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Remote Sens., Volume 7, Issue 11 (November 2015) – 65 articles , Pages 14276-15803

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Article
An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 µm MODIS Imagery
Remote Sens. 2015, 7(11), 15782-15803; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115782 - 24 Nov 2015
Cited by 44 | Viewed by 5813
Abstract
The Brazilian Cerrado is significantly affected by anthropic fires every year, which makes the region an important source of pyrogenic emissions. This study aims at generating improved 1 km monthly burned area maps for Cerrado based on remote-sensed information. The algorithm relies on [...] Read more.
The Brazilian Cerrado is significantly affected by anthropic fires every year, which makes the region an important source of pyrogenic emissions. This study aims at generating improved 1 km monthly burned area maps for Cerrado based on remote-sensed information. The algorithm relies on a burn-sensitive vegetation index based on MODIS daily values of near and middle infrared reflectance and makes use of active fire detection from multiple sensors. Validation is performed using reference burned area (BA) maps derived from Landsat imagery. Results are also compared with MODIS standard BA products. A monthly BA database for the Brazilian Cerrado is generated covering the period 2005–2014. Estimated value of BA is 1.3 times larger than the value derived from reference data, making the product suitable for applications in fire emission studies and ecosystem management. As expected the intra and inter-annual variability of estimated BA over the Brazilian Cerrado is in agreement with the regime of precipitation. This work represents the first step towards setting up a regional database of BA for Brazil to be developed in the framework of BrFLAS, an R and D project in the areas of fire emissions and ecosystem management planning. Full article
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Article
Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy
Remote Sens. 2015, 7(11), 15748-15781; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115748 - 23 Nov 2015
Cited by 20 | Viewed by 4943
Abstract
Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and [...] Read more.
Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Article
Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements
Remote Sens. 2015, 7(11), 15729-15747; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115729 - 23 Nov 2015
Cited by 63 | Viewed by 3777
Abstract
Long-term global satellite and reanalysis soil moisture products have been available for several years. In this study, in situ soil moisture measurements from 2008 to 2012 over Southwest China are used to evaluate the accuracy of four satellite-based products and one reanalysis soil [...] Read more.
Long-term global satellite and reanalysis soil moisture products have been available for several years. In this study, in situ soil moisture measurements from 2008 to 2012 over Southwest China are used to evaluate the accuracy of four satellite-based products and one reanalysis soil moisture product. These products are the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), the Advanced Scatterometer (ASCAT), the Soil Moisture and Ocean Salinity (SMOS), the European Space Agency’s Climate Change Initiative soil moisture (CCI SM), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim). The evaluation of soil moisture absolute values and anomalies shows that all the products can capture the temporal dynamics of in situ soil moisture well. For AMSR-E and SMOS, larger errors occur, which are likely due to the severe effects of radio frequency interference (RFI) over the test region. In general, the ERA-Interim (R = 0.782, ubRMSD = 0.035 m3/m3) and CCI SM (R = 0.723, ubRMSD = 0.046 m3/m3) perform the best compared to the other products. The accuracy levels obtained are comparable to validation results from other regions. Therefore, local hydrological applications and water resource management will benefit from the long-term ERA-Interim and CCI SM soil moisture products. Full article
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Article
On the Use of Global Flood Forecasts and Satellite-Derived Inundation Maps for Flood Monitoring in Data-Sparse Regions
Remote Sens. 2015, 7(11), 15702-15728; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115702 - 23 Nov 2015
Cited by 45 | Viewed by 5533
Abstract
Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, [...] Read more.
Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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Article
Towards an Interoperable Field Spectroscopy Metadata Standard with Extended Support for Marine Specific Applications
Remote Sens. 2015, 7(11), 15668-15701; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115668 - 20 Nov 2015
Cited by 5 | Viewed by 2539
Abstract
This paper presents an approach to developing robust metadata standards for specific applications that serves to ensure a high level of reliability and interoperability for a spectroscopy dataset. The challenges of designing a metadata standard that meets the unique requirements of specific user [...] Read more.
