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Remote Sens., Volume 5, Issue 7 (July 2013), Pages 3140-3636

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Open AccessArticle Performance Analysis of Mobile Laser Scanning Systems in Target Representation
Remote Sens. 2013, 5(7), 3140-3155; doi:10.3390/rs5073140
Received: 28 April 2013 / Revised: 9 June 2013 / Accepted: 13 June 2013 / Published: 24 June 2013
Cited by 4 | PDF Full-text (578 KB) | HTML Full-text | XML Full-text
Abstract
The technology of mobile laser scanning (MLS) has developed rapidly in recent years. This speedy development is evidenced by the emergence of a variety of MLS systems in commercial market and academic institutions. However, the producers tend to supply the specifications of the
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The technology of mobile laser scanning (MLS) has developed rapidly in recent years. This speedy development is evidenced by the emergence of a variety of MLS systems in commercial market and academic institutions. However, the producers tend to supply the specifications of the individual sensors in a generic sense, and this is not enough for guiding the choice of a MLS system for a specific application case. So far, the research efforts comparing the efficacy ranges of the existing MLS systems have been little reported. To fill this gap, this study examined the performance of three typical MLS systems (Riegl VMX-250, Roamer and Sensei) in terms of target representation. Retrievals of window areas and lighting pole radiuses served as representative cases, as these parameters correspond to the spatial scales from meter to centimeter. The evaluations showed that the VMX-250 with highest sampling density did best, and thus, it was preferred in the scenario of this study. If both the cost and efficacy were regarded, Roamer was a choice of compromise. Therefore, an application-oriented scheme was suggested for selecting MLS systems to acquire the desired performance. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Targeting Mineral Resources with Remote Sensing and Field Data in the Xiemisitai Area, West Junggar, Xinjiang, China
Remote Sens. 2013, 5(7), 3156-3171; doi:10.3390/rs5073156
Received: 30 April 2013 / Revised: 12 June 2013 / Accepted: 13 June 2013 / Published: 25 June 2013
Cited by 6 | PDF Full-text (1532 KB) | HTML Full-text | XML Full-text
Abstract
The Xiemisitai area, West Junggar, Xinjiang, China, is situated at a potential copper mineralization zone in association with small granitic intrusions. In order to identify the alteration zones and mineralization characteristics of the intrusions, Landsat Enhanced Thematic Mapper (ETM+) and Quickbird data of
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The Xiemisitai area, West Junggar, Xinjiang, China, is situated at a potential copper mineralization zone in association with small granitic intrusions. In order to identify the alteration zones and mineralization characteristics of the intrusions, Landsat Enhanced Thematic Mapper (ETM+) and Quickbird data of the study area were evaluated in mapping lithological units, small intrusions, and alteration zones. False color composites of the first principal component analyses (PCA1), PCA2, and PCA4 in red (R), green (G), and blue (B) of the ETM+ image, and relevant hue-saturation-intensity (HSI) color model transformations, were performed. This led to the identification of lithologic units and discrimination of granitic intrusions from wall-rocks. A new geological map was generated by integrating the remote sensing results with two internally published local geologic maps and field inspection data. For the selected region, false color composites from PCA and relevant HSI-transformed images of the Quickbird data delineated the details of small intrusions and identified other unknown similar intrusions nearby. Fifteen separate potash-feldspar granites and three separate hornblende biotite granites were identified using ETM+ and Quickbird data. The principal component analysis-based Crosta technique was employed to discriminate alteration minerals. Some of the mapped alteration zones using the Crosta technique agreed very well with the known copper deposits. Field verification led to the discovery of three copper mineralizations and two gold mineralizations for the first time. The results show that the PCA and HSI transformation techniques proved to be robust in processing remote sensing data with moderate to high spatial resolutions. It is concluded that the utilized methods are useful for mapping lithology and the targeting of small intrusion-type mineral resources within the sparsely vegetated regions of Northwest China. Full article
(This article belongs to the Special Issue Geological Remote Sensing)
Open AccessArticle Comparison of Typhoon Locations over Ocean Surface Observed by Various Satellite Sensors
Remote Sens. 2013, 5(7), 3172-3189; doi:10.3390/rs5073172
Received: 5 May 2013 / Revised: 13 June 2013 / Accepted: 13 June 2013 / Published: 28 June 2013
