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Remote Sens., Volume 1, Issue 3 (September 2009), Pages 122-619

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Open AccessArticle Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications
Remote Sens. 2009, 1(3), 122-143; doi:10.3390/rs1030122
Received: 6 May 2009 / Revised: 11 June 2009 / Accepted: 23 June 2009 / Published: 3 July 2009
Cited by 29 | PDF Full-text (725 KB) | HTML Full-text | XML Full-text
Abstract
Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data
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Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data have been acquired by different instruments. For this reason, it is interesting to study those indicators that do not rely completely on radiometric values. In this work a new approach is proposed based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to establish the limits of their applicability and their sensitivity to the occurred changes. Initial results are promising and suggest similarity measures as possiblechange detectors in multi-sensor configurations. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Radiometric Calibration of Terrestrial Laser Scanners with External Reference Targets
Remote Sens. 2009, 1(3), 144-158; doi:10.3390/rs1030144
Received: 2 June 2009 / Revised: 29 June 2009 / Accepted: 3 July 2009 / Published: 3 July 2009
Cited by 64 | PDF Full-text (481 KB) | HTML Full-text | XML Full-text
Abstract
The intensity data produced by terrestrial laser scanners has become a topic of increasing interest in the remote sensing community. We present a case study of radiometric calibration for two phase-shift continuous wave (CW) terrestrial scanners and discuss some major issues in correcting
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The intensity data produced by terrestrial laser scanners has become a topic of increasing interest in the remote sensing community. We present a case study of radiometric calibration for two phase-shift continuous wave (CW) terrestrial scanners and discuss some major issues in correcting and applying the intensity data, and a practical calibration scheme based on external reference targets. There are differences in the operation of detectors of different (although similar type) instruments, and the detector effects must be known in order to calibrate the intensity data into values representing the target reflectance. It is, therefore, important that the effects of distance and target reflectance on the recorded intensity are carefully studied before using the intensity data from any terrestrial laser scanner. Full article
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Open AccessArticle Modeling Net Ecosystem Exchange for Grassland in Central Kazakhstan by Combining Remote Sensing and Field Data
Remote Sens. 2009, 1(3), 159-183; doi:10.3390/rs1030159
Received: 1 June 2009 / Revised: 7 June 2009 / Accepted: 30 June 2009 / Published: 6 July 2009
Cited by 14 | PDF Full-text (331 KB) | HTML Full-text | XML Full-text
Abstract
Carbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 × 1 km were calculated as a product of photosynthetically active radiation
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Carbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 × 1 km were calculated as a product of photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (fPAR), the light use efficiency (LUE) and ecosystem respiration (Re). The PAR is obtained from a mathematical model incorporating Earth-Sun distance, solar inclination, solar elevation angle, geographical position and cloudiness information of localities. The fPAR was measured in field using hemispherical photography and was extrapolated to each pixel by combination with the Normalized Difference Vegetation Index (NDVI) obtained by the Vegetation instrument on board the Satellite Pour l’Observation de la Terra (SPOT) satellite. Gross Primary Production (GPP) of the aboveground and belowground vegetation of 14 sites along a 230 km west-east transect within the study region were determined at the peak of growing season in different land cover types and linearly related to the amount of PAR absorbed by vegetation (APAR). The product of this relationship is LUE = 0.61 and 0.97 g C/MJ APAR for short grassland and steppe, respectively. The Re is estimated using complex models driven by climatic data. Growing season carbon sequestration was calculated for the modelling year of 2004. Overall, the short grassland was a net carbon sink, whereas the steppe was carbon neutral. The evaluation of the modelled carbon sequestration against independent reference data sets proved high accuracy of the estimations. Full article
(This article belongs to the Special Issue Land Surface Fluxes)
Open AccessArticle Comparison of Topographic Correction Methods
Remote Sens. 2009, 1(3), 184-196; doi:10.3390/rs1030184
Received: 8 June 2009 / Revised: 30 June 2009 / Accepted: 3 July 2009 / Published: 6 July 2009
