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Remote Sens., Volume 6, Issue 8 (August 2014), Pages 6727-7856

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Editorial

Jump to: Research, Review, Other

Open AccessEditorial Remote Sensing Open Access Journal: Increasing Impact through Quality Publications
Remote Sens. 2014, 6(8), 7463-7468; doi:10.3390/rs6087463
Received: 10 June 2014 / Revised: 4 August 2014 / Accepted: 10 August 2014 / Published: 12 August 2014
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Abstract
Remote Sensing, an open access journal (http://www.mdpi.com/journal/remotesensing) has grown at rapid pace since its first publication five years ago, and has acquired a strong reputation. It is a “pathfinder” being the first open access journal in remote sensing. For those academics who
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Remote Sensing, an open access journal (http://www.mdpi.com/journal/remotesensing) has grown at rapid pace since its first publication five years ago, and has acquired a strong reputation. It is a “pathfinder” being the first open access journal in remote sensing. For those academics who were used to waiting a year or two for their peer-reviewed scientific work to be reviewed, revised, edited, and published, Remote Sensing offers a publication time frame that is unheard of (in most cases, less than four months). However, we do this after multiple peer-reviews, multiple revisions, much editorial scrutiny and decision-making, and professional editing by an editorial office before a paper is published online in our tight time frame, bringing a paradigm shift in scientific publication. As a result, there has been a swift increase in submissions of higher and higher quality manuscripts from the best authors and institutes working on Remote Sensing, Geographic Information Systems (GIS), Global Navigation Satellite System (GNSS), GIScience, and all related geospatial science and technologies from around the world. The purpose of this editorial is to update everyone interested in Remote Sensing on the progress made over the last year, and provide an outline of our vision for the immediate future. [...] Full article

Research

Jump to: Editorial, Review, Other

Open AccessArticle Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification
Remote Sens. 2014, 6(8), 6727-6764; doi:10.3390/rs6086727
Received: 19 March 2014 / Revised: 25 June 2014 / Accepted: 30 June 2014 / Published: 24 July 2014
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Abstract
Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF)
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Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i) a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii) an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle Relationship between the Growing Season Maximum Enhanced Vegetation Index and Climatic Factors on the Tibetan Plateau
Remote Sens. 2014, 6(8), 6765-6789; doi:10.3390/rs6086765
Received: 22 April 2014 / Revised: 15 July 2014 / Accepted: 15 July 2014 / Published: 24 July 2014
Cited by 10 | PDF Full-text (4552 KB) | HTML Full-text | XML Full-text
Abstract
Temperature and water conditions affect vegetation growth dynamics and associated spectral measures. We examined the response of the growing season maximum enhanced vegetation index (MEVI) to the growing season temperature, precipitation, vapor pressure and relative humidity on the Tibetan Plateau. The responses of
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Temperature and water conditions affect vegetation growth dynamics and associated spectral measures. We examined the response of the growing season maximum enhanced vegetation index (MEVI) to the growing season temperature, precipitation, vapor pressure and relative humidity on the Tibetan Plateau. The responses of the MEVI to climatic factors changed with the vegetation type, which may be attributed to the finding that the background values and climatic factor changes varied with the type of vegetation. The spatially averaged MEVI over the entire plateau exhibited a non-significant decreasing trend. Approximately 5% and 12% of the vegetation area exhibited significant MEVI decreasing and increasing trends, respectively. Both vapor pressure and relative humidity significantly affected the MEVI, whereas the temperature and precipitation did not significantly correlate with the MEVI over the entire plateau. Specifically, the environmental humidity dominated the MEVI variation over the entire plateau. Full article
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Open AccessArticle Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2
Remote Sens. 2014, 6(8), 6790-6816; doi:10.3390/rs6086790
Received: 7 March 2014 / Revised: 15 July 2014 / Accepted: 18 July 2014 / Published: 25 July 2014
Cited by 8 | PDF Full-text (8092 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste
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Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e.g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km × 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity. Full article
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Open AccessArticle Investigating Spatial Patterns of Persistent Scatterer Interferometry Point Targets and Landslide Occurrences in the Arno River Basin
Remote Sens. 2014, 6(8), 6817-6843; doi:10.3390/rs6086817
Received: 28 May 2014 / Revised: 21 July 2014 / Accepted: 21 July 2014 / Published: 25 July 2014
Cited by 8 | PDF Full-text (5191 KB) | HTML Full-text | XML Full-text
Abstract
Persistent Scatterer Interferometry (PSI) has been widely used for landslide studies in recent years. This paper investigated the spatial patterns of PSI point targets and landslide occurrences in the Arno River basin in Central Italy. The main purpose is to analyze whether spatial
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Persistent Scatterer Interferometry (PSI) has been widely used for landslide studies in recent years. This paper investigated the spatial patterns of PSI point targets and landslide occurrences in the Arno River basin in Central Italy. The main purpose is to analyze whether spatial patterns of Persistent Scatterers (PS) can be recognized as indicators of landslide occurrences throughout the whole basin. The bivariate K-function was employed to assess spatial relationships between PS and landslides. The PSI point targets were acquired from almost 4 years (from March 2003 to January 2007) of RADARSAT-1 images. The landslide inventory was collected from 15 years (from 1992–2007) of surveying and mapping data, mainly including remote sensing data, topographic maps and field investigations. The proposed approach is able to assess spatial patterns between a variety of PS and landslides, in particular, to understand if PSI point targets are spatially clustered (spatial attraction) or randomly distributed (spatial independency) on various types of landslides across the basin. Additionally, the degree and scale distances of PS clustering on a variety of landslides can be characterized. The results rejected the null hypothesis that PSI point targets appear to cluster similarly on four types of landslides (slides, flows, falls and creeps) in the Arno River basin. Significant influence of PS velocities and acquisition orbits can be noticed on detecting landslides with different states of activities. Despite that the assessment may be influenced by the quality of landslide inventory and Synthetic Aperture Radar (SAR) images, the proposed approach is expected to provide guidelines for studies trying to detect and investigate landslide occurrences at a regional scale through spatial statistical analysis of PS, for which an advanced understanding of the impact of scale distances on landslide clustering is fundamentally needed. Full article
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Open AccessArticle Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)
Remote Sens. 2014, 6(8), 6867-6896; doi:10.3390/rs6086867
Received: 9 June 2014 / Revised: 15 July 2014 / Accepted: 15 July 2014 / Published: 25 July 2014
Cited by 5 | PDF Full-text (23150 KB) | HTML Full-text | XML Full-text
Abstract
The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping
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The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping that arises from the integration of geomorphic features in classifications. The Mawat Ophiolite Complex (MOC) is located in the NE of Iraq and is known for its mineral deposits. The aims of this study are: (I) to refine the existing lithological map of the MOC; (II) to identify the best discriminatory datasets for lithological classification, including geomorphic features and textures; and (III) to identify potential locations with high concentrations of chromite. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The updated map allowed the identification of a new mafic body and a substantial improvement of the geometry of the known lithological units. The use of geomorphic features allowed for the increase of the overall accuracy from 73% to 79.3%. In addition, we detected chromite occurrences within the ophiolite by applying Spectral Angle Mapping (SAM) technique. We identified two new locations having high concentrations of chromite and verified one of these promising areas in the field. This new body covers ~0.3 km2 and has coarsely crystalline chromite within dunite host rock. The chromium (Cr2O3) concentration is ~8.46%. The SAM and SVM methods applied on ASTER satellite data show that these can be used as a powerful tool to explore ore deposits and to further improve lithological mapping in mountainous semi-arid regions. Full article
Open AccessArticle Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping
Remote Sens. 2014, 6(8), 6897-6928; doi:10.3390/rs6086897
Received: 4 April 2014 / Revised: 1 July 2014 / Accepted: 21 July 2014 / Published: 25 July 2014
Cited by 7 | PDF Full-text (15550 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector
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This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements. Full article
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Open AccessArticle A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series
Remote Sens. 2014, 6(8), 6929-6960; doi:10.3390/rs6086929
Received: 13 December 2013 / Revised: 12 June 2014 / Accepted: 4 July 2014 / Published: 25 July 2014
Cited by 96 | PDF Full-text (2362 KB) | HTML Full-text | XML Full-text
Abstract
The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting
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The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Assessing Long-Term Changes in the Beach Width of Reef Islands Based on Temporally Fragmented Remote Sensing Data
Remote Sens. 2014, 6(8), 6961-6987; doi:10.3390/rs6086961
Received: 30 May 2014 / Revised: 7 July 2014 / Accepted: 18 July 2014 / Published: 25 July 2014