Cited by 5 | PDF Full-text (1074 KB) | HTML Full-text | XML Full-text
Abstract
In this study, typhoon eyes have been delineated using wavelet analysis from the synthetic aperture radar (SAR) images of ocean surface roughness and from the warm area at the cloud top in the infrared (IR) images, respectively. Envisat SAR imagery, and multi-functional transport
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In this study, typhoon eyes have been delineated using wavelet analysis from the synthetic aperture radar (SAR) images of ocean surface roughness and from the warm area at the cloud top in the infrared (IR) images, respectively. Envisat SAR imagery, and multi-functional transport satellite (MTSAT) and Feng Yun (FY)-2 Chinese meteorological satellite IR imagery were used to examine the typhoons in the western North Pacific from 2005 to 2011. Three cases of various typhoons in different years, locations, and conditions have been used to compare the typhoon eyes derived from SAR (on the ocean surface) with IR (at the cloud-top level) images. Furthermore, the best track data from the Joint Typhoon Warning Center (JTWC), Chinese Meteorological Administration (CMA), and the Japan Meteorological Agency (JMA) are checked for the calibration. Because of the vertical wind shear, which acts as an upright tilt, the location of the typhoon eye on the ocean surface differs from that at the top of the clouds. Consequently, the large horizontal distance between typhoon eyes on the ocean surface and on the cloud top implies that the associated vertical wind shear profile is considerably more complex than generally expected. This result demonstrates that SAR can be a useful tool for typhoon monitoring study over the ocean surface. Full article
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Open AccessArticle Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method
Remote Sens. 2013, 5(7), 3190-3211; doi:10.3390/rs5073190
Received: 5 May 2013 / Revised: 23 June 2013 / Accepted: 24 June 2013 / Published: 1 July 2013
Cited by 12 | PDF Full-text (1745 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the use of high temporal resolution satellite data has been emerging as an important tool to study crop phenology. Most methods to detect phenological events based on satellite data use thresholds to identify key events in the lifecycle of the
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In recent years, the use of high temporal resolution satellite data has been emerging as an important tool to study crop phenology. Most methods to detect phenological events based on satellite data use thresholds to identify key events in the lifecycle of the crop. In this study, a new method was used to define such thresholds for identifying the start and end of the growing season (SOS/EOS) for 43 different agricultural zones in China. The method used 2000–2003 NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data with a spatial resolution of eight kilometers and a temporal resolution of 15 days. Following data pre-processing, time series for the normalized difference vegetation index (NDVI or N), slope of the NDVI curve (S), and difference (D) between the NDVI value and a base NDVI value for bare land without snow were constructed. For each zone, an optimal set of threshold values for N, D, and S was determined, based on the remote sensing data and observed SOS/EOS data for 2003 at 261 agro-meteorological stations. Results were verified by comparing the accuracy of the new proposed NDS threshold method with the results of three other methods for SOS/EOS detection with remote sensing data. The findings of all four methods were compared to in situ SOS/EOS data from 2000 to 2002 for 110 agro-meteorological stations. Results show that the developed NDS threshold method had a significantly higher accuracy compared with other methods. The method is mainly limited by the observed data and the necessity of reestablishing the thresholds periodically. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Open AccessArticle Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota
Remote Sens. 2013, 5(7), 3212-3238; doi:10.3390/rs5073212
Received: 5 April 2013 / Revised: 20 June 2013 / Accepted: 20 June 2013 / Published: 4 July 2013
Cited by 34 | PDF Full-text (2682 KB) | HTML Full-text | XML Full-text
Abstract
Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in
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Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota. Full article
Open AccessArticle Surface Imprints of Water-Column Turbulence: A Case Study of Tidal Flow over an Estuarine Sill
Remote Sens. 2013, 5(7), 3239-3258; doi:10.3390/rs5073239
Received: 5 May 2013 / Revised: 25 June 2013 / Accepted: 26 June 2013 / Published: 4 July 2013
Cited by 1 | PDF Full-text (1719 KB) | HTML Full-text | XML Full-text
Abstract
