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Remote Sens., Volume 10, Issue 6 (June 2018)

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Cover Story (view full-size image) Land use change and reservoir construction alter sediment transport within rivers. These changes [...] Read more.
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Open AccessArticle Parameterization of Spectral Particulate and Phytoplankton Absorption Coefficients in Sognefjord and Trondheimsfjord, Two Contrasting Norwegian Fjord Ecosystems
Remote Sens. 2018, 10(6), 977; https://doi.org/10.3390/rs10060977
Received: 1 May 2018 / Revised: 7 June 2018 / Accepted: 18 June 2018 / Published: 20 June 2018
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Abstract
We present here parameterizations of particulate and phytoplankton absorption coefficients as functions of pigment concentrations (Tchla) in Sognefjord and Trondheimsfjord along the northwestern coast of Norway. The total particulate and non-algal optical densities were measured via quantitative filter technique (QFT) in a spectrophotometer
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We present here parameterizations of particulate and phytoplankton absorption coefficients as functions of pigment concentrations (Tchla) in Sognefjord and Trondheimsfjord along the northwestern coast of Norway. The total particulate and non-algal optical densities were measured via quantitative filter technique (QFT) in a spectrophotometer with integrating sphere. The spectral parameter coefficients A(λ) and E(λ) of the power law describing variations of particulate and phytoplankton absorption coefficients as a function of Tchla, were not only different from those provided for open ocean case 1 waters, but also exhibited differences in the two fjords under investigation. Considering the influence of glacial meltwater leading to increased inorganic sediment load in Sognefjord we investigate differences in two different parameterizations, developed by excluding and including inner Sognefjord stations. Tchla are modelled to test the parameterizations and validated against data from the same cruise and that from a repeated campaign. Being less influenced by non-algal particles parameterizations performed well in Trondheimsfjord and yielded high coefficients of determination (R2) of modelled vs. measured Tchla. In Sognefjord, the modelled vs. measured Tchla resulted in better R2 with parameter coefficients developed excluding the inner-fjord stations influenced by glacial meltwater influx. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements
Remote Sens. 2018, 10(6), 976; https://doi.org/10.3390/rs10060976
Received: 17 May 2018 / Revised: 8 June 2018 / Accepted: 14 June 2018 / Published: 20 June 2018
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Abstract
A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously
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A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations. Full article
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Open AccessArticle A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment
Remote Sens. 2018, 10(6), 975; https://doi.org/10.3390/rs10060975
Received: 6 May 2018 / Revised: 30 May 2018 / Accepted: 15 June 2018 / Published: 19 June 2018
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Abstract
Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human
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Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management. Full article
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Open AccessArticle Combining a Two Source Energy Balance Model Driven by MODIS and MSG-SEVIRI Products with an Aggregation Approach to Estimate Turbulent Fluxes over Sparse and Heterogeneous Vegetation in Sahel Region (Niger)
Remote Sens. 2018, 10(6), 974; https://doi.org/10.3390/rs10060974
Received: 16 May 2018 / Revised: 12 June 2018 / Accepted: 12 June 2018 / Published: 19 June 2018
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Abstract
Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere
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Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network
Remote Sens. 2018, 10(6), 973; https://doi.org/10.3390/rs10060973
Received: 30 April 2018 / Revised: 15 June 2018 / Accepted: 17 June 2018 / Published: 19 June 2018
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Abstract
Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep
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Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1) First, we implement the multiclass version of the intersection-over-union (IoU) loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2) Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3) Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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Open AccessArticle Assessment of Irrigation Performance in Large River Basins under Data Scarce Environment—A Case of Kabul River Basin, Afghanistan
Remote Sens. 2018, 10(6), 972; https://doi.org/10.3390/rs10060972
Received: 10 April 2018 / Revised: 7 June 2018 / Accepted: 11 June 2018 / Published: 18 June 2018
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Abstract
The Kabul River basin (KRB) of Afghanistan, a lifeline of around 10 million people, has multiplicity of governance, management, and development-related challenges leading to inequity, inadequacy, and unreliability of irrigation water distribution. Prior to any uplifting intervention, there is a need to evaluate
