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Remote Sens., Volume 8, Issue 9 (September 2016)

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Cover Story (view full-size image) The use of unmanned aerial vehicles (UAVs) combined with Structure-from-Motion (SfM) now allows [...] Read more.
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Open AccessTechnical Note Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry
Remote Sens. 2016, 8(9), 786; https://doi.org/10.3390/rs8090786
Received: 5 July 2016 / Revised: 7 September 2016 / Accepted: 19 September 2016 / Published: 21 September 2016
Cited by 27 | PDF Full-text (6338 KB) | HTML Full-text | XML Full-text
Abstract
The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this
[...] Read more.
The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this aerial survey method by investigating aerial survey data quality in relation to the application of ground control points (GCPs) at a site of undulating topography (Ennerdale, Lake District, UK). Sixteen digital surface models (DSMs) were produced from a UAV survey using a varying number of GCPs (3-101). These DSMs were compared to 530 dGPS spot heights to calculate vertical error. All DSMs produced reasonable surface reconstructions (vertical root-mean-square-error (RMSE) of <0.2 m), however, an improvement in DSM quality was found where four or more GCPs (up to 101 GCPs) were applied, with errors falling to within the suggested point quality range of the survey equipment used for GCP acquisition (e.g., vertical RMSE of <0.09 m). The influence of a poor GCP distribution was also investigated by producing a DSM using an evenly distributed network of GCPs, and comparing it to a DSM produced using a clustered network of GCPs. The results accord with existing findings, where vertical error was found to increase with distance from the GCP cluster. Specifically vertical error and distance to the nearest GCP followed a strong polynomial trend (R2 = 0.792). These findings contribute to our understanding of the sources of error when conducting a UAV-SfM survey and provide guidance on the collection of GCPs. Evidence-driven UAV-SfM survey designs are essential for practitioners seeking reproducible, high quality topographic datasets for detecting surface change. Full article
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Open AccessArticle An Inter-Comparison of Techniques for Determining Velocities of Maritime Arctic Glaciers, Svalbard, Using Radarsat-2 Wide Fine Mode Data
Remote Sens. 2016, 8(9), 785; https://doi.org/10.3390/rs8090785
Received: 20 July 2016 / Revised: 12 September 2016 / Accepted: 16 September 2016 / Published: 21 September 2016
Cited by 7 | PDF Full-text (29991 KB) | HTML Full-text | XML Full-text
Abstract
Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and
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Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 × 150 km) and the high pixel resolution (9 m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m·d−1–2.55 m·d−1) that are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
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Open AccessCorrection Correction: Liu, Y. et al. Time-Dependent Afterslip of the 2009 Mw 6.3 Dachaidan Earthquake (China) and Viscosity beneath the Qaidam Basin Inferred from Postseismic Deformation Observations. Remote Sens. 2016, 8, 649
Remote Sens. 2016, 8(9), 784; https://doi.org/10.3390/rs8090784
Received: 6 September 2016 / Accepted: 16 September 2016 / Published: 21 September 2016
Cited by 1 | PDF Full-text (1350 KB) | HTML Full-text | XML Full-text
Abstract
After publication of the research paper [1] an error was recognized.[...] Full article
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Open AccessEditorial Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges
Remote Sens. 2016, 8(9), 783; https://doi.org/10.3390/rs8090783
Received: 22 August 2016 / Accepted: 11 September 2016 / Published: 21 September 2016
Cited by 7 | PDF Full-text (1741 KB) | HTML Full-text | XML Full-text
Abstract
Mangrove forests, distributed in the tropical and subtropical regions of the world, are in a constant flux. They provide important ecosystem goods and services to nature and society. In recent years, the carbon sequestration potential and protective role of mangrove forests from natural
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Mangrove forests, distributed in the tropical and subtropical regions of the world, are in a constant flux. They provide important ecosystem goods and services to nature and society. In recent years, the carbon sequestration potential and protective role of mangrove forests from natural disasters is being highlighted as an effective option for climate change adaptation and mitigation. The forests are under threat from both natural and anthropogenic forces. However, accurate, reliable, and timely information of the distribution and dynamics of mangrove forests of the world is not readily available. Recent developments in the availability and accessibility of remotely sensed data, advancement in image pre-processing and classification algorithms, significant improvement in computing, availability of expertise in handling remotely sensed data, and an increasing awareness of the applicability of remote sensing products has greatly improved our scientific understanding of changing mangrove forest cover attributes. As reported in this special issue, the use of both optical and radar satellite data at various spatial resolutions (i.e., 1 m to 30 m) to derive meaningful forest cover attributes (e.g., species discrimination, above ground biomass) is on the rise. This multi-sensor trend is likely to continue into the future providing a more complete inventory of global mangrove forest distributions and attribute inventories at enhanced temporal frequency. The papers presented in this “Special Issue” provide important remote sensing monitoring advancements needed to meet future scientific objectives for global mangrove forest monitoring from local to global scales. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Open AccessArticle An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
Remote Sens. 2016, 8(9), 782; https://doi.org/10.3390/rs8090782
Received: 1 July 2016 / Revised: 1 September 2016 / Accepted: 13 September 2016 / Published: 21 September 2016
Cited by 10 | PDF Full-text (6170 KB) | HTML Full-text | XML Full-text
Abstract
Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point
[...] Read more.
Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point next to a reference mast in complex terrain. This multi-lidar (ML) technique is also compared to a profiling lidar using the Doppler beam swinging (DBS) method. First- and second-order statistics of the radial wind velocities from the individual instruments and the horizontal wind components of several ML combinations are analysed in comparison to sonic anemometry and DBS measurements. The results for the wind speed show significantly reduced scatter and directional error for the ML method in comparison to the DBS lidar. The analysis of the second-order statistics also reveals a significantly better correlation for the ML technique than for the DBS lidar, when compared to the sonic. However, the probe volume averaging of the lidars leads to an attenuation of the turbulence at high wave numbers. Also the configuration (i.e., angles) of the WindScanners in the ML method seems to be more important for turbulence measurements. In summary, the results clearly show the advantages of the ML technique in complex terrain and indicate that it has the potential to achieve significantly higher accuracy in measuring turbulence quantities for wind-energy applications than classical profiling lidars. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Open AccessArticle Cultural Heritage Sites in Danger—Towards Automatic Damage Detection from Space
Remote Sens. 2016, 8(9), 781; https://doi.org/10.3390/rs8090781
Received: 1 July 2016 / Revised: 10 August 2016 / Accepted: 18 September 2016 / Published: 21 September 2016
Cited by 6 | PDF Full-text (16483 KB) | HTML Full-text | XML Full-text
Abstract
The intentional damage to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess this damage in such non-accessible areas, and automated image processing
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The intentional damage to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess this damage in such non-accessible areas, and automated image processing techniques will be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification
Remote Sens. 2016, 8(9), 780; https://doi.org/10.3390/rs8090780
Received: 27 May 2016 / Revised: 7 September 2016 / Accepted: 12 September 2016 / Published: 21 September 2016
Cited by 3 | PDF Full-text (6838 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually
[...] Read more.
Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission’s Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool. Full article
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Open AccessArticle An Image-Based Approach for the Co-Registration of Multi-Temporal UAV Image Datasets
Remote Sens. 2016, 8(9), 779; https://doi.org/10.3390/rs8090779
Received: 4 July 2016 / Revised: 3 September 2016 / Accepted: 13 September 2016 / Published: 21 September 2016
Cited by 10 | PDF Full-text (14744 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the
[...] Read more.
During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs), which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris
Remote Sens. 2016, 8(9), 778; https://doi.org/10.3390/rs8090778
Received: 22 June 2016 / Revised: 1 September 2016 / Accepted: 8 September 2016 / Published: 20 September 2016
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Abstract
Large woody debris (LWD) plays a critical structural role in riparian ecosystems, but it can be difficult and time-consuming to quantify and survey in the field. We demonstrate an automated method for quantifying LWD using aerial LiDAR and object-based image analysis techniques, as
[...] Read more.
Large woody debris (LWD) plays a critical structural role in riparian ecosystems, but it can be difficult and time-consuming to quantify and survey in the field. We demonstrate an automated method for quantifying LWD using aerial LiDAR and object-based image analysis techniques, as well as a manual method for quantifying LWD using image interpretation derived from LiDAR rasters and aerial four-band imagery. In addition, we employ an established method for estimating the number of individual trees within the riparian forest. These methods are compared to field data showing high accuracies for the LWD method and moderate accuracy for the individual tree method. These methods can be integrated to quantify the contemporary and recruitable LWD in a river system. Full article
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Open AccessArticle Sediment-Mass Accumulation Rate and Variability in the East China Sea Detected by GRACE
Remote Sens. 2016, 8(9), 777; https://doi.org/10.3390/rs8090777
Received: 17 June 2016 / Revised: 30 August 2016 / Accepted: 9 September 2016 / Published: 20 September 2016
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Abstract
The East China Sea (ECS) is a region with shallow continental shelves and a mixed oceanic circulation system allowing sediments to deposit on its inner shelf, particularly near the estuary of the Yangtze River. The seasonal northward-flowing Taiwan Warm Current and southward-flowing China
[...] Read more.