Turbulent mixing in the ocean can, in some cases, be so intense as to leave surface imprints, or “boils”, that are detectable from space. Examples include turbulent flow over a submerged obstacle and instability of large-amplitude internal waves. In this paper we examine
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Turbulent mixing in the ocean can, in some cases, be so intense as to leave surface imprints, or “boils”, that are detectable from space. Examples include turbulent flow over a submerged obstacle and instability of large-amplitude internal waves. In this paper we examine the particular case of tidal flow over a ~60-m-deep sill, which forms a barrier for the flow of dense water from the Pacific Ocean into the Strait of Georgia. The flow response during flood tide is illustrated using visible and thermal-band satellite and airborne imagery, the latter having high-resolution multi-looks that capture the formative stage of the boils. The image examples capture aspects of the expected flow response based on in situ measurements reported in the literature, but they also suggest differences, and they reveal the level of complexity of the surface structure. A new result is that, after the front is pushed well off the sill, boils emerge several hundred meters downstream from the sill crest, grow at a rate of ~60 m2/s, and attain a size of 3,800 m2 (an equivalent diameter of 70 m) after one minute. These boils appear to arise from vorticity generated by vertical shear at the sill crest, and provide an additional source of vertical mixing and (through wave breaking) air-sea gas exchange. Full article
(This article belongs to the Special Issue Observing the Ocean’s Interior from Satellite Remote Sensing)
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Open AccessArticle Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique
Remote Sens. 2013, 5(7), 3259-3279; doi:10.3390/rs5073259
Received: 28 April 2013 / Revised: 21 June 2013 / Accepted: 23 June 2013 / Published: 5 July 2013
Cited by 11 | PDF Full-text (2637 KB) | HTML Full-text | XML Full-text
Abstract
An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split
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An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation. Full article
(This article belongs to the Special Issue High Performance Computing in Remote Sensing)
Open AccessArticle Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model
Remote Sens. 2013, 5(7), 3280-3304; doi:10.3390/rs5073280
Received: 27 April 2013 / Revised: 25 June 2013 / Accepted: 26 June 2013 / Published: 9 July 2013
Cited by 18 | PDF Full-text (19659 KB) | HTML Full-text | XML Full-text
Abstract
Abstract: Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll
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Abstract: Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parameters to be tuned, and thus they can be immediately implemented in processing chains. The coupled leaf/canopy model PROSAIL was inverted against simulated Sentinel-2 imagery at 20 m spatial resolution (8 bands) and validated against field data from the ESA-led SPARC (Barrax, Spain) campaign. For all 18 considered CFs with noise introduction and opting for the mean of multiple best solutions considerably improved retrievals; relative errors can be twice reduced as opposed to those without these regularization options. M-estimates were found most successful, but also data normalization influences the accuracy of the retrievals. Here, best LCC retrievals were obtained using a normalized “L1 -estimate” function with a relative error of 17.6% (r2 : 0.73), while best LAI retrievals were obtained through non-normalized “least-squares estimator” (LSE) with a relative error of 15.3% (r2 : 0.74). Full article
Open AccessArticle Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data
Remote Sens. 2013, 5(7), 3305-3330; doi:10.3390/rs5073305
Received: 22 May 2013 / Revised: 27 June 2013 / Accepted: 1 July 2013 / Published: 10 July 2013
Cited by 6 | PDF Full-text (1729 KB) | HTML Full-text | XML Full-text
Abstract
The present study classified global Ecosystem Functional Types (EFTs) derived from seasonal vegetation dynamics of the GIMMS3g NDVI time-series. Rotated Principal Component Analysis (PCA) was run on the derived phenological and productivity variables, which selected the Standing Biomass (approximation of Net Primary Productivity),