Cited by 7 | PDF Full-text (11241 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Atoll islands are subject to a variety of processes that influence their geomorphological development. Analysis of historical shoreline changes using remotely sensed images has become an efficient approach to both quantify past changes and estimate future island response. However, the detection of long-term
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Atoll islands are subject to a variety of processes that influence their geomorphological development. Analysis of historical shoreline changes using remotely sensed images has become an efficient approach to both quantify past changes and estimate future island response. However, the detection of long-term changes in beach width is challenging mainly for two reasons: first, data availability is limited for many remote Pacific islands. Second, beach environments are highly dynamic and strongly influenced by seasonal or episodic shoreline oscillations. Consequently, remote-sensing studies on beach morphodynamics of atoll islands deal with dynamic features covered by a low sampling frequency. Here we present a study of beach dynamics for nine islands on Takú Atoll, Papua New Guinea, over a seven-decade period. A considerable chronological gap between aerial photographs and satellite images was addressed by applying a new method that reweighted positions of the beach limit by identifying “outlier” shoreline positions. On top of natural beach variability observed along the reweighted beach sections, we found that one third of the analyzed islands show a statistically significant decrease in reweighted beach width since 1943. The total loss of beach area for all islands corresponds to 44% of the initial beach area. Variable shoreline trajectories suggest that changes in beach width on Takú Atoll are dependent on local control (that is, human activity and longshore sediment transport). Our results show that remote imagery with a low sampling frequency may be sufficient to characterize prominent morphological changes in planform beach configuration of reef islands. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
Open AccessArticle Using Unmanned Aerial Vehicles (UAV) to Quantify Spatial Gap Patterns in Forests
Remote Sens. 2014, 6(8), 6988-7004; doi:10.3390/rs6086988
Received: 12 May 2014 / Revised: 22 July 2014 / Accepted: 22 July 2014 / Published: 29 July 2014
Cited by 14 | PDF Full-text (7768 KB) | HTML Full-text | XML Full-text
Abstract
Gap distributions in forests reflect the spatial impact of man-made tree harvesting or naturally-induced patterns of tree death being caused by windthrow, inter-tree competition, disease or senescence. Gap sizes can vary from large (>100 m2) to small (<10 m2),
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Gap distributions in forests reflect the spatial impact of man-made tree harvesting or naturally-induced patterns of tree death being caused by windthrow, inter-tree competition, disease or senescence. Gap sizes can vary from large (>100 m2) to small (<10 m2), and they may have contrasting spatial patterns, such as being aggregated or regularly distributed. However, very small gaps cannot easily be recorded with conventional aerial or satellite images, which calls for new and cost-effective methodologies of forest monitoring. Here, we used an unmanned aerial vehicle (UAV) and very high-resolution images to record the gaps in 10 temperate managed and unmanaged forests in two regions of Germany. All gaps were extracted for 1-ha study plots and subsequently analyzed with spatially-explicit statistics, such as the conventional pair correlation function (PCF), the polygon-based PCF and the mark correlation function. Gap-size frequency was dominated by small gaps of an area <5 m2, which were particularly frequent in unmanaged forests. We found that gap distances showed a variety of patterns. However, the polygon-based PCF was a better descriptor of patterns than the conventional PCF, because it showed randomness or aggregation for cases when the conventional PCF showed small-scale regularity; albeit, the latter was only a mathematical artifact. The mark correlation function revealed that gap areas were in half of the cases negatively correlated and in the other half independent. Negative size correlations may likely be the result of single-tree harvesting or of repeated gap formation, which both lead to nearby small gaps. Here, we emphasize the usefulness of UAV to record forest gaps of a very small size. These small gaps may originate from repeated gap-creating disturbances, and their spatial patterns should be monitored with spatially-explicit statistics at recurring intervals in order to further insights into forest dynamics. Full article
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Open AccessArticle Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic
Remote Sens. 2014, 6(8), 7005-7025; doi:10.3390/rs6087005
Received: 20 May 2014 / Revised: 17 July 2014 / Accepted: 22 July 2014 / Published: 29 July 2014
Cited by 9 | PDF Full-text (16815 KB) | HTML Full-text | XML Full-text
Abstract
Remote-sensing techniques offer an efficient alternative for mapping mining environments and assessing the impacts of mining activities. Airborne multispectral data in the thermal region and hyperspectral data in the optical region, acquired with the Airborne Hyperspectral Scanner (AHS) sensor over the Sokolov lignite
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Remote-sensing techniques offer an efficient alternative for mapping mining environments and assessing the impacts of mining activities. Airborne multispectral data in the thermal region and hyperspectral data in the optical region, acquired with the Airborne Hyperspectral Scanner (AHS) sensor over the Sokolov lignite open-pit mines in the Czech Republic, were analyzed. The emissivity spectrum was calculated for each vegetation-free land pixel in the longwave infrared (LWIR)-region image using the surface-emitted radiation, and the reflectance spectrum was derived from the visible, near-infrared and shortwave-infrared (VNIR–SWIR)-region image using the solar radiation reflected from the surface, after applying atmospheric correction. The combination of calculated emissivity, with the ability to detect quartz, and SWIR reflectance spectra, detecting phyllosilicates and kaolinite in particular, enabled estimating the content of the dominant minerals in the exposed surface. The difference between the emissivity values at λ = 9.68 µm and 8.77 µm was found to be a useful index for estimating the relative amount of quartz in each land pixel in the LWIR image. The absorption depth at around 2.2 µm in the reflectance spectra was used to estimate the relative amount of kaolinite in each land pixel in the SWIR image. The resulting maps of the spatial distribution of quartz and kaolinite were found to be in accordance with the geological nature and origin of the exposed surfaces and demonstrated the benefit of using data from both thermal and optical spectral regions to map the abundance of the major minerals around the mines. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
Open AccessArticle Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation
Remote Sens. 2014, 6(8), 7026-7049; doi:10.3390/rs6087026
Received: 15 May 2014 / Revised: 16 July 2014 / Accepted: 18 July 2014 / Published: 29 July 2014
Cited by 2 | PDF Full-text (16330 KB) | HTML Full-text | XML Full-text
Abstract
The estimation of evapotranspiration of blue water (ETb) from farmlands, due to irrigation, is crucial to improve water management, especially in regions where water resources are scarce. Large scale ETb was previously obtained, based on the differences between remote sensing
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The estimation of evapotranspiration of blue water (ETb) from farmlands, due to irrigation, is crucial to improve water management, especially in regions where water resources are scarce. Large scale ETb was previously obtained, based on the differences between remote sensing derived actual ET and values simulated from the Global Land Data Assimilation System (GLDAS). In this paper, we improve on the previous approach by enhancing the classification scheme employed so that it represents regions with common hydrometeorological conditions. Bias between the two data sets for reference areas (non-irrigated croplands) were identified per class, and used to adjust the remote sensing products. Different classifiers were compared and evaluated based on the generated bias curves per class and their variability. The results in Europe show that the k-means classifier was better suited to identify the bias curves per class, capturing the dynamic range of these curves and minimizing their variability within each corresponding class. The method was applied in Africa and the classification and bias results were consistent with the findings in Europe. The ETb results were compared with existing literature and provided differences up to 50 mm/year in Europe, while the comparison in Africa was found to be highly influenced by the assigned cover type and the heterogeneity of the pixel. Although further research is needed to fully understand the ETb values found, this paper shows a more robust approach to classify and characterize the bias between the two sets of ET data. Full article
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Open AccessArticle Small-Scale Surface Reconstruction and Volume Calculation of Soil Erosion in Complex Moroccan Gully Morphology Using Structure from Motion
Remote Sens. 2014, 6(8), 7050-7080; doi:10.3390/rs6087050
Received: 27 February 2014 / Revised: 16 July 2014 / Accepted: 21 July 2014 / Published: 29 July 2014
Cited by 21 | PDF Full-text (19817 KB) | HTML Full-text | XML Full-text
Abstract
This study presents a computer vision application of the structure from motion (SfM) technique in three dimensional high resolution gully monitoring in southern Morocco. Due to impractical use of terrestrial Light Detection and Ranging (LiDAR) in difficult to access gully systems, the inexpensive SfM
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This study presents a computer vision application of the structure from motion (SfM) technique in three dimensional high resolution gully monitoring in southern Morocco. Due to impractical use of terrestrial Light Detection and Ranging (LiDAR) in difficult to access gully systems, the inexpensive SfM is a promising tool for analyzing and monitoring soil loss, gully head retreat and plunge pool development following heavy rain events. Objects with known dimensions were placed around the gully scenes for scaling purposes as a workaround for ground control point (GCP) placement. Additionally, the free scaling with objects was compared to terrestrial laser scanner (TLS) data in a field laboratory in Germany. Results of the latter showed discrepancies of 5.6% in volume difference for erosion and 1.7% for accumulation between SfM and TLS. In the Moroccan research area soil loss varied between 0.58 t in an 18.65 m2 narrowly stretched gully incision and 5.25 t for 17.45 m2 in a widely expanded headcut area following two heavy rain events. Different techniques of data preparation were applied and the advantages of SfM for soil erosion monitoring under complex surface conditions were demonstrated. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