This paper presents an approach to developing robust metadata standards for specific applications that serves to ensure a high level of reliability and interoperability for a spectroscopy dataset. The challenges of designing a metadata standard that meets the unique requirements of specific user communities are examined, including in situ measurement of reflectance underwater, using coral as a case in point. Metadata schema mappings from seven existing metadata standards demonstrate that they consistently fail to meet the needs of field spectroscopy scientists for general and specific applications (μ = 22%, σ = 32% conformance with the core metadata requirements and μ = 19%, σ = 18% for the special case of a benthic (e.g., coral) reflectance metadataset). Issues such as field measurement methods, instrument calibration, and data representativeness for marine field spectroscopy campaigns are investigated within the context of submerged benthic measurements. The implication of semantics and syntax for a robust and flexible metadata standard are also considered. A hybrid standard that serves as a “best of breed” incorporating useful modules and parameters within the standards is proposed. This paper is Part 3 in a series of papers in this journal, examining the issues central to a metadata standard for field spectroscopy datasets. The results presented in this paper are an important step towards field spectroscopy metadata standards that address the specific needs of field spectroscopy data stakeholders while facilitating dataset documentation, quality assurance, discoverability and data exchange within large-scale information sharing platforms. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Article
Automatic Detection and Segmentation of Columns in As-Built Buildings from Point Clouds
Remote Sens. 2015, 7(11), 15651-15667; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115651 - 20 Nov 2015
Cited by 28 | Viewed by 3406
Abstract
Over the past few years, there has been an increasing need for tools that automate the processing of as-built 3D laser scanner data. Given that a fast and active dimensional analysis of constructive components is essential for construction monitoring, this paper is particularly [...] Read more.
Over the past few years, there has been an increasing need for tools that automate the processing of as-built 3D laser scanner data. Given that a fast and active dimensional analysis of constructive components is essential for construction monitoring, this paper is particularly focused on the detection and segmentation of columns in building interiors from incomplete point clouds acquired with a Terrestrial Laser Scanner. The methodology addresses two types of columns: round cross-section and rectangular cross-section. Considering columns as vertical elements, the global strategy for segmentation involves the rasterization of a point cloud onto the XY plane and the implementation of a model-driven approach based on the Hough Transform. The methodology is tested in two real case studies, and experiments are carried out under different levels of data completeness. The results show the robustness of the methodology to the presence of clutter and partial occlusion, typical in building indoors, even though false positives can be obtained if other elements with the same shape and size as columns are present in the raster. Full article
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Technical Note
An On-Demand Web Tool for the Unsupervised Retrieval of Earth’s Surface Deformation from SAR Data: The P-SBAS Service within the ESA G-POD Environment
Remote Sens. 2015, 7(11), 15630-15650; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115630 - 19 Nov 2015
Cited by 38 | Viewed by 4425
Abstract
This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) [...] Read more.
This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, within the Grid Processing on Demand (G-POD) environment that is a part of the ESA’s Geohazards Exploitation Platform (GEP). The developed on-demand web tool, which is specifically addressed to scientists that are non-expert in DInSAR data processing, permits to set up an efficient on-line P-SBAS processing service to produce surface deformation mean velocity maps and time series in an unsupervised manner. Such results are obtained by exploiting the available huge ERS and ENVISAT SAR data archives; moreover, the implementation of the Sentinel-1 P-SBAS processing chain is in a rather advanced status and first results are already available. Thanks to the adopted strategy to co-locate both DInSAR algorithms and computational resources close to the SAR data archives, as well as the provided capability to easily generate the DInSAR results, the presented web tool may contribute to drastically expand the user community exploiting the DInSAR products and methodologies. Full article
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Article
Automatic Object Extraction from Electrical Substation Point Clouds
Remote Sens. 2015, 7(11), 15605-15629; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115605 - 19 Nov 2015
Cited by 13 | Viewed by 4859
Abstract
The reliability of power delivery can be profoundly improved by preventing wildlife-related power outages. This can be achieved by insulating electrical substation components with non-conductive covers. The manufacture of custom-built covers requires as-built models of the salient components. This study presents new, automated [...] Read more.
The reliability of power delivery can be profoundly improved by preventing wildlife-related power outages. This can be achieved by insulating electrical substation components with non-conductive covers. The manufacture of custom-built covers requires as-built models of the salient components. This study presents new, automated methodology to recognize key components of electrical substations from 3D LiDAR data acquired using terrestrial laser scanning. The proposed methodology includes six novel algorithms to recognize key components (fence, cables, circuit breakers, bushings and bus pipes) of electrical substations. Three datasets with different resolutions and configurations are used in this study. A Leica HDS 6100 laser scanner was used to acquire the first dataset and a Faro Focus3D laser scanner was employed to collect the second and third datasets. The obtained results indicate that 178 and 171 out of 181 electrical substation elements were successfully recognized in the first and second dataset, respectively, and 183 out of 191 components were identified in the third dataset. The results also demonstrate that an average 97.8% accuracy and average 98.8% precision at the point cloud level can be achieved. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Article
Satellite Image Time Series Decomposition Based on EEMD
Remote Sens. 2015, 7(11), 15583-15604; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115583 - 19 Nov 2015
Cited by 20 | Viewed by 3029
Abstract
Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble [...] Read more.
Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes. Full article
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Article
Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data
Remote Sens. 2015, 7(11), 15561-15582; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115561 - 19 Nov 2015
Cited by 34 | Viewed by 3066
Abstract
Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the [...] Read more.
Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the influence of soil moisture on clay estimation by using soil spectra acquired in a laboratory and by simulating satellite hyperspectral data. Simulated satellite data were obtained according to the spectral characteristics of the forthcoming hyperspectral imager on board of the Italian PRISMA satellite mission. The soil datasets were split into four groups according to the water content. For each soil moisture level a prediction model was applied, using either spectral indices or partial least squares regression (PLSR). Prediction models were either specifically developed for the soil moisture level or calibrated using synthetically dry soil spectra, generated from wet soil data. Synthetically dry spectra were obtained using a new technique based on the effects caused by soil moisture on the optical spectrum from 400 to 2400 nm. The estimation of soil clay content, when using different prediction models according to soil moisture, was slightly more accurate as compared to the use of synthetically dry soil spectra, both employing clay indices and PLSR models. The results obtained in this study demonstrate that the a priori knowledge of the soil moisture class can reduce the error of clay estimation when using hyperspectral remote sensing data, such as those that will be provided by the PRISMA satellite mission in the near future. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Article
Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants
Remote Sens. 2015, 7(11), 15536-15560; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115536 - 18 Nov 2015
Cited by 7 | Viewed by 2931
Abstract
Albedo characterizes the radiometric interface of land surfaces, especially vegetation, and the atmosphere. Albedo is a critical input to many models, such as crop growth models, hydrological models and climate models. For the extensive attention to crop monitoring, a physical albedo model for [...] Read more.
Albedo characterizes the radiometric interface of land surfaces, especially vegetation, and the atmosphere. Albedo is a critical input to many models, such as crop growth models, hydrological models and climate models. For the extensive attention to crop monitoring, a physical albedo model for crops is developed based on the law of energy conservation and spectral invariants, which is derived from a prior forest albedo model. The model inputs have been efficiently and physically parameterized, including the dependency of albedo on the solar zenith/azimuth angle, the fraction of diffuse skylight in the incident radiance, the canopy structure, the leaf reflectance/transmittance and the soil reflectance characteristics. Both the anisotropy of soil reflectance and the clumping effect of crop leaves at the canopy scale are considered, which contribute to the improvement of the model accuracy. The comparison between the model results and Monte Carlo simulation results indicates that the canopy albedo has high accuracy with an RMSE < 0.005. The validation using ground measurements has also demonstrated the reliability of the model and that it can reflect the interaction mechanism between radiation and the canopy-soil system. Full article
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Article
Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012
Remote Sens. 2015, 7(11), 15517-15535; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115517 - 18 Nov 2015
Cited by 37 | Viewed by 2866
Abstract
In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during [...] Read more.
In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001–2012, with a linear trend of 1.4%/10 years (p < 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grass-forb community had the highest linear trend of 2%/10 years (p < 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade. Full article
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Article
A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements
Remote Sens. 2015, 7(11), 15494-15516; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115494 - 18 Nov 2015
Cited by 51 | Viewed by 5190
Abstract
The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and [...] Read more.
The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and FCOVER satellite products are derived moderate to coarse spatial resolution. The launch of Sentinel-2 in 2015 will provide data at decametric resolution with a high revisit frequency to allow quantifying the canopy functioning at the local to regional scales. The aim of this study is thus to evaluate the performances of a neural network based algorithm to derive LAI, FAPAR and FCOVER products at decametric spatial resolution and high temporal sampling. The algorithm is generic, i.e., it is applied without any knowledge of the landcover. A time series of high spatial resolution SPOT4_HRVIR (16 scenes) and Landsat 8 (18 scenes) images acquired in 2013 over the France southwestern site were used to generate the LAI, FAPAR and FCOVER products. For each sensor and each biophysical variable, a neural network was first trained over PROSPECT+SAIL radiative transfer model simulations of top of canopy reflectance data for green, red, near-infra red and short wave infra-red bands. Our results show a good spatial and temporal consistency between the variables derived from both sensors: almost half the pixels show an absolute difference between SPOT and LANDSAT estimates of lower that 0.5 unit for LAI, and 0.05 unit for FAPAR and FCOVER. Finally, downward-looking digital hemispherical cameras were completed over the main land cover types to validate the accuracy of the products. Results show that the derived products are strongly correlated with the field measurements (R2 > 0.79), corresponding to a RMSE = 0.49 for LAI, RMSE = 0.10 (RMSE = 0.12) for black-sky (white sky) FAPAR and RMSE = 0.15 for FCOVER. It is concluded that the proposed generic algorithm provides a good basis to monitor the seasonal variation of the vegetation biophysical variables for important crops at decametric resolution. Full article
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Article
Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level
Remote Sens. 2015, 7(11), 15467-15493; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115467 - 18 Nov 2015
Cited by 188 | Viewed by 9466
Abstract
Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of [...] Read more.
Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of insect induced forest disturbance has established a new demand for effective methods suitable in mapping and monitoring tasks. In this investigation, a novel miniaturized hyperspectral frame imaging sensor operating in the wavelength range of 500–900 nm was used to identify mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation, representing a different outbreak phase, by the European spruce bark beetle (Ips typographus L.). We developed a new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees. The dense point clouds, measured using image matching, enabled detection of single trees with an accuracy of 74.7%. We classified the trees into classes of healthy, infested and dead, and the results were promising. The best results for the overall accuracy were 76% (Cohen’s kappa 0.60), when using three color classes (healthy, infested, dead). For two color classes (healthy, dead), the best overall accuracy was 90% (kappa 0.80). The survey methodology based on high-resolution hyperspectral imaging will be of a high practical value for forest health management, indicating a status of bark beetle outbreak in time. Full article
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Article
Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM
Remote Sens. 2015, 7(11), 15443-15466; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115443 - 18 Nov 2015
Cited by 22 | Viewed by 3990
Abstract
Steep topographic reliefs and heavy vegetation severely limit visibility when examining geological structures and surface deformations in the field or when detecting these features with traditional approaches, such as aerial photography and satellite imagery. However, a light detection and ranging (LiDAR)-derived digital elevation [...] Read more.
Steep topographic reliefs and heavy vegetation severely limit visibility when examining geological structures and surface deformations in the field or when detecting these features with traditional approaches, such as aerial photography and satellite imagery. However, a light detection and ranging (LiDAR)-derived digital elevation model (DEM), which is directly related to the bare ground surface, is successfully employed to map topographic signatures with an appropriate scale and accuracy and facilitates measurements of fine topographic features. This study demonstrates the efficient use of 1-m-resolution LiDAR for tectonic geomorphology in forested areas and to identify a fault, a deep-seated landslide, and the regional cleavage attitude in southern Taiwan. Integrated approaches that use grayscale slope images, openness with a tint color slope visualization, the three-dimensional (3D) perspective of a red relief image map, and a field investigation are employed to identify the aforementioned features. In this study, the previously inferred Meilongshan Fault is confirmed as a NE–SW-trending, eastern dipping thrust with at least a 750 m-wide deformation zone. The site where future paleoseismological studies should be performed has been identified, and someone needs to work further on this site. Signatures of deep-seated landslides, such as double ridges, trenches, main escarpments, and extension cracks, are successfully differentiated in LiDAR DEM images through the use of different visualization techniques. Systematic parallel and continuous lineaments in the images are interpreted as the regional cleavage attitude of cleavage, and a field investigation confirms this interpretation. Full article
(This article belongs to the Special Issue Remote Sensing in Geology)
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Article
Polarimetric Scattering Properties of Landslides in Forested Areas and the Dependence on the Local Incidence Angle
Remote Sens. 2015, 7(11), 15424-15442; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115424 - 18 Nov 2015
Cited by 17 | Viewed by 3167
Abstract
This paper addresses the local incidence angle dependence of several polarimetric indices corresponding to landslides in forested areas. Landslide is deeply related to the loss of human lives and their property. Various kinds of remote sensing techniques, including aerial photography, high-resolution optical satellite [...] Read more.
This paper addresses the local incidence angle dependence of several polarimetric indices corresponding to landslides in forested areas. Landslide is deeply related to the loss of human lives and their property. Various kinds of remote sensing techniques, including aerial photography, high-resolution optical satellite imagery, LiDAR and SAR interferometry (InSAR), have been available for landslide investigations. SAR polarimetry is potentially an effective measure to investigate landslides because fully-polarimetric SAR (PolSAR) data contain more information compared to conventional single- or dual-polarization SAR data. However, research on landslide recognition utilizing polarimetric SAR (PolSAR) is quite limited. Polarimetric properties of landslides have not been examined quantitatively so far. Accordingly, we examined the polarimetric scattering properties of landslides by an assessment of how the decomposed scattering power components and the polarimetric correlation coefficient change with the local incidence angle. In the assessment, PolSAR data acquired from different directions with both spaceborne and airborne SARs were utilized. It was found that the surface scattering power and the polarimetric correlation coefficient of landslides significantly decrease with the local incidence angle, while these indices of surrounding forest do not. This fact leads to establishing a method of effective detection of landslide area by polarimetric information. Full article
(This article belongs to the Special Issue Remote Sensing in Geology)
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Article
Quality Assessment of the CCI ECV Soil Moisture Product Using ENVISAT ASAR Wide Swath Data over Spain, Ireland and Finland
Remote Sens. 2015, 7(11), 15388-15423; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115388 - 18 Nov 2015
Cited by 17 | Viewed by 3680
Abstract
During the last decade, great progress has been made by the scientific community in generating satellite-derived global surface soil moisture products, as a valuable source of information to be used in a variety of applications, such as hydrology, meteorology and climatic modeling. Through [...] Read more.