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The present study classified global Ecosystem Functional Types (EFTs) derived from seasonal vegetation dynamics of the GIMMS3g NDVI time-series. Rotated Principal Component Analysis (PCA) was run on the derived phenological and productivity variables, which selected the Standing Biomass (approximation of Net Primary Productivity), the Cyclic Fraction (seasonal vegetation productivity), the Permanent Fraction (permanent surface vegetation), the Maximum Day (day of maximum vegetation development) and the Season Length (length of vegetation growing season) variables, describing 98% of the variation in global ecosystems. EFTs were created based on Isodata classification of the spatial patterns of the Principal Components and were interpreted via gradient analysis using the selected remote sensing variables and climatic constraints (radiation, temperature, and water) of vegetation growth. The association of the EFTs with existing climate and land cover classifications was demonstrated via Detrended Correspondence Analysis (DCA). The ordination indicated good description of the global environmental gradient by the EFTs, supporting the understanding of phenological and productivity dynamics of global ecosystems. Climatic constraints of vegetation growth explained 50% of variation in the phenological data along the EFTs showing that part of the variation in the global phenological gradient is not climate related but is unique to the Earth Observation derived variables. DCA demonstrated good correspondence of the EFTs to global climate and also to land use classification. The results show the great potential of Earth Observation derived parameters for the quantification of ecosystem functional dynamics and for providing reference status information for future assessments of ecosystem changes. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields
Remote Sens. 2013, 5(7), 3331-3356; doi:10.3390/rs5073331
Received: 18 May 2013 / Revised: 28 June 2013 / Accepted: 5 July 2013 / Published: 12 July 2013
Cited by 6 | PDF Full-text (1081 KB) | HTML Full-text | XML Full-text
Abstract
Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would
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Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
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Open AccessArticle Harmonization of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from Sea-ViewingWide Field-of-View Sensor (SeaWiFS) and Medium Resolution Imaging Spectrometer Instrument (MERIS)
Remote Sens. 2013, 5(7), 3357-3376; doi:10.3390/rs5073357
Received: 24 May 2013 / Revised: 9 July 2013 / Accepted: 9 July 2013 / Published: 12 July 2013
Cited by 3 | PDF Full-text (4706 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale
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This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale monitoring of climate impacts on vegetation. In this work, we create an extensive dataset of FAPAR using a 10-day product at ∼1 km resolution from September, 1997, to April, 2012, combining information from two sensors: the NASA/Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the European Space Agency (ESA)/Medium Resolution Imaging Spectrometer Instrument (MERIS). The proposed methodology reduces the noise, fills the gaps and corrects for the spurious trends in the data, providing a time-consistent coverage of FAPAR. We develop a fast merging method and evaluate its performance over Europe and the Horn of Africa. Full article
(This article belongs to the Special Issue High Performance Computing in Remote Sensing)
Open AccessArticle A Texture-Based Land Cover Classification for the Delineation of a Shifting Cultivation Landscape in the Lao PDR Using Landscape Metrics
Remote Sens. 2013, 5(7), 3377-3396; doi:10.3390/rs5073377
Received: 3 June 2013 / Revised: 9 July 2013 / Accepted: 9 July 2013 / Published: 15 July 2013
Cited by 10 | PDF Full-text (1051 KB) | HTML Full-text | XML Full-text
Abstract
The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the
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The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset. Full article
Open AccessArticle Compact Setup of a Tunable Heterodyne Spectrometer for Infrared Observations of Atmospheric Trace-Gases
Remote Sens. 2013, 5(7), 3397-3414; doi:10.3390/rs5073397
Received: 29 April 2013 / Revised: 26 June 2013 / Accepted: 9 July 2013 / Published: 16 July 2013
Cited by 2 | PDF Full-text (2056 KB) | HTML Full-text | XML Full-text
Abstract
We report on the development and characterization of the new  compact infrared heterodyne receiver, iChips (Infrared Compact Heterodyne Instrument for Planetary Science). It is specially designed for ground-based observations of the terrestrial atmosphere in the mid-infrared wavelength region. Mid-infrared room temperature quantum cascade
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We report on the development and characterization of the new  compact infrared heterodyne receiver, iChips (Infrared Compact Heterodyne Instrument for Planetary Science). It is specially designed for ground-based observations of the terrestrial atmosphere in the mid-infrared wavelength region. Mid-infrared room temperature quantum cascade lasers are implemented into a heterodyne system for the first time. Their tunability allows the instrument to operate in two different modes.  The scanning mode covers a spectral range of few wavenumbers continuously with a resolution of approximately ν/∆ν ≥ 105. This mode allows the determination of the terrestrial atmospheric transmission. The staring mode, applied for observations of single molecular transition features, provides a spectral resolution of ν/∆ν ≥ 107 and a bandwidth of 1.4  GHz.  To demonstrate the instrument's capabilities, initial observations in both modes were performed by measuring the terrestrial transmittance at 7.8 µm (∼ 1,285 cm−1) and by probing terrestrial ozone features at 8.6 µm (∼ 1,160 cm−1), respectively. The receivers characteristics and performance are described. Full article