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The Kabul River basin (KRB) of Afghanistan, a lifeline of around 10 million people, has multiplicity of governance, management, and development-related challenges leading to inequity, inadequacy, and unreliability of irrigation water distribution. Prior to any uplifting intervention, there is a need to evaluate the performance of irrigation system on a long term basis to identify the existing bottlenecks. Although there are several indicators available for the performance evaluation of the irrigation schemes, we used the coefficient of variation (CV) of actual evapotranspiration (ETa) in space (basin, sub-basin, and provincial level), relative evapotranspiration (RET), and temporal CV of RET, to assess the equity, adequacy, and reliability of water distribution, respectively, from 2003 to 2013. The ETa was estimated through a surface energy balance system (SEBS) algorithm and the ETa estimates were validated using the advection aridity (AA) method with a R2 value of 0.81 and 0.77 at Nawabad and Sultanpur stations, respectively. The global land data assimilation system (GLDAS) and moderate-resolution imaging spectroradiometer (MODIS) products were used as main inputs to the SEBS. Results show that the mean seasonal sub-based RET values during summer (May–September) (0.37 ± 0.06) and winter (October–April) (0.40 ± 0.08) are below the target values (RET ≥ 0.75) during 2003–2013. The CV of the mean ETa, within sub-basins and provinces for the entire study period, has an equitable distribution of water from October–January (0.09 ± 0.04), whereas the highest inequity (0.24 ± 0.08) in water distribution is during early summer. The range of the CV of the mean ETa (0.04–0.06) on a monthly and seasonal basis shows the unreliability of water supplies in several provinces or sub-basins. The analysis of the temporal CV of mean RET highlights the unreliable water supplies across the entire basin. The maximum ETa during the study period was estimated for the Shamal sub-basin (552 ± 43 mm) while among the provinces, Kunar experienced the highest ETa (544 ± 39 mm). This study highlights the dire need for interventions to improve the irrigation performance in time and space. The proposed methodology can be used as a framework for monitoring and implementing water distribution plans in future. Full article
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Open AccessArticle Improving Geometric Performance for Imagery Captured by Non-Cartographic Optical Satellite: A Case Study of GF-1 WFV Imagery
Remote Sens. 2018, 10(6), 971; https://doi.org/10.3390/rs10060971
Received: 14 May 2018 / Revised: 8 June 2018 / Accepted: 13 June 2018 / Published: 18 June 2018
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Abstract
Numerous countries have established their own Earth observing systems (EOSs) for global change research. Data acquisition efforts are generally only concerned with the completion of the mission regardless of the potential to expand into other areas, which reduces the application effectiveness of Earth
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Numerous countries have established their own Earth observing systems (EOSs) for global change research. Data acquisition efforts are generally only concerned with the completion of the mission regardless of the potential to expand into other areas, which reduces the application effectiveness of Earth observation data. This paper explores the cartographic possibility of images being not initially intended for surveying and mapping, and a novel method is proposed to improve the geometric performance. First, the rigorous sensor model (RSM) is recovered from the rational function model (RFM), and then the system errors of the non-cartographic satellite’s imagery are compensated by using the conventional geometric calibration method based on RSM; finally, a new and improved RFM is generated. The advantage of the method over traditional ones is that it divides the errors into static errors and non-static errors for each image during the improvement process. Experiments using images collected with the Gaofen-1 (GF-1) wide-field view (WFV) camera demonstrate that the orientation accuracy of the proposed method is within 1 pixel for both calibration and validation images, and the obvious high-order system errors are eliminated. Moreover, a block adjustment test shows that the vertical accuracy is improved from 21 m to 11 m with four ground control points (GCPs) after compensation, which can fulfill requirements for 1:100,000 stereo mapping in mountainous areas. Generally, the proposed method can effectively improve the geometric potential for images captured by non-cartographic satellite. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia
Remote Sens. 2018, 10(6), 970; https://doi.org/10.3390/rs10060970
Received: 30 April 2018 / Revised: 30 May 2018 / Accepted: 13 June 2018 / Published: 17 June 2018
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Abstract
Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to
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Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Relation between Convective Rainfall Properties and Antecedent Soil Moisture Heterogeneity Conditions in North Africa
Remote Sens. 2018, 10(6), 969; https://doi.org/10.3390/rs10060969
Received: 5 May 2018 / Revised: 10 June 2018 / Accepted: 14 June 2018 / Published: 17 June 2018
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Abstract
Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall properties—such as rain volume or timing—has yet to be confirmed by observational data.
