand dunes landscapes. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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Open AccessArticle
High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features
Remote Sens. 2019, 11(12), 1505; https://doi.org/10.3390/rs11121505
Received: 27 May 2019 / Revised: 13 June 2019 / Accepted: 18 June 2019 / Published: 25 June 2019
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Abstract
Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the [...] Read more.
Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs. Full article
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Open AccessArticle
Climate and Land-Use Change Effects on Soil Carbon Stocks over 150 Years in Wisconsin, USA
Remote Sens. 2019, 11(12), 1504; https://doi.org/10.3390/rs11121504
Received: 1 May 2019 / Revised: 20 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
Soil organic carbon is a sink for mitigating increased atmospheric carbon. The international initiative “4 per 1000” aims at implementing practical actions on increasing soil carbon storage in soils under agriculture. This requires a fundamental understanding of the soil carbon changes across the [...] Read more.
Soil organic carbon is a sink for mitigating increased atmospheric carbon. The international initiative “4 per 1000” aims at implementing practical actions on increasing soil carbon storage in soils under agriculture. This requires a fundamental understanding of the soil carbon changes across the globe. Several studies have suggested that the global soil organic carbon stocks (SOCS) have decreased due to global warming and land cover change, while others reported SOCS may increase under climate change and improved soil management. To better understand how a changing climate, land cover, and agricultural activities influence SOCS across large extents and long periods, the spatial and temporal variations of SOCS were estimated using a modified space-for-time substitution method over a 150-year period in the state of Wisconsin, USA. We used legacy soil datasets and environmental factors collected and estimated at different times across the state (169,639 km2) coupled with a machine-learning algorithm. The legacy soil datasets were collected from 1980 to 2002 from 550 soil profiles and harmonized to 0.30 m depth. The environmental factors consisted of 100-m soil property maps, 1-km annual temperature and precipitation maps, 250-m remote-sensing (i.e., Landsat)-derived yearly land cover maps and a 30-m digital elevation model. The model performance was moderate but can provide insights on understanding the impacts of different factors on SOCS changes across a large spatial and temporal extent. SOCS at the 0–0.30 m decreased at a rate of 0.1 ton ha−1 year−1 between 1850 and 1938 and increased at 0.2 ton ha−1 year−1 between 1980 and 2002. The spatial variation in SOCS at 0–0.30 m was mainly affected by land cover and soil types with the largest SOCS found in forest and wetland and Spodosols. The loss between 1850 and 1980 was most likely due to land cover change while the increase between 1980 and 2002 was due to best soil management practices (e.g., decreased erosion, reduced tillage, crop rotation and use of legume and cover crops). Full article
(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)
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Open AccessArticle
Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning
Remote Sens. 2019, 11(12), 1503; https://doi.org/10.3390/rs11121503
Received: 27 May 2019 / Revised: 21 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
Spatially continuous estimates of forest aboveground biomass (AGB) are essential to supporting the sustainable management of forest ecosystems and providing invaluable information for quantifying and monitoring terrestrial carbon stocks. The launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) on September 15th, [...] Read more.
Spatially continuous estimates of forest aboveground biomass (AGB) are essential to supporting the sustainable management of forest ecosystems and providing invaluable information for quantifying and monitoring terrestrial carbon stocks. The launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) on September 15th, 2018 offers an unparalleled opportunity to assess AGB at large scales using along-track samples that will be provided during its three-year mission. The main goal of this study was to investigate deep learning (DL) neural networks for mapping AGB with ICESat-2, using simulated photon-counting lidar (PCL)-estimated AGB for daytime, nighttime, and no noise scenarios, Landsat imagery, canopy cover, and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using a simulated PCL-estimated AGB along two years of planned ICESat-2 profiles. The primary tasks were to investigate and determine neural network architecture, examine the hyper-parameter settings, and subsequently generate wall-to-wall AGB maps. A first set of models were developed using vegetation indices calculated from single-date Landsat imagery, canopy cover, and land cover, and a second set of models were generated using metrics from one year of Landsat imagery with canopy cover and land cover maps. To compare the effectiveness of final models, comparisons with Random Forests (RF) models were made. The deep neural network (DNN) models achieved R2 values of 0.42, 0.49, and 0.50 for the daytime, nighttime, and no noise scenarios respectively. With the extended dataset containing metrics calculated from Landsat images acquired on different dates, substantial improvements in model performance for all data scenarios were noted. The R2 values increased to 0.64, 0.66, and 0.67 for the daytime, nighttime, and no noise scenarios. Comparisons with Random forest (RF) prediction models highlighted similar results, with the same R2 and root mean square error (RMSE) range (15–16 Mg/ha) for daytime and nighttime scenarios. Findings suggest that there is potential for mapping AGB using a combinatory approach with ICESat-2 and Landsat-derived products with DL. Full article
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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Open AccessArticle
New Approach for Temporal Stability Evaluation of Pseudo-Invariant Calibration Sites (PICS)
Remote Sens. 2019, 11(12), 1502; https://doi.org/10.3390/rs11121502
Received: 24 May 2019 / Revised: 17 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
Pseudo-Invariant Calibration Sites (PICS) are one of the most popular methods for in-flight vicarious radiometric calibration of Earth remote sensing satellites. The fundamental question of PICS temporal stability has not been adequately addressed. However, the main purpose of this work is to evaluate [...] Read more.
