Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Remote Sens., Volume 9, Issue 12 (December 2017)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story (view full-size image) Release of methane (CH4) from the Arctic can affect global climate. Predicting future CH4 emissions [...] Read more.
View options order results:
result details:
Displaying articles 1-134
Export citation of selected articles as:
Open AccessArticle Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
Remote Sens. 2017, 9(12), 1340; https://doi.org/10.3390/rs9121340
Received: 14 October 2017 / Revised: 2 December 2017 / Accepted: 2 December 2017 / Published: 20 December 2017
PDF Full-text (3994 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we examined the phenology of the salt marsh ecosystem across coastal Louisiana (LA) for a 16-year time period (2000–2015) using NASA’s Moderate Resolution Imaging Spectroradiometer’s (MODIS) eight-day average surface reflectance images (500 m). We compared the performances of least squares
[...] Read more.
In this study, we examined the phenology of the salt marsh ecosystem across coastal Louisiana (LA) for a 16-year time period (2000–2015) using NASA’s Moderate Resolution Imaging Spectroradiometer’s (MODIS) eight-day average surface reflectance images (500 m). We compared the performances of least squares fitted asymmetric Gaussian (AG) and double logistic (DL) smoothing functions in terms of increasing the signal-to-noise ratio from the raw phenology derived from the time-series composites. We performed derivative analysis to determine the appropriate start of season (SOS) and end of season (EOS) thresholds. After that, we extracted the seasonality parameters in TIMESAT, and studied the effect of environmental disturbances/anomalies on the seasonality parameters. Finally, we performed trend analysis using the derived seasonality parameters such as base green biomass (GBM) value, maximum GBM value, seasonal amplitude, and small seasonal integral. Based on root mean square error (RMSE) values and residual plots, we selected the best thresholds for SOS (5% of amplitude) and EOS (20% of amplitude), along with the best smoothing function. The selected SOS and EOS thresholds were able to capture the environmental disturbances that have affected the salt marsh ecosystem during the 16-year time period. Our trend analysis results indicate positive trends in the base GBM values in the salt marshes of LA. However, we did not notice as much of a positive trend in the maximum GBM levels. Hence, we observed mostly negative changes in the GBM amplitude and small seasonal integral values. These negative changes indicated the overall progressive decline in the rates of photosynthesis and biomass allocation in the LA salt marsh ecosystem, which is most likely due to elevated atmospheric carbon dioxide levels and sea level rise. The results illustrate both the relative efficiency of MODIS-based biophysical models for analyzing salt marsh phenology, and performances of the smoothing techniques in terms of improving the signal-to-noise ratio of the MODIS-derived phenology. Full article
Figures

Graphical abstract

Open AccessArticle High-Resolution Mapping of Freeze/Thaw Status in China via Fusion of MODIS and AMSR2 Data
Remote Sens. 2017, 9(12), 1339; https://doi.org/10.3390/rs9121339
Received: 30 October 2017 / Revised: 30 November 2017 / Accepted: 13 December 2017 / Published: 20 December 2017
Cited by 1 | PDF Full-text (6727 KB) | HTML Full-text | XML Full-text
Abstract
Transition of freeze/thaw (F/T) affects land-atmospheric interactions and other biospheric dynamics. Global F/T statuses are normally monitored using microwave remote sensing, but at coarse resolutions (e.g., 25 km). Integration of coarse microwave remote sensing data with finer satellite products represents an opportunity to
[...] Read more.
Transition of freeze/thaw (F/T) affects land-atmospheric interactions and other biospheric dynamics. Global F/T statuses are normally monitored using microwave remote sensing, but at coarse resolutions (e.g., 25 km). Integration of coarse microwave remote sensing data with finer satellite products represents an opportunity to further enhance our ability to map F/T statuses regionally and globally. Here, we implemented and tested an approach to generate daily F/T status maps at a 5-km spatial resolution through the fusion of passive microwave data from AMSR2 and land surface temperature products from MODIS, using China as our study area for the year 2013 and 2014. Moreover, possible influences from elevation, vegetation, seasonality, etc., were also analyzed, as such analysis provides a direction to improve the approach. Overall, our freeze/thaw maps agreed well with ground reference observations, with an accuracy of ~86.6%. The new F/T maps helped to identify regions subject to frequent F/T transitions through the year, such as the Qinghai-Tibetan Plateau, Xinjiang, Gansu, Heilongjiang, Jilin, and Liaoning Province. This study indicates that the combination of AMSR2 and MODIS observations provides an effective method to obtain finer F/T maps (5-km or lower) for extensive regions. The finer F/T maps improve our knowledge of the F/T state detected by satellite remote sensing, and have a wide range of applications in regional studies considering land surface heterogeneity and models (e.g., community land models). Full article
Figures

