Remote Sensing

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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

by Toby N. Tonkin and Nicholas G. Midgley

Remote Sens. 2016, 8(9), 786; https://doi.org/10.3390/rs8090786 - 21 Sep 2016

Cited by 218 | Viewed by 13724

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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29991 KiB

Open AccessArticle

An Inter-Comparison of Techniques for Determining Velocities of Maritime Arctic Glaciers, Svalbard, Using Radarsat-2 Wide Fine Mode Data

by Thomas Schellenberger, Wesley Van Wychen, Luke Copland, Andreas Kääb and Laurence Gray

Remote Sens. 2016, 8(9), 785; https://doi.org/10.3390/rs8090785 - 21 Sep 2016

Cited by 21 | Viewed by 8335

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 [...] Read more.

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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1350 KiB

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

by Yang Liu, Caijun Xu, Zhenhong Li, Yangmao Wen, Jiajun Chen and Zhicai Li

Remote Sens. 2016, 8(9), 784; https://doi.org/10.3390/rs8090784 - 21 Sep 2016

Cited by 1 | Viewed by 4532

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

by Chandra Giri

Remote Sens. 2016, 8(9), 783; https://doi.org/10.3390/rs8090783 - 21 Sep 2016

Cited by 113 | Viewed by 17205

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 [...] Read more.

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

by Lukas Pauscher, Nikola Vasiljevic, Doron Callies, Guillaume Lea, Jakob Mann, Tobias Klaas, Julian Hieronimus, Julia Gottschall, Annedore Schwesig, Martin Kühn and Michael Courtney

Remote Sens. 2016, 8(9), 782; https://doi.org/10.3390/rs8090782 - 21 Sep 2016

Cited by 51 | Viewed by 9363

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

by Daniele Cerra, Simon Plank, Vasiliki Lysandrou and Jiaojiao Tian

Remote Sens. 2016, 8(9), 781; https://doi.org/10.3390/rs8090781 - 21 Sep 2016

Cited by 46 | Viewed by 8905

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 [...] Read more.

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

by Javier Martínez-López, Bastian Bertzky, Francisco Javier Bonet-García, Lucy Bastin and Grégoire Dubois

Remote Sens. 2016, 8(9), 780; https://doi.org/10.3390/rs8090780 - 21 Sep 2016

Cited by 10 | Viewed by 8122

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

by Irene Aicardi, Francesco Nex, Markus Gerke and Andrea Maria Lingua

Remote Sens. 2016, 8(9), 779; https://doi.org/10.3390/rs8090779 - 21 Sep 2016

Cited by 55 | Viewed by 9086

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

by Jeffrey J. Richardson and L. Monika Moskal

Remote Sens. 2016, 8(9), 778; https://doi.org/10.3390/rs8090778 - 20 Sep 2016

Cited by 14 | Viewed by 4947

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

by Ya-Chi Liu, Cheinway Hwang, Jiancheng Han, Ricky Kao, Chau-Ron Wu, Hsuan-Chang Shih and Natthachet Tangdamrongsub

Remote Sens. 2016, 8(9), 777; https://doi.org/10.3390/rs8090777 - 20 Sep 2016

Cited by 16 | Viewed by 7872

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

by Siqin Tong, Jiquan Zhang, Si Ha, Quan Lai and Qiyun Ma

Remote Sens. 2016, 8(9), 776; https://doi.org/10.3390/rs8090776 - 20 Sep 2016

Cited by 70 | Viewed by 7341

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)

by Ricardo Díaz-Delgado, David Aragonés, Isabel Afán and Javier Bustamante

Remote Sens. 2016, 8(9), 775; https://doi.org/10.3390/rs8090775 - 20 Sep 2016

Cited by 49 | Viewed by 10634

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 [...] Read more.