Pseudo-Invariant Calibration Sites (PICS) are one of the most popular methods for in-flight vicarious radiometric calibration of Earth remote sensing satellites. The fundamental question of PICS temporal stability has not been adequately addressed. However, the main purpose of this work is to evaluate the temporal stability of a few PICS using a new approach. The analysis was performed over six PICS (Libya 1, Libya 4, Niger 1, Niger 2, Egypt 1 and Sudan 1). The concept of a “Virtual Constellation” was developed to provide greater temporal coverage and also to overcome the dependence limitation of any specific characteristic derived from one particular sensor. TOA reflectance data from four sensors consistently demonstrating “stable” calibration to within 5%—the Landsat 7 ETM+ (Enhanced Thematic Mapper Plus), Landsat 8 OLI (Operational Land Imager), Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and Sentinel-2A MSI (Multispectral Instrument)–were merged into a seamless dataset. Instead of using the traditional method of trend analysis (Student’s T test), a nonparametric Seasonal Mann-Kendall test was used for determining the PICS stability. The analysis results indicate that Libya 4 and Egypt 1 do not exhibit any monotonic trend in six reflective solar bands common to all of the studied sensors, indicating temporal stability. A decreasing monotonic trend was statistically detected in all bands, except SWIR 2, for Sudan 1 and the Green and Red bands for Niger 1. An increasing trend was detected in the Blue band for Niger 2 and the NIR band for Libya 1. These results do not suggest abandoning PICS as a viable calibration source. Rather, they indicate that PICS temporal stability cannot be assumed and should be regularly monitored as part of the sensor calibration process. Full article
(This article belongs to the Special Issue Cross-Calibration and Interoperability of Remote Sensing Instruments)
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Open AccessArticle
Multi-Temporal Investigation of the Boulder Clay Glacier and Northern Foothills (Victoria Land, Antarctica) by Integrated Surveying Techniques
Remote Sens. 2019, 11(12), 1501; https://doi.org/10.3390/rs11121501
Received: 13 May 2019 / Revised: 18 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
The paper aims to detect the main changes that occurred in the area surrounding the Mario Zucchelli Station (MZS) through analysis of multi-temporal remote sensing integrated by geophysical measurements. Specific attention was directed at realizing an integrated geomorphological study of the Boulder Clay [...] Read more.
The paper aims to detect the main changes that occurred in the area surrounding the Mario Zucchelli Station (MZS) through analysis of multi-temporal remote sensing integrated by geophysical measurements. Specific attention was directed at realizing an integrated geomorphological study of the Boulder Clay Glacier, a partially debris-covered glacier belonging to the Northern Foothills (Victoria Land, Antarctica). This area was recently chosen as the location for the construction of a new semi-permanent gravel runway for MZS logistical airfreight operations. Photogrammetric analysis was performed by comparing three historical aerial photogrammetric surveys (carried out in 1956, 1985, and 1993) and Very High Resolution (VHR) GeoEye-1 satellite stereo-image coverage acquired in 2012. The comparison of geo-referenced orthophoto-mosaics allowed the main changes occurring in some particular areas along the coast nearby MZS to be established. Concerning the study of the Boulder Clay Glacier, it has to be considered that glaciers and moraines are not steady-state systems by definition. Several remote sensing and geophysical investigations were carried out with the main aim of determining the general assessment of this glacier: Ground Penetrating Radar (GPR); Geodetic Global Positioning System (GPS) network; multi-temporal satellite Synthetic Aperture Radar (SAR) interferometry. The analysis of Boulder Clay Glacier moraine pointed out a deformation of less than 74 mm y−1 in a time span of 56 years, value that agrees with velocity and deformation data observed by GPS and InSAR methods. The presence of unexpected brine ponds at the ice/bedrock interface and the deformation pattern observed in the central part of the moraine has to be monitored and studied, especially under the long-term maintenance of the future runway. Full article
(This article belongs to the Special Issue Remote Sensing of Engineering Geological Science)
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Open AccessArticle
Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids
Remote Sens. 2019, 11(12), 1500; https://doi.org/10.3390/rs11121500
Received: 22 April 2019 / Revised: 21 June 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of [...] Read more.
Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future. Full article
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Open AccessReview
A Review on Deep Learning Techniques for 3D Sensed Data Classification
Remote Sens. 2019, 11(12), 1499; https://doi.org/10.3390/rs11121499
Received: 15 April 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 25 June 2019
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Abstract
Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national [...] Read more.
Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable. Full article
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
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Open AccessArticle
Identify and Monitor Growth Faulting Using InSAR over Northern Greater Houston, Texas, USA
Remote Sens. 2019, 11(12), 1498; https://doi.org/10.3390/rs11121498
Received: 24 May 2019 / Revised: 18 June 2019 / Accepted: 22 June 2019 / Published: 25 June 2019
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Abstract
Growth faults are widely distributed in the Greater Houston (GH) region of Texas, USA, and the existence of faulting could interrupt groundwater flow and aggravate local deformation. Faulting-induced property damages have become more pronounced over the last few years, necessitating further investigation of [...] Read more.