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Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall properties—such as rain volume or timing—has yet to be confirmed by observational data. Here, we analyze 10 years of satellite-based sub-daily soil moisture and precipitation records and explore the potential of strong spatial gradients in morning soil moisture to influence the properties of afternoon rainfall in the North African region, at the 100-km scale. We find that the convective rain systems that form over locally drier soils and anomalously strong soil moisture gradients have a tendency to initiate earlier in the afternoon; they also yield lower volumes of rain, weaker intensity and lower spatial variability. The strongest sensitivity to antecedent soil conditions is identified for the timing of the rain onset; it is found to be correlated with the magnitude of the soil moisture gradient. Further analysis shows that the early initiation of rainfall over dry soils and strong surface gradients yet requires the presence of a very moist boundary layer on that day. Our findings agree well with the expected effects of thermally-induced circulation on rainfall properties suggested by theoretical studies and point to the potential of locally drier and heterogeneous soils to influence convective rainfall development. The systematic nature of the identified effect of soil moisture state on the onset time of rainstorms in the region is of particular relevance and may help foster research on rainfall predictability. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle The Use of Massive Deformation Datasets for the Analysis of Spatial and Temporal Evolution of Mauna Loa Volcano (Hawai’i)
Remote Sens. 2018, 10(6), 968; https://doi.org/10.3390/rs10060968
Received: 7 May 2018 / Revised: 5 June 2018 / Accepted: 5 June 2018 / Published: 17 June 2018
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Abstract
In this work, we exploited large DInSAR and GPS datasets to create a 4D image of the magma transfer processes at Mauna Loa Volcano (Island of Hawai’i) from 2005 to 2015. The datasets consist of 23 continuous GPS time series and 307 SAR
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In this work, we exploited large DInSAR and GPS datasets to create a 4D image of the magma transfer processes at Mauna Loa Volcano (Island of Hawai’i) from 2005 to 2015. The datasets consist of 23 continuous GPS time series and 307 SAR images acquired from ascending and descending orbits by ENVISAT (ENV) and COSMO-SkyMed (CSK) satellites. Our results highlight how the joint use of SAR data acquired from different orbits (thus with different look angles and wavelengths), together with deformation data from GPS networks and geological information can significantly improve the constraints on the geometry and location of the sources responsible for the observed deformation. The analysis of these datasets has been performed by using an innovative method that allows building a complex source configuration. The results suggest that the deformation pattern observed from 2005 to 2015 has been controlled by three deformation sources: the ascent of magma along a conduit, the opening of a dike and the slip along the basal decollement. This confirms that the intrusion of the magma within a tabular system (rift dikes) may trigger the sliding of the SE portion of the volcanic edifice along the basal decollement. This case study confirms that it is now possible to exploit large geodetic datasets to improve our knowledge of volcano dynamics. The same approach could also be easily applied in other geodynamical contexts such as geothermal reservoirs and regions with complex tectonics. Full article
(This article belongs to the Special Issue Remote Sensing of Tectonic Deformation)
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Open AccessArticle Investigation of Precipitable Water Vapor Obtained by Raman Lidar and Comprehensive Analyses with Meteorological Parameters in Xi’an
Remote Sens. 2018, 10(6), 967; https://doi.org/10.3390/rs10060967
Received: 28 May 2018 / Accepted: 14 June 2018 / Published: 17 June 2018
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Abstract
To evaluate the potential of Raman lidar observations for measuring precipitable water vapor (PWV), PWV variations and distribution characteristics were investigated in Xi’an (34.233°N, 108.911°E), and its comparisons with meteorological parameters were also analysed. Comparisons of lidar PWV with radiosonde PWV verified the