Cited by 82 | PDF Full-text (1300 KB) | HTML Full-text | XML Full-text
Abstract
A comparison of topographic correction methods is conducted for Landsat-5 TM, Landsat-7 ETM+, and SPOT-5 imagery from different geographic areas and seasons. Three successful and known methods are compared: the semi-empirical C correction, the Gamma correction depending on the incidence and exitance angles,
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A comparison of topographic correction methods is conducted for Landsat-5 TM, Landsat-7 ETM+, and SPOT-5 imagery from different geographic areas and seasons. Three successful and known methods are compared: the semi-empirical C correction, the Gamma correction depending on the incidence and exitance angles, and a modified Minnaert approach. In the majority of cases the modified Minnaert approach performed best, but no method is superior in all cases. Full article
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Open AccessArticle Canscan — An Algorithm for Automatic Extraction of Canyons
Remote Sens. 2009, 1(3), 197-209; doi:10.3390/rs1030197
Received: 14 May 2009 / Revised: 22 June 2009 / Accepted: 23 June 2009 / Published: 7 July 2009
Cited by 2 | PDF Full-text (493 KB) | HTML Full-text | XML Full-text
Abstract
This article introduces a novel algorithm for automatic extraction of canyons which was developed in the Department of the Remote Sensing and Land Information Systems at the University of Freiburg (FELIS). The algorithm detects canyons by means of user-defined dimension parameters and elevation
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This article introduces a novel algorithm for automatic extraction of canyons which was developed in the Department of the Remote Sensing and Land Information Systems at the University of Freiburg (FELIS). The algorithm detects canyons by means of user-defined dimension parameters and elevation information provided in a Digital Terrain Model (DTM). The extraction procedure is based on the geometric interpretation of canyons through which the input dimension parameters are identified. The dimension parameters are used for identifying cross-sections across DTM on the basis of which canyons are extracted. In addition to the detailed description of the extraction algorithm, this paper includes the results obtained in test regions as well as a thorough discussion. Full article
Open AccessArticle An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery
Remote Sens. 2009, 1(3), 243-265; doi:10.3390/rs1030243
Received: 10 June 2009 / Revised: 23 June 2009 / Accepted: 24 June 2009 / Published: 9 July 2009
Cited by 29 | PDF Full-text (461 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for
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This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA). The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC) classifications of a Landsat Thematic Mapper (TM) image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR) Digital Orthophoto Quarter Quad (DOQQ) data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data. Full article
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Open AccessArticle Deriving Ocean Surface Drift Using Multiple SAR Sensors
Remote Sens. 2009, 1(3), 266-277; doi:10.3390/rs1030266
Received: 3 June 2009 / Revised: 9 July 2009 / Accepted: 9 July 2009 / Published: 13 July 2009
Cited by 9 | PDF Full-text (693 KB) | HTML Full-text | XML Full-text
Abstract
Tracking and monitoring ocean features which have short coherent time periods from sequential satellite images requires that the images have both very high spatial resolutions and short temporal sampling intervals (i.e., repeated cycles). Satellite images from a single sensor in a polar-orbiting satellite
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Tracking and monitoring ocean features which have short coherent time periods from sequential satellite images requires that the images have both very high spatial resolutions and short temporal sampling intervals (i.e., repeated cycles). Satellite images from a single sensor in a polar-orbiting satellite usually cannot meet these requirements since high spatial resolution of the sensor may result in relatively long temporal sampling interval and vice versa, such as the case of Synthetic Aperture Radar (SAR). This paper presents a new multi-sensor approach to overcome the long temporal sampling interval associated with a single SAR sensor while taking advantage of high spatial resolution of SAR images for the application of ocean feature tracking.Currently, there are two SAR sensors on different satellites, the European Remote Sensing Satellite-2 (ERS-2) and the ENVIronment SATellite (ENVISAT), having acquisition time offset around 28 minutes with almost exactly the same path.That is, ERS-2 is following ENVISAT with a 28-minutes delay, which is a good time-scale for ocean mesoscale feature tracking.A pair of SAR images from ERS-2 and ENVISAT collected on April 27, 2005 has been chosen to track ocean surface features by using wavelet analysis. As demonstrated in the case studies, this technique is robust and capable to derive ocean surface drift near an oil slick and around a big eddy in the South China Sea (SCS). Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle A Highly Accurate Classification of TM Data through Correction of Atmospheric Effects