Graphical abstract

Open AccessArticle Short-Term Impacts of the Air Temperature on Greening and Senescence in Alaskan Arctic Plant Tundra Habitats
Remote Sens. 2017, 9(12), 1338; https://doi.org/10.3390/rs9121338
Received: 31 October 2017 / Revised: 12 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
PDF Full-text (7060 KB) | HTML Full-text | XML Full-text
Abstract
Climate change is warming the temperatures and lengthening the Arctic growing season with potentially important effects on plant phenology. The ability of plant species to acclimate to changing climatic conditions will dictate the level to which their spatial coverage and habitat-type dominance is
[...] Read more.
Climate change is warming the temperatures and lengthening the Arctic growing season with potentially important effects on plant phenology. The ability of plant species to acclimate to changing climatic conditions will dictate the level to which their spatial coverage and habitat-type dominance is different in the future. While the effect of changes in temperature on phenology and species composition have been observed at the plot and at the regional scale, a systematic assessment at medium spatial scales using new noninvasive sensor techniques has not been performed yet. At four sites across the North Slope of Alaska, changes in the Normalized Difference Vegetation Index (NDVI) signal were observed by Mobile Instrumented Sensor Platforms (MISP) that are suspended over 50 m transects spanning local moisture gradients. The rates of greening (measured in June) and senescence (measured in August) in response to the air temperature was estimated by changes in NDVI measured as the difference between the NDVI on a specific date and three days later. In June, graminoid- and shrub-dominated habitats showed the greatest rates of NDVI increase in response to the high air temperatures, while forb- and lichen-dominated habitats were less responsive. In August, the NDVI was more responsive to variations in the daily average temperature than spring greening at all sites. For graminoid- and shrub-dominated habitats, we observed a delayed decrease of the NDVI, reflecting a prolonged growing season, in response to high August temperatures. Consequently, the annual C assimilation capacity of these habitats is increased, which in turn may be partially responsible for shrub expansion and further increases in net summer CO2 fixation. Strong interannual differences highlight that long-term and noninvasive measurements of such complex feedback mechanisms in arctic ecosystems are critical to fully articulate the net effects of climate variability and climate change on plant community and ecosystem processes. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
Figures

Figure 1

Open AccessArticle Experimental Evaluation of Several Key Factors Affecting Root Biomass Estimation by 1500 MHz Ground-Penetrating Radar
Remote Sens. 2017, 9(12), 1337; https://doi.org/10.3390/rs9121337
Received: 3 November 2017 / Revised: 11 December 2017 / Accepted: 17 December 2017 / Published: 20 December 2017
Cited by 1 | PDF Full-text (4009 KB) | HTML Full-text | XML Full-text
Abstract
Accurate quantification of coarse roots without disturbance represents a gap in our understanding of belowground ecology. Ground penetrating radar (GPR) has shown significant promise for coarse root detection and measurement, however root orientation relative to scanning transect direction, the difficulty identifying dead root
[...] Read more.
Accurate quantification of coarse roots without disturbance represents a gap in our understanding of belowground ecology. Ground penetrating radar (GPR) has shown significant promise for coarse root detection and measurement, however root orientation relative to scanning transect direction, the difficulty identifying dead root mass, and the effects of root shadowing are all key factors affecting biomass estimation that require additional research. Specifically, many aspects of GPR applicability for coarse root measurement have not been tested with a full range of antenna frequencies. We tested the effects of multiple scanning directions, root crossover, and root versus soil moisture content in a sand-hill mixed oak community using a 1500 MHz antenna, which provides higher resolution than the oft used 900 MHz antenna. Combining four scanning directions produced a significant relationship between GPR signal reflectance and coarse root biomass (R2 = 0.75) (p < 0.01) and reduced variability encountered when fewer scanning directions were used. Additionally, significantly fewer roots were correctly identified when their moisture content was allowed to equalize with the surrounding soil (p < 0.01), providing evidence to support assertions that GPR cannot reliably identify dead root mass. The 1500 MHz antenna was able to identify roots in close proximity of each other as well as roots shadowed beneath shallower roots, providing higher precision than a 900 MHz antenna. As expected, using a 1500 MHz antenna eliminates some of the deficiency in precision observed in studies that utilized lower frequency antennas. Full article
Figures

Graphical abstract

Open AccessArticle High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão
Remote Sens. 2017, 9(12), 1336; https://doi.org/10.3390/rs9121336
Received: 17 October 2017 / Revised: 3 December 2017 / Accepted: 10 December 2017 / Published: 20 December 2017
Cited by 1 | PDF Full-text (3930 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess
[...] Read more.
Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3–0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes—trees, shrubs/grasses, and bare land—across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView) outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral) resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses). This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world’s semi-arid ecosystems, and we provide methods that could be scaled to do so. Full article
(This article belongs to the Special Issue Google Earth Engine Applications)
Figures