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To evaluate the potential of Raman lidar observations for measuring precipitable water vapor (PWV), PWV variations and distribution characteristics were investigated in Xi’an (34.233°N, 108.911°E), and its comparisons with meteorological parameters were also analysed. Comparisons of lidar PWV with radiosonde PWV verified the ability and accuracy of using Raman lidars for PWV measurements. The diurnal and monthly variation trends in PWV in different layers are first discussed via the statistical analysis of lidar data from November 2013 to July 2016; different proportions of PWV were found in different layers, and the PWV in each layer presented a slight diurnal change trend and consistent seasonal variation, which was relatively rich in summer, less so in spring and autumn, and relatively deficient in winter. Furthermore, correlation analyses between lidar PWV and meteorological parameters are explored. Water vapor pressure and surface temperature revealed the same inter-seasonal oscillation of PWV, with a correlation coefficient of ~0.90. However, incomplete synchronization was found between PWV and relative humidity and precipitation parameters. Higher humidity appeared in the late summer and the beginning of autumn of each year, which was also the case for precipitation and precipitation efficiency. In addition, atmospheric water vapor density profiles and the obtained PWV by Raman lidar are discussed employing a rainfall case, and a comprehensive analysis with meteorological parameters is undertaken. The intensifying characteristics of vertical change in water vapor and the accumulation of PWV in the lower troposphere can be captured by lidar before the onset of rainfall. In contrast to the obvious diurnal change trend, such meteorological parameters as relative humidity, water vapor pressure, and dew-point temperature difference are accompanied with stable trends with a change rate of close to 0 in the rainfall processes; they also show high correlated variations with lidar PWV. Thus, with the advantage of lidar detection, investigation of water vapor profiles and PWV by Raman lidar, and the comprehensive correlation analyses with synchronic meteorological parameters can prove to be good indications of rainfall. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Properties)
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Open AccessArticle Estimation of Water Level Changes of Large-Scale Amazon Wetlands Using ALOS2 ScanSAR Differential Interferometry
Remote Sens. 2018, 10(6), 966; https://doi.org/10.3390/rs10060966
Received: 23 May 2018 / Revised: 13 June 2018 / Accepted: 15 June 2018 / Published: 17 June 2018
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Abstract
Differential synthetic aperture radar (SAR) interferometry (DInSAR) has been successfully used to estimate water level changes (∂h/∂t) over wetlands and floodplains. Specifically, amongst ALOS PALSAR datasets, the fine-beam stripmap mode has been mostly implemented to estimate ∂h/∂t due to its availability of multitemporal
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Differential synthetic aperture radar (SAR) interferometry (DInSAR) has been successfully used to estimate water level changes (∂h/∂t) over wetlands and floodplains. Specifically, amongst ALOS PALSAR datasets, the fine-beam stripmap mode has been mostly implemented to estimate ∂h/∂t due to its availability of multitemporal images. However, the fine-beam observation mode provides limited swath coverage to study large floodplains and wetlands, such as the Amazon floodplains. Therefore, for the first time, this paper demonstrates that ALOS2 ScanSAR data can be used to estimate the large-scale ∂h/∂t in Amazon floodplains. The basic procedures and challenges of DInSAR processing with ALOS2 ScanSAR data are addressed and final ∂h/∂t maps are generated based on the Satellite with ARgos and ALtiKa (SARAL) altimetry’s reference data. This study reveals that the local ∂h/∂t patterns of Amazon floodplains are spatially complex with highly interconnected floodplain channels, but the large-scale (with 350 km swath) ∂h/∂t patterns are simply characterized by river water flow directions. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle The Heterogeneity of Air Temperature in Urban Residential Neighborhoods and Its Relationship with the Surrounding Greenspace
Remote Sens. 2018, 10(6), 965; https://doi.org/10.3390/rs10060965
Received: 18 April 2018 / Revised: 13 May 2018 / Accepted: 13 June 2018 / Published: 16 June 2018
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Abstract
The thermal environment in residential areas is directly related to the living quality of residents. Therefore, it is important to understand thermal heterogeneity and ways to regulate temperature in residential neighborhoods. We investigated the spatial heterogeneity and temporal dynamics of air temperatures in
