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 6, Issue 2 (February 2014), Pages 907-1761

  • 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.
View options order results:
result details:
Displaying articles 1-40
Export citation of selected articles as:
Open AccessCorrection Correction: Van Beek, J. et al. Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery. Remote Sens. 2013, 5, 6647–6666
Remote Sens. 2014, 6(2), 1760-1761; https://doi.org/10.3390/rs6021760
Received: 14 February 2014 / Accepted: 20 February 2014 / Published: 24 February 2014
PDF Full-text (148 KB) | HTML Full-text | XML Full-text
Abstract
The suitability of high resolution satellite imagery to provide the water status in orchard crops, i.e. stem water potential (Ψstem) was evaluated in [1]. However, the contribution of a number of collaborators was not properly acknowledged. Pieter Janssens, Wendy Odeurs, Hilde
[...] Read more.
The suitability of high resolution satellite imagery to provide the water status in orchard crops, i.e. stem water potential (Ψstem) was evaluated in [1]. However, the contribution of a number of collaborators was not properly acknowledged. Pieter Janssens, Wendy Odeurs, Hilde Vandendriessche and Tom Deckers all provided a substantial contribution to the conception and the design of the work. They furthermore had a leading role in the acquisition, processing, analysis, and interpretation of the reference evapotranspiration (ETo) and Ψstem data. The article [1] would not have been possible without their valuable input, and the authors would like to correct the authors list as follows. [...] Full article
Open AccessArticle GIS-Based Roughness Derivation for Flood Simulations: A Comparison of Orthophotos, LiDAR and Crowdsourced Geodata
Remote Sens. 2014, 6(2), 1739-1759; https://doi.org/10.3390/rs6021739
Received: 4 October 2013 / Revised: 28 January 2014 / Accepted: 12 February 2014 / Published: 24 February 2014
Cited by 18 | PDF Full-text (4239 KB) | HTML Full-text | XML Full-text
Abstract
Natural disasters like floods are a worldwide phenomenon and a serious threat to mankind. Flood simulations are applications of disaster control, which are used for the development of appropriate flood protection. Adequate simulations require not only the geometry but also the roughness of
[...] Read more.
Natural disasters like floods are a worldwide phenomenon and a serious threat to mankind. Flood simulations are applications of disaster control, which are used for the development of appropriate flood protection. Adequate simulations require not only the geometry but also the roughness of the Earth’s surface, as well as the roughness of the objects hereon. Usually, the floodplain roughness is based on land use/land cover maps derived from orthophotos. This study analyses the applicability of roughness map derivation approaches for flood simulations based on different datasets: orthophotos, LiDAR data, official land use data, OpenStreetMap data and CORINE Land Cover data. Object-based image analysis is applied to orthophotos and LiDAR raster data in order to generate land cover maps, which enable a roughness parameterization. The vertical vegetation structure within the LiDAR point cloud is used to derive an additional floodplain roughness map. Further roughness maps are derived from official land use data, OpenStreetMap and CORINE Land Cover datasets. Six different flood simulations are applied based on one elevation data but with the different roughness maps. The results of the hydrodynamic–numerical models include information on flow velocity and water depth from which the additional attribute flood intensity is calculated of. The results based on roughness maps derived from LiDAR data and OpenStreetMap data are comparable, whereas the results of the other datasets differ significantly. Full article
Open AccessEditorial Acknowledgement to Reviewers of Remote Sensing in 2013
Remote Sens. 2014, 6(2), 1725-1738; https://doi.org/10.3390/rs6021725
Received: 24 February 2014 / Accepted: 24 February 2014 / Published: 24 February 2014
PDF Full-text (215 KB) | HTML Full-text | XML Full-text
Abstract
The publisher and editors of the Remote Sensing would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2013 for Remote Sensing. [...] Full article
Open AccessArticle Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data
Remote Sens. 2014, 6(2), 1705-1724; https://doi.org/10.3390/rs6021705
Received: 9 December 2013 / Revised: 5 February 2014 / Accepted: 14 February 2014 / Published: 20 February 2014
Cited by 90 | PDF Full-text (986 KB) | HTML Full-text | XML Full-text
Abstract
The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were
[...] Read more.
The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the GDP and EPC (which is from the country’s statistical data) at provincial- and prefectural-level divisions of mainland China. The result of the linear regression shows that R2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
Open AccessArticle A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape
Remote Sens. 2014, 6(2), 1684-1704; https://doi.org/10.3390/rs6021684
Received: 31 December 2013 / Revised: 8 February 2014 / Accepted: 13 February 2014 / Published: 20 February 2014
Cited by 11 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future
[...] Read more.
Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future wildfires on ecosystem services. Our study aims to evaluate and compare the spectral and spatial information inherent in the EO-1 Hyperion, Quickbird and Landsat TM imagery. The analysis was based on a support vector machine classification approach in order to discriminate and map Mediterranean fuel types. The fuel classification scheme followed a site-specific fuel model within the study area, which is suitable for fire behavior prediction and spatial simulation. The overall accuracy of the Quickbird-based fuel type mapping was higher than 74% with a quantity disagreement of 9% and an allocation disagreement of 17%. Both classifications from the Hyperion and Landsat TM fuel type maps presented approximately 70% overall accuracy and 16% allocation disagreement. The McNemar’s test indicated that the overall accuracy differences between the three produced fuel type maps were not significant (p < 0.05). Based on both overall and individual higher accuracies obtained with the use of the Quickbird image, this study suggests that the high spatial resolution might be more decisive than the high spectral resolution in Mediterranean fuel type mapping. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
Figures