Graphical abstract

Open AccessArticle Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data
Remote Sens. 2017, 9(12), 1334; https://doi.org/10.3390/rs9121334
Received: 18 October 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
Cited by 1 | PDF Full-text (4381 KB) | HTML Full-text | XML Full-text
Abstract
Although lake surface water temperature (LSWT) is defined as an essential climate variable (ECV) within the global climate observing system (GCOS), current satellite-based retrieval techniques do not fulfill the GCOS accuracy requirements. The split-window (SW) retrieval method is well-established, and the split-window coefficients
[...] Read more.
Although lake surface water temperature (LSWT) is defined as an essential climate variable (ECV) within the global climate observing system (GCOS), current satellite-based retrieval techniques do not fulfill the GCOS accuracy requirements. The split-window (SW) retrieval method is well-established, and the split-window coefficients (SWC) are the key elements of its accuracy. Performances of SW depends on the degree of SWC customization with respect to its application, where accuracy increases when SWC is tailored for specific situations. In the literature, different SWC customization approaches have been investigated, however, no direct comparisons have been conducted among them. This paper presents the results of a sensitivity analysis to address this gap. We show that the performance of SWC is most sensitive to customizations for specific time-windows (Sensitivity Index SI of 0.85) or spatial extents (SI 0.27). Surprisingly, the study highlights that the use of separated SWC for daytime and night-time situations has limited impact (SI 0.10). The final validation with AVHRR satellite data showed that the subtle differences among different SWC customizations were not traceable to the final uncertainty of the LSWT product. Nevertheless, this study provides a basis to critically evaluate current assumptions regarding SWC generation by directly comparing the performance of multiple customization approaches for the first time. Full article
Figures

Graphical abstract

Open AccessFeature PaperArticle A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data
Remote Sens. 2017, 9(12), 1333; https://doi.org/10.3390/rs9121333
Received: 30 September 2017 / Revised: 13 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
Cited by 2 | PDF Full-text (18481 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in
[...] Read more.
Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download. Full article
(This article belongs to the Section Remote Sensing Image Processing)
Figures

Graphical abstract

Open AccessTechnical Note Land-Air Interactions over Urban-Rural Transects Using Satellite Observations: Analysis over Delhi, India from 1991–2016
Remote Sens. 2017, 9(12), 1283; https://doi.org/10.3390/rs9121283
Received: 28 September 2017 / Revised: 23 November 2017 / Accepted: 7 December 2017 / Published: 20 December 2017
PDF Full-text (3090 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic
[...] Read more.
Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic rise of 30% from 1977. The effect of changing LULC, at a local scale, on various variables-land surface temperature (LST), normalized difference vegetation index (NDVI), emissivity, albedo, evaporation, Bowen ratio, and planetary boundary layer (PBL) height, from 1991–2016, is investigated. To assess the spatio-temporal dynamics of land-air interactions, we select two different 100 km transects covering the NE-SW and NW-SE expanse of Delhi and its adjoining areas. High NDVI and emissivity is found for regions with green cover and drastic reduction is noted in built-up area clusters. In both of the transects, land surface variations manifest itself in patterns of LST variation. Parametric and non-parametric correlations are able to statistically establish the land-air interactions in the city. NDVI, an indirect indicator for LULC classes, significantly helps in understanding the modifications in LST and ultimately air temperature. Significant, strong positive relationships exist between skin temperature and evaporation, skin temperature and PBL height, and PBL height and evaporation, providing insights into the meteorological changes that are associated with urbanization. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
Figures

Graphical abstract

Open AccessArticle Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau
Remote Sens. 2017, 9(12), 1332; https://doi.org/10.3390/rs9121332
Received: 20 October 2017 / Revised: 6 December 2017 / Accepted: 17 December 2017 / Published: 19 December 2017
PDF Full-text (8009 KB) | HTML Full-text | XML Full-text
Abstract
Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral
[...] Read more.
Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau. Full article
Figures

Graphical abstract

Open AccessArticle Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site
Remote Sens. 2017, 9(12), 1331; https://doi.org/10.3390/rs9121331
Received: 18 October 2017 / Revised: 7 December 2017 / Accepted: 12 December 2017 / Published: 19 December 2017
PDF Full-text (3352 KB) | HTML Full-text | XML Full-text
Abstract
A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has
[...] Read more.
A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI), Optimal Interpolation (OI), Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code) and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS) over the ≥6 h forecasting period from the best model NDA attained 26% and 31% for east–west and north–south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations. Full article
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
Figures

Graphical abstract

Open AccessArticle Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Remote Sens. 2017, 9(12), 1330; https://doi.org/10.3390/rs9121330
Received: 12 November 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
Cited by 3 | PDF Full-text (2697 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and
[...] Read more.
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN. Full article
(This article belongs to the Section Remote Sensing Image Processing)
Figures