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The thermal environment in residential areas is directly related to the living quality of residents. Therefore, it is important to understand thermal heterogeneity and ways to regulate temperature in residential neighborhoods. We investigated the spatial heterogeneity and temporal dynamics of air temperatures in 20 residential neighborhoods within the 5th ring road of Beijing, China. We further explored how the variations in air temperature were related to the patterns of the surrounding greenspace at different scales. We found that: (1) large air temperature differences existed among residential neighborhoods, with hourly maximum differences in air temperature reaching 5.30 °C on hot summer days; (2) not only the percentage but also the spatial configuration (e.g., edge density) of greenspace affected the local air temperature; and (3) the effects of spatial greenspace patterns on air temperature were scale dependent and varied by season. For example, increasing the proportion of greenspace in surrounding areas within a 100-m radius and increasing the edge density within radii from 500 to 1000 m could lower air temperatures in summer but not affect air temperatures in winter. In addition, decreasing the edge density of greenspaces within a 100-m radius of the surrounding areas would lead to an increase in air temperature in winter but not affect the air temperature in summer. These results extend our understanding of thermal environments and their relationships with greenspace patterns at the microscale (i.e., residential neighborhoods). They also provide useful information for urban planners to optimize greenspace patterns under better thermal conditions at the neighborhood scale. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval
Remote Sens. 2018, 10(6), 964; https://doi.org/10.3390/rs10060964
Received: 11 May 2018 / Revised: 9 June 2018 / Accepted: 14 June 2018 / Published: 16 June 2018
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Abstract
Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image.
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Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels are required for more complex problems, such as RSIR. This motivated us to present a new benchmark dataset termed “MLRSIR” that was labeled from an existing single-labeled remote sensing archive. MLRSIR contained a total of 17 classes, and each image had at least one of 17 pre-defined labels. We evaluated the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep-learning-based ones on MLRSIR. More specifically, we compared the performances of RSIR methods from both single-label and multi-label perspectives. These results presented the advantages of multiple labels over single labels for interpreting complex remote sensing images, and serve as a baseline for future research on multi-label RSIR. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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Open AccessArticle A Seismic Capacity Evaluation Approach for Architectural Heritage Using Finite Element Analysis of Three-Dimensional Model: A Case Study of the Limestone Hall in the Ming Dynasty
Remote Sens. 2018, 10(6), 963; https://doi.org/10.3390/rs10060963
Received: 2 May 2018 / Revised: 28 May 2018 / Accepted: 13 June 2018 / Published: 15 June 2018
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Abstract
A lot of architectural heritage in China are urgently in need to carry out seismic assessment for further conservation. In this paper, a seismic capacity evaluation approach for architectural heritage using finite element analysis with precision three-dimensional data was proposed. The Limestone Hall
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A lot of architectural heritage in China are urgently in need to carry out seismic assessment for further conservation. In this paper, a seismic capacity evaluation approach for architectural heritage using finite element analysis with precision three-dimensional data was proposed. The Limestone Hall of Shaanxi Province was taken as an example. First, low attitude unmanned aerial vehicle photogrammetry and a close-range photogrammetry camera were used to collect multiple view images to obtain the precision three-dimensional current model of the Limestone. Second, the dimensions of internal structures of Limestone Hall are obtained by means of structural analysis; re-establishing the ideal model of Limestone Hall based on the modeling software. Third, a finite element analysis was conducted to find out the natural frequency and seismic stress in various conditions with the 3D model using ANSYS software. Finally, the seismic capacity analysis results were comprehensively evaluated for the risk assessment and simulation. The results showed that for architectural heritage with a multilayer structure, utilizing photogrammetric surveying and mapping, 3D software modeling, finite element software simulation, and seismic evaluation for simulation was feasible where the precision of the modeling and parameters determine the accuracy of the simulation. The precise degree of the three-dimensional model, the accurate degree of parameter measurement and estimation, the setting of component attributes in the finite element model and the strategy of finite element analysis have an important effect on the result of seismic assessment. The main body structure of the Limestone Hall could resist an VII-degree earthquake at most, and the ridge of the second floor could not resist a V-degree earthquake due to unsupported conditions. The maximum deformation of the Limestone Hall during the earthquake occurred in the tabia layer below the second roof. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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