Open AccessArticle Long Wavelength SAR Backscatter Modelling Trends as a Consequence of the Emergent Properties of Tree Populations
Remote Sens. 2014, 6(8), 7081-7109; doi:10.3390/rs6087081
Received: 14 May 2014 / Revised: 15 July 2014 / Accepted: 16 July 2014 / Published: 29 July 2014
Cited by 2 | PDF Full-text (1391 KB) | HTML Full-text | XML Full-text
Abstract
This study describes the novel use of a macroecological plant and forest structure model in conjunction with a Radiative Transfer (RT) model to better understand interactions between microwaves and forest canopies. Trends predicted by the RT model, resulting from interactions with mixed age,
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This study describes the novel use of a macroecological plant and forest structure model in conjunction with a Radiative Transfer (RT) model to better understand interactions between microwaves and forest canopies. Trends predicted by the RT model, resulting from interactions with mixed age, mono and multi species forests, are analysed in comparison to those predicted using a simplistic structure based scattering model. This model relates backscatter to scatterer cross sectional or volume specifications, dependent on the size. The Spatially Explicit Reiterative Algorithm (SERA) model is used to provide a widely varied tree size distribution while maintaining allometric consistency to produce a natural-like forest representation. The RT model is parameterised using structural information from SERA and microwave backscatter simulations are used to analyse the impact of changes to the forest stand. Results show that the slope of the saturation curve observed in the Synthetic Aperture Radar (SAR) backscatter-biomass relationship is sensitive to thinning and therefore forest basal area. Due to similarities displayed between the results of the RT and simplistic model, it is determined that forest SAR backscatter behaviour at long microwave wavelengths may be described generally using equations related to total stem volume and basal area. The nature of these equations is such that they describe saturating behaviour of forests in the absence of attenuation in comparable fashion to the trends exhibited using the RT model. Both modelled backscatter trends predict a   relationship to forest basal area from an early age when forest volume is increasing. When this is not the case, it is assumed to be a result of attenuation of the dominant stem-ground interaction due to the presence of excessive numbers of stems. This work shows how forest growth models can be successfully incorporated into existing independent scattering models and reveals, through the RT comparison with simplistic backscatter calculations, that saturation need not solely be a direct result of attenuation. Full article
Open AccessArticle Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests
Remote Sens. 2014, 6(8), 7110-7135; doi:10.3390/rs6087110
Received: 12 May 2014 / Revised: 14 July 2014 / Accepted: 18 July 2014 / Published: 30 July 2014
Cited by 12 | PDF Full-text (1514 KB) | HTML Full-text | XML Full-text
Abstract
An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR)
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An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D canopy structural information by discrete (XYZ) LiDAR metrics (DR-metrics), and (ii) the detailed geometric and radiometric information of the returned waveform by full-waveform LiDAR metrics (FW-metrics), and to evaluate the capacity of these metrics in predicting biomass and its components in subtropical forest ecosystems. This study was undertaken in a mixed subtropical forest in Yushan Mountain National Park, Jiangsu, China. LiDAR metrics derived from DR and FW LiDAR data were used alone, and in combination, in stepwise regression models to estimate total as well as above-ground, root, foliage, branch and trunk biomass. Overall, the results indicated that three sets of predictive models performed well across the different subtropical forest types (Adj-R2 = 0.42–0.93, excluding foliage biomass). Forest type-specific models (Adj-R2 = 0.18–0.93) were generally more accurate than the general model (Adj-R2 = 0.07–0.79) with the most accurate results obtained for coniferous stands (Adj-R2 = 0.50–0.93). In addition, LiDAR metrics related to vegetation heights were the strongest predictors of total biomass and its components. This research also illustrates the potential for the synergistic use of DR and FW LiDAR metrics to accurately assess biomass stocks in subtropical forests, which suggest significant potential in research and decision support in sustainable forest management, such as timber harvesting, biofuel characterization and fire hazard analyses. Full article
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Open AccessArticle An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling
Remote Sens. 2014, 6(8), 7136-7157; doi:10.3390/rs6087136
Received: 7 April 2014 / Revised: 18 July 2014 / Accepted: 23 July 2014 / Published: 31 July 2014
Cited by 5 | PDF Full-text (4318 KB) | HTML Full-text | XML Full-text
Abstract
Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct
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Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate. Full article
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Open AccessArticle Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels
Remote Sens. 2014, 6(8), 7158-7181; doi:10.3390/rs6087158
Received: 8 June 2014 / Revised: 16 July 2014 / Accepted: 18 July 2014 / Published: 31 July 2014
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Abstract
In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse representation-based classifiers (SRCs). We propose to take advantage of both polarimetric information
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In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse representation-based classifiers (SRCs). We propose to take advantage of both polarimetric information and contextual information by combining sparsity-based classification methods with the concept of superpixels. Based on polarimetric feature vectors constructed by stacking a variety of polarimetric signatures and a superpixel map, two strategies are considered to perform polarimetric-contextual classification of PolSAR images. The first strategy starts by classifying the PolSAR image with pixel-wise SRC. Then, spatial regularization is imposed on the pixel-wise classification map by using majority voting within superpixels. In the second strategy, the PolSAR image is classified by taking superpixels as processing elements. The joint sparse representation-based classifier (JSRC) is employed to combine the polarimetric information contained in feature vectors and the contextual information provided by superpixels. Experimental results on real PolSAR datasets demonstrate the feasibility of the proposed approaches. It is proven that the classification performance is improved by using contextual information. A comparison with several other approaches also verifies the effectiveness of the proposed approach. Full article
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Open AccessArticle Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data
Remote Sens. 2014, 6(8), 7182-7211; doi:10.3390/rs6087182
Received: 25 March 2014 / Revised: 26 June 2014 / Accepted: 27 June 2014 / Published: 4 August 2014
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Abstract
This paper presents two algorithms used to derive Aerosol Optical Depth (AOD) from a synergy of satellite and ground-based observations, as well as aerosol transport model output. The Spinning Enhanced Visible Infrared Radiometer (SEVIRI) instrument on board Meteosat Second Generation (MSG) allows us
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This paper presents two algorithms used to derive Aerosol Optical Depth (AOD) from a synergy of satellite and ground-based observations, as well as aerosol transport model output. The Spinning Enhanced Visible Infrared Radiometer (SEVIRI) instrument on board Meteosat Second Generation (MSG) allows us to monitor aerosol loading over land at high temporal and spatial resolution. We present the algorithms which were fed with the data acquired via the SEVIRI channel 1, and also channels 1 and 3 in conjunction. In both cases, the surface reflectance is the most important parameter that should be estimated during the retrieval process. The surface properties are estimated during days with a low AOD (less than 0.1 at 500 nm) based on the radiance measured by the SEVIRI detector and aerosol optical properties modeled with the aerosol transport model or measured by the MODIS sensor. For data from the model and the MODIS, ground-based stations equipped with a sun photometer have been applied to correct the AOD fields using the optimal interpolation method. By assuming that surface reflectance at the SEVIRI resolution changes slowly over time, the AOD has been computed. Comparison of the SEVIRI AOD with the sun photometer observations shows good agreement/correlation. The mean bias is small (an order of 0.01–0.02) and the root mean square (rms) is about 0.05 for both one- and two-channel methods. In addition, the rms for the one-channel method does not change with the AOD. Full article
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Open AccessArticle Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures
Remote Sens. 2014, 6(8), 7212-7232; doi:10.3390/rs6087212
Received: 22 May 2014 / Revised: 3 July 2014 / Accepted: 28 July 2014 / Published: 4 August 2014
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Abstract
As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge
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As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge computational cost and Input/Output (I/O) bottlenecks. The progressive TIN (Triangulated Irregular Network) densification (PTD) filter is a commonly employed iterative method that mainly consists of the TIN generation and the judging functions. However, better quality from the progressive process comes at the cost of increasing computing time. Fortunately, it is possible to take advantage of state-of-the-art multi-core computing facilities to speed up this computationally intensive task. A streaming framework for filtering point clouds by encapsulating the PTD filter into independent computing units is proposed in this paper. Through overlapping multiple computing units and the I/O events, the efficiency of the proposed method is improved greatly. More importantly, this framework is adaptive to many filters. Experiments suggest that the proposed streaming PTD (SPTD) is able to improve the performance of massive point clouds processing and alleviate the I/O bottlenecks. The experiments also demonstrate that this SPTD allows the quick processing of massive point clouds with better adaptability. In a 12-core environment, the SPTD gains a speedup of 7.0 for filtering 249 million points. Full article
