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Remote Sens., Volume 3, Issue 1 (January 2011), Pages 1-202

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Research

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Open AccessArticle Tracking Desertification in California Using Remote Sensing: A Sand Dune Encroachment Approach
Remote Sens. 2011, 3(1), 1-13; doi:10.3390/rs3010001
Received: 15 October 2010 / Revised: 3 December 2010 / Accepted: 20 December 2010 / Published: 24 December 2010
Cited by 3 | PDF Full-text (360 KB) | HTML Full-text | XML Full-text
Abstract
Most remote sensing studies in deserts focus solely on vegetation monitoring to assess the extent of desertification. However, the application of sand dune encroachment into such studies would greatly improve the accuracy in the prediction criteria of risk-prone areas. This study applies [...] Read more.
Most remote sensing studies in deserts focus solely on vegetation monitoring to assess the extent of desertification. However, the application of sand dune encroachment into such studies would greatly improve the accuracy in the prediction criteria of risk-prone areas. This study applies the latter methodology for tracking desertification using sand dunes in the Kelso Dunes (in Newberry-Baker, CA, USA). The approach involves the comparison of spectral characteristics of the dunes in Landsat Thematic Mapper (TM) images over a 24-year period (1982, 1988, 1994, 2000, and 2006). During this 24-year period, two El Niño events occurred (1983 and 1993); it was concluded that despite the shift in predominant winds, the short-term variation in wind direction did not make a noticeable change in dune formation, but greatly influences vegetation cover. Therefore, relying solely on vegetation monitoring to assess desertification can lead to overestimations in prediction analysis. Results from this study indicate that the Kelso Dunes are experiencing an encroachment rate of approximately 5.9 m3/m/yr over the 24-year period. While quantifying the Kelso Dunes or any natural dynamic system is subject to uncertainties, the encroachment rate approach reflects the highly heterogeneous nature of the sand dunes (in regards to spectral variability in brightness) at Kelso Dunes and serves as an exemplar for future research. Full article
Open AccessArticle Description and Performance of an L-Band Radiometer with Digital Beamforming
Remote Sens. 2011, 3(1), 14-40; doi:10.3390/rs3010014
Received: 27 October 2010 / Revised: 3 December 2010 / Accepted: 22 December 2010 / Published: 30 December 2010
Cited by 3 | PDF Full-text (981 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the description and performance tests of an L-band microwave radiometer with Digital Beamforming (DBF), developed for the Passive Advanced Unit (PAU) for ocean monitoring project. PAU is an instrument that combines, in a single receiver and without time multiplexing, [...] Read more.
This paper presents the description and performance tests of an L-band microwave radiometer with Digital Beamforming (DBF), developed for the Passive Advanced Unit (PAU) for ocean monitoring project. PAU is an instrument that combines, in a single receiver and without time multiplexing, a microwave radiometer at L-band (PAU-RAD) and a GPS-reflectometer (PAU-GNSS-R). This paper focuses on the PAU‑RAD beamformer’s first results, analyzing the hardware and software required for the developed prototype. Finally, it discusses the first results measured in the Universitat Politècnica de Catalunya (UPC) anechoic chamber. Full article
Open AccessArticle Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007
Remote Sens. 2011, 3(1), 42-64; doi:10.3390/rs3010042
Received: 28 October 2010 / Revised: 24 December 2010 / Accepted: 31 December 2010 / Published: 6 January 2011
Cited by 39 | PDF Full-text (1857 KB) | HTML Full-text | XML Full-text
Abstract
Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and [...] Read more.
Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and marine ecosystem processes. The capacity to accurately and consistently map and monitor these benthic habitats is critical to developing and implementing management applications. This paper presents a method for integrating field survey data and high spatial resolution, multi-spectral satellite image data to map bathymetry and seagrass in shallow coastal waters. Using Quickbird 2 satellite images from 2004 and 2007, acoustic field survey data were used to map bathymetry using a linear and ratio algorithm method; benthic survey field data were used to calibrate and validate classifications of seagrass percentage cover and seagrass species composition; and a change detection analysis of seagrass cover was performed. The bathymetry mapping showed that only the linear algorithm could effectively and accurately predict water depth; overall benthic map accuracies ranged from 57–95%; and the change detection produced a reliable change map and showed a net decrease in seagrass cover levels, but the majority of the study area showed no change in seagrass cover level. This study demonstrates that multiple spatial products (bathymetry, seagrass and change maps) can be produced from single satellite images and a concurrent field survey dataset. Moreover, the products were produced at higher spatial resolution and accuracy levels than previous studies in Moreton Bay. The methods are developed from previous work in the study area and are continuing to be implemented, as well as being developed to be repeatable in similar shallow coastal water environments. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Open AccessArticle Automatic Registration of Airborne and Spaceborne Images by Topology Map Matching with SURF Processor Algorithm
Remote Sens. 2011, 3(1), 65-82; doi:10.3390/rs3010065
Received: 16 November 2010 / Revised: 24 December 2010 / Accepted: 4 January 2011 / Published: 6 January 2011
Cited by 12 | PDF Full-text (1307 KB) | HTML Full-text | XML Full-text
Abstract
Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. [...] Read more.
Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. Conventional image registration algorithms have proven to be inaccurate, time-consuming, and unfeasible due to image complexity which makes it cumbersome or even impossible to discern the appropriate control points. In this study, we propose a novel method for automatic image registration based on topology (AIRTop) for change detection and multi‑sensor (airborne and spaceborne) fusion. In this algorithm, we first apply image‑processing methods (SURF—Speeded-Up Robust Features) to extract the landmark structures (roads and buildings) and convert them to a features (vector) map. The following stages are applied in GIS (Geographic Information System), where topology rules, which define the permissible spatial relationships between features, are defined. The relationships between features are established by weight-based topological map-matching algorithm (tMM). The suggested algorithm presents a robust method for image registration. The main focus in this study is on scale and image rotation, when the quality of the scanning system is constant. These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. The skew and the anisotropic scaling are assumed to be second-order effects that are covered to some degree by the overall robustness of the sensor. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Satellite-Observed Urbanization Characters in Shanghai, China: Aerosols, Urban Heat Island Effect, and Land–Atmosphere Interactions
Remote Sens. 2011, 3(1), 83-99; doi:10.3390/rs3010083
Received: 16 November 2010 / Revised: 20 December 2010 / Accepted: 4 January 2011 / Published: 7 January 2011
Cited by 17 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
Urbanization reflects how human-activities affect natural climate system. Accurately assessing the urban system by comparing it with the nearby rural regions helps to identify the impacts of urbanization. This work uses the recent satellite observed aerosol, skin temperature, land cover, albedo, cloud [...] Read more.