Graphical abstract

Open AccessArticle Response of Grassland Degradation to Drought at Different Time-Scales in Qinghai Province: Spatio-Temporal Characteristics, Correlation, and Implications
Remote Sens. 2017, 9(12), 1329; https://doi.org/10.3390/rs9121329
Received: 28 October 2017 / Revised: 4 December 2017 / Accepted: 17 December 2017 / Published: 19 December 2017
PDF Full-text (4490 KB) | HTML Full-text | XML Full-text
Abstract
Grassland, as the primary vegetation on the Qinghai-Tibet Plateau, has been increasingly influenced by water availability due to climate change in last decades. Therefore, identifying the evolution of drought becomes crucial to the efficient management of grassland. However, it is not yet well
[...] Read more.
Grassland, as the primary vegetation on the Qinghai-Tibet Plateau, has been increasingly influenced by water availability due to climate change in last decades. Therefore, identifying the evolution of drought becomes crucial to the efficient management of grassland. However, it is not yet well understood as to the quantitative relationship between vegetation variations and drought at different time scales. Taking Qinghai Province as a case, the effects of meteorological drought on vegetation were investigated. Multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) considering evapotranspiration variables was used to indicate drought, and time series Normal Difference Vegetation Index (NDVI) to indicate the vegetation response. The results showed that SPEI values at different time scales reflected a complex dry and wet variation in this region. On a seasonal scale, more droughts occurred in summer and autumn. In general, the NDVI presented a rising trend in the east and southwest part and a decreasing trend in the northwest part of Qinghai Province from 1998 to 2012. Hurst indexes of NDVI revealed that 69.2% of the total vegetation was positively persistent (64.1% of persistent improvement and 5.1% of persistent degradation). Significant correlations were found for most of the SPEI values and the one year lagged NDVI, indicating vegetation made a time-lag response to drought. In addition, one month lagged NDVI made an obvious response to SPEI values at annual and biennial scales. Further analysis showed that all multiscale SPEI values have positive relationships with the NDVI trend and corresponding grassland degradation. The study highlighted the response of vegetation to meteorological drought at different time scales, which is available to predict vegetation change and further help to improve the utilization efficiency of water resources in the study region. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
Figures

Graphical abstract

Open AccessArticle GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification
Remote Sens. 2017, 9(12), 1328; https://doi.org/10.3390/rs9121328
Received: 17 October 2017 / Revised: 30 November 2017 / Accepted: 16 December 2017 / Published: 18 December 2017
Cited by 1 | PDF Full-text (10383 KB) | HTML Full-text | XML Full-text
Abstract
Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore
[...] Read more.
Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, with a main stream utilizing fine spatial-resolution panchromatic images to retain low-level information under a supervised residual network structure. An auxiliary line employed an unsupervised net to extract high-level abstract and discriminative features from multispectral images to supplement the spectral information in the main stream. Various feature extraction types from the neural network were selected and jointed in the novel net, as the combined high- and low-level features could provide a superior solution to image classification. In traditional convolutional neural networks, increased network depth might not influence the network performance perceptibly; however, we introduced a residual neural network to develop the expressive ability of the deeper net, increasing the role of net depth in feature extraction. To enhance feature robustness, we proposed a novel consolidation part in feature extraction. An adversarial net improved the feature extraction capabilities and aided digging the inherent and discriminative features from data, with increased extraction efficacy. Tests on satellite images indicated the high overall accuracy of our novel net, verifying that net depth or number of convolution kernels affected the classification capability. Various comparative tests proved the structural rationality for our two-stream structure. Full article
(This article belongs to the Section Remote Sensing Image Processing)
Figures

Figure 1

Open AccessArticle Modeling the Observed Microwave Emission from Shallow Multi-Layer Tundra Snow Using DMRT-ML
Remote Sens. 2017, 9(12), 1327; https://doi.org/10.3390/rs9121327
Received: 1 November 2017 / Revised: 7 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
PDF Full-text (2566 KB) | HTML Full-text | XML Full-text
Abstract
The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases,
[...] Read more.
The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The DMRT-ML was parameterized with the in situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. With these two configurations, the calibrated DMRT-ML successfully predicted the Tb V 37 GHz response (R correlation of 0.83) when compared with the observed airborne Tb footprints containing snow pits measurements. Using this calibrated model, the DMRT-ML was applied to the whole study region. At the satellite observation scale, observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) over the study area reflected seasonal differences between Tb V 37 GHz and Tb V 19 GHz that supports the hypothesis of the development of an early season volume scattering depth hoar layer, followed by the growth of the late season emission-dominated wind slab layer. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack at 37 GHz Tb. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
Figures

Figure 1

Open AccessArticle MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms
Remote Sens. 2017, 9(12), 1326; https://doi.org/10.3390/rs9121326
Received: 3 October 2017 / Revised: 18 November 2017 / Accepted: 14 December 2017 / Published: 16 December 2017
Cited by 2 | PDF Full-text (7914 KB) | HTML Full-text | XML Full-text
Abstract
Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different
[...] Read more.
Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN); support vector machines (SVM); and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000–2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002–2004 using a spatial resolution of 0.05°, which proved to be useful for estimating the long-term LE over North America. Full article
Figures