The East China Sea (ECS) is a region with shallow continental shelves and a mixed oceanic circulation system allowing sediments to deposit on its inner shelf, particularly near the estuary of the Yangtze River. The seasonal northward-flowing Taiwan Warm Current and southward-flowing China Coastal Current trap sediments from the Yangtze River, which are accumulated over time at rates of up to a few mm/year in equivalent water height. Here, we use the Gravity Recovery and Climate Experiment (GRACE) gravity products from three data centres to determine sediment mass accumulation rates (MARs) and variability on the ECS inner shelf. We restore the atmospheric and oceanic effects to avoid model contaminations on gravity signals associated with sediment masses. We apply destriping and spatial filters to improve the gravity signals from GRACE and use the Global Land Data Assimilation System to reduce land leakage. The GRACE-derived MARs over April 2002–March 2015 on the ECS inner shelf are about 6 mm/year and have magnitudes and spatial patterns consistent with those from sediment-core measurements. The GRACE-derived monthly sediment depositions show variations at time scales ranging from six months to more than two years. Typically, a positive mass balance of sediment deposition occurs in late fall to early winter when the southward coastal currents prevail. A negative mass balance happens in summer when the coastal currents are northward. We identify quasi-biennial sediment variations, which are likely to be caused by quasi-biennial variations in rain and erosion in the Yangtze River basin. We briefly explain the mechanisms of such frequency-dependent variations in the GRACE-derived ECS sediment deposition. There is no clear perturbation on sediment deposition over the ECS inner shelf induced by the Three Gorges Dam. The limitations of GRACE in resolving sediment deposition are its low spatial resolution (about 250 km) and possible contaminations by land hydrological and oceanic signals. Potential GRACE-derived sediment depositions in six major estuaries are presented. Full article
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Open AccessArticle Dynamics of Fractional Vegetation Coverage and Its Relationship with Climate and Human Activities in Inner Mongolia, China
Remote Sens. 2016, 8(9), 776; https://doi.org/10.3390/rs8090776
Received: 28 June 2016 / Revised: 13 September 2016 / Accepted: 15 September 2016 / Published: 20 September 2016
Cited by 5 | PDF Full-text (5218 KB) | HTML Full-text | XML Full-text
Abstract
Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity
[...] Read more.
Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity analysis, and residual analysis were used to analyze the changes of vegetation coverage in Inner Mongolia—from 1982 to 2010—and their relationships with climate and human activities. This study also explored vegetation changes in Inner Mongolia with respect to natural factors and human activities. The results showed that the estimated vegetation coverage exhibited a high correlation (0.836) with the actual measured values. The increased vegetation coverage area (49.2% of the total area) was larger than the decreased area (43.3%) from the 1980s to the 1990s, whereas the decreased area (57.1%) was larger than the increased area (35.6%) from the 1990s to the early 21st century. This finding indicates that vegetation growth in the 1990s was better than that in the other two decades. Intensity analysis revealed that changes in the average annual rate from the 1990s to the early 21st century were relatively faster than those in the 1980s–1990s. During the 1980s–1990s, the gain of high vegetation coverage areas was active, and the loss was dormant; in contrast, the gain and loss of low vegetation coverage areas were both dormant. In the 1990s to the early 21st century, the gains of high and low vegetation coverage areas were both dormant, whereas the losses were active. During the study period, areas of low vegetation coverage were converted into ones with higher coverage, and areas of high vegetation coverage were converted into ones with lower coverage. The vegetation coverage exhibited a good correlation (R2 = 0.60) with precipitation, and the positively correlated area was larger than the negatively correlated area. Human activities not only promote the vegetation coverage, but also have a destructive effect on vegetation, and the promotion effect during 1982 to 2000 was larger than from 2001 to 2010, while, the destructive effect was larger from 2000 to 2010. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)
Remote Sens. 2016, 8(9), 775; https://doi.org/10.3390/rs8090775
Received: 31 May 2016 / Revised: 6 September 2016 / Accepted: 12 September 2016 / Published: 20 September 2016
Cited by 9 | PDF Full-text (4217 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974–2014). Extensive field campaigns for ground
