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Remote Sens., Volume 6, Issue 11 (November 2014) , Pages 10252-11672

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Open AccessArticle
Evaluation of Satellite Rainfall Estimates over the Chinese Mainland
Remote Sens. 2014, 6(11), 11649-11672; https://doi.org/10.3390/rs61111649
Received: 14 July 2014 / Revised: 31 October 2014 / Accepted: 13 November 2014 / Published: 24 November 2014
Cited by 46 | Viewed by 3245 | PDF Full-text (1989 KB) | HTML Full-text | XML Full-text
Abstract
Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is [...] Read more.
Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is crucial to quantify and document their error characteristics. In this study, four satellite-based precipitation products (TRMM-3B42, TRMM-3B42RT, CMORPH, GSMaP) were evaluated using gauge-based rainfall analysis based on a high-density gauge network throughout the Chinese Mainland during 2003–2006. To quantitatively evaluate satellite-based precipitation products, continuous (e.g., ME, RMSE, CC) and categorical (e.g., POD, FAR) verification statistics were used in this study. The results are as follows: (1) GSMaP and CMORPH underestimated precipitation (about −0.53 and −0.14 mm/day, respectively); TRMM-3B42RT overestimated precipitation (about 0.73 mm/day); TRMM-3B42, which is the only dataset corrected by gauges, had the best estimation of precipitation amongst all four products; (2) GSMaP, CMORPH and TRMM-3B42RT overestimated the frequency of low-intensity rainfall events; TRMM-3B42 underestimated the frequency of low-intensity rainfall events; GSMaP underestimated the frequency of high-intensity rainfall events; TRMM-3B42RT tended to overestimate the frequency of high-intensity rainfall events; TRMM-3B42 and CMORPH produced estimations of high-intensity rainfall frequency that best aligned with observations; (3) All four satellite-based precipitation products performed better in summer than in winter. They also had better performance over wet southern region than dry northern or high altitude regions. Overall, this study documented error characteristics of four satellite-based precipitation products over the Chinese Mainland. The results help to understand features of these datasets for users and improve algorithms for algorithm developers in the future. Full article
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Open AccessArticle
A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data
Remote Sens. 2014, 6(11), 11627-11648; https://doi.org/10.3390/rs61111627
Received: 16 June 2014 / Revised: 16 November 2014 / Accepted: 17 November 2014 / Published: 21 November 2014
Cited by 17 | Viewed by 3500 | PDF Full-text (1290 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands [...] Read more.
Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories. Full article
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Open AccessArticle
Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration
Remote Sens. 2014, 6(11), 11607-11626; https://doi.org/10.3390/rs61111607
Received: 29 July 2014 / Revised: 31 October 2014 / Accepted: 4 November 2014 / Published: 21 November 2014
Cited by 80 | Viewed by 5360 | PDF Full-text (3255 KB) | HTML Full-text | XML Full-text
Abstract
Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 [...] Read more.
Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m2·sr·μm or −2.1 K and −4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m2·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle
Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch
Remote Sens. 2014, 6(11), 11579-11606; https://doi.org/10.3390/rs61111579
Received: 30 August 2014 / Revised: 29 October 2014 / Accepted: 12 November 2014 / Published: 20 November 2014
Cited by 45 | Viewed by 6695 | PDF Full-text (13361 KB) | HTML Full-text | XML Full-text
Abstract
The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for [...] Read more.
The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for higher resolution products by taking advantage of new satellites, sensors and algorithms. Improvements of the 5-km products over CRW’s heritage global 50-km products are derived from: (1) the higher resolution and greater data density of NOAA’s next-generation operational daily global 5-km geo-polar blended sea surface temperature (SST) analysis; and (2) implementation of a new SST climatology derived from the Pathfinder SST climate data record. The new products increase near-shore coverage and now allow direct monitoring of 95% of coral reefs and significantly reduce data gaps caused by cloud cover. The 5-km product suite includes SST Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks and Bleaching Alert Area, matching existing CRW products. When compared with the 50-km products and in situ bleaching observations for 2013–2014, the 5-km products identified known thermal stress events and matched bleaching observations. These near reef-scale products significantly advance the ability of coral reef researchers and managers to monitor coral thermal stress in near-real-time. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification
Remote Sens. 2014, 6(11), 11558-11578; https://doi.org/10.3390/rs61111558
Received: 27 June 2014 / Revised: 27 October 2014 / Accepted: 27 October 2014 / Published: 20 November 2014
Cited by 15 | Viewed by 2749 | PDF Full-text (3767 KB) | HTML Full-text | XML Full-text
Abstract
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories [...] Read more.