Growth faults are widely distributed in the Greater Houston (GH) region of Texas, USA, and the existence of faulting could interrupt groundwater flow and aggravate local deformation. Faulting-induced property damages have become more pronounced over the last few years, necessitating further investigation of these faults. Interferometric synthetic aperture radar (InSAR) has been proved to be an effective way for mapping deformations along and/or across fault traces. However, extracting short-wavelength small-amplitude creep signal (about 10–20 mm/yr) from long time span interferograms is extremely difficult, especially in agricultural or vegetated areas. This study aims to position, map and monitor the rate, extent, and temporal evolution of faulting over GH at the highest spatial density using Multi-temporal InSAR (MTI) technique. The MTI method, which maximizes usable signal and correlation, has the ability to identify and monitor faulting and provide accurate and detailed depiction of active faults. Two neighboring L-band Advanced Land Observing (ALOS) tracks (2007–2011) are utilized in this research. Numerous areas of sharp phase discontinuities have been discerned from MTI-derived velocity map. InSAR measurements allow us to position both previously known faults traces as well as nucleation of new fractures not previously revealed by other ground/space techniques. Faulting damages and surface scarps were evident at most InSAR-mapped fault locations through our site investigations. The newly discovered fault activation appears to be related to excessive groundwater exploitation from the Jasper aquifer in Montgomery County. The continuous mining of groundwater from the Jasper aquifer formed new water-level decline cones over Montgomery County, corroborating the intensity of new fractures. Finally, we elaborate the localized fault activities and evaluate the characteristics of faulting (locking depth and slip rate) through modeling MTI-derived deformation maps. The SW–NE-oriented faults pertain to normal faulting with an average slip rate of 7–13 mm/yr at a shallow locking depth of less than 4 km. Identifying and characterizing active faults through MTI and deformation modeling can provide insights into faulting, its causal mechanism and potential damages to infrastructure over the GH. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle
An Enhanced Mapping Function with Ionospheric Varying Height
Remote Sens. 2019, 11(12), 1497; https://doi.org/10.3390/rs11121497
Received: 23 May 2019 / Accepted: 1 June 2019 / Published: 25 June 2019
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Abstract
Mapping function (MF) converts the line-of-sight slant total electron content (STEC) into the vertical total electron content (VTEC), and vice versa. In an MF, an essential parameter is the ionospheric effective height. However, the inhomogeneous ionosphere makes this height vary spatially and temporally, [...] Read more.
Mapping function (MF) converts the line-of-sight slant total electron content (STEC) into the vertical total electron content (VTEC), and vice versa. In an MF, an essential parameter is the ionospheric effective height. However, the inhomogeneous ionosphere makes this height vary spatially and temporally, meaning it is not a global constant. In the paper, we review several mapping functions and propose a mapping function that utilizes the ionospheric varying height (IVH). We investigate impacts of the IVH on mapping errors and on the ionospheric modeling, as well as on the satellite and receiver differential code biases (DCBs). Our analysis results indicate that the mapping errors using IVH are smaller than those from the fixed height of 450 km. The integral height achieves smaller mapping errors than using a fixed height of 450 km, an improvement of about 8% when compared with the fixed height of 450 km. And 35% smaller mapping errors were found using HmF2 at the lower latitude. Also, the effects of IVH on the satellite DCBs are about 0.1 ns, and larger impacts on the receiver DCBs at 1.0 ns. Full article
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Open AccessArticle
Obtaining High-Resolution Seabed Topography and Surface Details by Co-Registration of Side-Scan Sonar and Multibeam Echo Sounder Images
Remote Sens. 2019, 11(12), 1496; https://doi.org/10.3390/rs11121496
Received: 20 May 2019 / Revised: 16 June 2019 / Accepted: 21 June 2019 / Published: 24 June 2019
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Abstract
Side-scan sonar (SSS) is used for obtaining high-resolution seabed images, but with low position accuracy without using the Ultra Short Base Line (USBL) or Short Base Line (SBL). Multibeam echo sounder (MBES), which can simultaneously obtain high-accuracy seabed topography as well as seabed [...] Read more.
Side-scan sonar (SSS) is used for obtaining high-resolution seabed images, but with low position accuracy without using the Ultra Short Base Line (USBL) or Short Base Line (SBL). Multibeam echo sounder (MBES), which can simultaneously obtain high-accuracy seabed topography as well as seabed images with low resolution in deep water. Based on the complementarity of SSS and MBES data, this paper proposes a new method for acquiring high-resolution seabed topography and surface details that are difficult to obtain using MBES or SSS alone. Firstly, according to the common seabed features presented in both images, the Speeded-Up Robust Features (SURF) algorithm, with the constraint of image geographic coordinates, is adopted for initial image matching. Secondly, to further improve the matching performance, a template matching strategy using the dense local self-similarity (DLSS) descriptor is adopted according to the self-similarities within these two images. Next, the random sample consensus (RANSAC) algorithm is used for removing the mismatches and the SSS backscatter image geographic coordinates are rectified by the transformation model established based on the correct matched points. Finally, the superimposition of this rectified SSS backscatter image on MBES seabed topography is performed and the high-resolution and high-accuracy seabed topography and surface details can be obtained. Full article
(This article belongs to the Special Issue Radar and Sonar Imaging and Processing)
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Open AccessArticle
Remotely Sensed Spatial Structure as an Indicator of Internal Changes of Vegetation Communities in Desert Landscapes
Remote Sens. 2019, 11(12), 1495; https://doi.org/10.3390/rs11121495
Received: 9 May 2019 / Revised: 5 June 2019 / Accepted: 18 June 2019 / Published: 24 June 2019
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Abstract
Desert environments are sensitive to disturbances, and their functions and processes can take many years to recover. Detecting early signs of disturbance is critical, but developing such a capability for expansive remote desert regions is challenging. Using a variogram and 15-cm resolution Visible [...] Read more.