Graphical abstract

Open AccessReview A Review of Swidden Agriculture in Southeast Asia
Remote Sens. 2014, 6(2), 1654-1683; https://doi.org/10.3390/rs6021654
Received: 29 November 2013 / Revised: 17 February 2014 / Accepted: 18 February 2014 / Published: 20 February 2014
Cited by 25 | PDF Full-text (448 KB) | HTML Full-text | XML Full-text
Abstract
Swidden agriculture is by far the dominant land use system in the mountainous regions of Southeast Asia (SEA). It provides various valuable subsistence products to local farmers, mostly the poor ethnic minority groups. Controversially, it is also closely connected with a number of
[...] Read more.
Swidden agriculture is by far the dominant land use system in the mountainous regions of Southeast Asia (SEA). It provides various valuable subsistence products to local farmers, mostly the poor ethnic minority groups. Controversially, it is also closely connected with a number of environmental issues. With the strengthening regional economic cooperation in SEA, swidden agriculture has experienced drastic transformations into other diverse market-oriented land use types since the 1990s. However, there is very limited information on the basic geographical and demographic data of swidden agriculture and the socio-economic and biophysical effects of the transformations. International programs, such as the Reducing Emissions from Deforestation and forest Degradation (REDD), underscore the importance of monitoring and evaluating swidden agriculture and its transition to reduce carbon emission due to deforestation and forest degradation. In this context, along with the accessibility of Landsat historical imagery, remote sensing based techniques will offer an effective way to detect and monitor the locations and extent of swidden agriculture. Many approaches for investigating fire occurrence and burned area can be introduced for swidden agriculture mapping due to the common feature of fire relatedness. In this review paper, four broad approaches involving spectral signatures, phenological characteristics, statistical theory and landscape ecology were summarized for swidden agriculture delineation. Five research priorities about swidden agriculture involving remote sensing techniques, spatial pattern, change, drivers and impacts were proposed accordingly. To our knowledge, a synthesis review on the remote sensing and outlook on swidden agriculture has not been reported yet. This review paper aims to give a comprehensive overview of swidden agriculture studies in the domains of debated definition, trends, remote sensing methods and outlook research in SEA undertaken in the past two decades. Full article
Open AccessArticle Performance Analysis of MODIS 500-m Spatial Resolution Products for Estimating Chlorophyll-a Concentrations in Oligo- to Meso-Trophic Waters Case Study: Itumbiara Reservoir, Brazil
Remote Sens. 2014, 6(2), 1634-1653; https://doi.org/10.3390/rs6021634
Received: 8 January 2014 / Revised: 7 February 2014 / Accepted: 12 February 2014 / Published: 20 February 2014
Cited by 7 | PDF Full-text (1104 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a
[...] Read more.
Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a in time and space through remote sensing techniques. However, one of the limitations of remote sensing is the resolution. To achieve a high temporal resolution and medium space resolution, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance product, MOD09GA, and limnological parameters from the Itumbiara Reservoir. With these data, an empirical (O14a) and semi-empirical (O14b) algorithm were developed. Algorithms were cross-calibrated and validated using three datasets: one for each campaign and a third consisting of a combination of the two individual campaigns. Algorithm O14a produced the best validation with a root mean square error (RMSE) of 30.4%, whereas O14b produced an RMSE of 32.41% using the mixed dataset calibration. O14a was applied to MOD09GA to build a time series for the reservoir for the year of 2009. The time-series analysis revealed that there were occurrences of algal blooms in the summer that were likely related to the additional input of nutrients caused by rainfall runoff. During the winter, however, the few observed algal blooms events were related to periods of atmospheric meteorological variations that represented an enhanced external influence on the processes of mixing and stratification of the water column. Finally, the use of remote sensing techniques can be an important tool for policy makers, environmental managers and the scientific community with which to monitor water quality. Full article
Open AccessArticle Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland
Remote Sens. 2014, 6(2), 1605-1633; https://doi.org/10.3390/rs6021605