During the last decade, great progress has been made by the scientific community in generating satellite-derived global surface soil moisture products, as a valuable source of information to be used in a variety of applications, such as hydrology, meteorology and climatic modeling. Through the European Space Agency Climate Change Initiative (ESA CCI), the most complete and consistent global soil moisture (SM) data record based on active and passive microwaves sensors is being developed. However, the coarse spatial resolution characterizing such data may be not sufficient to accurately represent the moisture conditions. The objective of this work is to assess the quality of the CCI Essential Climate Variable (ECV) SM product by using finer spatial resolution Advanced Synthetic Aperture Radar (ASAR) Wide Swath and in situ soil moisture data taken over three regions in Europe. Ireland, Spain, and Finland have been selected with the aim of assessing the spatial and temporal representativeness of the ECV SM product over areas that differ in climate, topography, land cover and soil type. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values. A good temporal and spatial agreement has been observed between the three soil moisture datasets for the Irish and Spanish sites, while poorer results have been found at the Finnish sites. Overall, the two different satellite derived products capture the soil moisture temporal variations well and are in good agreement with each other. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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Article
Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale
Remote Sens. 2015, 7(11), 15361-15387; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115361 - 18 Nov 2015
Cited by 27 | Viewed by 4689
Abstract
Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to [...] Read more.
Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference fractional abundances, as well as spectral measurements of the canopy components, could be precisely determined from a crane measurement platform situated in a deciduous forest in North-East Germany. In contrast to most other studies, which only use leaf and soil endmembers, this experimental setup allowed for the inclusion of a bark endmember for the unmixing of components within the canopy. This study demonstrates that the inclusion of additional endmembers markedly improves the accuracy. A mean absolute error of 7.9% could be achieved for the fractional occurrence of the leaf endmember and 5.9% for the bark endmember. In order to evaluate the results of this field-based study for airborne and satellite-based remote sensing applications, a transfer to Airborne Imaging Spectrometer for Applications (AISA) and simulated Environmental Mapping and Analysis Program (EnMAP) and Sentinel-2 imagery was carried out. All sensors were capable of unmixing crown components with a mean absolute error ranging between 3% and 21%. Full article
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Article
A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance
Remote Sens. 2015, 7(11), 15340-15360; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115340 - 17 Nov 2015
Cited by 35 | Viewed by 2900
Abstract
Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to [...] Read more.
Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605−720), the sum of reconstructed detail reflectance at fourth decomposition level over 605−720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350−2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = -0.719, p < 0.001). Based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean R2Val (determination coefficient of validation) value of 0.706 and an RPD (residual prediction deviation) value of 1.75 for Cu estimation. The bootstrapping and ANOVA test results showed that SWT (605−720) significantly (p < 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. It was concluded here that the wavelet-based area parameter (i.e., SWT (605−720)) has potential ability to indirectly estimate Cu concentrations in Carex leaves through the strong correlation between Cu and chlorophyll. The method presented in this pilot study may be used to estimate the concentrations of other heavy metals. However, further research is needed to test its transferability and robustness for estimating Cu concentrations on other plant species in different biological and environmental conditions. Full article
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Technical Note
Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing
Remote Sens. 2015, 7(11), 15318-15339; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115318 - 13 Nov 2015
Cited by 18 | Viewed by 3965
Abstract
In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM) trained [...] Read more.
In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM) trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient. Full article
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Article
Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling
Remote Sens. 2015, 7(11), 15295-15317; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115295 - 13 Nov 2015
Cited by 17 | Viewed by 3198
Abstract
Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of [...] Read more.
Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We present summary results for this methodology applied to a land cover classification of Zambia’s Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%. Full article
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Article
An Autonomous System to Take Angular Thermal-Infrared Measurements for Validating Satellite Products
Remote Sens. 2015, 7(11), 15269-15294; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115269 - 13 Nov 2015
Cited by 7 | Viewed by 2729
Abstract
An autonomous system for field land surface temperature (LST) measurements taken at different observation angles was developed to be deployed easily at any conventional meteorological tower station. The system permits ground-truth data to be acquired on a continuous basis, and angularly scans land [...] Read more.
An autonomous system for field land surface temperature (LST) measurements taken at different observation angles was developed to be deployed easily at any conventional meteorological tower station. The system permits ground-truth data to be acquired on a continuous basis, and angularly scans land and sky hemispheres with a single thermal-infrared (TIR) radiometer. This paper describes the autonomous angular system and the methodology to assess ground-truth LST and relative-to-nadir emissivity data from system measurements. Ground-truth LSTs were used to validate satellite-retrieved LST products at two experimental sites (rice crop and shrubland areas). The relative-to-nadir emissivity values were used to analyze the anisotropy of surface emissive properties over thermally-homogeneous covers. The EOS-MODIS MOD11_L2/MYD11_L2 LST product was evaluated and shown to work within expected uncertainties (<2.0 K) when tested against the system data. A slight underestimation of around −0.15 K was observed, which became greater for the off-nadir observation angles at the shrubland site. The system took angular measurements for the different seasonal homogeneous covers at the rice crop site. These measurements showed emissivity angular anisotropies, which were in good agreement with previously published data. The dual-view ENVISAT-AATSR data reproduced them, and revealed that the system data collected for thermally-homogeneous surfaces could be used to test future satellite TIR sensors with multi-angular or bi-angular capabilities, like the forthcoming SLSTR on board Copernicus Sentinel-3A. Full article
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Article
Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data
Remote Sens. 2015, 7(11), 15244-15268; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115244 - 13 Nov 2015
Cited by 14 | Viewed by 3712
Abstract
The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their [...] Read more.
The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area. Full article
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Article
Analysis of the 2014 “APEC Blue” in Beijing Using More than One Decade of Satellite Observations: Lessons Learned from Radical Emission Control Measures
Remote Sens. 2015, 7(11), 15224-15243; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115224 - 13 Nov 2015
Cited by 21 | Viewed by 3163
Abstract
During the 2014 Asia-Pacific Economic Cooperation (APEC) Economic Leaders’ Meetings in Beijing, the Chinese government made significant efforts to clear Beijing’s sky. The emission control measures were very effective and the improved air quality during the APEC Meetings was called the “APEC Blue”. [...] Read more.
During the 2014 Asia-Pacific Economic Cooperation (APEC) Economic Leaders’ Meetings in Beijing, the Chinese government made significant efforts to clear Beijing’s sky. The emission control measures were very effective and the improved air quality during the APEC Meetings was called the “APEC Blue”. To monitor and estimate how these emission control measures affected air quality in Beijing and its five neighboring large cities (Tianjin, Shijiazhuang, Tangshan, Jinan, and Qingdao), we compared and analyzed the satellite-retrieved Aerosol Optical Thickness (AOT) products of the pre-APEC (18–31 October), APEC (1–11 November), and post-APEC periods (11–31 November) in 2002–2014 and daily PM2.5 measurements of the three periods in 2014 on the ground. Compared with the pre- and post-APEC periods, both ground and satellite observations indicated significantly reduced aerosol loading during the 2014 APEC period in Beijing and its surroundings, but with apparent spatial heterogeneity. For example, the peak value of PM2.5 in Beijing were around 100 µg∙m−3 during the APEC period, however, during the pre- and post-APEC periods, the peak values were up to 290 µg∙m−3. The following temporal correlation analysis of mean AOT values between Beijing and other five cities for the past thirteen years (2002–2014) indicated that the potential emission source regions strongly impacting air quality of Beijing were confined within central and southern Hebei as well as northern and southwestern Shandong, in correspondence with the spatial pattern of Digital Earth Model (DEM) of the study region. In addition to stringent emission control measures, back trajectory analysis indicated that the relatively favorable regional transport pattern might also have contributed to the “APEC Blue” in Beijing. These results suggest that the “APEC Blue” is a temporarily regional phenomenon; a long-term improvement of air quality in Beijing is still challenging and joint efforts of the whole region are needed. Full article
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Article
Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China
Remote Sens. 2015, 7(11), 15203-15223; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115203 - 12 Nov 2015
Cited by 30 | Viewed by 3510
Abstract
Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level (\({\overline{EWT}}\)) based on in [...] Read more.
Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level (\({\overline{EWT}}\)) based on in situ measurements of Lambertian equivalent reflectance and important biological and environmental factors during the 2013−2014 growing seasons in the North China Plain. Among ten VIs, the performances of green chlorophyll index (CIgreen), red edge chlorophyll index (CIred edge), and the red edge normalized ratio (NRred edge) were most sensitive to the variations of CWC and \({\overline{EWT}}\). Simulated drought in two differently managed irrigation years did not affect the sensitivities of VIs to the variations in CWC and \({\overline{EWT}}\). However, the relationships between CWC and VIs were more noticeable in 2014 than in 2013. In contrast, \({\overline{EWT}}\) and VIs were more closely related in 2013 than in 2014. CWC and relative soil water content (RSWC) obviously exhibited a two-dimensional trapezoid space, which illustrated that CWC was determined not only by soil water status but also by crop growth and stage of development. This study demonstrated that nearly half of the variation in CWC explained by spectral information was derived from the variation in leaf area index (LAI). Full article
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Article
Magma Pathways and Their Interactions Inferred from InSAR and Stress Modeling at Nyamulagira Volcano, D.R. Congo
Remote Sens. 2015, 7(11), 15179-15202; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115179 - 12 Nov 2015
Cited by 16 | Viewed by 3825
Abstract
A summit and upper flank eruption occurred at Nyamulagira volcano, Democratic Republic of Congo, from 2–27 January 2010. Eruptions at Nyamulagira during 1996–2010 occurred from eruptive fissures on the upper flanks or within the summit caldera and were distributed along the ~N155E rift [...] Read more.
A summit and upper flank eruption occurred at Nyamulagira volcano, Democratic Republic of Congo, from 2–27 January 2010. Eruptions at Nyamulagira during 1996–2010 occurred from eruptive fissures on the upper flanks or within the summit caldera and were distributed along the ~N155E rift zone, whereas the 2011–2012 eruption occurred ~12 km ENE of the summit. 3D numerical modeling of Interferometric Synthetic Aperture Radar (InSAR) geodetic measurements of the co-eruptive deformation in 2010 reveals that magma stored in a shallow (~3.5 km below the summit) reservoir intruded as two subvertical dikes beneath the summit and southeastern flank of the volcano. The northern dike is connected to an ~N45E-trending intra-caldera eruptive fissure, extending to an ~2.5 km maximum depth. The southern dike is connected to an ~N175E-trending flank fissure extending to the depth of the inferred reservoir at ~3.5 km. The inferred reservoir location is coincident with the reservoir that was active during previous eruptions in 1938–1940 and 2006. The volumetric ratio of total emitted magma (intruded in dikes + erupted) to the contraction of the reservoir (rv) is 9.3, consistent with pressure recovery by gas exsolution in the small, shallow modeled magma reservoir. We derive a modified analytical expression for rv, accounting for changes in reservoir volume induced by gas exsolution, as well as eruptive volume. By using the precise magma composition, we estimate a magma compressibility of 1.9–3.2 × 109 Pa−1 and rv of 6.5–10.1. From a normal-stress change analysis, we infer that intrusions in 2010 could have encouraged the ascent of magma from a deeper reservoir along an ~N45E orientation, corresponding to the strike of the rift transfer zone structures and possibly resulting in the 2011–2012 intrusion. The intrusion of magma to greater distances from the summit may be enhanced along the N45E orientation, as it is more favorable to the regional rift extension (compared to the local volcanic rift zone, trending N155E). Repeated dike intrusions beneath Nyamulagira’s SSE flank may encourage intrusions beneath the nearby Nyiragongo volcano. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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Technical Note
Energy Analysis of Road Accidents Based on Close-Range Photogrammetry
Remote Sens. 2015, 7(11), 15161-15178; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115161 - 12 Nov 2015
Cited by 5 | Viewed by 3315
Abstract
This paper presents an efficient and low-cost approach for energy analysis of road accidents using images obtained using consumer-grade digital cameras and smartphones. The developed method could be used by security forces in order to improve the qualitative and quantitative analysis of traffic [...] Read more.