Graphical abstract

Open AccessArticle Adjustment of Sentinel-2 Multi-Spectral Instrument (MSI) Red-Edge Band Reflectance to Nadir BRDF Adjusted Reflectance (NBAR) and Quantification of Red-Edge Band BRDF Effects
Remote Sens. 2017, 9(12), 1325; https://doi.org/10.3390/rs9121325
Received: 10 November 2017 / Revised: 7 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
Cited by 3 | PDF Full-text (3438 KB) | HTML Full-text | XML Full-text
Abstract
Optical wavelength satellite data have directional reflectance effects over non-Lambertian surfaces, described by the bidirectional reflectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6° field of view that have been shown to have non-negligible BRDF effects in the
[...] Read more.
Optical wavelength satellite data have directional reflectance effects over non-Lambertian surfaces, described by the bidirectional reflectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6° field of view that have been shown to have non-negligible BRDF effects in the visible, near-infrared, and short wave infrared bands. MSI red-edge BRDF effects have not been investigated. In this study, they are quantified by an examination of 6.6 million (January 2016) and 10.7 million (April 2016) pairs of forward and back scatter reflectance observations extracted over approximately 20° × 10° of southern Africa. Non-negligible MSI red-edge BRDF effects up to 0.08 (reflectance units) across the 290 km wide MSI swath are documented. A recently published MODIS BRDF parameter c-factor approach to adjust MSI visible, near-infrared, and short wave infrared reflectance to nadir BRDF-adjusted reflectance (NBAR) is adapted for application to the MSI red-edge bands. The red-edge band BRDF parameters needed to implement the algorithm are provided. The parameters are derived by a linear wavelength interpolation of fixed global MODIS red and NIR BRDF model parameters. The efficacy of the interpolation is investigated using POLDER red, red-edge, and NIR BRDF model parameters, and is shown to be appropriate for the c-factor NBAR generation approach. After adjustment to NBAR, red-edge MSI BRDF effects were reduced for the January data (acquired close to the solar principal where BRDF effects are maximal) and the April data (acquired close to the orthogonal plane) for all the MSI red-edge bands. Full article
Figures

Graphical abstract

Open AccessArticle MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data
Remote Sens. 2017, 9(12), 1324; https://doi.org/10.3390/rs9121324
Received: 8 November 2017 / Revised: 7 December 2017 / Accepted: 14 December 2017 / Published: 16 December 2017
Cited by 1 | PDF Full-text (10677 KB) | HTML Full-text | XML Full-text
Abstract
We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water–sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance (R) swath data, respectively. The resolution
[...] Read more.
We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water–sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance ( R ) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R . This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing II)
Figures

Graphical abstract

Open AccessArticle A New Regionalization Scheme for Effective Ecological Restoration on the Loess Plateau in China
Remote Sens. 2017, 9(12), 1323; https://doi.org/10.3390/rs9121323
Received: 15 October 2017 / Revised: 6 December 2017 / Accepted: 14 December 2017 / Published: 15 December 2017
PDF Full-text (13662 KB) | HTML Full-text | XML Full-text
Abstract
To prevent potentially unsuitable activities during vegetation restoration, it is important to examine the impact of historical restoration activities on the target ecological system to inform future restoration policies. Taking the Loess Plateau of China as an example, a regionalization method and corresponding
[...] Read more.
To prevent potentially unsuitable activities during vegetation restoration, it is important to examine the impact of historical restoration activities on the target ecological system to inform future restoration policies. Taking the Loess Plateau of China as an example, a regionalization method and corresponding scheme were proposed to select suitable vegetation types (forested lands, woody grasslands/bushlands, grasslands, or xerophytic shrublands and semi-shrublands) for a given location using remote sensing technology in order to analyze the vegetation growth status before and after the largest ecological conservation project in the country: The Grain for Green Program (GTGP). To design the scheme, remote sensing data covering the periods before and after the implementation of the GTGP (the 1980s and 2001–2013) were collected, along with soil, meteorological, and topographic data. The net primary production (NPP) values for 2001–2013 were calculated using the Carnegie-Ames-Stanford Approach (CASA) model. Locations representing the native vegetation and the restored vegetation were first recognized using maps of vegetation cover. Then, for the restored vegetation area, the places suitable for planting the covered vegetation type were selected by comparing the NPP value of the corresponding vegetation type in the native vegetation area to the NPP value in the site under consideration. Third, half of these sites were uniformly selected based on their NPP value, and these areas and the native vegetation area were used as training regions. Based on weather, soil, and topographic data, a new regionalization scheme was designed using standardized Euclidean distances. Finally, data from the remainder of the Loess Plateau were used to validate the new regionalization scheme, which was also compared to an existing Chinese eco-geographical regionalization scheme. The results showed that the new regionalization scheme performed well, with an average potential classification accuracy of 81.81%. Compared with the eco-geographical regionalization scheme, the new scheme exhibited improved the consistency of vegetation dynamics, reflecting the potential to better guide vegetation restoration activities on the Loess Plateau. Full article
Figures