Urbanization reflects how human-activities affect natural climate system. Accurately assessing the urban system by comparing it with the nearby rural regions helps to identify the impacts of urbanization. This work uses the recent satellite observed aerosol, skin temperature, land cover, albedo, cloud fraction and water vapor measurements to reveal how the city of Shanghai, one of the biggest, dense urban areas in East Asia, affects land surface and atmosphere conditions. In addition, the National Aeronautics and Space Administration (NASA) ground observations from AErosol RObotic NETwork (AERONET) is also used to reveal diurnal, seasonal, and interannual variations of the heavy aerosol load over Shanghai region. Furthermore, Shanghai reduces surface albedo, total column water vapor, cloud fraction and increases land skin temperature than rural region. These observations prove that Shanghai significantly modifies local and regional land surface physical properties as well as physical processes, which lead to the urban heat island effect (UHI). Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Airborne Laser Scanning for the Site Type Identification of Mature Boreal Forest Stands
Remote Sens. 2011, 3(1), 100-116; doi:10.3390/rs3010100
Received: 8 November 2010 / Revised: 17 December 2010 / Accepted: 27 December 2010 / Published: 10 January 2011
Cited by 7 | PDF Full-text (552 KB) | HTML Full-text | XML Full-text
Abstract
In Finland, forest site types are used to assess the need of silvicultural operations and the growth potential of the forests and, therefore, provide important inventory information. This study introduces airborne laser scanner (ALS) data and the k-NN classifier data analysis technique [...] Read more.
In Finland, forest site types are used to assess the need of silvicultural operations and the growth potential of the forests and, therefore, provide important inventory information. This study introduces airborne laser scanner (ALS) data and the k-NN classifier data analysis technique applicable to the site quality assessment of mature forests. Both the echo height and the intensity value percentiles of different echo types of ALS data were used in the analysis. The data are of 274 mature forest stands of different sizes, belonging to five forest site types, varying from very fertile to poor forests, in Koli National Park, eastern Finland. The k-NN classifier was applied with values of k varying from 1 to 5. The best overall classification accuracy achieved for all the forest site types and for a single type, were 58% and 73%, respectively. The conclusion is that when conducting large-scale forest inventories ALS-data based analysis would be a useful technology for the identification of mature boreal site types. However, the technique could still be improved and further studies are needed to ensure its applicability under different local conditions and with data representing earlier stages of stand development. Full article
Open AccessArticle SAR-Based Wind Resource Statistics in the Baltic Sea
Remote Sens. 2011, 3(1), 117-144; doi:10.3390/rs3010117
Received: 16 November 2010 / Revised: 22 December 2010 / Accepted: 31 December 2010 / Published: 11 January 2011
Cited by 34 | PDF Full-text (1093 KB) | HTML Full-text | XML Full-text
Abstract
Ocean winds in the Baltic Sea are expected to power many wind farms in the coming years. This study examines satellite Synthetic Aperture Radar (SAR) images from Envisat ASAR for mapping wind resources with high spatial resolution. Around 900 collocated pairs of [...] Read more.
Ocean winds in the Baltic Sea are expected to power many wind farms in the coming years. This study examines satellite Synthetic Aperture Radar (SAR) images from Envisat ASAR for mapping wind resources with high spatial resolution. Around 900 collocated pairs of wind speed from SAR wind maps and from 10 meteorological masts, established specifically for wind energy in the study area, are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a root mean square error of 1.17 m s−1, bias of −0.25 m s−1, standard deviation of 1.88 m s−1 and correlation coefficient of R2 0.783. Wind directions from a global atmospheric model, interpolated in time and space, are used as input to the geophysical model function CMOD-5 for SAR wind retrieval. Wind directions compared to mast observations show a root mean square error of 6.29° with a bias of 7.75°, standard deviation of 20.11° and R2 of 0.950. The scale and shape parameters, A and k, respectively, from the Weibull probability density function are compared at only one available mast and the results deviate ~2% for A but ~16% for k. Maps of A and k, and wind power density based on more than 1000 satellite images show wind power density values to range from 300 to 800 W m−2 for the 14 existing and 42 planned wind farms. Full article
Open AccessArticle Terrestrial Laser Scanner Resolution: Numerical Simulations and Experiments on Spatial Sampling Optimization
Remote Sens. 2011, 3(1), 167-184; doi:10.3390/rs3010167
Received: 3 December 2010 / Revised: 4 January 2011 / Accepted: 7 January 2011 / Published: 14 January 2011
Cited by 29 | PDF Full-text (1412 KB) | HTML Full-text | XML Full-text
Abstract
An empirical approach is proposed in order to evaluate the largest spot spacing allowing the appropriate resolution to recognize the required surface details in a terrestrial laser scanner (TLS) survey. The suitable combination of laser beam divergence and spot spacing for the [...] Read more.
An empirical approach is proposed in order to evaluate the largest spot spacing allowing the appropriate resolution to recognize the required surface details in a terrestrial laser scanner (TLS) survey. The suitable combination of laser beam divergence and spot spacing for the effective scanning angular resolution has been studied by numerical simulation experiments with an artificial target taken from distances between 25 m and 100 m, and observations of real surfaces. The tests have been performed by using the Optech ILRIS-3D instrument. Results show that the discrimination of elements smaller than a third of the beam divergence (D) is not possible and that the ratio between the used spot-spacing (ss) and the element size (TS) is linearly related to the acquisition range. The zero and first order parameters of this linear trend are computed and used to solve for the maximum efficient ss at defined ranges for a defined TS. Despite the fact that the parameters are obtained for the Optech ILRIS-3D scanner case, and depend on its specific technical data and performances, the proposed method has general validity and it can be used to estimate the corresponding parameters for other instruments. The obtained results allow the optimization of a TLS survey in terms of acquisition time and surface details recognition. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
Open AccessArticle The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images
Remote Sens. 2011, 3(1), 185-202; doi:10.3390/rs3010185
Received: 16 November 2010 / Revised: 22 December 2010 / Accepted: 29 December 2010 / Published: 18 January 2011
Cited by 45 | PDF Full-text (1617 KB) | HTML Full-text | XML Full-text
Abstract
The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the [...] Read more.
The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the MOD13Q1 product and TM/Landsat-5 images followed by aerial survey and field inspection. In the 2009/2010 crop year, 6.3 thousand ha of soybean (0.25% of the total deforestation) were identified in areas deforested during the moratorium period. The use of remote sensing satellite images reduced by almost 80% the need for aerial survey to identify soybean planting and allowed monitoring of all deforested areas greater than 25 ha. It is still premature to attribute the recent low deforestation rates in the Amazon biome to the Soy Moratorium, but the initiative has certainly exerted an inhibitory effect on the soybean frontier expansion in this biome. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)
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Review