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Open AccessArticle Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China
Remote Sens. 2014, 6(8), 7260-7275; doi:10.3390/rs6087260
Received: 30 June 2014 / Revised: 28 July 2014 / Accepted: 29 July 2014 / Published: 4 August 2014
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Abstract
There exists a spatial mismatch between socioeconomic data, such as Gross Domestic Product (GDP), and physical and environmental datasets. This study provides a dasymetric approach for GDP estimation at a fine scale by combining the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS)
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There exists a spatial mismatch between socioeconomic data, such as Gross Domestic Product (GDP), and physical and environmental datasets. This study provides a dasymetric approach for GDP estimation at a fine scale by combining the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) nighttime imagery, enhanced vegetation index (EVI), and land cover data. Despite the advantages of DMSP/OLS nighttime imagery in estimating human activities, its drawbacks, including coarse resolution, overglow, and saturation effects, limit its application. Hence, high-resolution EVI data were integrated with DMSP/OLS in this study to create a Human Settlement Index (HSI) for estimating the GDP of secondary and tertiary industries. The GDP of the primary industry was then estimated on the basis of land cover data, and the area with the GDP of the primary industry was classified by a threshold technique (DN ≤ 8). The regression model for GDP distribution estimation was implemented in Zhejiang Province in southeast China, and a GDP density map was generated at a resolution of 250 m × 250 m. Compared with the outcome of taking DMSP/OLS as a unique parameter, estimation errors obviously decreased. This study offers a low-cost and accurate approach for rapidly estimating high-resolution GDP distribution to construct an important database for the government when formulating developmental strategies. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California
Remote Sens. 2014, 6(8), 7276-7302; doi:10.3390/rs6087276
Received: 31 January 2014 / Revised: 23 May 2014 / Accepted: 24 July 2014 / Published: 5 August 2014
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Abstract
Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing
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Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu’s method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle The Penetration Depth Derived from the Synthesis of ALOS/PALSAR InSAR Data and ASTER GDEM for the Mapping of Forest Biomass
Remote Sens. 2014, 6(8), 7303-7319; doi:10.3390/rs6087303
Received: 28 April 2014 / Revised: 16 July 2014 / Accepted: 16 July 2014 / Published: 5 August 2014
PDF Full-text (6446 KB) | HTML Full-text | XML Full-text
Abstract
The Global Digital Elevation Model produced from stereo images of Advanced Spaceborne Thermal Emission and Reflection Radiometer data (ASTER GDEM) covers land surfaces between latitudes of 83°N and 83°S. The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite
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The Global Digital Elevation Model produced from stereo images of Advanced Spaceborne Thermal Emission and Reflection Radiometer data (ASTER GDEM) covers land surfaces between latitudes of 83°N and 83°S. The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite (ALOS) collected many SAR images since it was launched on 24 January 2006. The combination of ALOS/PALSAR interferometric data and ASTER GDEM should provide the penetration depth of SAR data assuming ASTER GDEM was the elevation of vegetation canopy top. It would be correlated with forest biomass because penetration depth could be affected by forest density and forest canopy height. Their combination held great promises for the forest biomass mapping over large area. The feasibility of forest biomass mapping through the data synthesis of ALOS/PALSAR InSAR data and ASTER GDEM was investigated in this study. A procedure for the extraction of penetration depth was firstly proposed. Then three models were built for biomass estimation: (I) model only using backscattering coefficients of ALOS/PALSAR data; (II) model only using penetration depth; (III) model using both of them. The biomass estimated from Lidar data was taken as reference data to evaluate the three different models. The results showed that the combination of backscattering coefficients and penetration depth gave the best accuracy. The forest disturbance has to be considered in forest biomass estimation because of the long time span of ASTER data for generating ASTER GDEM. The spatial homogeneity could be used to improve estimation accuracy. Full article
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Open AccessArticle Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution
Remote Sens. 2014, 6(8), 7320-7338; doi:10.3390/rs6087320
Received: 6 January 2014 / Revised: 28 June 2014 / Accepted: 9 July 2014 / Published: 6 August 2014
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Abstract
Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008–2011.
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Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008–2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI), MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes. Full article
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Open AccessArticle Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture
Remote Sens. 2014, 6(8), 7339-7359; doi:10.3390/rs6087339
Received: 1 May 2014 / Revised: 5 July 2014 / Accepted: 30 July 2014 / Published: 6 August 2014
Cited by 6 | PDF Full-text (11438 KB) | HTML Full-text | XML Full-text
Abstract
Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In
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Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is proposed. The multivariate textures are separately extracted from multispectral data using a multivariate variogram with different distance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. The multivariate textures and the spectral bands are then combined for urban built-up area extraction. Because the urban built-up area is the only target class, a one-class classifier, one-class support vector machine, is used. For comparison, the classical gray-level co-occurrence matrix (GLCM) is also used to extract image texture. The proposed method was evaluated using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Results demonstrated that the proposed method outperformed the use of spectral information alone and the joint use of the spectral information and the GLCM texture. In particular, the inclusion of multivariate variogram textures with spectral angle distance achieved the best results. The proposed method provides an effective way of extracting urban built-up areas from Landsat series images and could be applicable to other applications. Full article
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Open AccessArticle Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS Products
Remote Sens. 2014, 6(8), 7360-7378; doi:10.3390/rs6087360
Received: 14 May 2014 / Revised: 25 July 2014 / Accepted: 28 July 2014 / Published: 6 August 2014
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Abstract
The use of land surface temperature and vertical temperature profile data from Moderate Resolution Imaging Spectroradiometer (MODIS), to estimate high spatial resolution daily and monthly maximum and minimum 2 m above ground level (AGL) air temperatures for regions with limited in situ data
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The use of land surface temperature and vertical temperature profile data from Moderate Resolution Imaging Spectroradiometer (MODIS), to estimate high spatial resolution daily and monthly maximum and minimum 2 m above ground level (AGL) air temperatures for regions with limited in situ data was investigated. A diurnal air temperature change model was proposed to consider the differences between the MODIS overpass times and the times of daily maximum and minimum temperatures, resulting in the improvements of the estimation in terms of error values, especially for minimum air temperature. Both land surface temperature and vertical temperature profile data produced relatively high coefficient of determination values and small Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for air temperature estimation. The correction of the estimates using two gridded datasets, National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and Climate Research Unit (CRU), was performed and the errors were reduced, especially for maximum air temperature. The correction of daily and monthly air temperature estimates using the NCEP/NCAR reanalysis data, however, still produced relatively large error values compared to existing studies, while the correction of monthly air temperature estimates using the CRU data significantly reduced the errors; the MAE values for estimating monthly maximum air temperature range between 1.73 °C and 1.86 °C. Uncorrected land surface temperature generally performed better for estimating monthly minimum air temperature and the MAE values range from 1.18 °C to 1.89 °C. The suggested methodology on a monthly time scale may be applied in many data sparse areas to be used for regional environmental and agricultural studies that require high spatial resolution air temperature data. Full article
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Open AccessArticle Application of the Regional Water Mass Variations from GRACE Satellite Gravimetry to Large-Scale Water Management in Africa
Remote Sens. 2014, 6(8), 7379-7405; doi:10.3390/rs6087379
Received: 30 March 2014 / Revised: 16 July 2014 / Accepted: 18 July 2014 / Published: 7 August 2014
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Abstract
Time series of regional 2° × 2° Gravity Recovery and Climate Experiment (GRACE) solutions of surface water mass change have been computed over Africa from 2003 to 2012 with a 10-day resolution by using a new regional approach. These regional maps are used