Graphical abstract

Open AccessArticle Utilization of Integrated Geophysical Techniques to Delineate the Extraction of Mining Bench of Ornamental Rocks (Marble)
Remote Sens. 2017, 9(12), 1322; https://doi.org/10.3390/rs9121322
Received: 24 October 2017 / Revised: 5 December 2017 / Accepted: 8 December 2017 / Published: 15 December 2017
Cited by 1 | PDF Full-text (17811 KB) | HTML Full-text | XML Full-text
Abstract
Low yields in ornamental rock mining remain one of the most important problems in this industry. This fact is usually associated with the presence of anisotropies in the rock, which makes it difficult to extract the blocks. An optimised planning of the exploitation,
[...] Read more.
Low yields in ornamental rock mining remain one of the most important problems in this industry. This fact is usually associated with the presence of anisotropies in the rock, which makes it difficult to extract the blocks. An optimised planning of the exploitation, together with an improved geological understanding of the deposit, could increase these yields. In this work, marble mining in Macael (Spain) was studied to test the capacity of non-destructive geophysical prospecting methods (GPR and ERI) as tools to characterize the geology of the deposit. It is well-known that the ERI method provides a greater penetration depth. By using this technique, it is possible to distinguish the boundaries between the marble and the underlying micaschists, the morphology of the unit to be exploited, and even fracture zones to be identified. Therefore, this technique could be used in the early stages of research, to estimate the reserves of the deposit. The GPR methodology, with a lower penetration depth, is able to offer more detailed information. Specifically, it detects lateral and vertical changes of the facies inside the marble unit, as well as the anisotropies of the rock (fractures or holes). This technique would be suitable for use in a second stage of research. On the one hand, it is very useful for characterization of the texture and fabric of the rock, which allows us to determine in advance its properties, and therefore, the quality for ornamental use. On the other hand, the localization of anisotropy using the GPR technique will make it possible to improve the planning of the rock exploitation in order to increase yields. Both integrated geophysical techniques are effective for assessing the quality of ornamental rock and thus can serve as useful tools in mine planning to improve yields and costs. Full article
(This article belongs to the Special Issue Recent Advances in GPR Imaging)
Figures

Figure 1

Open AccessArticle Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data
Remote Sens. 2017, 9(12), 1321; https://doi.org/10.3390/rs9121321
Received: 22 September 2017 / Revised: 28 November 2017 / Accepted: 13 December 2017 / Published: 15 December 2017
Cited by 1 | PDF Full-text (13086 KB) | HTML Full-text | XML Full-text
Abstract
A total of 168 fully polarimetric synthetic-aperture radar (SAR) images are selected together with the buoy measurements of ocean surface wind fields and high-frequency radar measurements of ocean surface currents. Our objective is to investigate the effect of the ocean currents on the
[...] Read more.
A total of 168 fully polarimetric synthetic-aperture radar (SAR) images are selected together with the buoy measurements of ocean surface wind fields and high-frequency radar measurements of ocean surface currents. Our objective is to investigate the effect of the ocean currents on the retrieved SAR ocean surface wind fields. The results show that, compared to SAR wind fields that are retrieved without taking into account the ocean currents, the accuracy of the winds obtained when ocean currents are taken into account is increased by 0.2–0.3 m/s; the accuracy of the wind direction is improved by 3 4 ° . Based on these results, a semi-empirical formula for the errors in the winds and the ocean currents is derived. Verification is achieved by analysis of 52 SAR images, buoy measurements of the corresponding ocean surface winds, and high-frequency radar measurements of ocean currents. Results of the comparisons between data obtained by the semi-empirical formula and data measured by the high-frequency radar show that the root-mean-square error in the ocean current speed is 12.32 cm/s and the error in the current direction is 6.32°. Full article
(This article belongs to the Special Issue Ocean Surface Currents: Progress in Remote Sensing and Validation)
Figures

Graphical abstract

Open AccessArticle Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach
Remote Sens. 2017, 9(12), 1320; https://doi.org/10.3390/rs9121320
Received: 22 September 2017 / Revised: 1 December 2017 / Accepted: 8 December 2017 / Published: 15 December 2017
Cited by 1 | PDF Full-text (9447 KB) | HTML Full-text | XML Full-text
Abstract
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along
[...] Read more.
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights ( H s and H s 50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents ( U b a r and V b a r ) from the Iberian–Biscay–Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error— N R M S E of less than 16%) is the first step in demonstrating the robustness of the method. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
Figures

Graphical abstract

Open AccessArticle Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy
Remote Sens. 2017, 9(12), 1319; https://doi.org/10.3390/rs9121319
Received: 7 November 2017 / Revised: 6 December 2017 / Accepted: 12 December 2017 / Published: 15 December 2017
Cited by 1 | PDF Full-text (21236 KB) | HTML Full-text | XML Full-text
Abstract
The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery.
[...] Read more.
The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
Figures