(This article belongs to the Special Issue Optical Remote Sensing of the Atmosphere)
Open AccessArticle Estimating Total Discharge in the Yangtze River Basin Using Satellite-Based Observations
Remote Sens. 2013, 5(7), 3415-3430; doi:10.3390/rs5073415
Received: 20 May 2013 / Revised: 11 July 2013 / Accepted: 12 July 2013 / Published: 17 July 2013
Cited by 8 | PDF Full-text (1357 KB) | HTML Full-text | XML Full-text
Abstract
The measurement of total basin discharge along coastal regions is necessary for understanding the hydrological and oceanographic issues related to the water and energy cycles. However, only the observed streamflow (gauge-based observation) is used to estimate the total fluxes from the river basin
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The measurement of total basin discharge along coastal regions is necessary for understanding the hydrological and oceanographic issues related to the water and energy cycles. However, only the observed streamflow (gauge-based observation) is used to estimate the total fluxes from the river basin to the ocean, neglecting the portion of discharge that infiltrates to underground and directly discharges into the ocean. Hence, the aim of this study is to assess the total discharge of the Yangtze River (Chang Jiang) basin. In this study, we explore the potential response of total discharge to changes in precipitation (from the Tropical Rainfall Measuring Mission—TRMM), evaporation (from four versions of the Global Land Data Assimilation—GLDAS, namely, CLM, Mosaic, Noah and VIC), and water-storage changes (from the Gravity Recovery and Climate Experiment—GRACE) by using the terrestrial water budget method. This method has been validated by comparison with the observed streamflow, and shows an agreement with a root mean square error (RMSE) of 14.30 mm/month for GRACE-based discharge and 20.98 mm/month for that derived from precipitation minus evaporation (P E). This improvement of approximately 32% indicates that monthly terrestrial water-storage changes, as estimated by GRACE, cannot be considered negligible over Yangtze basin. The results for the proposed method are more accurate than the results previously reported in the literature. Full article
Open AccessArticle Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011)
Remote Sens. 2013, 5(7), 3431-3456; doi:10.3390/rs5073431
Received: 16 May 2013 / Revised: 5 July 2013 / Accepted: 8 July 2013 / Published: 18 July 2013
Cited by 5 | PDF Full-text (2897 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a comparison of CO2 products derived from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS), with reference to calibration data obtained using the high-resolution ground-based Fourier Transform Spectrometers (g-b
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This paper presents a comparison of CO2 products derived from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS), with reference to calibration data obtained using the high-resolution ground-based Fourier Transform Spectrometers (g-b FTS) in the Total Carbon Column Observing Network (TCCON). Based on the monthly averages, we calculate the global offsets and regional relative precisions between satellite products and g-b FTS measurements. The results are as follows: the monthly means of SCIAMACHY data are systemically slightly lower than g-b FTS, but limited in coverage; the GOSAT data are superior in stability, but inferior in systematic error; the mean difference between AIRS data and that of g-b FTS is small; and the monthly global coverage is above 95%. Therefore, the AIRS data are better than the other two satellite products in both coverage and accuracy. We also estimate linear trends based on monthly mean data and find that the differences between the satellite products and the g-b FTS data range from 0.25 ppm (SCIAMACHY) to 1.26 ppm (AIRS). The latitudinal distributions of the zonal means of the three satellite products show similar spatial features. The seasonal cycle of satellite products also illustrates the same trend with g-b FTS observations. Full article
Open AccessArticle Subsurface Ocean Signals from an Orbiting Polarization Lidar
Remote Sens. 2013, 5(7), 3457-3475; doi:10.3390/rs5073457
Received: 28 May 2013 / Revised: 15 July 2013 / Accepted: 17 July 2013 / Published: 19 July 2013
Cited by 6 | PDF Full-text (596 KB) | HTML Full-text | XML Full-text
Abstract
Detection of subsurface returns from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite were demonstrated. Despite the coarse range resolution of this aerosol lidar, evidence of subsurface scattering was observed as a delay