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Time series of regional 2° × 2° Gravity Recovery and Climate Experiment (GRACE) solutions of surface water mass change have been computed over Africa from 2003 to 2012 with a 10-day resolution by using a new regional approach. These regional maps are used to describe and quantify water mass change. The contribution of African hydrology to actual sea level rise is negative and small in magnitude (i.e., −0.1 mm/y of equivalent sea level (ESL)) mainly explained by the water retained in the Zambezi River basin. Analysis of the regional water mass maps is used to distinguish different zones of important water mass variations, with the exception of the dominant seasonal cycle of the African monsoon in the Sahel and Central Africa. The analysis of the regional solutions reveals the accumulation in the Okavango swamp and South Niger. It confirms the continuous depletion of water in the North Sahara aquifer at the rate of −2.3 km3/y, with a decrease in early 2008. Synergistic use of altimetry-based lake water volume with total water storage (TWS) from GRACE permits a continuous monitoring of sub-surface water storage for large lake drainage areas. These different applications demonstrate the potential of the GRACE mission for the management of water resources at the regional scale. Full article
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Open AccessArticle Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa
Remote Sens. 2014, 6(8), 7406-7423; doi:10.3390/rs6087406
Received: 1 April 2014 / Revised: 10 July 2014 / Accepted: 14 July 2014 / Published: 11 August 2014
Cited by 9 | PDF Full-text (2849 KB) | HTML Full-text | XML Full-text
Abstract
Globally, water is an important resource required for the survival of human beings. Water is a scarce resource in the semi-arid environments, including South Africa. In South Africa, several studies have quantified evapotranspiration (ET) in different ecosystems at a local scale. Accurate spatially
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Globally, water is an important resource required for the survival of human beings. Water is a scarce resource in the semi-arid environments, including South Africa. In South Africa, several studies have quantified evapotranspiration (ET) in different ecosystems at a local scale. Accurate spatially explicit information on ET is rare in the country mainly due to lack of appropriate tools. In recent years, a remote sensing ET product from the MODerate Resolution Imaging Spectrometer (MOD16) has been developed. However, its accuracy is not known in South African ecosystems. The objective of this study was to validate the MOD16 ET product using data from two eddy covariance flux towers, namely; Skukuza and Malopeni installed in a savanna and woodland ecosystem within the Kruger National Park, South Africa. Eight day cumulative ET data from the flux towers was calculated to coincide with the eight day MOD16 products over a period of 10 years from 2000 to 2010. The Skukuza flux tower results showed inconsistent comparisons with MOD16 ET. The Malopeni site achieved a poorer comparison with MOD16 ET compared to the Skukuza, and due to a shorter measurement period, data validation was performed for 2009 only. The inconsistent comparison of MOD16 and flux tower-based ET can be attributed to, among other things, the parameterization of the Penman-Monteith model, flux tower measurement errors, and flux tower footprint vs. MODIS pixel. MOD16 is important for global inference of ET, but for use in South Africa's integrated water management, a locally parameterized and improved product should be developed. Full article
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Open AccessArticle The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data
Remote Sens. 2014, 6(8), 7424-7441; doi:10.3390/rs6087424
Received: 6 May 2014 / Revised: 16 July 2014 / Accepted: 24 July 2014 / Published: 11 August 2014
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Abstract
The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has
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The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans. Full article
Open AccessArticle A New Method for Modifying Thresholds in the Classification of Tree Models for Mapping Aquatic Vegetation in Taihu Lake with Satellite Images
Remote Sens. 2014, 6(8), 7442-7462; doi:10.3390/rs6087442
Received: 17 March 2014 / Revised: 24 July 2014 / Accepted: 25 July 2014 / Published: 12 August 2014
Cited by 5 | PDF Full-text (3561 KB) | HTML Full-text | XML Full-text
Abstract
Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely
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Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely sensed images. However, due to the effects of extrinsic and intrinsic factors, applying a CT model developed for imagery from one date to imagery from another date or a different dataset likely would reduce the classification accuracy. In this study, three spectral features (SFs) were selected to develop a CT model for identifying aquatic vegetation in Taihu Lake. Three traditional CT models with three SFs were developed using CT analysis based on satellite images acquired on 11 July, 16 August and 26 September 2013, and corresponding ground-truth samples, from the Huangjing-1A/B Charge-Coupled Device (HJ-CCD) images, environment and disaster reduction small satellites that were launched by China Center for Resources Satellite Data and Application (CRESDA). The overall accuracies of traditional CT models were 82%, 80% and 84%. We then tested two methods to modify CT model thresholds to adjust the traditional CT models based on image date to determine if the results would enable us to map and classify aquatic vegetation for periods when no ground-based data were available. We assessed the results with ground-truth samples and area agreement with traditional CT models. Results showed that CT models modified from a linear adjustment based on the relationship between ranked values of SFs between two image dates produced map accuracies comparable with those obtained from the traditional CT models and suggest that the method we proposed is feasible for mapping aquatic vegetation types in lakes when ground data are not available. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
Open AccessArticle Soil Surface Sealing Effect on Soil Moisture at a Semiarid Hillslope: Implications for Remote Sensing Estimation
Remote Sens. 2014, 6(8), 7469-7490; doi:10.3390/rs6087469
Received: 16 April 2014 / Revised: 17 July 2014 / Accepted: 18 July 2014 / Published: 13 August 2014
Cited by 5 | PDF Full-text (3281 KB) | HTML Full-text | XML Full-text
Abstract
Robust estimation of soil moisture using microwave remote sensing depends on extensive ground sampling for calibration and validation of the data. Soil surface sealing is a frequent phenomenon in dry environments. It modulates soil moisture close to the soil surface and, thus, has
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Robust estimation of soil moisture using microwave remote sensing depends on extensive ground sampling for calibration and validation of the data. Soil surface sealing is a frequent phenomenon in dry environments. It modulates soil moisture close to the soil surface and, thus, has the potential to affect the retrieval of soil moisture from microwave remote sensing and the validation of these data based on ground observations. We addressed this issue using a physically-based modeling approach that accounts explicitly for surface sealing at the hillslope scale. Simulated mean soil moisture at the respective layers corresponding to both the ground validation probe and the radar beam’s typical effective penetration depth were considered. A cyclic pattern was found in which, as compared to an unsealed profile, the seal layer intensifies the bias in validation during rainfall events and substantially reduces it during subsequent drying periods. The analysis of this cyclic pattern showed that, accounting for soil moisture dynamics at the soil surface, the optimal time for soil sampling following a rainfall event is a few hours in the case of an unsealed system and a few days in the case of a sealed one. Surface sealing was found to increase the temporal stability of soil moisture. In both sealed and unsealed systems, the greatest temporal stability was observed at positions with moderate slope inclination. Soil porosity was the best predictor of soil moisture temporal stability, indicating that prior knowledge regarding the soil texture distribution is crucial for the application of remote sensing validation schemes. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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Open AccessArticle Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization
Remote Sens. 2014, 6(8), 7491-7521; doi:10.3390/rs6087491
Received: 27 April 2014 / Revised: 24 July 2014 / Accepted: 5 August 2014 / Published: 13 August 2014
Cited by 5 | PDF Full-text (4364 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a blind restoration method is presented to remove the blur in remote sensing images. An alternating minimization (AM) framework is employed to simultaneously recover the image and the point spread function (PSF), and an adaptive-norm prior is used to apply
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In this paper, a blind restoration method is presented to remove the blur in remote sensing images. An alternating minimization (AM) framework is employed to simultaneously recover the image and the point spread function (PSF), and an adaptive-norm prior is used to apply different constraints to smooth regions and edges. Moreover, with the use of the knife-edge features in remote sensing images, an automatic knife-edge detection method is used to obtain a good initial PSF for the AM framework. In addition, a no-reference (NR) sharpness index is used to stop the iterations of the AM framework automatically at the best visual quality. Results in both simulated and real data experiments indicate that the proposed AM-KEdge method, which combines the automatic knife-edge detection and the AM framework, is robust, converges quickly, and can stop automatically to obtain satisfactory results. Full article
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Open AccessArticle Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data
Remote Sens. 2014, 6(8), 7522-7545; doi:10.3390/rs6087522
Received: 31 March 2014 / Revised: 28 July 2014 / Accepted: 7 August 2014 / Published: 13 August 2014
Cited by 6 | PDF Full-text (2420 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the feasibility of estimating discharges from Roseires Reservoir (Sudan) for the period from 2002 to 2010 and Aswan High Dam/Lake Nasser (Egypt) for the periods 1999–2002 and 2005–2009 using satellite altimetry and imagery with limited in situ data. Discharges were