Graphical abstract

Open AccessArticle UAV Remote Sensing Surveillance of a Mine Tailings Impoundment in Sub-Arctic Conditions
Remote Sens. 2017, 9(12), 1318; https://doi.org/10.3390/rs9121318
Received: 22 November 2017 / Revised: 12 December 2017 / Accepted: 12 December 2017 / Published: 15 December 2017
PDF Full-text (9934 KB) | HTML Full-text | XML Full-text
Abstract
Mining typically involves extensive areas where environmental monitoring is spatially sporadic. New remote sensing techniques and platforms such as Structure from Motion (SfM) and unmanned aerial vehicles (UAVs) may offer one solution for more comprehensive and spatially continuous measurements. We conducted UAV campaigns
[...] Read more.
Mining typically involves extensive areas where environmental monitoring is spatially sporadic. New remote sensing techniques and platforms such as Structure from Motion (SfM) and unmanned aerial vehicles (UAVs) may offer one solution for more comprehensive and spatially continuous measurements. We conducted UAV campaigns in three consecutive summers (2015–2017) at a sub-Arctic mining site where production was temporarily suspended. The aim was to monitor a 0.5 km2 tailings impoundment and measure potential subsidence of tailings. SfM photogrammetry was used to produce yearly topographical models of the tailings surface, which allowed the amount of surface displacement between years to be tracked. Ground checkpoints surveyed in stable areas of the impoundment were utilized in assessing the vertical accuracy of the models. Observed surface displacements were linked to a combination of erosion, tailings settlement, and possible compaction of the peat layer underlying the tailings. The accuracy obtained indicated that UAV-assisted monitoring of tailings impoundments is sufficiently accurate for supporting impoundment management operations and for tracking surface displacements in the decimeter range. Full article
Figures

Graphical abstract

Open AccessArticle Real-Time Tropospheric Delays Retrieved from Multi-GNSS Observations and IGS Real-Time Product Streams
Remote Sens. 2017, 9(12), 1317; https://doi.org/10.3390/rs9121317
Received: 20 October 2017 / Revised: 8 December 2017 / Accepted: 13 December 2017 / Published: 15 December 2017
Cited by 7 | PDF Full-text (3066 KB) | HTML Full-text | XML Full-text
Abstract
The multi-constellation Global Navigation Satellite Systems (GNSS) offers promising potential for the retrieval of real-time (RT) atmospheric parameters to support time-critical meteorological applications, such as nowcasting or regional short-term forecasts. In this study, we processed GNSS data from the globally distributed Multi-GNSS Experiment
[...] Read more.
The multi-constellation Global Navigation Satellite Systems (GNSS) offers promising potential for the retrieval of real-time (RT) atmospheric parameters to support time-critical meteorological applications, such as nowcasting or regional short-term forecasts. In this study, we processed GNSS data from the globally distributed Multi-GNSS Experiment (MGEX) network of about 30 ground stations by using the precise point positioning (PPP) technique for retrieving RT multi-GNSS tropospheric delays. RT satellite orbit and clock product streams from the International GNSS Service (IGS) were used. Meanwhile, we assessed the quality of clock and orbit products provided by different IGS RT services, called CLK01, CLK81, CLK92, GFZC2, and GFZD2, respectively. Using the RT orbit and clock products, the performances of the RT zenith total delays (ZTD) retrieved from single-system as well as from multi-GNSS combined observations were evaluated by comparing with the U.S. Naval Observatory (USNO) final troposphere products. With the addition of multi-GNSS observations, RT ZTD estimates with higher accuracy and enhanced reliability compared to the single-system solution can be obtained. Compared with the Global Positioning System (GPS)-only solution, the improvements in the initialization time of ZTD estimates are about 5.8% and 8.1% with the dual-system and the four-system combinations, respectively. The RT ZTD estimates retrieved with the GFZC2 products outperform those derived from the other IGS-RT products. In the GFZC2 solution, the accuracy of about 5.05 mm for the RT estimated ZTD can be achieved with fixing station coordinates. The results also confirm that the accuracy improvement (about 22.2%) can be achieved for the real-time estimated ZTDs by using multi-GNSS observables, compared to the GPS-only solution. In the multi-GNSS solution, the accuracy of real-time retrieved ZTDs can be improved by a factor of up to 2.7 in the fixing coordinate mode, compared with that in the kinematic mode. Full article
Figures