Remote Sens. 2009, 1(3), 278-299; doi:10.3390/rs1030278
Received: 11 May 2009 / Revised: 29 June 2009 / Accepted: 8 July 2009 / Published: 15 July 2009
Cited by 6 | PDF Full-text (553 KB) | HTML Full-text | XML Full-text
Abstract
Atmospheric correction impacts on the accuracy of satellite image-based land cover classification are a growing concern among scientists. In this study, the principle objective was to enhance classification accuracy by minimizing contamination effects from aerosol scattering in Landsat TM images due to the
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Atmospheric correction impacts on the accuracy of satellite image-based land cover classification are a growing concern among scientists. In this study, the principle objective was to enhance classification accuracy by minimizing contamination effects from aerosol scattering in Landsat TM images due to the variation in solar zenith angle corresponding to cloud-free earth targets. We have derived a mathematical model for aerosols to compute and subtract the aerosol scattering noise per pixel of different vegetation classes from TM images of Nicolet in north-eastern Wisconsin. An algorithm in C++ has been developed with iterations to simulate, model, and correct for the solar zenith angle influences on scattering. Results from a supervised classification with corrected TM images showed increased class accuracy for land cover types over uncorrected images. The overall accuracy of the supervised classification was improved substantially (between 13% and 18%). The z-score shows significant difference between the corrected data and the raw data (between 4.0 and 12.0). Therefore, the atmospheric correction was essential for enhancing the image classification. Full article
Open AccessArticle Numerical Simulation of the Full-Polarimetric Emissivity of Vines and Comparison with Experimental Data
Remote Sens. 2009, 1(3), 300-317; doi:10.3390/rs1030300
Received: 30 April 2009 / Revised: 2 July 2009 / Accepted: 11 July 2009 / Published: 20 July 2009
Cited by 3 | PDF Full-text (776 KB) | HTML Full-text | XML Full-text
Abstract
Surface soil moisture is a key variable needed to understand and predict the climate. L-band microwave radiometry seems to be the best technique to remotely measure the soil moisture content, since the influence of other effects such as surface roughness and vegetation is
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Surface soil moisture is a key variable needed to understand and predict the climate. L-band microwave radiometry seems to be the best technique to remotely measure the soil moisture content, since the influence of other effects such as surface roughness and vegetation is comparatively small. This work describes a numerical model developed to efficiently compute the four elements of the Stokes emission vector (Th, Tv, TU and TV) of vegetation-covered soils at low microwave frequencies, as well as the single-scattering albedo and the extinction coefficient of the vegetation layer over a wide range of incidence angles. A comparison with L-band (1.400–1.427 MHz) experimental radiometric data gathered during the SMOS REFLEX 2003 field experiment over vines is presented and discussed. The measured and simulated emissivities at vertical polarization agree very well. However, at horizontal polarization there is some disagreement introduced by the soil emission model. Important radiometric parameters, such as the albedo, the attenuation and the transmissivity are computed and analyzed in terms of their values and trends, as well as their dependence on the observation and scene parameters. It is found that the vegetation attenuation is mainly driven by the presence of branches and leaves, while the albedo is mainly driven by the branches. The comparison of the simulated parameters with the values obtained by fitting the experimental data with the t-w model is very satisfactory. Full article
Open AccessArticle Remote Sensing and Mapping of Tamarisk along the Colorado River, USA: A Comparative Use of Summer-Acquired Hyperion, Thematic Mapper and QuickBird Data
Remote Sens. 2009, 1(3), 318-329; doi:10.3390/rs1030318
Received: 17 July 2009 / Revised: 28 July 2009 / Accepted: 28 July 2009 / Published: 31 July 2009
Cited by 25 | PDF Full-text (274 KB) | HTML Full-text | XML Full-text
Abstract
Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De