Received: 7 January 2014 / Revised: 21 January 2014 / Accepted: 12 February 2014 / Published: 20 February 2014
Cited by 11 | PDF Full-text (2585 KB) | HTML Full-text | XML Full-text
Abstract
The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet.
[...] Read more.
The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. The authors propose applying satellite remote sensing data acquired in the optical and microwave spectrums to classify wetlands vegetation habitats for the assessment of vegetation changes and estimation of wetlands’ biophysical properties to improve monitoring of these unique, very often physically impenetrable, areas. The backscattering coefficients (σ°) calculated from ALOS PALSAR FBD (Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar, Fine Beam Dual Mode) images registered at cross polarization HV on 12 May 2008 were used to classify the main wetland communities using ground truth observations and the visual interpretation method. As a result, the σ° values were distributed among the six wetlands’ vegetation classes: scrubs, sedges-scrubs, sedges, reeds, sedges-reeds, rushes, and the areas of each community and changes were assessed. Also, the change in the biophysical variable as Leaf Area Index (LAI) is described using the information from PALSAR data. Strong linear relationships have been found between LAI and σ° derived for particular wetland classes, which then were applied to elaborate the maps of LAI distribution. The other variables used to characterize the changing environmental conditions are: surface temperature (Ts) calculated from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and Normalized Difference Vegetation Index (NDVI) from ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer). Differences of almost double Ts between “dry” and “wet” years were noticed that reflect observed weather conditions. The highest values of NDVI occurred in years with a sufficient amount of precipitation with the lowest in “dry” years. NDVI values variances within the same wetlands class resulted mainly from the differences in soil moisture. The results of this study show that the satellite data from microwave and optical spectrum gave the repetitive spatial information about vegetation growth conditions and could be used for monitoring wetland ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
Open AccessArticle Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution
Remote Sens. 2014, 6(2), 1587-1604; https://doi.org/10.3390/rs6021587
Received: 12 December 2013 / Revised: 29 January 2014 / Accepted: 12 February 2014 / Published: 20 February 2014
Cited by 6 | PDF Full-text (1145 KB) | HTML Full-text | XML Full-text
Abstract
Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in
[...] Read more.
Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease. Full article
Open AccessArticle Slope Superficial Displacement Monitoring by Small Baseline SAR Interferometry Using Data from L-band ALOS PALSAR and X-band TerraSAR: A Case Study of Hong Kong, China
Remote Sens. 2014, 6(2), 1564-1586; https://doi.org/10.3390/rs6021564
Received: 12 December 2013 / Revised: 27 January 2014 / Accepted: 10 February 2014 / Published: 20 February 2014
Cited by 11 | PDF Full-text (2189 KB) | HTML Full-text | XML Full-text
Abstract
Owing to the development of spaceborne synthetic aperture radar (SAR) platforms, and in particular the increase in the availability of multi-source (multi-band and multi-resolution) data, it is now feasible to design a surface displacement monitoring application using multi-temporal SAR interferometry (MT-InSAR). Landslides have
[...] Read more.
Owing to the development of spaceborne synthetic aperture radar (SAR) platforms, and in particular the increase in the availability of multi-source (multi-band and multi-resolution) data, it is now feasible to design a surface displacement monitoring application using multi-temporal SAR interferometry (MT-InSAR). Landslides have high socio-economic impacts in many countries because of potential geo-hazards and heavy casualties. In this study, taking into account the merits of ALOS PALSAR (L-band, good coherence preservation) and TerraSAR (X-band, high resolution and short revisit times) data, we applied an improved small baseline InSAR (SB-InSAR) with 3-D phase unwrapping approach, to monitor slope superficial displacement in Hong Kong, China, a mountainous subtropical zone city influenced by over-urbanization and heavy monsoonal rains. Results revealed that the synergistic use of PALSAR and TerraSAR data produces different outcomes in relation to data reliability and spatial-temporal resolution, and hence could be of significant value for a comprehensive understanding and monitoring of unstable slopes. Full article
Figures