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Detection of subsurface returns from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite were demonstrated. Despite the coarse range resolution of this aerosol lidar, evidence of subsurface scattering was observed as a delay and broadening of the cross-polarized signal relative to the co-polarized signal in the three near-surface range bins. These two effects contributed to an increased depolarization at the nominal depth of 25 m. These features were all correlated with near-surface chlorophyll concentrations. An increase in the depolarization was also seen at a depth of 50 m under certain conditions, suggesting that chlorophyll concentration at that depth could be estimated if an appropriate retrieval technique can be developed. At greater depths, the signal is dominated by the temporal response of the detectors, which was approximated by an analytical expression. The depolarization caused by aerosols in the atmosphere was calculated and eliminated as a possible artifact. Full article
(This article belongs to the Special Issue Remote Sensing of Phytoplankton)
Open AccessArticle Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures
Remote Sens. 2013, 5(7), 3476-3494; doi:10.3390/rs5073476
Received: 13 June 2013 / Revised: 15 July 2013 / Accepted: 16 July 2013 / Published: 19 July 2013
Cited by 25 | PDF Full-text (1426 KB) | HTML Full-text | XML Full-text
Abstract
The world is rapidly urbanizing, but there is no single urbanization process. Rather, urban areas in different regions of the world are undergoing myriad types of transformation processes. The purpose of this paper is to examine how well data from DMSP/OLS nighttime lights
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The world is rapidly urbanizing, but there is no single urbanization process. Rather, urban areas in different regions of the world are undergoing myriad types of transformation processes. The purpose of this paper is to examine how well data from DMSP/OLS nighttime lights (NTL) can identify different types of urbanization processes. Although data from DMSP/OLS NTL are increasingly used for the study of urban areas, to date there is no systematic assessment of how well these data identify different types of urban change. Here, we randomly select 240 sample locations distributed across all world regions to generate urbanization typologies with the DMSP/OLS NTL data and use Google Earth imagery to assess the validity of the NTL results. Our results indicate that where urbanization occurred, NTL have a high accuracy (93%) of characterizing these changes. There is also a relatively high error of commission (42%), where NTL identified urban change when no change occurred. This leads to an overestimation of urbanization by NTL. Our analysis shows that time series NTL data more accurately identifies urbanization in developed countries, but is less accurate in developing countries, suggesting the need to exert caution when using or interpreting NTL in developing countries. Full article
Open AccessArticle Exploring Patterns and Effects of Aerosol Quantity Flag Anomalies in MODIS Surface Reflectance Products in the Tropics
Remote Sens. 2013, 5(7), 3495-3515; doi:10.3390/rs5073495
Received: 18 May 2013 / Revised: 10 July 2013 / Accepted: 11 July 2013 / Published: 19 July 2013
Cited by 8 | PDF Full-text (2511 KB) | HTML Full-text | XML Full-text
Abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) has been supplying a continuous data stream since 2000, lending to detailed time series analysis of the global terrestrial environment. This paper explores a quality anomaly present in the tropics relating to the aerosol quantity flag in
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The Moderate Resolution Imaging Spectroradiometer (MODIS) has been supplying a continuous data stream since 2000, lending to detailed time series analysis of the global terrestrial environment. This paper explores a quality anomaly present in the tropics relating to the aerosol quantity flag in the daily MODIS surface reflectance products (MOD09 series) and the 16-day Vegetation Index (VI) composite products (MOD13 series) derived from the daily observations. While the anomaly is to some extent a known issue reported by the MODIS Land Quality Assessment group, very little is known about the scale of the issue, the nature and patterns of its occurrence, and potential consequences for data analysis, which explains why it is not adequately recognized throughout the literature. Two tropical regions were used to explore the anomaly and demonstrate the effects it has on the quality of selected MODIS products—one in the South American Amazon, the other in mainland Southeast Asia. The origins of the anomaly are described qualitatively in detail, and quantitative estimates of affected evergreen forest area in the MOD13A1 time series are made using blue band thresholding. The anomaly originates in the 1 km State dataset, whereby, under certain conditions, high aerosol quantity pixels are given a low aerosol quantity label, resulting in poor quality pixels with “good” quality labels. MODIS users are advised to investigate whether this anomaly has significant implications for their respective analysis and to consider the effects it may have on past studies. Full article