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

by Carl Salk, Tobias Sturn, Linda See and Steffen Fritz

Remote Sens. 2016, 8(9), 774; https://doi.org/10.3390/rs8090774 - 20 Sep 2016

Cited by 13 | Viewed by 7082

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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Full article ">Figure 1
Distribution of players of the Cropland Capture game among the top 10 countries with the most participants. Only participants who rated at least 1000 images are included in this figure. An additional 105 players from 53 other countries were included in the analyses in this paper. Full article ">Figure 2
An example of pasture land in an image from the Cropland Capture game. (A) The image as seen by players of the game. Players overwhelmingly classified this as being cropland; and (B) a view of the same parcel from ground-level as seen in Google Street View, showing that this field is pasture, not cropland. Full article ">Figure 3
Predicted probability of agreement with the majority of ratings for an image in the Cropland Capture game. (A) As a function of the image location’s great circle distance from home and a volunteer’s home region (‘western’ includes North America, Europe, and Oceania; ‘non-western’ is Asia, Africa, and Latin America); (B) As a function of self-reported profession. RS/LC stands for ‘Remote sensing/land cover’. Differences among groups are non-significant (see main text); (C) As a function of image distance from home and professional background. The ‘specialist’ professional background applies to users who reported their profession as ‘remote sensing or land cover’, while the ‘non-specialist category applies to everyone else. The dotted lines show ±2 standard error confidence intervals. The y-axes in panels A and C are on a logit scale due to the binomial probability model used for the response variable. Note the small range of values on these y-axes—this indicates low explanatory power of the models, in spite of their statistical significance (see main text). Full article ">

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Open AccessArticle

Improved Detection of Human Respiration Using Data Fusion Basedon a Multistatic UWB Radar

by Hao Lv, Fugui Qi, Yang Zhang, Teng Jiao, Fulai Liang, Zhao Li and Jianqi Wang

Remote Sens. 2016, 8(9), 773; https://doi.org/10.3390/rs8090773 - 20 Sep 2016

Cited by 32 | Viewed by 6046

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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Systematic diagram of the multistatic UWB radar. Full article ">Figure 2
Processing flow of the data fusion algorithm based on Kalman filtering. Full article ">Figure 3
Schematic diagram of the Kalman filtering. Full article ">Figure 4
Experimental setup. The multistatic UWB radar’s antennas were deployed as a line array, in which the receiving antennas were placed close to the transmitting one. Full article ">Figure 5
Measurement of the volunteer that curled upon a camp bed with his feet pointing to the transmitting antenna. In the figure, he was positioned approximately at (6 m, −20°) away from the origin, namely the transmitting antenna. Full article ">Figure 6
Measurement of the artificial breathing object: (a) photo of the object; (b) the multistatic UWB radar’s receiving antennas were separated from the transmitting one; (c) the transmitting antenna was moved away from the receiving ones. Full article ">Figure 7
UWB data after the preprocessing. The rows from top to bottom correspond to the data of channel 1, 2, 3 and 4, respectively. The blue broken line indicates the waveforms manually picked on the basis of the a priori range information of the target. Full article ">Figure 8
Four waveforms detected by the target association and the data fusion result (a) and their corresponding power spectrum in the frequency domain (b) when the volunteer was measured at the position (4 m, 0°). The rows from top to bottom are the waveforms from channel 1, 2, 3, 4 and the detected respiration, respectively. The detected respiration was fused by two methods: one is the Kalman filtering proposed in this paper and the other is a simple method based on averaging all four waveforms. Full article ">Figure 9
Four waveforms detected by the target association and the data fusion result (a) and their corresponding power spectrum in the frequency domain (b) when the volunteer was measured at the position (6 m, 0°). The rows from top to bottom are the waveforms from channel 1, 2, 3, 4 and the detected respiration, respectively. The detected respiration was fused by two methods: one is the Kalman filtering proposed in this paper and the other is a simple method based on averaging all four waveforms. Full article ">Figure 10
Four waveforms detected by the target association and the data fusion result and (a) their corresponding power spectrum in the frequency domain (b) when the volunteer was measured at the position (6 m, −20°). The rows from top to bottom were the waveforms from channel 1, 2, 3, 4 and the detected respiration, respectively. The detected respiration was fused by two methods: one is the Kalman filtering proposed in this paper and the other is a simple method based on averaging all four waveforms. Full article ">Figure 11
Results processed by the reference algorithm in [<a href="#B6-remotesensing-08-00773" class="html-bibr">6</a>]: the volunteer was at the position (4 m, 0°) (a); the volunteer was at the position (6 m, 0°) (b); and the volunteer was at the position (6 m, −20°) (c). The rows from top to bottom corresponds to channel 1, 2, 3 and 4, respectively. Full article ">Figure 12
Results processed by the single-channel Kalman filtering: the volunteer was at the position (4 m, 0°) (a); the volunteer was at the position (6 m, 0°) (b); and the volunteer was at the position (6 m, −20°) (c). The rows from top to bottom corresponds to channel 1, 2, 3 and 4, respectively. Full article ">Figure 13
Power spectrum of the detected respiration by fusing channels 1, 3 and 4 (the blue line) and fusing all the channels (the red line). The rectangular shadow denotes the band expansion in the BRIR. The data was measured when the volunteer was (4 m, 0°) away from the radar. Full article ">Figure 14
Detection results of the artificial breathing object (left column) and their corresponding power spectrum (right column) with different antenna deployments: (a,b) the transmitting and receiving antennas were placed close together; (c,d) the transmitting and receiving antennas were placed separately; and (e,f) the transmitting antenna was moving away from the receiving ones. Full article ">