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Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De Beque, Colorado, USA. As a result of highly correlated reflectance among the spectral bands provided by each sensor, relatively standard image analysis methods were employed. Multispectral data at high spatial resolution (QB, 2.5 m Ground Spatial Distance or GSD) proved more effective in tamarisk delineation than either multispectral (TM5) or hyperspectral (Hyperion) data at moderate spatial resolution (30 m GSD). Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
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Open AccessArticle Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement
Remote Sens. 2009, 1(3), 330-344; doi:10.3390/rs1030330
Received: 2 July 2009 / Revised: 14 July 2009 / Accepted: 23 July 2009 / Published: 31 July 2009
Cited by 51 | PDF Full-text (432 KB) | HTML Full-text | XML Full-text
Abstract
Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information still remains a challenge that depends on many factors such as complexity of landscape, the remote sensing data selected, image processing and classification methods, etc. The aim
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Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information still remains a challenge that depends on many factors such as complexity of landscape, the remote sensing data selected, image processing and classification methods, etc. The aim of this paper is to extract reliable LULC information from Landsat imageries of the Lower Hunter region of New South Wales, Australia. The classical maximum likelihood classifier (MLC) was first applied to classify Landsat-MSS of 1985 and Landsat-TM of 1995 and 2005. The major LULC identified were Woodland, Pasture/scrubland, Vineyard, Built-up and Water-body. By applying post-classification correction (PCC) using ancillary data and knowledge-based logic rules the overall classification accuracy was improved from about 72% to 91% for 1985 map, 76% to 90% for 1995 map and 79% to 87% for 2005 map. The improved overall Kappa statistics due to PCC were 0.88 for the 1985 map, 0.86 for 1995 and 0.83 for 2005. The PCC maps, assessed by McNemar’s test, were found to have much higher accuracy in comparison to their counterpart MLC maps. The overall improvement in classification accuracy of the LULC maps is significant in terms of their potential use for land change modelling of the region. Full article
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Open AccessArticle A Simple Algorithm for Large-Scale Mapping of Evergreen Forests in Tropical America, Africa and Asia
Remote Sens. 2009, 1(3), 355-374; doi:10.3390/rs1030355
Received: 28 April 2009 / Revised: 30 May 2009 / Accepted: 3 August 2009 / Published: 12 August 2009
Cited by 17 | PDF Full-text (1931 KB) | HTML Full-text | XML Full-text
Abstract
The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we
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The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution. Full article
Open AccessArticle Operational Ship Monitoring System Based on Synthetic Aperture Radar Processing
Remote Sens. 2009, 1(3), 375-392; doi:10.3390/rs1030375
Received: 8 June 2009 / Revised: 30 July 2009 / Accepted: 4 August 2009 / Published: 14 August 2009
Cited by 25 | PDF Full-text (2069 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a Ship Monitoring System (SIMONS) working with Synthetic Aperture Radar (SAR) images. It is able to infer ship detection and classification information, and merge the results with other input channels, such as polls from the Automatic Identification System (AIS). Two
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This paper presents a Ship Monitoring System (SIMONS) working with Synthetic Aperture Radar (SAR) images. It is able to infer ship detection and classification information, and merge the results with other input channels, such as polls from the Automatic Identification System (AIS). Two main stages can be identified, namely: SAR processing and data dissemination. The former has three independent modules, which are related to Coastline Detection (CD), Ship Detection (SD) and Ship Classification (SC). The later is solved via an advanced web interface, which is compliant with the OpenSource standards fixed by the Open Geospatial Consortium (OGC). SIMONS has been designed to be a modular, unsupervised and reliable system that meets Near-Real Time (NRT) delivery requirements. From data ingestion to product delivery, the processing chain is fully automatic accepting ERS and ENVISAT formats. SIMONS has been developed by GMV Aerospace, S.A. with three main goals, namely: 1) To limit the dependence on the ancillary information provided by systems such as AIS. 2) To achieve the maximum level of automatism and restrict human manipulation. 3) To limit the error sources and their propagation. Spanish authorities have validated SIMONS. The results have been satisfactory and have confirmed that the system is useful for improving decision making. For single-polarimetric images with a resolution of 30 m, SIMONS permits the location of ships larger than 40 m with a classification ratio around 50% of positive matches. These values are expected to be improved with SAR data from new sensors. In the paper, the performance of SD and SC modules is assessed by cross-check of SAR data with AIS reports. Full article