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization
Remote Sens. 2014, 6(11), 11533-11557; https://doi.org/10.3390/rs61111533
Received: 26 June 2014 / Revised: 31 October 2014 / Accepted: 4 November 2014 / Published: 20 November 2014
Cited by 23 | Viewed by 3890 | PDF Full-text (31263 KB) | HTML Full-text | XML Full-text
Abstract
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here [...] Read more.
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle
Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data
Remote Sens. 2014, 6(11), 11518-11532; https://doi.org/10.3390/rs61111518
Received: 10 June 2014 / Revised: 21 October 2014 / Accepted: 4 November 2014 / Published: 19 November 2014
Cited by 44 | Viewed by 4031 | PDF Full-text (2243 KB) | HTML Full-text | XML Full-text
Abstract
Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. [...] Read more.
Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to obtain NDVI data at the Landsat spatial resolution. Next, phenological features, including the beginning and ending dates of the growing season, the length of the growing season, seasonal amplitude, and the maximum fitted NDVI value, were extracted from the fused time series NDVI data using the TIMESAT tool. The extracted data were integrated with the spectral data of the Landsat data to improve classification accuracy using a maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. The results indicated that phenological features had a statistically significant effect on improving the land cover classification accuracy of single Landsat data (an approximately 3% increase in overall classification accuracy), especially for vegetation type discrimination. However, the phenological features did not improve on statistical measures including the maximum, the minimum, the mean, and the standard deviation values of the time series NDVI dataset, especially for human-managed vegetation types. Regarding different classifiers, SVM could achieve better classification accuracy than the traditional MLC classifier, but the improvement in accuracy obtained using advanced classifiers was inferior to that achieved by involving the temporally derived features for land cover classification. Full article
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Open AccessArticle
Evaluation of MODIS LST Products Using Longwave Radiation Ground Measurements in the Northern Arid Region of China
Remote Sens. 2014, 6(11), 11494-11517; https://doi.org/10.3390/rs61111494
Received: 23 June 2014 / Revised: 3 November 2014 / Accepted: 7 November 2014 / Published: 19 November 2014
Cited by 15 | Viewed by 2151 | PDF Full-text (12069 KB) | HTML Full-text | XML Full-text
Abstract
This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. [...] Read more.
This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. In this evaluation process, the broadband emissivity at each station was obtained from the ASTER Spectral Library or estimated from the MODIS narrowband emissivity Collection 5. A comparison of the validation results based on those two methods shows that no significant differences occur in the short-term validation, and a sensitivity analysis of the broadband emissivity demonstrates that it has a limited effect on the evaluation results. In general, the results at the 12 stations indicate that the LST products have a lower accuracy in China’s arid and semi-arid areas than in other areas, with a mean absolute error of 2–3 K. Compared with the temporal mismatch, the spatial mismatch has a stronger effect on the validation results in this study, and the stations with homogeneous land cover have more comparable MODIS LST accuracies. Comparisons between the stations indicate that the spatial mismatch can increase the influence of the temporal mismatch. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle
TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 2: Line Segments Linking and Merging
Remote Sens. 2014, 6(11), 11468-11493; https://doi.org/10.3390/rs61111468
Received: 14 May 2014 / Revised: 11 September 2014 / Accepted: 29 October 2014 / Published: 18 November 2014
Cited by 14 | Viewed by 4076 | PDF Full-text (2836 KB) | HTML Full-text | XML Full-text
Abstract
Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and [...] Read more.
Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and obtain vectors similar to what an analyst would manually extract. The objective of this study is the implementation, evaluation and comparison of Hough transform, segment merging and polynomial fitting methods towards automated tectonic lineament mapping. For this purpose we developed a new MATLAB-based toolbox (TecLines). The proposed toolbox capabilities were validated using a synthetic Digital Elevation Model (DEM) and tested along in the Andarab fault zone (Afghanistan) where specific fault structures are known. In this study, we used filters in both frequency and spatial domains and the tensor voting framework to produce binary edge maps. We used the Hough transform to extract linear image discontinuities. We used B-spline as a polynomial curve fitting method to eliminate artificial line segments that are out of interest and to link discontinuous segments with similar trends. We performed statistical analyses in order to compare the final image discontinuities maps with existing references map. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
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Open AccessArticle
An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery
Remote Sens. 2014, 6(11), 11444-11467; https://doi.org/10.3390/rs61111444
Received: 27 August 2014 / Revised: 5 November 2014 / Accepted: 12 November 2014 / Published: 17 November 2014
Cited by 26 | Viewed by 3508 | PDF Full-text (10153 KB) | HTML Full-text | XML Full-text
Abstract
Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and [...] Read more.
Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle
Use of Satellite SAR for Understanding Long-Term Human Occupation Dynamics in the Monsoonal Semi-Arid Plains of North Gujarat, India
Remote Sens. 2014, 6(11), 11420-11443; https://doi.org/10.3390/rs61111420
Received: 7 July 2014 / Revised: 17 October 2014 / Accepted: 20 October 2014 / Published: 14 November 2014
Cited by 12 | Viewed by 3338 | PDF Full-text (17794 KB) | HTML Full-text | XML Full-text
Abstract
This work explores the spatial distribution of monsoonal flooded areas using ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR) in the semi-arid region of N. Gujarat, India. The amplitude component of SAR Single Look Complex (SLC) images has been used to estimate the extent [...] Read more.
This work explores the spatial distribution of monsoonal flooded areas using ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR) in the semi-arid region of N. Gujarat, India. The amplitude component of SAR Single Look Complex (SLC) images has been used to estimate the extent of surface and near-surface water dynamics using the mean amplitude (MA) of monsoonal (July to September) and post-monsoonal (October to January) seasons. The integration of SAR-derived maps (seasonal flooding maps and seasonal MA change) with archaeological data has provided new insights to understand present-day landscape dynamics affecting archaeological preservation and visibility. Furthermore, preliminary results suggest a good correlation between Mid-Holocene settlement patterns and the distribution and extension of seasonal floodable areas within river basin areas, opening interesting inroads to study settlement distribution and resource availability in past socio-ecological systems in semi-arid areas. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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Open AccessArticle
Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case Study for Spain
Remote Sens. 2014, 6(11), 11391-11419; https://doi.org/10.3390/rs61111391
Received: 22 July 2014 / Revised: 21 October 2014 / Accepted: 24 October 2014 / Published: 14 November 2014
Cited by 9 | Viewed by 4326 | PDF Full-text (15907 KB) | HTML Full-text | XML Full-text
Abstract
Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a [...] Read more.
Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a major identification of Ecosystem Functional Types (EFTs) in Spain to characterize the patterns of ecosystem functional diversity and status, from several functional attributes as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Albedo. For this purpose, several metrics, related to the spatial variability in seasonal and annual patterns (e.g., relative range), have been derived from remote sensing time series of 1 km MODIS over the period 2000–2009. Moreover, precipitation maps from data provided by the AEMet (Agencia Estatal de Meteorología) and the corresponding aridity and humidity indices were also included in the analysis. To create the EFTs, the potential of the joint use of Kohonen’s Self-Organizing Map (SOM) and the k-means clustering algorithm was tested. The EFTs were analyzed using different remote sensing (i.e., Gross Primary Production) and climatic variables. The relationship of the EFTs with existing land cover datasets and climatic data were analyzed through a correspondence analysis (CA). The trained SOM have shown feasible in providing a comprehensive view on the functional attributes patterns and a remarkable potential for the quantification of ecosystem function. The results highlight the potential of this technique to delineate ecosystem functional types as well as to monitor the spatial pattern of the ecosystem status as a reference for changes due to human or climate impacts. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle
Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA
Remote Sens. 2014, 6(11), 11372-11390; https://doi.org/10.3390/rs61111372
Received: 16 July 2014 / Revised: 15 October 2014 / Accepted: 24 October 2014 / Published: 14 November 2014
Cited by 29 | Viewed by 3841 | PDF Full-text (2858 KB) | HTML Full-text | XML Full-text
Abstract
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods [...] Read more.
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle
Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard
Remote Sens. 2014, 6(11), 11342-11371; https://doi.org/10.3390/rs61111342
Received: 30 May 2014 / Revised: 17 October 2014 / Accepted: 21 October 2014 / Published: 14 November 2014
Cited by 19 | Viewed by 3047 | PDF Full-text (7118 KB) | HTML Full-text | XML Full-text
Abstract
A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi [...] Read more.