2880 KiB

Open AccessArticle

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

by Charlotte Bay Hasager, Poul Astrup, Rong Zhu, Rui Chang, Merete Badger and Andrea N. Hahmann

Remote Sens. 2016, 8(9), 769; https://doi.org/10.3390/rs8090769 - 20 Sep 2016

Cited by 13 | Viewed by 7521

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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7255 KiB

Open AccessArticle

Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS

by Nicholas A. Forfinski-Sarkozi and Christopher E. Parrish

Remote Sens. 2016, 8(9), 772; https://doi.org/10.3390/rs8090772 - 19 Sep 2016

Cited by 64 | Viewed by 9165

Abstract

In 2018, the National Aeronautics and Space Administration (NASA) is scheduled to launch the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), with a new six-beam, green-wavelength, photon-counting lidar system, Advanced Topographic Laser Altimeter System (ATLAS). The primary objectives of the ICESat-2 mission are [...] Read more.

In 2018, the National Aeronautics and Space Administration (NASA) is scheduled to launch the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), with a new six-beam, green-wavelength, photon-counting lidar system, Advanced Topographic Laser Altimeter System (ATLAS). The primary objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass. However, if bathymetry can be reliably retrieved from ATLAS data, this could assist in addressing a key data need in many coastal and inland water body areas, including areas that are poorly-mapped and/or difficult to access. Additionally, ATLAS-derived bathymetry could be used to constrain bathymetry derived from complementary data, such as passive, multispectral imagery and synthetic aperture radar (SAR). As an important first step in evaluating the ability to map bathymetry from ATLAS, this study involves a detailed assessment of bathymetry from the Multiple Altimeter Beam Experimental Lidar (MABEL), NASA’s airborne ICESat-2 simulator, flown on the Earth Resources 2 (ER-2) high-altitude aircraft. An interactive, web interface, MABEL Viewer, was developed and used to identify bottom returns in Keweenaw Bay, Lake Superior. After applying corrections for refraction and channel-specific elevation biases, MABEL bathymetry was compared against National Oceanic and Atmospheric Administration (NOAA) data acquired two years earlier. The results indicate that MABEL reliably detected bathymetry in depths of up to 8 m, with a root mean square (RMS) difference of 0.7 m, with respect to the reference data. Additionally, a version of the lidar equation was developed for predicting bottom-return signal levels in MABEL and tested using the Keweenaw Bay data. Future work will entail extending these results to ATLAS, as the technical specifications of the sensor become available. Full article

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2182 KiB

Open AccessArticle

Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

by Hao Tang, Anu Swatantran, Terence Barrett, Phil DeCola and Ralph Dubayah

Remote Sens. 2016, 8(9), 771; https://doi.org/10.3390/rs8090771 - 19 Sep 2016

Cited by 46 | Viewed by 7627

Abstract

Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength [...] Read more.

Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r² = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidar heights (r² = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r² = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r² = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data. Full article

(This article belongs to the Special Issue Airborne Laser Scanning)

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Full article ">Figure 1
An overview of single photon lidar (SPL) data acquired from the High-Resolution Quantum Lidar System (HRQLS): (a) The conical scanning mechanism of HRQLS with both forward and backward scans; (b) An example of point clouds acquired in an individual scan; (c) An example of cross sectional profile composited from multiple scans; (d) 3D point clouds including photons from canopy, terrain and solar noise. Full article ">Figure 2
A flowchart of deriving canopy height from SPL data using two independent methods: the histogram based method and the spatial filtering method. Full article ">Figure 3
(a) Comparisons between field heights and canopy heights (p99) derived from SPL data (using both histogram method and the spatial-filtering method); and (b) height differences between field data and SPL as a function of averaged slope value at each plot. For the histogram method ΔH = 0.43 × Slope − 4.05 with r2 = 0.28 (p < 0.01), and for the spatial-filtering method ΔH = 0.26 × Slope − 2.07 with r2 = 0.18 (p < 0.01). Symbols of different color and shape stand for different processing methods (red: histogram method, and blue: spatial-filtering method) over different types of forests (dbf: deciduous broadleaf forests, conif: coniferous forests, and mix: mixed forests). Full article ">Figure 4
(a) Comparisons between canopy heights derived from discrete return lidar (DRL) and SPL data (using both histogram method and the spatial-filtering method); and (b) height differences between DRL and SPL as a function of averaged slope value at each plot. For the histogram method ΔH = 0.21 × Slope − 1.88 with r2 = 0.11 (p < 0.01). There was no significant relationship between ΔH and slope for the spatial-filtering method (p = 0.77). Same legend as <a href="#remotesensing-08-00771-f003" class="html-fig">Figure 3</a>. Full article ">Figure 5
A comparison example of plot-level canopy height products derived from SPL data using both histogram method and the spatial-filtering method. The plot is on a slope of about 30° with a DRL measured canopy height of 33.87 m and a field measured height of 30.3 m. The left part shows the raw level 1 HRQLS data over the plot with noises distributed both above and below the canopy-terrain layer. The center shows the pseudo-waveform generated from the histogram method, with identified canopy top (616.15 m), ground peak (574.35 m) and canopy height (42.3 m) in dash lines. The right part shows the noise-removal and point-classification results of HRQLS data using the spatial-filtering method. The ground points are in blue, and canopy points are in red with an estimated canopy height (p99) of 35.46 m. Full article ">Figure 6
An illustrative example of the impact of different voxel sizes on noise removal at the individual tree level. The voxel sizes are expressed as combinations of different horizontal resolutions (xy, unit: m) and vertical resolutions (z, unit: m) in (a–c). All the three voxels of different sizes can identify the majority of noise photons (green points) both above canopy and below ground. However, an extra-fine resolution voxel may fail to capture the top of individual trees (a), and an extra-coarse horizontal resolution voxel may miss the entire small tree in open space (c). Full article ">

1928 KiB

Open AccessArticle

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

by Austin Stanforth, Max J. Moreno-Madriñán and Jeffrey Ashby

Remote Sens. 2016, 8(9), 770; https://doi.org/10.3390/rs8090770 - 19 Sep 2016

Cited by 12 | Viewed by 7542

Abstract

Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the [...] Read more.

Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations. Full article

(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)

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9699 KiB

Open AccessArticle

High-Resolution NDVI from Planet’s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture

by Rasmus Houborg and Matthew F. McCabe

Remote Sens. 2016, 8(9), 768; https://doi.org/10.3390/rs8090768 - 19 Sep 2016

Cited by 150 | Viewed by 21900

Abstract

Planet Labs (“Planet”) operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3–5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility [...] Read more.

Planet Labs (“Planet”) operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3–5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility of this rich information source. In this study, Planet’s RGB imagery was translated into a Normalized Difference Vegetation Index (NDVI): a common metric for vegetation growth and condition. Our framework employs a data mining approach to build a set of rule-based regression models that relate RGB data to atmospherically corrected Landsat-8 NDVI. The approach was evaluated over a desert agricultural landscape in Saudi Arabia where the use of near-coincident (within five days) Planet and Landsat-8 acquisitions in the training of the regression models resulted in NDVI predictabilities with an r² of approximately 0.97 and a Mean Absolute Deviation (MAD) on the order of 0.014 (~9%). The MAD increased to 0.021 (~14%) when the Landsat NDVI training image was further away (i.e., 11–16 days) from the corrected Planet image. In these cases, the use of MODIS observations to inform on the change in NDVI occurring between overpasses was shown to significantly improve prediction accuracies. MAD levels ranged from 0.002 to 0.011 (3.9% to 9.1%) for the best performing 80% of the data. The technique is generic and extendable to any region of interest, increasing the utility of Planet’s dense time-series of RGB imagery. Full article

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Overview of the multi-sensor processing framework used for converting the Planet (P) RGB information into Landsat-8 (L) consistent estimates of atmospherically corrected NDVI. The integration of MODIS 250 data is needed to correct for changes in cover conditions occurring between the Planet and Landsat scene acquisitions when they are more than ~4 days apart. Specific details are provided in <a href="#sec2dot3-remotesensing-08-00768" class="html-sec">Section 2.3</a>. Full article ">Figure 2
(a) True color RGB imagery from Landsat-8 and Planet acquisitions two days apart. The insert in the Planet image is included to demonstrate the impact of pivot irrigation on the RGB signal; (b) Intercomparison of Landsat-8 NDVI and Planet NDVI computed from RGB imagery using Cubist regressions generated on the basis of Landsat-8 training data from DOY 320. The two white arrows highlight fields that have undergone significant change in cover conditions between the two acquisitions. Zoomed images are included to demonstrate the enhanced spatial detail achievable with Planet data. Full article ">Figure 3
(a) Density scatter plot intercomparing Landsat-8 and Planet NDVI. The Planet NDVI at DOY 318 was estimated from multi-variate regression models using Landsat NDVI from DOY 320 as training data; (b) Normalized frequency distributions of the Landsat-8 and Planet NDVI using DOY 320 as the Landsat training base. Note that the Planet data have been resampled to 30 m resolution to facilitate a scale consistent intercomparison and statistic evaluation. Full article ">Figure 4
(a) Landsat-8 NDVI on DOY 304 (top) and 320 (bottom); (b) Day-specific (i.e., interpolated) MODIS NDVI on DOY 304 (top) and 318 (bottom); (c) Planet NDVI on DOY 318 predicted on the basis of Landsat-8 NDVI from DOY 304. The bottom plot demonstrates the utility of using higher frequency MODIS data to inform the Cubist-based regression models when near-coincident Landsat and Planet acquisitions do not exist. Full article ">Figure 5
(a) Density scatter plots intercomparing Landsat-8 and Planet NDVI. In the leftmost plot, Planet NDVI on DOY 318 was estimated from Cubist regression models using Landsat NDVI from DOY 304 as training data. In the rightmost plot, MODIS NDVI from DOY 304 and 318 were used to inform on the change in cover conditions occurring between the Landsat and Planet overpasses; (b) Normalized frequency distributions of the Landsat-8 and Planet NDVI using DOY 304 as the Landsat training base. Note that the Planet data have been resampled to 30 m resolution to facilitate a scale consistent intercomparison and statistic evaluation. Full article ">Figure 6
Time-series of (a) 250 m resolution MODIS, (b) 30 m resolution Landsat-8, and (c,d) 3 m resolution Planet NDVI over a subset of the farm. In (c), NDVI was predicted on the basis of a near-coincident (i.e., within 0–5 days) Landsat acquisition that is indicated by a red boundary box (b). In (d), the Landsat-8 NDVI image used as training base is within 11–16 days of the Planet acquisition, and MODIS NDVI is used to inform on the change occurring over these timespans. Full article ">