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Open AccessArticle Potential Species Distribution of Balsam Fir Based on the Integration of Biophysical Variables Derived with Remote Sensing and Process-Based Methods
Remote Sens. 2009, 1(3), 393-407; doi:10.3390/rs1030393
Received: 14 July 2009 / Revised: 13 August 2009 / Accepted: 13 August 2009 / Published: 17 August 2009
Cited by 9 | PDF Full-text (595 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we present a framework for modelling potential species distribution (PSD) of balsam fir [bF; Abies balsamea (L.) Mill.] as a function of landscape-level descriptions of: (i) growing degree days (GDD: a temperature related index), (ii) land-surface wetness, (iii) incident photosynthetically
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In this paper we present a framework for modelling potential species distribution (PSD) of balsam fir [bF; Abies balsamea (L.) Mill.] as a function of landscape-level descriptions of: (i) growing degree days (GDD: a temperature related index), (ii) land-surface wetness, (iii) incident photosynthetically active radiation (PAR), and (iv) tree habitat suitability. GDD and land-surface wetness are derived primarily from remote sensing data acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite. PAR is calculated with an existing spatial model of solar radiation. Raster-based calculations of habitat suitability and PSD are obtained by multiplying normalized values of species environmental-response functions (one for each environmental variable) parameterized for balsam fir. As a demonstration of the procedure, we apply the calculations to a high bF-content area in northwest New Brunswick, Canada, at 250-m resolution. Location of medium-to-high habitat suitability values (i.e., >0.50) and actual forests, with >50% bF, matched on average 92% of the time. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
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Open AccessCommunication Evaluating Principal Components Analysis for Identifying Optimal Bands Using Wetland Hyperspectral Measurements From the Great Lakes, USA
Remote Sens. 2009, 1(3), 408-417; doi:10.3390/rs1030408
Received: 13 May 2009 / Revised: 12 August 2009 / Accepted: 12 August 2009 / Published: 18 August 2009
Cited by 11 | PDF Full-text (398 KB) | HTML Full-text | XML Full-text
Abstract
Mapping species composition is a focus of the wetland science community as this information will substantially enhance assessment and monitoring abilities. Hyperspectral remote sensing has been utilized as a cost-efficient approach. While hyperspectral instruments can record hundreds of contiguous narrow bands, much of
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Mapping species composition is a focus of the wetland science community as this information will substantially enhance assessment and monitoring abilities. Hyperspectral remote sensing has been utilized as a cost-efficient approach. While hyperspectral instruments can record hundreds of contiguous narrow bands, much of the data are redundant and/or provide no increase in utility for distinguishing objects. Knowledge of the optimal bands allows users to efficiently focus on bands that provide the most information and several data reduction tools are available. The objective of this Communication was to evaluate Principal Components Analysis (PCA) for identifying optimal bands to discriminate wetland plant species. In-situ hyperspectral reflectance measurements were obtained for thirty-five species in two diverse Great Lakes wetlands. PCA was executed on a suite of categories based on botanical plant/substrate characteristics and spectral configuration schemes. Results showed that the data dependency of PCA makes it a poor, stand alone tool for selecting optimal wavelengths. PCA does not allow diagnostic comparison across sites and wavelengths identified by PCA do not necessarily represent wavelengths that indicate biophysical attributes of interest. Further, narrow bands captured by hyperspectral sensors need to be substantially re-sampled and/or smoothed in order for PCA to identify useful information. Full article
Open AccessArticle Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery
Remote Sens. 2009, 1(3), 418-444; doi:10.3390/rs1030418
Received: 15 May 2009 / Revised: 12 August 2009 / Accepted: 13 August 2009 / Published: 18 August 2009
Cited by 36 | PDF Full-text (855 KB) | HTML Full-text | XML Full-text
Abstract
Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity have many useful applications. Developing alternative methods for making estimates of these economic activities may prove to be useful when other measures are of suspect accuracy or unavailable.