A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi), sensible (Hi) and latent heat (LEi) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure αi, Rni and Gi. In the same tower an Eddy Covariance (EC) system was mounted to measure Hi and LEi. Specific sub-models to estimate Gi, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (zom) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, zom and Gi were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated αi in 21% and Rni in 11%. Also, METRIC using the calibrated functions overestimated Hi and LEi with errors of 16% and 17%, respectively while it using the original functions overestimated Hi and LEi with errors of 13% and 15%, respectively. Finally, LEi was estimated with root mean square error (RMSE) between 43 and 60 W∙m−2 and mean absolute error (MAE) between 35 and 48 W∙m−2 for both calibrated and original functions, respectively. These results suggested that biases observed for instantaneous pixel-by-pixel values of Rni, Gi and other intermediate components of the algorithm were presumably absorbed into the computation of sensible heat flux as a result of the internal self-calibration of METRIC. Full article
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Open AccessArticle
An Operational System for Estimating Road Traffic Information from Aerial Images
Remote Sens. 2014, 6(11), 11315-11341; https://doi.org/10.3390/rs61111315
Received: 4 June 2014 / Revised: 3 November 2014 / Accepted: 4 November 2014 / Published: 13 November 2014
Cited by 32 | Viewed by 3727 | PDF Full-text (25798 KB) | HTML Full-text | XML Full-text
Abstract
Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic [...] Read more.
Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks. Full article
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Open AccessArticle
Daily Area of Snow Melt Onset on Arctic Sea Ice from Passive Microwave Satellite Observations 1979–2012
Remote Sens. 2014, 6(11), 11283-11314; https://doi.org/10.3390/rs61111283
Received: 6 August 2014 / Revised: 6 October 2014 / Accepted: 24 October 2014 / Published: 13 November 2014
Cited by 3 | Viewed by 3871 | PDF Full-text (24126 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Variability in snow melt onset (MO) on Arctic sea ice since 1979 is examined by determining the area of sea ice experiencing the onset of melting during the melt season on a daily basis. The daily MO area of the snow and ice [...] Read more.
Variability in snow melt onset (MO) on Arctic sea ice since 1979 is examined by determining the area of sea ice experiencing the onset of melting during the melt season on a daily basis. The daily MO area of the snow and ice surface is determined from passive microwave satellite-derived MO dates for the Arctic Ocean and sub-regions. Annual accumulations of MO area are determined by summing the time series of daily MO area through the melt season. Daily areas and annual accumulations of MO area highlight inter-annual and regional variability in the timing of MO area, which is sensitive to day-to-day variations in spring weather conditions. Two distinct spatial patterns in MO area accumulations including an intense, fast accumulating melt area pattern and a slow accumulating melt pattern are examined for two melting events in the Kara Sea. In comparing the 34 years of MO dates for the Arctic Ocean and sub-regions, melt accumulations have changed during the period. In the earlier years, 1979–1987, the MO generally was later in the year than the mean, while in more recent years, the MO accumulations have been occurring earlier in the melt season. The sub-regions of the Arctic Ocean also exhibit greater annual variability than the Arctic Ocean. Full article
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Open AccessArticle
Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas
Remote Sens. 2014, 6(11), 11267-11282; https://doi.org/10.3390/rs61111267
Received: 11 August 2014 / Revised: 27 October 2014 / Accepted: 29 October 2014 / Published: 13 November 2014
Cited by 28 | Viewed by 3939 | PDF Full-text (1764 KB) | HTML Full-text | XML Full-text
Abstract
High-voltage power lines can be quite easily mapped using laser scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On [...] Read more.