Graphical abstract

Open AccessArticle Multi-Spectral Remote Sensing of Phytoplankton Pigment Absorption Properties in Cyanobacteria Bloom Waters: A Regional Example in the Western Basin of Lake Erie
Remote Sens. 2017, 9(12), 1309; https://doi.org/10.3390/rs9121309
Received: 5 October 2017 / Revised: 7 December 2017 / Accepted: 8 December 2017 / Published: 15 December 2017
Cited by 1 | PDF Full-text (7979 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Phytoplankton pigments absorb sunlight for photosynthesis, protect the chloroplast from damage caused by excess light energy, and influence the color of the water. Some pigments act as bio-markers and are important for separation of phytoplankton functional types. Among many efforts that have been
[...] Read more.
Phytoplankton pigments absorb sunlight for photosynthesis, protect the chloroplast from damage caused by excess light energy, and influence the color of the water. Some pigments act as bio-markers and are important for separation of phytoplankton functional types. Among many efforts that have been made to obtain information on phytoplankton pigments from bio-optical properties, Gaussian curves decomposed from phytoplankton absorption spectrum have been used to represent the light absorption of different pigments. We incorporated the Gaussian scheme into a semi-analytical model and obtained the Gaussian curves from remote sensing reflectance. In this study, a series of sensitivity tests were conducted to explore the potential of obtaining the Gaussian curves from multi-spectral satellite remote sensing. Results showed that the Gaussian curves can be retrieved with 35% or less mean unbiased absolute percentage differences from MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-like sensors. Further, using Lake Erie as an example, the spatial distribution of chlorophyll a and phycocyanin concentrations were obtained from the Gaussian curves and used as metrics for the spatial extent of an intense cyanobacterial bloom occurred in Lake Erie in 2014. The seasonal variations of Gaussian absorption properties in 2011 were further obtained from MERIS imagery. This study shows that it is feasible to obtain Gaussian curves from multi-spectral satellite remote sensing data, and the obtained chlorophyll a and phycocyanin concentrations from these Gaussian peak heights demonstrated potential application to monitor harmful algal blooms (HABs) and identification of phytoplankton groups from satellite ocean color remote sensing semi-analytically. Full article
(This article belongs to the Section Ocean Remote Sensing)
Figures

Graphical abstract

Open AccessArticle Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping
Remote Sens. 2017, 9(12), 1315; https://doi.org/10.3390/rs9121315
Received: 12 October 2017 / Revised: 30 November 2017 / Accepted: 7 December 2017 / Published: 14 December 2017
Cited by 2 | PDF Full-text (78885 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of
[...] Read more.
Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives. Full article
(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)
Figures

Graphical abstract

Open AccessArticle Time Series Analysis of Very Slow Landslides in the Three Gorges Region through Small Baseline SAR Offset Tracking
Remote Sens. 2017, 9(12), 1314; https://doi.org/10.3390/rs9121314
Received: 19 October 2017 / Revised: 9 December 2017 / Accepted: 11 December 2017 / Published: 14 December 2017
Cited by 3 | PDF Full-text (29363 KB) | HTML Full-text | XML Full-text
Abstract
Sub-pixel offset tracking has been used in various applications, including measurements of glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor
[...] Read more.
Sub-pixel offset tracking has been used in various applications, including measurements of glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de-correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data, with sufficient density of observations, we estimate the precision of the SBAS offset tracking approach to be 2–3 cm on average. The results demonstrated accord well with corresponding GPS measurements. Full article
Figures

Graphical abstract

Open AccessTechnical Note Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature
Remote Sens. 2017, 9(12), 1313; https://doi.org/10.3390/rs9121313
Received: 19 October 2017 / Revised: 28 November 2017 / Accepted: 11 December 2017 / Published: 14 December 2017
PDF Full-text (5555 KB) | HTML Full-text | XML Full-text
Abstract
Month-to-month air temperature (Tair) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while
[...] Read more.
Month-to-month air temperature (Tair) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month Tair mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical Tair modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum Tair, but also for maximum Tair. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum Tair (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Figures

Figure 1

Open AccessArticle Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery
Remote Sens. 2017, 9(12), 1312; https://doi.org/10.3390/rs9121312
Received: 11 October 2017 / Revised: 12 December 2017 / Accepted: 13 December 2017 / Published: 13 December 2017
Cited by 2 | PDF Full-text (5079 KB) | HTML Full-text | XML Full-text
Abstract
Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address
[...] Read more.
Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address geometric modeling in object recognition. By adding offsets to the convolution layers, feature mapping of CNN can be applied to unfixed locations, enhancing CNNs’ visual appearance understanding. In our work, a deformable region-based fully convolutional networks (R-FCN) was constructed by substituting the regular convolution layer with a deformable convolution layer. To efficiently use this deformable convolutional neural network (ConvNet), a training mechanism is developed in our work. We first set the pre-trained R-FCN natural image model as the default network parameters in deformable R-FCN. Then, this deformable ConvNet was fine-tuned on very high resolution (VHR) remote sensing images. To remedy the increase in lines like false region proposals, we developed aspect ratio constrained non maximum suppression (arcNMS). The precision of deformable ConvNet for detecting objects was then improved. An end-to-end approach was then developed by combining deformable R-FCN, a smart fine-tuning strategy and aspect ratio constrained NMS. The developed method was better than a state-of-the-art benchmark in object detection without data augmentation. Full article
Figures

Graphical abstract

Open AccessArticle SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter
Remote Sens. 2017, 9(12), 1311; https://doi.org/10.3390/rs9121311
Received: 7 November 2017 / Revised: 10 December 2017 / Accepted: 12 December 2017 / Published: 13 December 2017
PDF Full-text (2209 KB) | HTML Full-text | XML Full-text
Abstract
Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image,
[...] Read more.
Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction. Full article
(This article belongs to the Section Remote Sensing Image Processing)
Figures

Graphical abstract

Back to Top