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Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity have many useful applications. Developing alternative methods for making estimates of these economic activities may prove to be useful when other measures are of suspect accuracy or unavailable. This research explores the potential for estimating the formal and informal economy for Mexico using known relationships between the spatial patterns of nighttime satellite imagery and economic activity in the United States (U.S.). Regression models have been developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (AGSP) for the U.S. states. These regression parameters derived from the regression models of the U.S. were ‘blindly’ applied to Mexico to estimate the Estimated Gross State Income (EGSI) at the sub-national level and the Estimated Gross Domestic Income (EGDI) at the national level. Comparison of the EGDI estimate of Mexico against the official Gross National Income (GNI) estimate suggests that the magnitude of Mexico’s informal economy and the inflow of remittances are 150 percent larger than their existing official estimates in the GNI. Full article
Open AccessArticle Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas
Remote Sens. 2009, 1(3), 445-465; doi:10.3390/rs1030445
Received: 20 May 2009 / Revised: 10 July 2009 / Accepted: 3 August 2009 / Published: 20 August 2009
Cited by 13 | PDF Full-text (752 KB)
Abstract
Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would
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Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use. Full article
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Open AccessArticle Evaluation and Normalization of Cloud Obscuration Related BRDF Effects in Field Spectroscopy
Remote Sens. 2009, 1(3), 496-518; doi:10.3390/rs1030496
Received: 1 July 2009 / Revised: 7 August 2009 / Accepted: 11 August 2009 / Published: 25 August 2009
Cited by 4 | PDF Full-text (569 KB) | HTML Full-text | XML Full-text
Abstract
The impact of target bidirectional reflectance in dual field of view spectroscopy was described and quantified using field measurements and ray-tracing simulations. A data-driven normalization method was developed to convert reflectance factors under cloud obscured conditions into clear sky reflectance by decomposing the
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The impact of target bidirectional reflectance in dual field of view spectroscopy was described and quantified using field measurements and ray-tracing simulations. A data-driven normalization method was developed to convert reflectance factors under cloud obscured conditions into clear sky reflectance by decomposing the target bidirectional reflectance into an isotropic target-specific component and a group-specific bidirectional component. An evaluation on tree, grass and gravel targets suggests a reduction in relative reflectance error obtained by normalization from 15% to less than 5% between 400 and 1800 nm. At higher wavelengths a decreased signal-to-noise ratio increases the errors. Full article
Open AccessArticle Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data
Remote Sens. 2009, 1(3), 519-533; doi:10.3390/rs1030519
Received: 23 June 2009 / Revised: 14 August 2009 / Accepted: 21 August 2009 / Published: 31 August 2009
Cited by 35 | PDF Full-text (322 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for
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In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for our study area along the Arkansas River in Colorado. Satellite scenes were selected for April, May, June, August, September, and October and tested in single-scene and time-series analyses. The best model was a time-series analysis fit with all spectral variables, which had an AUC = 0.96, overall accuracy = 0.90, and Kappa = 0.79. The top predictor variables were June tasselled cap wetness, September tasselled cap wetness, and October band 3. A second time-series analysis, where the variables that were highly correlated and demonstrated low predictive strengths were removed, was the second best model. The third best model was the October single-scene analysis. Our results may prove to be an effective approach for mapping Tamarix sp., which has been a challenge for resource managers. Of equal importance is the positive performance of the Maxent model in handling remotely sensed datasets. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Open AccessArticle Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches
Remote Sens. 2009, 1(3), 534-556; doi:10.3390/rs1030534
Received: 29 July 2009 / Revised: 26 August 2009 / Accepted: 28 August 2009 / Published: 3 September 2009
Cited by 32 | PDF Full-text (2734 KB) | HTML Full-text | XML Full-text
Abstract
This paper examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on 33-years of time series data compiled from satellite images. Along with new developments within the city fringes and rural villages
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This paper examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on 33-years of time series data compiled from satellite images. Along with new developments within the city fringes and rural villages in the valley, shifts in the natural environment and newly developed socioeconomic strains between residents are emerging. A highly dynamic spatial pattern of urbanization is observed in the valley. Urban built-up areas had a slow trend of growth in the 1960s and 1970s but have grown rapidly since the 1980s. The urbanization process has developed fragmented and heterogeneous land use combinations in the valley. However, the refill type of development process in the city core and immediate fringe areas has shown a decreasing trend in the neighborhood distances between land use patches, and an increasing trend towards physical connectedness, which indicates a higher probability of homogenous landscape development in the upcoming decades. Full article
Open AccessArticle Aerosol Optical Depth Measured at Different Coastal Boundary Layers and Its Links with Synoptic-Scale Features
Remote Sens. 2009, 1(3), 557-576; doi:10.3390/rs1030557
Received: 8 June 2009 / Revised: 25 June 2009 / Accepted: 1 September 2009 / Published: 4 September 2009
Cited by 4 | PDF Full-text (1723 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the results of measurements of aerosol optical properties which were made between 2006 and 2008 within the framework of various international projects in different locations such as Spitsbergen, northern Norway and Crete. The investigations were made under different baric topography
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This paper presents the results of measurements of aerosol optical properties which were made between 2006 and 2008 within the framework of various international projects in different locations such as Spitsbergen, northern Norway and Crete. The investigations were made under different baric topography conditions and in various seasons of the year which facilitated the investigations of spatial and temporal dependencies between upper troposphere mass state and spectral variations of aerosol properties. The results of aerosol optical depth (AOD) measurements showed significant episodes during which jet stream events (300 hPa surface) over the Arctic were present. The mean spectral characteristics of AOD from “before” and “after” the event differ by 0.14 versus the “during” phase of the episode. The macrometeorological relative topography charts shown also the relationships between the 500 hPa, close sea-level pressure SLP (1,000 hPa) charts surfaces and the attenuation caused by aerosol scattering and absorption in vertical profiles during the afternoon hours. Full article
Open AccessArticle Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images
Remote Sens. 2009, 1(3), 577-605; doi:10.3390/rs1030577
Received: 21 July 2009 / Revised: 17 August 2009 / Accepted: 2 September 2009 / Published: 10 September 2009
Cited by 52 | PDF Full-text (232 KB) | HTML Full-text | XML Full-text
Abstract
The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images.