Desert environments are sensitive to disturbances, and their functions and processes can take many years to recover. Detecting early signs of disturbance is critical, but developing such a capability for expansive remote desert regions is challenging. Using a variogram and 15-cm resolution Visible Atmospherically Resistant Index (VARI) imagery, we examined the usefulness of the spatial structure of desert lands for monitoring early signs of habitat changes using the Riverside East solar energy zone located within Riverside County, California. We tested the method on four habitat types in the region, Parkinsonia floridaOlneya tesota, Chorizanthe rigidaGeraea canescens, Larrea tridentataAmbrosia dumosa, and Larrea tridentataEncelia farinosa alliances. The results showed that the sill, range, form, and partial sill of the variogram generated from VARI strongly correlate with overall vegetation cover, average canopy size, canopy size variation, and spatial structure within a dryland habitat, respectively. Establishing a baseline of variogram parameters for each habitat and comparing to subsequent monitoring parameters would be most effective for detecting internal changes because values of variogram parameters would not match absolute values of landscape properties. When monitoring habitats across varying landscape characteristics, a single appropriate image resolution would likely be the resolution that could adequately characterize the habitat dominated by the smallest vegetation. For the variogram generated from VARI, which correlates to vegetation greenness, the sills may indicate the health of vegetation communities. However, further studies are warranted to determine the effectiveness of variograms for monitoring habitat health. Remotely sensed landscape structure obtained from variograms could provide complementary information to traditional methods for monitoring internal changes in dryland vegetation communities. Full article
(This article belongs to the Special Issue Remote Sensing in Dryland Assessment and Monitoring)
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Open AccessArticle
Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry
Remote Sens. 2019, 11(12), 1494; https://doi.org/10.3390/rs11121494
Received: 5 May 2019 / Revised: 20 June 2019 / Accepted: 21 June 2019 / Published: 24 June 2019
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Abstract
Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and [...] Read more.
Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015–2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed. Full article
(This article belongs to the Special Issue Applications of Sentinel Satellite for Geohazards Prevention)
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Open AccessArticle
Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography
Remote Sens. 2019, 11(12), 1493; https://doi.org/10.3390/rs11121493
Received: 3 May 2019 / Revised: 16 June 2019 / Accepted: 17 June 2019 / Published: 24 June 2019
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Abstract
Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for [...] Read more.
Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for a better understanding of Earth surface processes and landforms. However, quantitative identification and monitoring of such anthropogenic signature still represent a challenge for the Earth science community. The purpose of this contribution is to explore a method for monitoring geomorphic changes and identifying the driving forces of such changes. The study was carried out on the Eibar watershed in Spain. The proposed method is able to quantitatively detect anthropogenic geomorphic changes based on multi-temporal LiDAR topography, and it is based on a combination of two techniques: the DEM of Difference (DoD) and the Slope Local Length of Auto-correlation (SLLAC). First, we tested the capability of the SLLAC and derived parameters to distinguish different types of anthropogenic geomorphologies in 5 study case at a small scale. Second, we calculated the DoD to quantify the geomorphic changes between 2008 and 2016. Based on the proposed approach, we classified the whole basin into three categories of geomorphic changes (natural, urban or mosaic areas). The urban area had the most clustered and largest geomorphic changes, followed by the mosaic area and the natural area. This research might help to identify and monitoring anthropogenic geomorphic changes over large areas, to schedule sustainable environmental planning, and to mitigate the consequences of anthropogenic alteration. Full article
(This article belongs to the Special Issue Remote Sensing of Anthropogenic Change)
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Open AccessArticle
Transfer and Association: A Novel Detection Method for Targets without Prior Homogeneous Samples
Remote Sens. 2019, 11(12), 1492; https://doi.org/10.3390/rs11121492
Received: 9 May 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 24 June 2019
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Abstract
A primary problem faced during previous research was the gap in limited and unbalanced quantity of prior samples between computer classification tasks and targeted remote sensing applications. This paper presents the fusion method to overcome this limitation. It offers a novel method based [...] Read more.
A primary problem faced during previous research was the gap in limited and unbalanced quantity of prior samples between computer classification tasks and targeted remote sensing applications. This paper presents the fusion method to overcome this limitation. It offers a novel method based on knowledge transfer and feature association, a strong combination of transfer learning and data fusion. The former reuses layers trained on complete data sets to compute a mid-level representation of the specific target. The latter brings additional information from heterogeneous sources to enrich the features in the target domain. Firstly, a basic convolutional neural network (B_CNN) is pretrained on to the CIFAR-10 dataset to produce a stable model responsible for general feature extraction from multiple inputs. Secondly, a transfer CNN (Trans_CNN) with fine-tuned and transferred parameters is constraint-trained to fit and switch between differing tasks. Meanwhile, the feature association (FA) frames a new feature space to achieve integration between training and testing samples from different sensors. Finally, on-line detection can be completed based on Trans_CNN to explore a state-of-the-art method to overcome the inadequate sample problems in real remote sensing applications rather than produce an unrolled version of training methods or structural improvement in CNN. Experimental results show that target detection rates without homogeneous prior samples can reach 85%. Under these conditions, the traditional CNN model is invalid. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessLetter
Short-Term Variation of the Surface Flow Pattern South of Lombok Strait Observed from the Himawari-8 Sea Surface Temperature
Remote Sens. 2019, 11(12), 1491; https://doi.org/10.3390/rs11121491
Received: 6 May 2019 / Revised: 10 June 2019 / Accepted: 18 June 2019 / Published: 24 June 2019
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Abstract
Spatial and temporal information on oceanic flow is fundamental to oceanography and crucial for marine-related social activities. This study attempts to describe the short-term surface flow variation in the area south of the Lombok Strait in the northern summer using the hourly Himawari-8 [...] Read more.