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This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974–2014). Extensive field campaigns for ground truth data retrieval were carried out simultaneous to Landsat overpasses. Ground truth was used as training and testing areas to check the performance of the method. Simple thresholds on TM and ETM band 5 (1.55–1.75 μm) worked significantly better than other empirical modeling techniques and supervised classification methods to delineate flooded areas at Doñana marshes. A classification tree was applied to band 5 reflectance values to classify flooded versus non-flooded pixels for every scene. Inter-scene cross-validation identified the most accurate threshold on band 5 reflectance (ρ < 0.186) to classify flooded areas (Kappa = 0.65). A joint TM-MSS acquisition was used to find the MSS band 4 (0.8 a 1.1 μm) threshold. The TM flooded area was identical to the results from MSS 4 band threshold ρ < 0.10 despite spectral and spatial resolution differences. Band slicing was retrospectively applied to the complete time series of MSS and TM images. About 391 flood masks were used to reconstruct historical spatial and temporal patterns of Doñana marshes flooding, including hydroperiod. Hydroperiod historical trends were used as a baseline to understand Doñana’s flooding regime, test hydrodynamic models, and give an assessment of relevant management and restoration decisions. The historical trends in the hydroperiod of Doñana marshes show two opposite spatial patterns. While the north-western part of the marsh is increasing its hydroperiod, the southwestern part shows a steady decline. Anomalies in each flooding cycle allowed us to assess recent management decisions and monitor their hydrological effects. Full article
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
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Open AccessArticle Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task
Remote Sens. 2016, 8(9), 774; https://doi.org/10.3390/rs8090774
Received: 29 July 2016 / Revised: 2 September 2016 / Accepted: 12 September 2016 / Published: 20 September 2016
Cited by 3 | PDF Full-text (845 KB) | HTML Full-text | XML Full-text
Abstract
The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen
[...] Read more.
The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Improved Detection of Human Respiration Using Data Fusion Basedon a Multistatic UWB Radar
Remote Sens. 2016, 8(9), 773; https://doi.org/10.3390/rs8090773
Received: 30 June 2016 / Revised: 24 August 2016 / Accepted: 13 September 2016 / Published: 20 September 2016
Cited by 2 | PDF Full-text (3978 KB) | HTML Full-text | XML Full-text
Abstract
This paper investigated the feasibility for improved detection of human respiration using data fusion based on a multistatic ultra-wideband (UWB) radar. UWB-radar-based respiration detection is an emerging technology that has great promise in practice. It can be applied to remotely sense the presence
[...] Read more.
This paper investigated the feasibility for improved detection of human respiration using data fusion based on a multistatic ultra-wideband (UWB) radar. UWB-radar-based respiration detection is an emerging technology that has great promise in practice. It can be applied to remotely sense the presence of a human target for through-wall surveillance, post-earthquake search and rescue, etc. In these applications, a human target’s position and posture are not known a priori. Uncertainty of the two factors results in a body orientation issue of UWB radar, namely the human target’s thorax is not always facing the radar. Thus, the radial component of the thorax motion due to respiration decreases and the respiratory motion response contained in UWB radar echoes is too weak to be detected. To cope with the issue, this paper used multisensory information provided by the multistatic UWB radar, which took the form of impulse radios and comprised one transmitting and four separated receiving antennas. An adaptive Kalman filtering algorithm was then designed to fuse the UWB echo data from all the receiving channels to detect the respiratory-motion response contained in those data. In the experiment, a volunteer’s respiration was correctly detected when he curled upon a camp bed behind a brick wall. Under the same scenario, the volunteer’s respiration was detected based on the radar’s single transmitting-receiving channels without data fusion using conventional algorithm, such as adaptive line enhancer and single-channel Kalman filtering. Moreover, performance of the data fusion algorithm was experimentally investigated with different channel combinations and antenna deployments. The experimental results show that the body orientation issue for human respiration detection via UWB radar can be dealt well with the multistatic UWB radar and the Kalman-filter-based data fusion, which can be applied to improve performance of UWB radar in real applications. Full article
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Open AccessArticle Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea
Remote Sens. 2016, 8(9), 769; https://doi.org/10.3390/rs8090769
Received: 4 April 2016 / Revised: 24 August 2016 / Accepted: 12 September 2016 / Published: 20 September 2016
Cited by 2 | PDF Full-text (2880 KB) | HTML Full-text | XML Full-text
Abstract
We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor
[...] Read more.
We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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