High-voltage power lines can be quite easily mapped using laser scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On the contrary, lower voltage power lines are located in the middle of dense forests, and it is difficult to classify power lines in such an environment. This paper proposes an automated power line detection method for forest environments. Our method was developed based on statistical analysis and 2D image-based processing technology. During the process of statistical analysis, a set of criteria (e.g., height criteria, density criteria and histogram thresholds) is applied for selecting the candidates for power lines. After transforming the candidates to a binary image, image-based processing technology is employed. Object geometric properties are considered as criteria for power line detection. This method was conducted in six sets of airborne laser scanning (ALS) data from different forest environments. By comparison with reference data, 93.26% of power line points were correctly classified. The advantages and disadvantages of the methods were analyzed and discussed. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Open AccessArticle
Development of an Operational Calibration Methodology for the Landsat Thermal Data Archive and Initial Testing of the Atmospheric Compensation Component of a Land Surface Temperature (LST) Product from the Archive
Remote Sens. 2014, 6(11), 11244-11266; https://doi.org/10.3390/rs61111244
Received: 5 August 2014 / Revised: 21 October 2014 / Accepted: 28 October 2014 / Published: 13 November 2014
Cited by 18 | Viewed by 2689 | PDF Full-text (2362 KB) | HTML Full-text | XML Full-text
Abstract
The Landsat program has been producing an archive of thermal imagery that spans the globe and covers 30 years of the thermal history of the planet at human scales (60–120 m). Most of that archive’s absolute radiometric calibration has been fixed through vicarious [...] Read more.
The Landsat program has been producing an archive of thermal imagery that spans the globe and covers 30 years of the thermal history of the planet at human scales (60–120 m). Most of that archive’s absolute radiometric calibration has been fixed through vicarious calibration techniques. These calibration ties to trusted values have often taken a year or more to gather sufficient data and, in some cases, it has been over a decade before calibration certainty has been established. With temperature being such a critical factor for all living systems and the ongoing concern over the impacts of climate change, NASA and the United States Geological Survey (USGS) are leading efforts to provide timely and accurate temperature data from the Landsat thermal data archive. This paper discusses two closely related advances that are critical steps toward providing timely and reliable temperature image maps from Landsat. The first advance involves the development and testing of an autonomous procedure for gathering and performing initial screening of large amounts of vicarious calibration data. The second advance discussed in this paper is the per-pixel atmospheric compensation of the data to permit calculation of the emitted surface radiance (using ancillary sources of emissivity data) and the corresponding land surface temperature (LST). Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle
Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data
Remote Sens. 2014, 6(11), 11225-11243; https://doi.org/10.3390/rs61111225
Received: 4 September 2014 / Revised: 1 November 2014 / Accepted: 4 November 2014 / Published: 13 November 2014
Cited by 8 | Viewed by 2000 | PDF Full-text (1036 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a hybrid ensemble method that is comprised of a sequential and a parallel architecture for the classification of tree genus using LiDAR (Light Detection and Ranging) data. The two classifiers use different sets of features: (1) features derived from geometric [...] Read more.
This paper presents a hybrid ensemble method that is comprised of a sequential and a parallel architecture for the classification of tree genus using LiDAR (Light Detection and Ranging) data. The two classifiers use different sets of features: (1) features derived from geometric information, and (2) features derived from vertical profiles using Random Forests as the base classifier. This classification result is also compared with that obtained by replacing the base classifier by LDA (Linear Discriminant Analysis), kNN (k Nearest Neighbor) and SVM (Support Vector Machine). The uniqueness of this research is in the development, implementation and application of three main ideas: (1) the hybrid ensemble method, which aims to improve classification accuracy, (2) a pseudo-margin criterion for assessing the quality of predictions and (3) an automatic feature reduction method using results drawn from Random Forests. An additional point-density analysis is performed to study the influence of decreased point density on classification accuracy results. By using Random Forests as the base classifier, the average classification accuracies for the geometric classifier and vertical profile classifier are 88.0% and 88.8%, respectively, with improvement to 91.2% using the ensemble method. The training genera include pine, poplar, and maple within a study area located north of Thessalon, Ontario, Canada. Full article
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Open AccessArticle
Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment
Remote Sens. 2014, 6(11), 11204-11224; https://doi.org/10.3390/rs61111204
Received: 25 July 2014 / Revised: 16 October 2014 / Accepted: 23 October 2014 / Published: 13 November 2014
Cited by 2 | Viewed by 2422 | PDF Full-text (6474 KB) | HTML Full-text | XML Full-text
Abstract
Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral [...] Read more.
Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral (HS) sensors means that image spectroscopy has the potential to become a modern method for monitoring polluted surface waters. This paper describes an approach employing linear Spectral Unmixing (LSU) for analysis of hyperspectral image data to map the relative abundances of mine water components (dissolved Fe—Fediss, dissolved organic carbon—DOC, undissolved particles). The ground truth data (8 monitored ponds) were used to validate the results of spectral mapping. The same approach applied to HS data was tested using the image data resampled to WorldView2 (WV2) spectral resolution. A key aspect of the image data processing was to define the proper pure image end members for the fundamental water types. The highest correlations detected between the studied water parameters and the fractional images using the HyMap and the resampled WV2 data, respectively, were: dissolved Fe (R2 = 0.74 and R2vw2 = 0.6), undissolved particles (R2 = 0.57 and R2vw2 = 0.49) and DOC (R2 = 0.42 and R2vw2 < 0.40). These fractional images were further classified to create semi-quantitative maps. In conclusion, the classification still benefited from the higher spectral resolution of the HyMap data; however the WV2 reflectance data can be suitable for mapping specific inherent optical properties (SIOPs), which significantly differ from one another from an optical point of view (e.g., mineral suspension, dissolved Fe and phytoplankton), but it seems difficult to differentiate among diverse suspension particles, especially when the waters have more complex properties (e.g., mineral particles, DOC together with tripton or other particles, etc.). Full article
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Open AccessArticle
A Life-Size and Near Real-Time Test of Irrigation Scheduling with a Sentinel-2 Like Time Series (SPOT4-Take5) in Morocco
Remote Sens. 2014, 6(11), 11182-11203; https://doi.org/10.3390/rs61111182
Received: 16 July 2014 / Revised: 15 September 2014 / Accepted: 29 October 2014 / Published: 11 November 2014
Cited by 9 | Viewed by 3530 | PDF Full-text (3171 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. [...] Read more.
This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. For the purpose of this experiment, the irrigation of a reference plot was driven by the farmer according to, mainly empirical, irrigation scheduling while test plot irrigations were being managed following the FAO-56 method, driven by remote sensing. Images were issued from the SPOT4 (Take5) data set, which aimed at delivering image time series at a decametric resolution with less than five-day satellite overpass similar to the time series ESA Sentinel-2 satellites will produce in the coming years. With a Root Mean Square Error (RMSE) of 0.91mm per day, the comparison between daily actual evapotranspiration measured by eddy-covariance and the simulated one is satisfactory, but even better at a five-day integration (0.59mm per day). Finally, despite a chaotic beginning of the experiment—the experimental plot had not been irrigated to get rid of a slaking crust, which prevented good emergence—our plot caught up and yielded almost the same grain crop with 14% less irrigation water. This experiment opens up interesting opportunities for operational scheduling of flooding irrigation sectors that dominate in the semi-arid Mediterranean area. Full article
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Open AccessArticle
Landsat 8 Thermal Infrared Sensor Geometric Characterization and Calibration
Remote Sens. 2014, 6(11), 11153-11181; https://doi.org/10.3390/rs61111153
Received: 29 July 2014 / Revised: 23 October 2014 / Accepted: 4 November 2014 / Published: 11 November 2014
Cited by 6 | Viewed by 3636 | PDF Full-text (4586 KB) | HTML Full-text | XML Full-text
Abstract
The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization [...] Read more.
The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization challenging. TIRS geometric calibration thus relied heavily on on-orbit measurements. Since the two Landsat 8 payloads are complementary and generate combined Level 1 data products, the TIRS geometric performance requirements emphasize the co-alignment of the OLI and TIRS instrument fields of view and the registration of the OLI reflective bands to the TIRS long-wave infrared emissive bands. The TIRS on-orbit calibration procedures include measuring the TIRS-to-OLI alignment, refining the alignment of the three TIRS sensor chips, and ensuring the alignment of the two TIRS spectral bands. The two key TIRS performance metrics are the OLI reflective to TIRS emissive band registration accuracy, and the registration accuracy between the TIRS thermal bands. The on-orbit calibration campaign conducted during the commissioning period provided an accurate TIRS geometric model that enabled TIRS Level 1 data to meet all geometric accuracy requirements. Seasonal variations in TIRS-to-OLI alignment have led to several small calibration parameter adjustments since commissioning. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle
Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance
Remote Sens. 2014, 6(11), 11127-11152; https://doi.org/10.3390/rs61111127
Received: 21 July 2014 / Revised: 2 November 2014 / Accepted: 3 November 2014 / Published: 11 November 2014
Cited by 74 | Viewed by 4542 | PDF Full-text (3130 KB) | HTML Full-text | XML Full-text
Abstract
The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric [...] Read more.