Spatial and temporal information on oceanic flow is fundamental to oceanography and crucial for marine-related social activities. This study attempts to describe the short-term surface flow variation in the area south of the Lombok Strait in the northern summer using the hourly Himawari-8 sea surface temperature (SST). Although the uncertainty of this temperature is relatively high (about 0.6 C), it could be used to discuss the flow variation with high spatial resolution because sufficient SST differences are found between the areas north and south of the strait. The maximum cross-correlation (MCC) method is used to estimate the surface velocity. The Himawari-8 SST clearly shows Flores Sea water intruding into the Indian Ocean with the high-SST water forming a warm thermal plume on a tidal cycle. This thermal plume flows southward at a speed of about 2 m / s . The Himawari-8 SST indicates a southward flow from the Lombok Strait to the Indian Ocean, which blocks the South Java Current flowing eastward along the southern coast of Nusa Tenggara. Although the satellite data is limited to the surface, we found it useful for understanding the spatial and temporal variations in the surface flow field. Full article
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Open AccessArticle
A Study of the Technology Used to Distinguish Sea Ice and Seawater on the Haiyang-2A/B (HY-2A/B) Altimeter Data
Remote Sens. 2019, 11(12), 1490; https://doi.org/10.3390/rs11121490
Received: 23 May 2019 / Revised: 14 June 2019 / Accepted: 15 June 2019 / Published: 24 June 2019
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Abstract
When the Haiyang-2B (HY-2B) was launched into space to form a star network with the Haiyang-2A (HY-2A), it provided new data sources for the sea ice research of the Earth’s polar regions. The ability of altimeter echoes to distinguish sea ice and sea [...] Read more.
When the Haiyang-2B (HY-2B) was launched into space to form a star network with the Haiyang-2A (HY-2A), it provided new data sources for the sea ice research of the Earth’s polar regions. The ability of altimeter echoes to distinguish sea ice and sea water is usable in operational ice charting. In this research study, the level 1B (L1B) data of HY-2A/B altimeter from November 2018 was used to analyze the altimeter waveforms from the polar regions. The Suboptimal Maximum Likelihood Estimation (SMLE) and Offset Center of Gravity (OCOG) tracking packages could maintain the waveform characteristics of diffused and quasi-specular surfaces by comparison. Also, they could be utilized to distinguish sea ice from seawater in the polar regions. It was determined that the types of echoes obtained from the seawater were diffuse. Also, some “ocean-like” waveform data had existed for the old ice formations in the Arctic regions during the study period. The types of echoes obtained from Arctic sea ice were found to be mainly quasi-specular. In the present study, three methods (Threshold segmentation, K-nearest-neighbor (KNN), and Lib-Support Vector machine (LIBSVM)) with four waveform parameters (Automatic Gain Control (AGC) and Pulse Peaking (PP) values of the Ku and C Bands) were adopted to distinguish between the sea ice and seawater areas. The accuracy rate of the separation results for the LIBSVM except band Ku from HY-2B ALT was found to be less than 40% in Antarctic. Meanwhile, the other two methods were observed to have maintained the waveforms correctly at accuracy rates of approximately 80% in Antarctic and the Arctic. In addition, the observed distinguishing errors were located in the regions of the old ice of the Arctic region. In addition, due to the summer melting processes, the large number of ice floes and the snow cover had made it difficult to distinguish the seawater and sea ice in the Antarctic regions. Full article
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Open AccessCorrection
Correction: Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E. Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. Remote Sensing 2019, 11, 575
Remote Sens. 2019, 11(12), 1489; https://doi.org/10.3390/rs11121489
Received: 12 June 2019 / Accepted: 19 June 2019 / Published: 24 June 2019
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Abstract
The authors wish to make the following correction to the paper [...] Full article
(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)
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Open AccessArticle
Discovery of the Fastest Ice Flow along the Central Flow Line of Austre Lovénbreen, a Poly-thermal Valley Glacier in Svalbard
Remote Sens. 2019, 11(12), 1488; https://doi.org/10.3390/rs11121488
Received: 28 May 2019 / Revised: 11 June 2019 / Accepted: 12 June 2019 / Published: 24 June 2019
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Abstract
Ice flow velocity is a sensitive indicator of glacier variations both controlling and representing the delivery of ice and affecting the future stability of ice masses in a warming climate. As one of the poly-thermal glaciers in the high Arctic, Austre Lovénbreen (AL) [...] Read more.
Ice flow velocity is a sensitive indicator of glacier variations both controlling and representing the delivery of ice and affecting the future stability of ice masses in a warming climate. As one of the poly-thermal glaciers in the high Arctic, Austre Lovénbreen (AL) is on the northwestern coast of Spitsbergen, Svalbard. The ice flow velocity of AL was investigated using in situ global positioning system (GPS) observations over 14 years and numerical modelling with Elmer/Ice. First, the ice flow velocity field of AL along central flow line was presented and the ice flow velocity is approximately 4 m/a. Obvious seasonal changes of ice flow velocity can be found in the middle of the glacier, where the velocity in spring-summer is 47% larger than in autumn–winter in 2016, and the mean annual velocity increased 14% from 2009 until 2016. Second, the numerical simulation was performed considering the poly-thermal character of the glacier, and indicated that there are two peak ice flow regions on the glacier, and not just one peak ice flow region as previously believed. The new peak ice flow zone found by simulation was verified by field work, which also demonstrated that the velocity of the newly identified zone is 8% faster than the previously identified zone. Third, although our field observations showed that the ice flow velocity is slowly increasing recently, the maximum ice flow velocity will soon begin to decrease gradually in the long term according to glacier evolution modelling of AL. Full article
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Open AccessArticle
Estimation of Ground Surface and Accuracy Assessments of Growth Parameters for a Sweet Potato Community in Ridge Cultivation
Remote Sens. 2019, 11(12), 1487; https://doi.org/10.3390/rs11121487
Received: 19 May 2019 / Revised: 17 June 2019 / Accepted: 18 June 2019 / Published: 23 June 2019
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Abstract
There are only a few studies that have been made on accuracy assessments of Leaf Area Index (LAI) and biomass estimation using three-dimensional (3D) models generated by structure from motion (SfM) image processing. In this study, sweet potato was grown with different amounts [...] Read more.