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This paper presents the feasibility of estimating discharges from Roseires Reservoir (Sudan) for the period from 2002 to 2010 and Aswan High Dam/Lake Nasser (Egypt) for the periods 1999–2002 and 2005–2009 using satellite altimetry and imagery with limited in situ data. Discharges were computed using the water balance of the reservoirs. Rainfall and evaporation data were obtained from public domain data sources. In situ measurements of inflow and outflow (for validation) were obtained, as well. The other water balance components, such as the water level and surface area, for derivation of the change of storage volume were derived from satellite measurements. Water levels were obtained from Hydroweb for Roseires Reservoir and Hydroweb and Global Reservoir and Lake Monitor (GRLM) for Lake Nasser. Water surface areas were derived from Landsat TM/ETM+ images using the Normalized Difference Water Index (NDWI). The water volume variations were estimated by integrating the area-level relationship of each reservoir. For Roseires Reservoir, the water levels from Hydroweb agreed well with in situ water levels (RMSE = 0.92 m; R2 = 0.96). Good agreement with in situ measurements were also obtained for estimated water volume (RMSE = 23%; R2 = 0.94) and computed discharge (RMSE = 18%; R2 = 0.98). The accuracy of the computed discharge was considered acceptable for typical reservoir operation applications. For Lake Nasser, the altimetry water levels also agreed well with in situ levels, both for Hydroweb (RMSE = 0.72 m; R2 = 0.81) and GRLM (RMSE = 0.62 m; R2 = 0.96) data. Similar agreements were also observed for the estimated water volumes (RMSE = 10%–15%). However, the estimated discharge from satellite data agreed poorly with observed discharge, Hydroweb (RMSE = 70%; R2 = 0.09) and GRLM (RMSE = 139%; R2 = 0.36). The error could be attributed to the high sensitivity of discharge to errors in storage volume because of the immense reservoir compared to inflow/outflow series. It may also be related to unaccounted spills into the Toshka Depression, overestimation of water inflow and errors in open water evaporation. Therefore, altimetry water levels and satellite imagery data can be used as a source of information for monitoring the operation of Roseires Reservoir with a fairly low uncertainty, while the errors of Lake Nasser are too large to allow for the monitoring of its operation. Full article
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Open AccessArticle On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods
Remote Sens. 2014, 6(8), 7546-7565; doi:10.3390/rs6087546
Received: 4 May 2014 / Revised: 7 August 2014 / Accepted: 8 August 2014 / Published: 14 August 2014
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Abstract
Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in
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Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans. Full article
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
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Open AccessArticle Industrial Wastewater Discharge Retrieval Based on Stable Nighttime Light Imagery in China from 1992 to 2010
Remote Sens. 2014, 6(8), 7566-7579; doi:10.3390/rs6087566
Received: 14 May 2014 / Revised: 8 August 2014 / Accepted: 11 August 2014 / Published: 14 August 2014
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Abstract
Industrial wastewater (IW) discharge, which is a known point source of pollution, is a major water pollution source. Increasing IW discharge has imposed considerable pressure on regional or global water environments. It is important to estimate the IW distribution in grid units to
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Industrial wastewater (IW) discharge, which is a known point source of pollution, is a major water pollution source. Increasing IW discharge has imposed considerable pressure on regional or global water environments. It is important to estimate the IW distribution in grid units to improve basin-scale hydrological processes and water quality modeling. For the first time, we use the nighttime light imagery produced by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) to estimate the spatial and temporal variations in the IW distribution from 1992 to 2010 in China. The digital number values per unit area (DNP) of each stable light image were calculated using nighttime light imagery and were regressed against the IW per unit area (IWP) to estimate the total industrial wastewater (TIW) for each province. The results indicated strong positive correlations between the DNP and the IWP for each province during different years. The fitted linear regression models were used to estimate IW discharge in China with reliable accuracy. The IW estimation using the satellite data was consistent with the statistical results. The results also revealed that the IW discharge coverage expanded, whereas the IW discharge intensity decreased from 1992 to 2010 in China. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle Sub-Compartment Variation in Tree Height, Stem Diameter and Stocking in a Pinus radiata D. Don Plantation Examined Using Airborne LiDAR Data
Remote Sens. 2014, 6(8), 7592-7609; doi:10.3390/rs6087592
Received: 15 April 2014 / Revised: 28 July 2014 / Accepted: 28 July 2014 / Published: 15 August 2014
Cited by 4 | PDF Full-text (2870 KB) | HTML Full-text | XML Full-text
Abstract
Better information regarding the spatial variability of height, Diameter at Breast Height (DBH) and stocking could improve inventory estimates at the operational Planning Unit since these parameters are used extensively in allometric equations, including stem volume, biomass and carbon calculations. In this study,
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Better information regarding the spatial variability of height, Diameter at Breast Height (DBH) and stocking could improve inventory estimates at the operational Planning Unit since these parameters are used extensively in allometric equations, including stem volume, biomass and carbon calculations. In this study, the influence of stand stocking on height and DBH of two even aged radiata pine (Pinus radiata D. Don) stands were investigated using airborne Light Detection and Ranging (LiDAR) data at a study site in New South Wales, Australia. Both stands were characterized by irregular stocking due to patchy establishment and self-thinning in the absence of any silvicultural thinning events. For the purpose of this study, a total of 34 plots from a 34 year old site and 43 plots from a nine year old site were established, from which a total of 447 trees were sampled. Within these plots, DBH and height measurements were measured and their relationships with stocking were evaluated. LiDAR was used for height estimation as well as stem counts in fixed plots (stocking). The results showed a significant relationship between stem DBH and stocking. At both locations, trees with larger diameters were found on lower stocking sites. Height values were also significantly correlated with stocking, with taller trees associated with high stocking. These results were further verified of additional tree samples, with independent field surveys for DBH and LiDAR-derived metrics for height analysis. This study confirmed the relationship between P. radiata tree heights and stem diameter with stocking and demonstrated the capacity of LiDAR to capture sub-compartment variation in these tree-level attributes. Full article
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Open AccessArticle The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China
Remote Sens. 2014, 6(8), 7610-7631; doi:10.3390/rs6087610
Received: 2 January 2014 / Revised: 4 August 2014 / Accepted: 5 August 2014 / Published: 19 August 2014
Cited by 8 | PDF Full-text (10777 KB) | HTML Full-text | XML Full-text
Abstract
Time series data capture crop growth dynamics and are some of the most effective data sources for crop mapping. However, a drawback of precise crop classification at medium resolution (30 m) using multi-temporal data is that some images at crucial time periods are
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Time series data capture crop growth dynamics and are some of the most effective data sources for crop mapping. However, a drawback of precise crop classification at medium resolution (30 m) using multi-temporal data is that some images at crucial time periods are absent from a single sensor. In this research, a medium-resolution, 15-day time series was obtained by merging Landsat-5 TM and HJ-1 CCD data (with similar radiometric performances in multi-spectral bands). Subsequently, optimal temporal windows for accurate crop mapping were evaluated using an extension of the Jeffries–Matusita (JM) distance from the merged time series. A support vector machine (SVM) was then used to compare the classification accuracy of the optimal temporal windows and the entire time series. In addition, different training sample sizes (10% to 90% of the entire training sample in 10% increments; five repetitions for each sample size) were used to investigate the stability of optimal temporal windows. The results showed that time series in optimal temporal windows can achieve high classification accuracies. The optimal temporal windows were robust when the training sample size was sufficiently large. However, they were not stable when the sample size was too small (i.e., less than 300) and may shift in different agro-ecosystems, because of different classes. In addition, merged time series had higher temporal resolution and were more likely to comprise the optimal temporal periods than time series from single-sensor data. Therefore, the use of merged time series increased the possibility of precise crop classification. Full article
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Open AccessArticle Development of a Novel Bidirectional Canopy Reflectance Model for Row-Planted Rice and Wheat
Remote Sens. 2014, 6(8), 7632-7659; doi:10.3390/rs6087632
Received: 16 April 2014 / Revised: 24 July 2014 / Accepted: 25 July 2014 / Published: 19 August 2014
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Abstract
Rice and wheat are mainly planted in a row structure in China. Radiative transfer models have the potential to provide an accurate description of the bidirectional reflectance characteristics of the canopies of row-planted crops, but few of them have addressed the problem of
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Rice and wheat are mainly planted in a row structure in China. Radiative transfer models have the potential to provide an accurate description of the bidirectional reflectance characteristics of the canopies of row-planted crops, but few of them have addressed the problem of row-planted structures. In this paper, a new 4SAIL-RowCrop model for row-planted rice and wheat canopies was developed by integrating the 4SAIL model and the Kimes geometric model. The Kimes model and the Kimes–Porous geometric optics (GO) module were used to simulate different scene component proportions. Spectral reflectance and transmittance were subsequently calculated using the 4SAIL model to determine the reflectance of crucial scene components: the illuminated canopy, illuminated background and shadowed background. The model was validated by measuring the reflectance of rice and wheat cultivars at different growth stages, planting densities and nitrogen fertilization rates. The directional and nadir reflectance simulated by the model agreed well with experimental data, with squared correlation coefficients of 0.69 and 0.98, root mean square errors of 0.013 and 0.009 and normalized root mean square errors of 15.8% and 12.4%, respectively. The results indicate that the 4SAIL-RowCrop model is suitable for simulating the spectral reflectance of the canopy of row-planted rice and wheat. Full article