This paper presents an efficient and low-cost approach for energy analysis of road accidents using images obtained using consumer-grade digital cameras and smartphones. The developed method could be used by security forces in order to improve the qualitative and quantitative analysis of traffic accidents. This role of the security forces is crucial to settle arguments; consequently, the remote and non-invasive collection of accident related data before the scene is modified proves to be essential. These data, taken in situ, are the basis to perform the necessary calculations, basically the energy analysis of the road accident, for the corresponding expert reports and the reconstruction of the accident itself, especially in those accidents with important damages and consequences. Therefore, the method presented in this paper provides the security forces with an accurate, three-dimensional, and scaled reconstruction of a road accident, so that it may be considered as a support tool for the energy analysis. This method has been validated and tested with a real crash scene simulated by the local police in the Academy of Public Safety of Extremadura, Spain. Full article
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Article
Inter-Band Radiometric Comparison and Calibration of ASTER Visible and Near-Infrared Bands
Remote Sens. 2015, 7(11), 15140-15160; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115140 - 12 Nov 2015
Cited by 9 | Viewed by 3189
Abstract
The present study evaluates inter-band radiometric consistency across the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near-infrared (VNIR) bands and develops an inter-band calibration algorithm to improve radiometric consistency. Inter-band radiometric comparison of current ASTER data shows a root mean [...] Read more.
The present study evaluates inter-band radiometric consistency across the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near-infrared (VNIR) bands and develops an inter-band calibration algorithm to improve radiometric consistency. Inter-band radiometric comparison of current ASTER data shows a root mean square error (RMSE) of 3.8%–5.7% among radiance outputs of spectral bands due primarily to differences between calibration strategies of the NIR band for nadir-looking (Band 3N) and the other two bands (green and red bands, corresponding to Bands 1 and 2). An algorithm for radiometric calibration of Bands 2 and 3N with reference to Band 1 is developed based on the band translation technique and is used to obtain new radiometric calibration coefficients (RCCs) for sensor sensitivity degradation. The systematic errors between radiance outputs are decreased by applying the derived RCCs, which result in reducing the RMSE from 3.8%–5.7% to 2.2%–2.9%. The remaining errors are approximately equal to or smaller than the intrinsic uncertainties of inter-band calibration derived by sensitivity analysis. Improvement of the radiometric consistency would increase the accuracy of band algebra (e.g., vegetation indices) and its application. The algorithm can be used to evaluate inter-band radiometric consistency, as well as for the calibration of other sensors. Full article
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Article
Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis
Remote Sens. 2015, 7(11), 15114-15139; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115114 - 11 Nov 2015
Cited by 20 | Viewed by 2708
Abstract
Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) [...] Read more.
Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), to map the forest carbon density of Shenzhen City of China, using Landsat 8 imagery and sample plot data collected in 2014. The independent variables that contributed to statistically significantly improving the fit of a model to data and reducing the sum of squared errors were first selected from a total of 284 spectral variables derived from the image bands. The vegetation fraction from LSUA was then added as an independent variable. The results obtained using cross-validation showed that: (1) Compared to the methods without the vegetation information, adding the vegetation fraction increased the accuracy of mapping carbon density by 1%–9.3%; (2) As the observed values increased, the LSR and kNN residuals showed overestimates and underestimates for the smaller and larger observations, respectively, while LMSR improved the systematical over and underestimations; (3) LSR resulted in illogically negative and unreasonably large estimates, while KNN produced the greatest values of root mean square error (RMSE). The results indicate that combining the spatial modeling method LMSR and the spectral unmixing analysis LUSA, coupled with Landsat imagery, is most promising for increasing the accuracy of urban forest carbon density maps. In addition, this method has considerable potential for accurate, rapid and nondestructive prediction of urban and peri-urban forest carbon stocks with an acceptable level of error and low cost. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Article
Estimation of CO2 Sequestration by the Forests in Japan by Discriminating Precise Tree Age Category using Remote Sensing Techniques
Remote Sens. 2015, 7(11), 15082-15113; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115082 - 11 Nov 2015
Cited by 22 | Viewed by 4238
Abstract
This study estimates CO2 sequestration by forests in Japan using Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing data for the in-depth retrieval of forest [...] Read more.
This study estimates CO2 sequestration by forests in Japan using Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing data for the in-depth retrieval of forest growth stages (tree age). Landsat imagery was used to develop a detailed forest cover map, while the PALSAR data were used to estimate the volume information. The volume was converted to tree age information for each of the three forest types in Japan. An estimation of CO2 sequestration values for each forest type and for each tree age from the forest inventory data was made. The forest cover map results in four classes, and the overall accuracy yields approximately 74%. For the volume estimation, Root Mean Square Error (RMSE) was computed with the ground reference information resulting in 105.58 m3/ha. The final result showed that total CO2 sequestration in Japan based on tree age forest subclasses yields 85.0 Mt∙CO2 (coniferous), 4.76 Mt∙CO2 (evergreen broadleaf) and 21.61 Mt∙CO2 (deciduous broadleaf), which in total is 111.27 Mt∙CO2. Using remote sensing techniques to quantitatively estimate CO2 sequestration in Japanese forests has been shown both to have advantages and to offer further possibilities. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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