There are only a few studies that have been made on accuracy assessments of Leaf Area Index (LAI) and biomass estimation using three-dimensional (3D) models generated by structure from motion (SfM) image processing. In this study, sweet potato was grown with different amounts of nitrogen fertilization in ridge cultivation at an experimental farm. Three-dimensional dense point cloud models were constructed from a series of two-dimensional (2D) color images measured by a small unmanned aerial vehicle (UAV) paired with SfM image processing. Although it was in the early stage of cultivation, a complex ground surface model for ridge cultivation with vegetation was generated, and the uneven ground surface could be estimated with an accuracy of 1.4 cm. Furthermore, in order to accurately estimate growth parameters from the early growth to the harvest period, a 3D model was constructed using a root mean square error (RMSE) of 3.3 cm for plant height estimation. By using a color index, voxel models were generated and LAIs were estimated using a regression model with an RMSE accuracy of 0.123. Further, regression models were used to estimate above-ground and below-ground biomass, or tuberous root weights, based on estimated LAIs. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle
Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves
Remote Sens. 2019, 11(12), 1486; https://doi.org/10.3390/rs11121486
Received: 16 May 2019 / Revised: 17 June 2019 / Accepted: 17 June 2019 / Published: 22 June 2019
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Abstract
Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and [...] Read more.
Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and mapped. The possibility to recognize, investigate, and monitor these phenomena play a key role to avoid further occurrences and consequences. The number of applications of Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) analysis for landslides monitoring and mapping greatly increased in the last decades thanks to the technological advances and the development of advanced processing algorithms. In this work, landslide-induced damage on structures recognized and classified by field survey and velocity of displacement re-projected along the steepest slope were combined in order to extract fragility curves for the hamlets of Patigno and Coloretta, in the Zeri municipality (Tuscany, northern Italy). Images using ERS1/2, ENVISAT, COSMO-SkyMed (CSK) and Sentinel-1 SAR (Synthetic Aperture Radar) were employed to investigate an approximate 25 years of deformation affecting both hamlets. Three field surveys were conducted for recognizing, identifying, and classifying the landslide-induced damage on structures and infrastructures. At the end, the damage probability maps were designed by means of the use of the fragility curves between Sentinel-1 velocities and recorded levels of damage. The results were conceived to be useful for the local authorities and civil protection authorities to improve the land managing and, more generally, for planning mitigation strategies. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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Open AccessArticle
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction
Remote Sens. 2019, 11(12), 1485; https://doi.org/10.3390/rs11121485
Received: 16 May 2019 / Revised: 16 June 2019 / Accepted: 17 June 2019 / Published: 22 June 2019
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Abstract
Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. [...] Read more.
Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Multiple-Scale Variations of Sea Ice and Ocean Circulation in the Bering Sea Using Remote Sensing Observations and Numerical Modeling
Remote Sens. 2019, 11(12), 1484; https://doi.org/10.3390/rs11121484
Received: 21 May 2019 / Revised: 13 June 2019 / Accepted: 20 June 2019 / Published: 22 June 2019
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Abstract
The Bering Sea is located between the Aleutian Low and Siberian High, with strong seasonal variations in the oceanic circulation and the sea ice coverage. Within such a large-scale system, the physical processes in the Bering Sea carry interannual variability. The special topography [...] Read more.
The Bering Sea is located between the Aleutian Low and Siberian High, with strong seasonal variations in the oceanic circulation and the sea ice coverage. Within such a large-scale system, the physical processes in the Bering Sea carry interannual variability. The special topography in the Bering Sea traps a strong jet along the Bering Slope, whose instability enriches the eddy activity in the region. A Regional Oceanic Modeling System (ROMS), coupled with a sea ice module, is employed to study multiple-scale variability in the sea ice and oceanic circulation in the Bering Sea for interannual, seasonal, and intra-seasonal eddy variations. The model domain covers the whole Bering Sea and a part of the Chukchi Sea and south of Aleutian Islands, with an averaged spatial resolution of 5 km. The external forcings are momentum, heat, and freshwater flux at the surface and adaptive nudging to reanalysis fields at the boundaries. The oceanic model starts in an equilibrium state from a multiple year cyclical climatology run, and then it is integrated from years 1990 through 2004. The 15 year simulation is analyzed and assessed against the observational data. The model accurately reproduces the seasonal and interannual variations in the sea ice coverage compared with the satellite-observed sea ice data from the National Snow and Ice Data Center (NSIDC). Sea surface temperature and eddy kinetic energy patterns from the ROMS agree with satellite remote sensing data. The transportation through the Bering Strait is also comparable with the estimate of mooring data. The mechanism for seasonal and interannual variation in the Bering Sea is connected to the Siberia-Aleutian index. Eddy variation along the Bering Slope is discussed. The model also simulates polynya generation and evolution around the St. Lawrence Island. Full article
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Open AccessArticle
Drought Monitoring Utility using Satellite-Based Precipitation Products over the Xiang River Basin in China
Remote Sens. 2019, 11(12), 1483; https://doi.org/10.3390/rs11121483
Received: 21 May 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 22 June 2019
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Abstract
Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of [...] Read more.
Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of rain gauges limit the access of long-term and reliable in situ observations. Remote sensing techniques enrich the precipitation data at different temporal–spatial resolutions. In this study, the climate prediction center morphing (CMORPH) technique (CMORPH-CRT), the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TRMM 3B42V7), and the integrated multi-satellite retrievals for global precipitation measurement (IMERG V05) were evaluated and compared with in situ observations for the drought monitoring in the Xiang River Basin, a humid region in China. A widely-used drought index, the standardized precipitation index (SPI), was chosen to evaluate the drought monitoring utility. The atmospheric water deficit (AWD) was used for comparison of the drought estimation with SPI. The results were as follows: (1) IMERG V05 precipitation products showed the highest accuracy against grid-based precipitation, followed by CMORPH-CRT, which performed better than TRMM 3B42V7; (2) IMERG V05 showed the best performance in SPI-1 (one-month SPI) estimations compared with CMORPH-CRT and TRMM 3B42V7; (3) SPI-1 was more suitable for drought monitoring than AWD in the Xiang River Basin, because its high R-values and low root mean square error (RMSE) compared with the corresponding index based on in situ observations; (4) drought conditions in 2015 were apparently more severe than that in 2016 and 2017, with the driest area mainly distributed in the southwest part of the Xiang River Basin. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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Open AccessArticle
Heterogeneous Behavior of the Campotosto Normal Fault (Central Italy) Imaged by InSAR GPS and Strong-Motion Data: Insights from the 18 January 2017 Events
Remote Sens. 2019, 11(12), 1482; https://doi.org/10.3390/rs11121482
Received: 13 May 2019 / Revised: 10 June 2019 / Accepted: 18 June 2019 / Published: 22 June 2019
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Abstract
On 18 January 2017, the 2016–2017 central Italy seismic sequence reached the Campotosto area with four events with magnitude larger than 5 in three hours (major event MW 5.5). To study the slip behavior on the causative fault/faults we followed two different [...] Read more.
On 18 January 2017, the 2016–2017 central Italy seismic sequence reached the Campotosto area with four events with magnitude larger than 5 in three hours (major event MW 5.5). To study the slip behavior on the causative fault/faults we followed two different methodologies: (1) we use Interferometric Synthetic Aperture Radar (InSAR) interferograms (Sentinel-1 satellites) and Global Positioning System (GPS) coseismic displacements to constrain the fault geometry and the cumulative slip distribution; (2) we invert near-source strong-motion, high-sampling-rate GPS waveforms, and high-rate GPS-derived static offsets to retrieve the rupture history of the two largest events. The geodetic inversion shows that the earthquake sequence occurred along the southern segment of the SW-dipping Mts. Laga normal fault system with an average slip of about 40 cm and an estimated cumulative geodetic moment of 9.29 × 1017 Nm (equivalent to a MW~6). This latter estimate is larger than the cumulative seismic moment of all the events, with MW > 4 which occurred in the corresponding time interval, suggesting that a fraction (~35%) of the overall deformation imaged by InSAR and GPS may have been released aseismically. Geodetic and seismological data agree with the geological information pointing out the Campotosto fault segment as the causative structure of the main shocks. The position of the hypocenters supports the evidence of an up-dip and northwestward rupture directivity during the major shocks of the sequence for both static and kinematic inferred slip models. The activated two main slip patches are characterized by rise time and peak slip velocity in the ranges 0.7–1.1 s and 2.3–3.2 km/s, respectively, and by ~35–50 cm of slip mainly concentrated in the shallower northern part of causative fault. Our results show that shallow slip (depth < 5 km) is required by the geodetic and seismological observations and that the inferred slip distribution is complementary with respect to the previous April 2009 seismic sequence affecting the southern half of the Campotosto fault. The recent moderate strain-release episodes (multiple M~5–5.5 earthquakes) and the paleoseismological evidence of surface-rupturing events (M~6.5) suggests therefore a heterogeneous behavior of the Campotosto fault. Full article
(This article belongs to the Special Issue Remote Sensing of Engineering Geological Science)
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Open AccessArticle
Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia
Remote Sens. 2019, 11(12), 1481; https://doi.org/10.3390/rs11121481
Received: 19 May 2019 / Revised: 14 June 2019 / Accepted: 18 June 2019 / Published: 22 June 2019
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Abstract
In the last few years, the world has been turning to the exploitation of renewable energy sources due to increased awareness of environmental protection and increased consumption of fossil fuels. In this research, by applying geographic information systems and integrating them with multi-criteria [...] Read more.
In the last few years, the world has been turning to the exploitation of renewable energy sources due to increased awareness of environmental protection and increased consumption of fossil fuels. In this research, by applying geographic information systems and integrating them with multi-criteria decision making methods, an area suitable for the construction and exploitation of renewable energy sources is determined. The research uses not only climate, spatial, environmental, and geomorphological parameters but also socioeconomic parameters, population, unemployment, and number of tourist nights as well as electricity consumption. By applying spatial analysis, rasters of all parameters were created using GRASS GIS software. Using the analytic hierarchy process, the calculated rasters are assigned with weight coefficients, and the sum of all those rasters gives the final raster of optimal locations for the construction of solar power plants in Croatia. To test the accuracy of the obtained results, sensitivity analysis was performed using different weight coefficients of the parameters. From the sensitivity analysis results, as well as a histogram and statistical indicators of the three rasters, it is apparent that raster F1 gives the best results. The most decisive parameters in determining the optimal solar plant locations that result from this research are GHI, land cover, and distance to the electricity network. Full article
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Open AccessArticle
GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
Remote Sens. 2019, 11(12), 1480; https://doi.org/10.3390/rs11121480
Received: 2 May 2019 / Revised: 13 June 2019 / Accepted: 20 June 2019 / Published: 21 June 2019
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Abstract
The network real-time differential positioning technique is a good choice for meter and sub-meter level’s navigation. More attention has been paid to the Global Positioning System (GPS) and GPS + GLONASS (GLObal NAvigation Satellite System) network real-time differential positioning, but less on the [...] Read more.