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Open AccessArticle Evaluation of the Surface Water Distribution in North-Central Namibia Based on MODIS and AMSR Series
Remote Sens. 2014, 6(8), 7660-7682; doi:10.3390/rs6087660
Received: 31 March 2014 / Revised: 5 August 2014 / Accepted: 6 August 2014 / Published: 19 August 2014
Cited by 3 | PDF Full-text (6646 KB) | HTML Full-text | XML Full-text
Abstract
Semi-arid North-central Namibia has high potential for rice cultivation because large seasonal wetlands (oshana) form during the rainy season. Evaluating the distribution of surface water would reveal the area potentially suitable for rice cultivation. In this study, we detected the distribution
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Semi-arid North-central Namibia has high potential for rice cultivation because large seasonal wetlands (oshana) form during the rainy season. Evaluating the distribution of surface water would reveal the area potentially suitable for rice cultivation. In this study, we detected the distribution of surface water with high spatial and temporal resolution by using two types of complementary satellite data: MODIS (MODerate-resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer–Earth Observing System), using AMSR2 after AMSR-E became unavailable. We combined the modified normalized-difference water index (MNDWI) from the MODIS data with the normalized-difference polarization index (NDPI) from the AMSR-E and AMSR2 data to determine the area of surface water. We developed a simple gap-filling method (“database unmixing”) with the two indices, thereby providing daily 500-m-resolution MNDWI maps of north-central Namibia regardless of whether the sky was clear. Moreover, through receiver-operator characteristics (ROC) analysis, we determined the threshold MNDWI (−0.316) for wetlands. Using ROC analysis, MNDWI had moderate performance (the area under the ROC curve was 0.747), and the recognition error for seasonal wetlands and dry land was 21.2%. The threshold MNDWI let us calculate probability of water presence (PWP) maps for the rainy season and the whole year. The PWP maps revealed the total area potentially suitable for rice cultivation: 1255 km2 (1.6% of the study area). Full article
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Open AccessArticle Seven Years of Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) of Surface Soil Moisture over Africa
Remote Sens. 2014, 6(8), 7683-7707; doi:10.3390/rs6087683
Received: 14 May 2014 / Revised: 8 August 2014 / Accepted: 11 August 2014 / Published: 19 August 2014
Cited by 4 | PDF Full-text (11268 KB) | HTML Full-text | XML Full-text
Abstract
A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning
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A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = ~0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products. Full article
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Open AccessArticle Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP/OLS Night Light Data
Remote Sens. 2014, 6(8), 7708-7731; doi:10.3390/rs6087708
Received: 24 June 2014 / Revised: 21 July 2014 / Accepted: 12 August 2014 / Published: 20 August 2014
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Abstract
Stable nighttime light (NTL) data, derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS), are typically considered a proxy measure of the dynamics of human settlements and have been extensively used to quantitative estimates of demographic variables, economic activity, and land-use
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Stable nighttime light (NTL) data, derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS), are typically considered a proxy measure of the dynamics of human settlements and have been extensively used to quantitative estimates of demographic variables, economic activity, and land-use change in previous studies at both regional and global scales. The utility of DMSP data for characterizing spatio-temporal trends in urban development at a local scale, however, has received less attention. In this study, we utilize a time series of DMSP data to examine the spatio-temporal characteristics of urban development in 285 Chinese cities from 1992 to 2009, at both the local and national levels. We compare linear models and piecewise linear models to identify the turning points of nighttime lights and calculate the trends in nighttime light growth at the pixel level. An unsupervised classification is applied to identify the patterns in the nighttime light time series quantitatively. Our results indicate that nighttime light brightness in most areas of China exhibit a positive, multi-stage process over the last two decades; however, the average trends in nighttime light growth differ significantly. Through the piecewise linear model, we identify the saturation of nighttime light brightness in the urban center and significant increases in suburban areas. The maps of turning points indicate the greater the distance to the city center or sub-center, the later the turning point occurs. Six patterns derived from the classification illustrate the various characteristics of the nighttime light time series from the local to the national level. The results portray spatially explicit patterns and conspicuous temporal trends of urbanization dynamics for individual Chinese cities from 1992 to 2009. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery
Remote Sens. 2014, 6(8), 7732-7761; doi:10.3390/rs6087732
Received: 8 April 2014 / Revised: 30 July 2014 / Accepted: 30 July 2014 / Published: 20 August 2014
Cited by 8 | PDF Full-text (5355 KB) | HTML Full-text | XML Full-text
Abstract
Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed
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Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
Open AccessArticle Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria
Remote Sens. 2014, 6(8), 7762-7782; doi:10.3390/rs6087762
Received: 14 February 2014 / Revised: 4 August 2014 / Accepted: 5 August 2014 / Published: 20 August 2014
Cited by 2 | PDF Full-text (2949 KB) | HTML Full-text | XML Full-text
Abstract
Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may
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Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aquatic plants. The information can be derived from products with large swath and high observation frequency, but with coarse resolution; and the quality of so derived information must be eventually assessed using finer resolution data. In this study, we evaluate two methods: Normalized Difference Vegetation Index (NDVI) slicing and maximum likelihood in terms of delineation; and two methods: Gutman and Ignatov’s NDVI-based fractional cover retrieval and linear spectral unmixing in terms of area estimation of aquatic plants from 300 m Medium Resolution Imaging Spectrometer (MERIS) data, using as reference results obtained with 30 m Landsat-7 ETM+. Our results show for delineation, that maximum likelihood with an average classification accuracy of 80% is better than NDVI slicing at 75%, both methods showing larger errors over sparse vegetation. In area estimation, we found that Gutman and Ignatov’s method and spectral unmixing produce almost the same root mean square (RMS) error of about 0.10, but the former shows larger errors of about 0.15 over sparse vegetation while the latter remains invariant. Where an endmember spectral library is available, we recommend the spectral unmixing approach to estimate extent of vegetation with coarse resolution data, as its performance is relatively invariant to the fragmentation of aquatic vegetation cover. Full article
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Open AccessArticle Salt Content Distribution and Paleoclimatic Significance of the Lop Nur “Ear” Feature: Results from Analysis of EO-1 Hyperion Imagery
Remote Sens. 2014, 6(8), 7783-7799; doi:10.3390/rs6087783
Received: 12 June 2014 / Revised: 14 August 2014 / Accepted: 14 August 2014 / Published: 21 August 2014
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Abstract
Lop Nur, a playa lake located on the eastern margin of Tarim Basin in northwestern China, is famous for the “Ear” feature of its salt crust, which appears in remote-sensing images. In this study, partial least squares (PLS) regression was used to estimated
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Lop Nur, a playa lake located on the eastern margin of Tarim Basin in northwestern China, is famous for the “Ear” feature of its salt crust, which appears in remote-sensing images. In this study, partial least squares (PLS) regression was used to estimated Lop Nur playa salt-crust properties, including total salt, Ca2+, Mg2+, Na+, Si2+, and Fe2+ using laboratory hyperspectral data. PLS results for laboratory-measured spectra were compared with those for resampled laboratory spectra with the same spectral resolution as Hyperion using the coefficient of determination (R2) and the ratio of standard deviation of sample chemical concentration to root mean squared error (RPD). Based on R2 and RPD, the results suggest that PLS can predict Ca2+ using Hyperion reflectance spectra. The Ca2+ distribution was compared to the “Ear area” shown in a Landsat Thematic Mapper (TM) 5 image. The mean value of reflectance from visible bands for a 14 km transversal profile to the “Ear area” rings was extracted with the TM 5 image. The reflectance was used to build a correlation with Ca2+ content estimated with PLS using Hyperion. Results show that the correlation between Ca2+ content and reflectance is in accordance with the evolution of the salt lake. Ca2+ content variation was consistent with salt deposition. Some areas show a negative correlation between Ca2+ content and reflectance, indicating that there could have been a small-scale temporary runoff event under an arid environmental background. Further work is needed to determine whether these areas of small-scale runoff are due to natural (climate events) or human factors (upstream channel changes). Full article
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Open AccessArticle Enabling the Use of Earth Observation Data for Integrated Water Resource Management in Africa with the Water Observation and Information System
Remote Sens. 2014, 6(8), 7819-7839; doi:10.3390/rs6087819
Received: 28 March 2014 / Revised: 13 August 2014 / Accepted: 14 August 2014 / Published: 21 August 2014
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Abstract
The Water Observation and Information System (WOIS) is an open source software tool for monitoring, assessing and inventorying water resources in a cost-effective manner using Earth Observation (EO) data. The WOIS has been developed by, among others, the authors of this paper under