Open AccessArticle Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach
Remote Sens. 2013, 5(7), 3544-3561; doi:10.3390/rs5073544
Received: 28 May 2013 / Revised: 16 July 2013 / Accepted: 16 July 2013 / Published: 19 July 2013
Cited by 20 | PDF Full-text (3928 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Mosquito-borne diseases affect millions of people worldwide. In the United States, since 1999, West Nile Virus (WNV) has infected 36,801 people and has caused the deaths of 1,580. In California, since 2002, nearly 3,600 people have been infected with WNV with an additional
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Mosquito-borne diseases affect millions of people worldwide. In the United States, since 1999, West Nile Virus (WNV) has infected 36,801 people and has caused the deaths of 1,580. In California, since 2002, nearly 3,600 people have been infected with WNV with an additional 124 fatalities. Analyses of remotely- and spatially-based data have proven to facilitate the study of mosquito-borne diseases, including WNV. This study proposes an efficient procedure to identify swimming pools that may serve as potential mosquito habitat. The procedure derives the Normalized Difference Water Index (NDWI) from high resolution, multi-spectral imagery to detect the presence of surface water, and then incorporates vector-based data layers within a GIS to identify residential land parcels with detectable water. This study compared the parcels identified as having water (535) with parcels known to have swimming pools (682) resulting in an accuracy of 78.4%. Nineteen of the 147 land parcels with swimming pools had backyards with enough vegetation to obscure the presence of a swimming pool from the satellite. The remaining 128 parcels lacked enough surface water for the NDWI to indicate them as actually having surface water. It is likely then that swimming pools, associated with such parcels, may have enough water in them to provide adequate habitat for mosquitoes, and so field inspection by mosquito abatement personnel would be justified. Full article
Open AccessArticle Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data
Remote Sens. 2013, 5(7), 3562-3582; doi:10.3390/rs5073562
Received: 28 May 2013 / Revised: 18 July 2013 / Accepted: 18 July 2013 / Published: 22 July 2013
Cited by 14 | PDF Full-text (1905 KB) | HTML Full-text | XML Full-text
Abstract
Understanding the dynamics of mangroves at the species level is the key for securing sustainable conservation of mangrove forests around the globe. This study demonstrates the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species. It was found that
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Understanding the dynamics of mangroves at the species level is the key for securing sustainable conservation of mangrove forests around the globe. This study demonstrates the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species. It was found that five different tropical mangrove species of Southern Thailand, including Avicennia alba, Avicennia marina, Bruguiera parviflora, Rhizophora apiculata, and Rhizophora mucronata, were correctly classified. The selected data treatment (a well-established spectral band selector) helped improve the overall accuracy from 86% to 92%, despite the remaining confusion between the two members of the Rhizophoraceae family and the pioneer species. It is therefore anticipated that the methodology presented in this study can be used as a practical guideline for detailed mangrove species mapping in other study areas. The next stage of this work will be to exploit the differences between the leaf textures of the two Rhizophoraceae mangroves in order to refine the classification outcome. Full article
Open AccessArticle Bridging Ridge-to-Reef Patches: Seamless Classification of the Coast Using Very High Resolution Satellite
Remote Sens. 2013, 5(7), 3583-3610; doi:10.3390/rs5073583
Received: 28 May 2013 / Revised: 11 July 2013 / Accepted: 12 July 2013 / Published: 22 July 2013
Cited by 4 | PDF Full-text (2258 KB) | HTML Full-text | XML Full-text
Abstract
The structure and functioning of coral reef coastal zones are currently coping with an increasing variety of threats, thereby altering the coastal spatial patterns at an accelerated pace. Understanding and predicting the evolution of these highly valuable coastal ecosystems require reliable and frequent