6526 KiB

Open AccessFeature PaperArticle

Evaluation of Single Photon and Geiger Mode Lidar for the 3D Elevation Program

by Jason M. Stoker, Qassim A. Abdullah, Amar Nayegandhi and Jayna Winehouse

Remote Sens. 2016, 8(9), 767; https://doi.org/10.3390/rs8090767 - 19 Sep 2016

Cited by 71 | Viewed by 11852

Abstract

Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these [...] Read more.

Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these new technologies for the 3DEP. While not able to collect data currently to meet USGS lidar base specification, this is partially due to the fact that the specification was written for linear-mode systems specifically. With little effort on part of the manufacturers of the new lidar systems and the USGS Lidar specifications team, data from these systems could soon serve the 3DEP program and its users. Many of the shortcomings noted in this study have been reported to have been corrected or improved upon in the next generation sensors. Full article

(This article belongs to the Special Issue Airborne Laser Scanning)

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Areas of interest used for processing and assessing bare earth. Full article ">Figure 2
Location of leaf-on linear mode collection (LMWptLO15), IntelliEarth™ daytime collection (GMHarLO15_7.5kDT), and IntelliEarth™ sensor 2 leaf-off collection (GMHarLF15_26k). Full article ">Figure 3
Location of checkpoints used for Vertical Accuracy Assessments. Plus signs are NVA checkpoints. Triangles are VVA checkpoints. Full article ">Figure 4
Example of range walk. Full article ">Figure 5
Cross section used for comparisons, overlaid on imagery (image top). Sample profiles of HRQLS, leaf on (top profile); linear-mode lidar, leaf-on (middle-top profile); linear-mode lidar, leaf-off; (middle-bottom profile); and IntelliEarth™ lidar, leaf on (bottom profile). Full article ">Figure 6
Example of an intensity image from: a linear-mode system (top); and the IntelliEarth™ system (bottom). Full article ">Figure 7
Correlations between Dewberry’s and Woolpert’s IntelliEarth™ DEM differences (top); and HRQLS DEM differences (bottom) from accepted 3DEP lidar. r = 0.74 and 0.55, respectively. Full article ">

4757 KiB

Open AccessArticle

Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

by Heather A. Kramer, Brandon M. Collins, Frank K. Lake, Marek K. Jakubowski, Scott L. Stephens and Maggi Kelly

Remote Sens. 2016, 8(9), 766; https://doi.org/10.3390/rs8090766 - 17 Sep 2016

Cited by 27 | Viewed by 8155

Abstract

Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical [...] Read more.

Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)

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809 KiB

Open AccessArticle

The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data

by Yerong Wu, Martin De Graaf and Massimo Menenti

Remote Sens. 2016, 8(9), 765; https://doi.org/10.3390/rs8090765 - 17 Sep 2016

Cited by 17 | Viewed by 6471

Abstract

This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible [...] Read more.