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The Water Observation and Information System (WOIS) is an open source software tool for monitoring, assessing and inventorying water resources in a cost-effective manner using Earth Observation (EO) data. The WOIS has been developed by, among others, the authors of this paper under the TIGER-NET project, which is a major component of the TIGER initiative of the European Space Agency (ESA) and whose main goal is to support the African Earth Observation Capacity for Water Resource Monitoring. TIGER-NET aims to support the satellite-based assessment and monitoring of water resources from watershed to cross-border basin levels through the provision of a free and powerful software package, with associated capacity building, to African authorities. More than 28 EO data processing solutions for water resource management tasks have been developed, in correspondence with the requirements of the participating key African water authorities, and demonstrated with dedicated case studies utilizing the software in operational scenarios. They cover a wide range of themes and information products, including basin-wide characterization of land and water resources, lake water quality monitoring, hydrological modeling and flood forecasting and mapping. For each monitoring task, step-by-step workflows were developed, which can either be adjusted by the user or largely automatized to feed into existing data streams and reporting schemes. The WOIS enables African water authorities to fully exploit the increasing EO capacity offered by current and upcoming generations of satellites, including the Sentinel missions. Full article
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Open AccessArticle Comparative Estimation of Urban Development in China’s Cities Using Socioeconomic and DMSP/OLS Night Light Data
Remote Sens. 2014, 6(8), 7840-7856; doi:10.3390/rs6087840
Received: 24 June 2014 / Revised: 29 July 2014 / Accepted: 12 August 2014 / Published: 22 August 2014
Cited by 7 | PDF Full-text (5321 KB) | HTML Full-text | XML Full-text
Abstract
China has been undergoing a remarkably rapid urbanization process in the last several decades. Urbanization is a complicated phenomenon involving imbalanced transformation processes, such as population migrations, economic advancements and human activity dynamics. It is important to evaluate the imbalances between transformation processes
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China has been undergoing a remarkably rapid urbanization process in the last several decades. Urbanization is a complicated phenomenon involving imbalanced transformation processes, such as population migrations, economic advancements and human activity dynamics. It is important to evaluate the imbalances between transformation processes to support policy making in the realms of environmental management and urban planning. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime lights time series imagery provides a consistent and timely measure to estimate socioeconomic dynamics and changes in human activity. In this study, we jointly compared the annual ranks of three variables: the population, the gross domestic product (GDP) and the sum of weighted DMSP/OLS nighttime lights to estimate spatial and temporal imbalances in the urbanization processes of 226 cities in China between 1994 and 2011. We used ternary plots and a Euclidean distance-based method to quantitatively estimate the spatial and temporal imbalances between cities and to classify diverse urban development patterns in China. Our results suggest that, from 1994 to 2011, the imbalances of urbanization processes observed in the eastern, western and middle cities decreased, respectively, by 35.26%, 29.04% and 25.84%; however, imbalances in the northeast increased by 33.29%. The average decrement in imbalances across all urbanization processes in the 226 cities was 17.58%. Cities in the eastern region displayed relatively strong attractions to population, more rapid economic development processes and lower imbalances between socioeconomic and anthropogenic dynamics than cities in other regions. Several types of urban development patterns can be identified by comparing the morphological characteristics of temporal ternary plots of the 226 cities in China. More than one third (35.40%) of the 226 cities presented balanced states during the period studied; however, the remainder showed alternative urban development patterns. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessReview Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review
Remote Sens. 2014, 6(8), 6844-6866; doi:10.3390/rs6086844
Received: 14 June 2014 / Revised: 18 July 2014 / Accepted: 18 July 2014 / Published: 25 July 2014
Cited by 19 | PDF Full-text (1308 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena.
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Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. However, differences among these datasets and how they have been applied may potentially confuse researchers working with these data. In this paper, we review the ways in which data from DMSP/OLS nighttime light images have been applied over the past two decades, focusing on differences in data processing, research trends, and the methods used among the different application areas. Five main datasets extracted from this database have led to many studies in various research areas over the last 20 years, and each dataset has its own strengths and limitations. The number of publications based on this database and the diversity of authors and institutions involved have shown promising growth. In addition, researchers have accumulated vast experience retrieving data on the spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters, which are “hotspot” applications in this field. Researchers continue to develop novel ways to extract more information from the DMSP/OLS database and apply the data to interdisciplinary research topics. We believe that DMSP/OLS nighttime light data will play an important role in monitoring and analyzing human activities and natural phenomena from space in the future, particularly over the long term. A transparent platform that encourages data sharing, communication, and discussion of extraction methods and synthesis activities will benefit researchers as well as public and political stakeholders. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessReview Remote Geophysical Observatory in Antarctica with HF Data Transmission: A Review
Remote Sens. 2014, 6(8), 7233-7259; doi:10.3390/rs6087233
Received: 19 June 2014 / Revised: 23 July 2014 / Accepted: 24 July 2014 / Published: 4 August 2014
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Abstract
The geophysical observatory in the Antarctic Spanish Station, Juan Carlos I (ASJI), on Livingston Island, has been monitoring the magnetic field in the Antarctic region for more than fifteen years. In 2004, a vertical incidence ionospheric sounder completed the observatory, which brings a
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The geophysical observatory in the Antarctic Spanish Station, Juan Carlos I (ASJI), on Livingston Island, has been monitoring the magnetic field in the Antarctic region for more than fifteen years. In 2004, a vertical incidence ionospheric sounder completed the observatory, which brings a significant added value in a region with low density of geophysical data. Although the ASJI is only operative during the austral summer, the geomagnetic station records the data throughout the year. A High Frequency (HF) transmission system was installed in 2004 in order to have the geomagnetic data available during the whole year. As the power supply is very limited when the station is not operative, we had to design a low-power HF transceiver with a very simple antenna, due to environmental aspects. Moreover, the flow of information was unidirectional, so the modulation had to be extremely robust since there is no retransmission in case of error. This led us to study the main parameters of the ionospheric channel and to design new modulations specially adapted to very low signal to noise scenarios with high levels of interference. In this paper, a review of the results of our remote geophysical observatory and associated transmission system in Antarctica during the last decade is presented. Full article
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Open AccessTechnical Note Improvements in Sample Selection Methods for Image Classification
Remote Sens. 2014, 6(8), 7580-7591; doi:10.3390/rs6087580
Received: 2 April 2014 / Revised: 7 August 2014 / Accepted: 8 August 2014 / Published: 15 August 2014
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Abstract
Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative
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Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. Recent improvements in human-computer interaction (HCI) enable the construction of more intuitive graphic user interfaces (GUIs) to help users obtain desired results. In remote sensing image classification, GUIs still need advancements. In this work, we describe our efforts to develop an improved GUI for selecting the representative samples needed to estimate the classification model. The idea is to identify changes in the common strategies for sample selection to create a user-driven sample selection, which focuses on different views of each sample, and to help domain experts identify explicit classification rules, which is a well-established technique in geographic object-based image analysis (GEOBIA). We also propose the use of the well-known nearest neighbor algorithm to identify similar samples and accelerate the classification. Full article
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Open AccessLetter Successful Applications of Geotechnologies for the Evaluation of Road Infrastructures
Remote Sens. 2014, 6(8), 7800-7818; doi:10.3390/rs6087800
Received: 1 July 2014 / Revised: 4 August 2014 / Accepted: 7 August 2014 / Published: 21 August 2014
Cited by 4 | PDF Full-text (7904 KB) | HTML Full-text | XML Full-text
Abstract
This work reports the results obtained over several years of research into the application of different geomatic techniques in the field of civil engineering and, in particular, in their application to the management of road systems and associated structures. Among the main advances
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This work reports the results obtained over several years of research into the application of different geomatic techniques in the field of civil engineering and, in particular, in their application to the management of road systems and associated structures. Among the main advances obtained are the quantification of parameters during the inventorying and inspection of infrastructures, the metric quality of the results and the development of hardware and software tools for the automation of road systems management. Full article

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