The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric calibration activities were performed to refine the prelaunch calibration parameters. The results of these calibration activities were subsequently used to measure geometric performance characteristics in order to verify the OLI geometric requirements. Three types of geometric calibrations were performed including: (1) updating the OLI-to-spacecraft alignment knowledge; (2) refining the alignment of the sub-images from the multiple OLI sensor chips; and (3) refining the alignment of the OLI spectral bands. The aspects of geometric performance that were measured and verified included: (1) geolocation accuracy with terrain correction, but without ground control (L1Gt); (2) Level 1 product accuracy with terrain correction and ground control (L1T); (3) band-to-band registration accuracy; and (4) multi-temporal image-to-image registration accuracy. Using the results of the on-orbit calibration update, all aspects of geometric performance were shown to meet or exceed system requirements. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessCase Report
The Integration of Geotechnologies in the Evaluation of a Wine Cellar Structure through the Finite Element Method
Remote Sens. 2014, 6(11), 11107-11126; https://doi.org/10.3390/rs61111107
Received: 23 June 2014 / Revised: 29 October 2014 / Accepted: 30 October 2014 / Published: 11 November 2014
Cited by 5 | Viewed by 2132 | PDF Full-text (13414 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a multidisciplinary methodology to evaluate an underground wine cellar structure using non-invasive techniques. In particular, a historical subterranean wine cellar that presents a complex structure and whose physical properties are unknown is recorded and analyzed using geomatics and geophysics synergies. [...] Read more.
This paper presents a multidisciplinary methodology to evaluate an underground wine cellar structure using non-invasive techniques. In particular, a historical subterranean wine cellar that presents a complex structure and whose physical properties are unknown is recorded and analyzed using geomatics and geophysics synergies. To this end, an approach that integrates terrestrial laser scanning and ground penetrating radar is used to properly define a finite element-based structural model, which is then used as a decision tool to plan architectural restoration actions. The combination of both techniques implies the registration of external and internal information that eases the construction of structural models. Structural simulation for both stresses and deformations through FEM allowed identifying critical structural elements under great stress or excessive deformations. In this investigation, the ultimate limit state of cracking was considered to determine allowable loads due to the brittle nature of the material. This allowed us to set limit values of loading on the cellar structure in order to minimize possible damage. Full article
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Open AccessArticle
Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions
Remote Sens. 2014, 6(11), 11082-11106; https://doi.org/10.3390/rs61111082
Received: 5 September 2014 / Revised: 30 October 2014 / Accepted: 4 November 2014 / Published: 11 November 2014
Cited by 18 | Viewed by 3404 | PDF Full-text (15695 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics [...] Read more.
Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics based units such as radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing the photoelectric effect. Temporal variations of the detector characteristics that differ with foregoing radiometric calibration cause visually perceptible along-track stripes in the at-sensor radiance data that aggravate succeeding image-based analyses. Especially, variations of the thermally induced dark current dominate and have to be reduced. In this work, a new approach is presented that efficiently reduces dark current related stripe noise. It integrates an across-effect gradient minimization principle. The performance has been evaluated using artificially degraded whiskbroom (reference) and real pushbroom acquisitions from EO-1 Hyperion and AISA DUAL that are significantly covered by stripe noise. A set of quality indicators has been used for the accuracy assessment. They clearly show that the new approach outperforms a limited set of tested state-of-the-art approaches and achieves a very high accuracy related to ground-truth for selected tests. It may substitute recent algorithms in the Reduction of Miscalibration Effects (ROME) framework that is broadly used to reduce radiometric miscalibrations of pushbroom data takes. Full article
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Open AccessReview
UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas
Remote Sens. 2014, 6(11), 11051-11081; https://doi.org/10.3390/rs61111051
Received: 3 June 2014 / Revised: 21 October 2014 / Accepted: 22 October 2014 / Published: 11 November 2014
Cited by 82 | Viewed by 7141 | PDF Full-text (9784 KB) | HTML Full-text | XML Full-text
Abstract
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting [...] Read more.