This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible non-dust aerosol models and 14 vertical distributions. The algorithm intrinsic uncertainty was investigated as well as the interplay effect of aerosol vertical profile and type on the retrieval. The results show that the AOD retrieval is highly sensitive to aerosol vertical profile and type. With 4 aerosol vertical distributions, the algorithm with a fixed vertical distribution gives about 5% error in the AOD retrieval with aerosol loading

τ \leq 0.5

. With pure aerosols (smoke and dust), the retrieval of AOD shows errors ranging from 2% to 30% for a series of vertical distributions. Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval. The interplay effect can give the AOD retrieval errors by over 6%. In addition, intrinsic algorithm errors were found, with a value of >3% when τ> 3.0. This is due to the incorrect estimation of the surface reflectance. The results suggest that the MODIS algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors. Full article

(This article belongs to the Special Issue Land Surface Processes and Interactions—From HCMM to Sentinel Missions and beyond)

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3002 KiB

Open AccessArticle

Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data

by Xifeng Wang and Kaoru Ichikawa

Remote Sens. 2016, 8(9), 764; https://doi.org/10.3390/rs8090764 - 17 Sep 2016

Cited by 7 | Viewed by 4730

Abstract

Wave periods estimated from satellite altimetry data behave differently from those calculated from buoy data, especially in low-wind conditions. In this paper, the geometric mean wave period

T_{a}

is calculated from buoy data, rather than the commonly used zero-crossing wave period [...] Read more.

Wave periods estimated from satellite altimetry data behave differently from those calculated from buoy data, especially in low-wind conditions. In this paper, the geometric mean wave period

T_{a}

is calculated from buoy data, rather than the commonly used zero-crossing wave period

T_{z}

. The geometric mean wave period uses the fourth moment of the wave frequency spectrum and is related to the mean-square slope of the sea surface measured using altimeters. The values of

T_{a}

obtained from buoys and altimeters agree well (root mean square difference: 0.2 s) only when the contribution of high-frequency sea waves is estimated by a wavenumber spectral model to complement the buoy data, because a buoy cannot obtain data from waves having wavelengths that are shorter than the characteristic dimension of the buoy. Full article

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2736 KiB

Open AccessArticle

A Simple Harmonic Model for FAPAR Temporal Dynamics in the Wetlands of the Volga-Akhtuba Floodplain

by Alexander Kozlov, Maria Kozlova and Nikolai Skorik

Remote Sens. 2016, 8(9), 762; https://doi.org/10.3390/rs8090762 - 17 Sep 2016

Cited by 9 | Viewed by 5015

Abstract

The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR) based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral [...] Read more.

The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR) based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral index for estimating plant cover health and its seasonal and annual dynamics. Since FAPAR algorithms are developed for multiple satellite sensors, all FAPAR-based models are suggested to be universal and useful for future studies and long-term monitoring of plant cover, particularly in wetlands. The model developed in the present work for FAPAR temporal dynamics clearly reflects the field-observed seasonal and annual changes of plant cover in the Volga-Akhtuba floodplain wetlands. Various types of wetland plant communities were categorized by the specific parameters of the model seasonal vegetation curve. In addition, the values derived from the model function allow quantitative estimation of wetland plant cover health. This information is particularly important for the Volga-Akhtuba floodplain, because its hydrological regime is regulated by the Volzhskaya hydropower plant. The ecosystem is extremely fragile and sensitive to human impact, and wetland plant cover health is a key indicator of regulatory efficiency. The present study is another step towards developing a methodology focused on arid wetland vegetation monitoring and conservation of its biodiversity and natural conditions. Full article

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8763 KiB

Open AccessArticle

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

by Żaneta Kaszta, Ruben Van De Kerchove, Abel Ramoelo, Moses Azong Cho, Sabelo Madonsela, Renaud Mathieu and Eléonore Wolff

Remote Sens. 2016, 8(9), 763; https://doi.org/10.3390/rs8090763 - 16 Sep 2016

Cited by 49 | Viewed by 6817

Abstract

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify [...] Read more.