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The structure and functioning of coral reef coastal zones are currently coping with an increasing variety of threats, thereby altering the coastal spatial patterns at an accelerated pace. Understanding and predicting the evolution of these highly valuable coastal ecosystems require reliable and frequent mapping and monitoring of both inhabited terrestrial and marine areas at the individual tree and coral colony spatial scale. The very high spatial resolution (VHR) mapping that was recently spearheaded by WorldView-2 (WV2) sensor with 2 m and 0.5 m multispectral (MS) and panchromatic (Pan) bands has the potential to address this burning issue. The objective of this study was to classify nine terrestrial and twelve marine patch classes with respect to spatial resolution enhancement and coast integrity using eight bands of the WV2 sensor on a coastal zone of Moorea Island, French Polynesia. The contribution of the novel WV2 spectral bands towards classification accuracy at 2 m and 0.5 m were tested using traditional and innovative Pan-sharpening techniques. The land and water classes were examined both separately and combinedly. All spectral combinations that were built only with the novel WV2 bands systematically increased the overall classification accuracy of the standard four band classification. The overall best contribution was attributed to the coastal-red edge-near infrared (NIR) 2 combination (Kappagain = 0.0287), which significantly increased the fleshy and encrusting algae (User’s Accuracygain = 18.18%) class. However, the addition of the yellow-NIR2 combination dramatically impacted the hard coral/algae patches class (Producer’s Accuracyloss = −20.88%). Enhancement of the spatial resolution reduced the standard classification accuracy, depending on the Pan-sharpening technique. The proposed composite method (local maximum) provided better overall results than the commonly used sensor method (systematic). However, the sensor technique produced the highest contribution to the hard coral thicket (PAgain = 30.36%) class with the coastal-red edge-NIR2 combination. Partitioning the coast into its terrestrial and aquatic components lowered the overall standard classification accuracy, while strongly enhancing the hard coral bommie class with the coastal-NIR2 combination (UAgain = 40%) and the green-coastal Normalized Difference Ratio (UAgain = 11.06%). VHR spaceborne remote sensing has the potential to gain substantial innovative insights into the evolution of tropical coastal ecosystems from local to regional scales, to predict the influence of anthropogenic and climate changes and to help design optimized management and conservation frameworks. Full article
Figures

Open AccessArticle Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia
Remote Sens. 2013, 5(7), 3611-3636; doi:10.3390/rs5073611
Received: 30 May 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 22 July 2013
Cited by 11 | PDF Full-text (3215 KB) | HTML Full-text | XML Full-text
Abstract
Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites
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Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration. Full article

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Open AccessReview Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives
Remote Sens. 2013, 5(7), 3516-3543; doi:10.3390/rs5073516
Received: 8 May 2013 / Revised: 15 July 2013 / Accepted: 16 July 2013 / Published: 19 July 2013
Cited by 7 | PDF Full-text (290 KB) | HTML Full-text | XML Full-text
Abstract
Moisture content in the soil and snow in the alpine environment is an important factor, not only for environmentally oriented research, but also for decision making in agriculture and hazard management. Current observation techniques quantifying soil moisture or characterizing a snow pack often
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Moisture content in the soil and snow in the alpine environment is an important factor, not only for environmentally oriented research, but also for decision making in agriculture and hazard management. Current observation techniques quantifying soil moisture or characterizing a snow pack often require dedicated instrumentation that measures either at point scale or at very large (satellite pixel) scale. Given the heterogeneity of both snow cover and soil moisture in alpine terrain, observations of the spatial distribution of moisture and snow-cover are lacking at spatial scales relevant for alpine hydrometeorology. This paper provides an overview of the challenges and status of the determination of soil moisture and snow properties in alpine environments. Current measurement techniques and newly proposed ones, based on the reception of reflected Global Navigation Satellite Signals (i.e., GNSS Reflectometry or GNSS-R), or the use of laser scanning are reviewed, and the perspectives offered by these new techniques to fill the current gap in the instrumentation level are discussed. Some key enabling technologies including the availability of modernized GNSS signals and GNSS array beamforming techniques are also considered and discussed. Full article

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