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Open AccessReview Detection and Measurement of Snowfall from Space
Remote Sens. 2011, 3(1), 145-166; doi:10.3390/rs3010145
Received: 21 November 2010 / Revised: 5 January 2011 / Accepted: 5 January 2011 / Published: 11 January 2011
Cited by 11 | PDF Full-text (708 KB) | HTML Full-text | XML Full-text
Abstract
Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that [...] Read more.
Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)

Other

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Open AccessRetraction Retraction: Najafabadi, A.T. Study of Soil Scattering Coefficients in Combination with Diesel for a Slightly Rough Surface in the Cj Band. Remote Sens. 2010, 2, 115-123.
Remote Sens. 2011, 3(1), 41; doi:10.3390/rs3010041
Received: 5 January 2011 / Published: 5 January 2011
PDF Full-text (127 KB) | HTML Full-text | XML Full-text
Abstract
It has been brought to our attention by a reader of Remote Sensing that substantial portions of this article [1] have been copied verbatim from an earlier published thesis without credit. After confirming this plagiarism with the authors, we have determined that [...] Read more.
It has been brought to our attention by a reader of Remote Sensing that substantial portions of this article [1] have been copied verbatim from an earlier published thesis without credit. After confirming this plagiarism with the authors, we have determined that indeed this manuscript clearly violates our policy on originality of all material submitted for publication and the generally accepted ethics of scientific publication. Consequently, the Editorial Team and Publisher have determined that it should be retracted. We apologize for any inconvenience this may cause. Full article

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