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna. Full article

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5481 KiB

Open AccessArticle

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

by Lei Ma, Manchun Li, Thomas Blaschke, Xiaoxue Ma, Dirk Tiede, Liang Cheng, Zhenjie Chen and Dong Chen

Remote Sens. 2016, 8(9), 761; https://doi.org/10.3390/rs8090761 - 16 Sep 2016

Cited by 81 | Viewed by 9181

Abstract

Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived [...] Read more.

Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters. Full article

(This article belongs to the Special Issue Monitoring of Land Changes)

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13314 KiB

Open AccessArticle

Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model

by Xiaobo Luo and Yidong Peng

Remote Sens. 2016, 8(9), 760; https://doi.org/10.3390/rs8090760 - 15 Sep 2016

Cited by 39 | Viewed by 7704

Abstract

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this [...] Read more.

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R²), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (< 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

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Full article ">Figure 1
Location of the study area ((a) China, (b) Chongqing, and (c) the study area). Full article ">Figure 2
The derivation results of SAVI, IBI, NDSI, LST, and the classification result of the study area at the spatial resolution of 30 m ((a) SAVI; (b) NDSI; (c) IBI; (d) LST; and (e) the classification result of the study area). Full article ">Figure 3
The spatial distribution of the local coefficients estimated by GWR models at a spatial resolution of 30 m. (a–c) are the slope coefficients for the SAVI, IBI, and NDSI in single-factor models, respectively; and (d–f) are the coefficients for the SAVI, IBI, and NDSI in multi-factor models, respectively. Moreover, the yellow area represents water. Full article ">Figure 4
The spatial distribution of the local R2 in the GWR models at a spatial resolution of 30 m, (a) for the LST–SAVI model; (b) for the LST–IBI model; (c) for the LST–NDSI model; and (d) for the multi-factor model (using the SAVI, IBI, and NDSI as the explanatory variables simultaneously). Moreover, the yellow area represents water. Full article ">Figure 5
The spatial distribution of the local R2 in the multi-factor GWR models (using the SAVI, IBI, and NDSI as the explanatory variables simultaneously) at different spatial resolutions, (a–f) represent the spatial resolutions of 30 m, 60 m, 120 m, 240 m, 480 m, and 960 m, respectively. Moreover, the yellow area represents water. Full article ">Figure 6
The spatial distribution of the local residuals in the multi-factor GWR models (using the SAVI, IBI, and NDSI as the explanatory variables simultaneously) at different spatial resolutions; (a)–(f) represent the spatial resolutions of 30 m, 60 m, 120 m, 240 m, 480 m, and 960 m, respectively. Moreover, the blue area represents water. Full article ">

21023 KiB

Open AccessArticle

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

by Meryl L. McDowell and Fred A. Kruse

Remote Sens. 2016, 8(9), 757; https://doi.org/10.3390/rs8090757 - 15 Sep 2016

Cited by 10 | Viewed by 7063

Abstract

We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first [...] Read more.

We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach. Full article

(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)

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25904 KiB

Open AccessArticle

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

by Katarzyna Dabrowska-Zielinska, Maria Budzynska, Monika Tomaszewska, Alicja Malinska, Martyna Gatkowska, Maciej Bartold and Iwona Malek

Remote Sens. 2016, 8(9), 756; https://doi.org/10.3390/rs8090756 - 15 Sep 2016

Cited by 38 | Viewed by 10471

Abstract

The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) [...] Read more.

The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R² = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R² = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO₂ to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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12684 KiB

Open AccessArticle

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

by Shuai Xie, Jianbo Duan, Shibin Liu, Qin Dai, Wei Liu, Yong Ma, Rui Guo and Caihong Ma

Remote Sens. 2016, 8(9), 759; https://doi.org/10.3390/rs8090759 - 14 Sep 2016

Cited by 32 | Viewed by 6660

Abstract

Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing [...] Read more.

Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image. Full article

(This article belongs to the Special Issue Earth Observations for Geohazards)

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