Graphical abstract

Open AccessArticle Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data
Remote Sens. 2014, 6(2), 1538-1563; https://doi.org/10.3390/rs6021538
Received: 13 December 2013 / Revised: 29 January 2014 / Accepted: 3 February 2014 / Published: 20 February 2014
Cited by 10 | PDF Full-text (783 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis
[...] Read more.
In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features. Full article
Open AccessArticle Modeling Accumulated Volume of Landslides Using Remote Sensing and DTM Data
Remote Sens. 2014, 6(2), 1514-1537; https://doi.org/10.3390/rs6021514
Received: 21 November 2013 / Revised: 30 December 2013 / Accepted: 24 January 2014 / Published: 19 February 2014
Cited by 9 | PDF Full-text (1245 KB) | HTML Full-text | XML Full-text
Abstract
Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital
[...] Read more.
Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital terrain model (DTM) data, this paper analyzes errors that can occur in calculating landslide volumes using conventional models. To improve existing models, the mechanisms and laws governing the material deposited by landslides are studied and then the mass balance principle and mass balance line are defined. Based on these ideas, a novel and improved model (Mass Balance Model, MBM) is proposed. By using a parameter called the “height adaptor”, MBM translates the volume calculation into an automatic search for the mass balance line within the scope of the landslide. Due to the use of mass balance constraints and the height adaptor, MBM is much more effective and reliable. A test of MBM was carried out for the case of a typical landslide, triggered by the Wenchuan Earthquake of 12 May 2008. Full article
Open AccessArticle Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China
Remote Sens. 2014, 6(2), 1496-1513; https://doi.org/10.3390/rs6021496
Received: 28 December 2013 / Revised: 5 February 2014 / Accepted: 9 February 2014 / Published: 19 February 2014
Cited by 32 | PDF Full-text (802 KB) | HTML Full-text | XML Full-text
Abstract
Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation
[...] Read more.
Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006–2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe. Full article
Open AccessArticle Evaluation of InSAR and TomoSAR for Monitoring Deformations Caused by Mining in a Mountainous Area with High Resolution Satellite-Based SAR
Remote Sens. 2014, 6(2), 1476-1495; https://doi.org/10.3390/rs6021476
Received: 20 December 2013 / Revised: 8 February 2014 / Accepted: 10 February 2014 / Published: 19 February 2014
Cited by 15 | PDF Full-text (2104 KB) | HTML Full-text | XML Full-text
Abstract
Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation
[...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation and atmospheric effects, which are common in interferograms. However, DInSAR or PSI applications in rural areas, especially in mountainous regions, can be extremely challenging. In this study we have employed a combined technique, i.e., SBAS-DInSAR, to a mountainous area that is severely affected by mining activities. In addition, L-band (ALOS) and C-band (ENVISAT) data sets, 21 TerraSAR-X images provided by German Aerospace Center (DLR) with a high resolution have been used. In order to evaluate the ability of TerraSAR-X for mining monitoring, we present a case study of TerraSAR-X SAR images for Subsidence Hazard Boundary (SHB) extraction. The resulting data analysis gives an initial evaluation of InSAR applications within a mountainous region where fast movements and big phase gradients are common. Moreover, the experiment of four-dimension (4-D) Tomography SAR (TomoSAR) for structure monitoring inside the mining area indicates a potential near all-wave monitoring, which is an extension of conventional InSAR. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
Remote Sens. 2014, 6(2), 1451-1475; https://doi.org/10.3390/rs6021451
Received: 17 December 2013 / Revised: 31 January 2014 / Accepted: 10 February 2014 / Published: 18 February 2014
Cited by 13 | PDF Full-text (5226 KB) | HTML Full-text | XML Full-text
Abstract
We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new
[...] Read more.
We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the progressive probabilistic Hough transform to automatically infer lead positions and orientations from daily AMSR-E satellite observations. Because the progressive probabilistic Hough transform often detects an identical lead several times the algorithm clusters neighboring leads that belong to one lead position. A first comparison of automatically detected lead positions and orientations with manually detected lead positions and orientations reveals that 57% of the reference leads are correctly determined. Around 11% of automatically detected leads are located where no reference lead occurs. The automatically detected lead orientations are distributed slightly differently from the reference lead orientations. A second comparison of automatically detected leads in the Fram Strait to leads in a wide swath mode Advanced Synthetic Aperture Radar scene shows a good agreement. We provide an Arctic-wide time series of lead orientations for winters from 2002 to 2011. For example, while a lead orientation of 110° with respect to the Greenwich meridian prevails in the Fram Strait, lead orientations in the Beaufort Sea are more isotropically distributed. We find significant preferred lead orientations almost everywhere in the Arctic Ocean when averaged over the entire AMSR-E time series. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing)
Figures

Graphical abstract

Back to Top