The network real-time differential positioning technique is a good choice for meter and sub-meter level’s navigation. More attention has been paid to the Global Positioning System (GPS) and GPS + GLONASS (GLObal NAvigation Satellite System) network real-time differential positioning, but less on the GPS + BDS (BeiDou Navigation Satellite System) combination. This paper focuses on the GPS + BDS network real-time differential positioning. Since the noise of pseudorange observation is large, carrier-phase-smoothed pseudorange is usually used in the network real-time differential positioning to improve the positioning accuracy, while it will be interrupted once the satellite signal is lost or a cycle slip occurs. An improved algorithm in the position domain based on position variation information is proposed. The improved method is immune to the smoothing window and only depends on the number of available satellites. The performance of the network real-time differential positioning using the improved method is evaluated. The performance of GPS + BDS combination is compared with GPS-only solution as well. The results show that the positioning accuracy can be increased by around 10%–40% using the improved method compared with the traditional one. The improved method is less affected by the satellite constellation. The positioning accuracy of GPS + BDS solution is better than that of GPS-only solution, and can reach up to 0.217 m, 0.159 m and 0.330 m in the north, east and up components for the static user station, and 0.122 m, 0.133 m and 0.432 m for the dynamic user station. The positioning accuracy variation does not only depend on whether the user is inside or outside the network, but also on the position relation between the user and network. Full article
(This article belongs to the Special Issue Global Navigation Satellite Systems for Earth Observing System)
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Open AccessArticle
Raindrop Size Distributions and Rain Characteristics Observed by a PARSIVEL Disdrometer in Beijing, Northern China
Remote Sens. 2019, 11(12), 1479; https://doi.org/10.3390/rs11121479
Received: 12 May 2019 / Revised: 12 June 2019 / Accepted: 19 June 2019 / Published: 21 June 2019
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Abstract
Fourteen-month precipitation measurements from a second-generation PARSIVEL disdrometer deployed in Beijing, northern China, were analyzed to investigate the microphysical structure of raindrop size distribution and its implications on polarimetric radar applications. Rainfall types are classified and analyzed in the domain of median volume [...] Read more.
Fourteen-month precipitation measurements from a second-generation PARSIVEL disdrometer deployed in Beijing, northern China, were analyzed to investigate the microphysical structure of raindrop size distribution and its implications on polarimetric radar applications. Rainfall types are classified and analyzed in the domain of median volume diameter D 0 and the normalized intercept parameter N w . The separation line between convective and stratiform rain is almost equivalent to rain rate at 8.6 mm h−1 and radar reflectivity at 36.8 dBZ. Convective rain in Beijing shows distinct seasonal variations in log 10 N w D 0 domain. X-band dual-polarization variables are simulated using the T-matrix method to derive radar-based quantitative precipitation estimation (QPE) estimators, and rainfall products at hourly scale are evaluated for four radar QPE estimators using collocated but independent rain gauge observations. This study also combines the advantages of individual estimators based on the thresholds on polarimetric variables. Results show that the blended QPE estimator has better performance than others. The rainfall microphysical analysis presented in this study is expected to facilitate the development of a high-resolution X-band radar network for urban QPE applications. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation: Part II)
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Open AccessArticle
The Messapic Site of Muro Leccese: New Results from Integrated Geophysical and Archaeological Surveys
Remote Sens. 2019, 11(12), 1478; https://doi.org/10.3390/rs11121478
Received: 7 May 2019 / Revised: 10 June 2019 / Accepted: 18 June 2019 / Published: 21 June 2019
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Abstract
The regular application of geophysical survey techniques to evaluate archaeological sites is well established as a method for locating, defining, and mapping buried archaeological materials. However, it is not always feasible to apply a range of different methods over a particular site or [...] Read more.
The regular application of geophysical survey techniques to evaluate archaeological sites is well established as a method for locating, defining, and mapping buried archaeological materials. However, it is not always feasible to apply a range of different methods over a particular site or landscape due to constraints in time or funding. This paper addresses the integrated application of three geophysical survey methods over an important archaeological site located in south Italy. In particular, it is focused on the results achieved from a past geophysical survey and the ongoing excavations performed by archaeologists in the site of Muro Leccese. Muro Leccese (Lecce) is one of the most important Messapian archaeological sites in southern Italy. The archaeological interest of the site was generated since the discovery of the remains of Messapian walls (late 4th–3rd centuries BC). With the aim of widening the archaeological knowledge of the Messapian settlement, several integrated methods, including magnetometry, ground-penetrating radar, and electrical resistivity tomography were used on site to fulfill a number of different research objectives. Since the most important targets were expected to be located at shallow soil depth, a three-dimensional (3D) ground-penetrating radar (GPR) survey was carried out in two zones, which were labeled respectively as zone 1 and zone 2, and were both quite close to the archaeological excavations. The GPR investigations were integrated with a 3D electrical resistivity tomography (ERT) survey in zone 1 and with a magnetometric, in gradiometry configuration survey in zone 2. The integration of several techniques allowed mapping the structural remains of this area and leading the excavation project. The geophysical results show a good correspondence with the archaeological features that were found after the excavation. Current work on the geophysical survey data using different codes for the processing of the data and merging different datasets using a Geographic Information System allowed achieving a user-friendly visualization that was presented to the archaeologists. Full article
(This article belongs to the Special Issue Recent Progress in Ground Penetrating Radar Remote Sensing)
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