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The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images. The analysis is based on a literature research and a questionnaire submitted to various interest groups related to the photogrammetric process. An important contribution to this paper is a characterization of the photogrammetric image acquisition and image product generation systems. The questionnaire revealed many weaknesses in current processes, but the future prospects of radiometrically quantitative photogrammetry are promising. Full article
Open AccessArticle Accounting for Uncertainties of the TRMM Satellite Estimates
Remote Sens. 2009, 1(3), 606-619; doi:10.3390/rs1030606
Received: 12 August 2009 / Revised: 8 September 2009 / Accepted: 8 September 2009 / Published: 11 September 2009
Cited by 30 | PDF Full-text (2387 KB) | HTML Full-text | XML Full-text
Abstract
Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related
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Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges. Full article
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Review

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Open AccessReview Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques
Remote Sens. 2009, 1(3), 210-242; doi:10.3390/rs1030210
Received: 17 June 2009 / Revised: 1 July 2009 / Accepted: 2 July 2009 / Published: 7 July 2009
Cited by 51 | PDF Full-text (425 KB) | HTML Full-text | XML Full-text
Abstract
The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively
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The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches. Full article
Open AccessReview Ultrawideband Microwave Sensing and Imaging Using Time-Reversal Techniques: A Review
Remote Sens. 2009, 1(3), 466-495; doi:10.3390/rs1030466
Received: 27 May 2009 / Revised: 13 July 2009 / Accepted: 10 August 2009 / Published: 24 August 2009
Cited by 36 | PDF Full-text (1131 KB) | HTML Full-text | XML Full-text
Abstract
This paper provides an overview of some time-reversal (TR) techniques for remote sensing and imaging using ultrawideband (UWB) electromagnetic signals in the microwave and millimeter wave range. The TR techniques explore the TR invariance of the wave equation in lossless and stationary media.
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This paper provides an overview of some time-reversal (TR) techniques for remote sensing and imaging using ultrawideband (UWB) electromagnetic signals in the microwave and millimeter wave range. The TR techniques explore the TR invariance of the wave equation in lossless and stationary media. They provide superresolution and statistical stability, and are therefore quite useful for a number of remote sensing applications. We first discuss the TR concept through a prototypal TR experiment with a discrete scatterer embedded in continuous random media. We then discuss a series of TR-based imaging algorithms employing UWB signals: DORT, space-frequency (SF) imaging and TR-MUSIC. Finally, we consider a dispersion/loss compensation approach for TR applications in dispersive/lossy media, where TR invariance is broken. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessLetter Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover
Remote Sens. 2009, 1(3), 345-354; doi:10.3390/rs1030345
Received: 7 July 2009 / Revised: 17 July 2009 / Accepted: 17 July 2009 / Published: 3 August 2009
Cited by 121 | PDF Full-text (970 KB) | HTML Full-text | XML Full-text
Abstract
Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images
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Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach is inexpensive and allows Internet users from any region of the world to get involved in this global validation exercise. The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g., to determine the potential of additional agricultural land available to grow crops in Africa), volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved hybrid global land cover map. Full article

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