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting modeling, mapping and monitoring applications in rangelands, forests and agricultural environments. This review provides an overview of recent research that has reported UAV flight experiments on the remote sensing of vegetated areas. To provide a differential trend to other reviews, this paper is not limited to crops and precision agriculture applications, but also includes forest and rangeland applications. This work follows a top-down categorization strategy and attempts to fill the gap between application requirements and the characteristics of selected tools, payloads and platforms. Furthermore, correlations between common requirements and the most frequently used solutions are highlighted. Full article
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Open AccessArticle
Surface Daytime Net Radiation Estimation Using Artificial Neural Networks
Remote Sens. 2014, 6(11), 11031-11050; https://doi.org/10.3390/rs61111031
Received: 7 July 2014 / Revised: 8 October 2014 / Accepted: 13 October 2014 / Published: 11 November 2014
Cited by 11 | Viewed by 2829 | PDF Full-text (1483 KB) | HTML Full-text | XML Full-text
Abstract
Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical R [...] Read more.
Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W∙m−2, and a bias of −0.61 W∙m−2 in global mode based on the validation dataset. This study concluded that ANN methods are a potentially powerful tool for global Rn estimation. Full article
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Open AccessArticle
A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles
Remote Sens. 2014, 6(11), 11013-11030; https://doi.org/10.3390/rs61111013
Received: 3 June 2014 / Revised: 20 October 2014 / Accepted: 3 November 2014 / Published: 10 November 2014
Cited by 63 | Viewed by 6280 | PDF Full-text (846 KB) | HTML Full-text | XML Full-text
Abstract
During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. [...] Read more.
During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution. Full article
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Open AccessArticle
Integrated Geophysical and Aerial Sensing Methods for Archaeology: A Case History in the Punic Site of Villamar (Sardinia, Italy)
Remote Sens. 2014, 6(11), 10986-11012; https://doi.org/10.3390/rs61110986
Received: 27 June 2014 / Revised: 22 October 2014 / Accepted: 27 October 2014 / Published: 10 November 2014
Cited by 3 | Viewed by 3638 | PDF Full-text (15997 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the authors present a recent integrated survey carried out on an archaeological urban site, generally free of buildings, except some temporary structures related to excavated areas where multi-chamber tombs were found. The two methods used to investigate this site were [...] Read more.
In this paper, the authors present a recent integrated survey carried out on an archaeological urban site, generally free of buildings, except some temporary structures related to excavated areas where multi-chamber tombs were found. The two methods used to investigate this site were thermal infrared and ground penetrating radar (GPR). The thermography was carried out with the sensor mounted under a helium balloon simultaneously with a photographic camera. In order to have a synthetic view of the surface thermal behavior, a simplified version of the existing night thermal gradient algorithm was applied. By this approach, we have a wide extension of thermal maps due to the balloon oscillation, because we are able to compute the maps despite collecting few acquisition samples. By the integration of GPR and the thermal imaging, we can evaluate the depth of the thermal influence of possible archaeological targets, such as buried Punic tombs or walls belonging to the succeeding medieval buildings, which have been subsequently destroyed. The thermal anomalies present correspondences to the radar time slices obtained from 30 to 50 cm. Furthermore, by superimposing historical aerial pictures on the GPR and thermal imaging data, we can identify these anomalies as the foundations of the destroyed buildings. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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Open AccessArticle
A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data
Remote Sens. 2014, 6(11), 10966-10985; https://doi.org/10.3390/rs61110966
Received: 22 June 2014 / Revised: 18 September 2014 / Accepted: 29 September 2014 / Published: 10 November 2014
Cited by 22 | Viewed by 2254 | PDF Full-text (7018 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The methodology adequately represents the scattering behavior of the vegetation-covered area by defining the scattering of the vegetation and the soil below. The Integral Equation Method (IEM) was [...] Read more.
This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The methodology adequately represents the scattering behavior of the vegetation-covered area by defining the scattering of the vegetation and the soil below. The Integral Equation Method (IEM) was employed to determine the backscattering of the underlying soil. The modified Water Cloud Model (WCM) was used to reduce the effect of vegetation. Vegetation coverage, which can be easily derived from optical data, was incorporated in this method to account for the vegetation gap information. Then, an inversion scheme of soil moisture was developed that made use of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data. The method developed in this study was assessed by comparing the reproduction of the backscattering, which was calculated from an area with full vegetation cover to that with relatively sparse cover. The accuracy and sources of error in this soil moisture retrieval method were evaluated. The results showed a good correlation between the measured and estimated soil moisture (R2 = 0.71, RMSE = 3.32 vol.%, p < 0.01). Therefore, this method has operational potential for estimating soil moisture under the vegetated area of an alpine prairie. Full article
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