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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of

Issue 2 • Date June 2011

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  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing publication information

    Publication Year: 2011 , Page(s): C2
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  • Table of contents

    Publication Year: 2011 , Page(s): 249 - 250
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  • Introduction to the Special Section on the Fifth International Workshop on Multi-temporal Imagery Analysis

    Publication Year: 2011 , Page(s): 251
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  • Monitoring Landscape Change for LANDFIRE Using Multi-Temporal Satellite Imagery and Ancillary Data

    Publication Year: 2011 , Page(s): 252 - 264
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2262 KB) |  | HTML iconHTML  

    LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applications. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three different approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2)incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spectroradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community. View full abstract»

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  • A Conceptual Model for Multi-Temporal Landscape Monitoring in an Object-Based Environment

    Publication Year: 2011 , Page(s): 265 - 271
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (947 KB) |  | HTML iconHTML  

    Remote sensing plays a critical role in contemporary monitoring programs, but our strategies for processing these data using automated procedures are not always reliable. In particular, the task of separating real from spurious changes remains problematic, especially in an object-based environment where differential errors in classification quality, spatial registration, scene illumination, resolution, and object delineation have forced some operators to adopt labor-intensive visual-interpretation strategies, or employ manual interaction on an object-by-object basis. In this paper, we present an updated summary of our new disturbance-inventory approach to land-cover monitoring that combines object-based classification and change-detection strategies with boundary-conditioning routines designed to maximize the spatial and thematic integrity of the finished products. With this approach, the final maps are only altered in regions of confirmed change, and spurious gaps, slivers, stretches, and encroachments are avoided. The approach constitutes an innovative, efficient, and transparent framework that can handle all the basic landscape dynamics, including feature appearance, disappearance, succession, expansion, and shrinkage, without the need for manual editing. View full abstract»

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  • Stepwise Automated Pixel-Based Generation of Time Series Using Ranked Data Quality Indicators

    Publication Year: 2011 , Page(s): 272 - 280
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1354 KB) |  | HTML iconHTML  

    High-quality time series of remote sensing data are needed for long-term global change studies. Since newer sensors such as MODIS provide pixel-level data quality indicators, these datasets can be employed to filter time series and interpolate invalid data with statistical or contextual methodologies. This study presents a novel automated technique for time-series generation using ranked data quality indicators and stepwise temporal interpolation of short data gaps. The methodology focuses exclusively on the temporal characteristics of each pixel as they would have been observed with good observations. The methodology is exemplarily applied to MODIS NDVI data of the entire country of Germany. Multiple time series, also those generated with other techniques, were compared with a reference set to evaluate the performance of selected parameters. The automated time-series generation approach is less time consuming, and, if parameters are specified with care, the quality is comparable to other approaches. View full abstract»

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  • The Use of Historical Imagery in the Remediation of an Urban Hazardous Waste Site

    Publication Year: 2011 , Page(s): 281 - 291
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4377 KB) |  | HTML iconHTML  

    The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity. View full abstract»

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  • An Environmental Normal of Vegetation Vigour for the Northern Great Plains

    Publication Year: 2011 , Page(s): 292 - 302
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1141 KB) |  | HTML iconHTML  

    Normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) satellites were used to create a spatially detailed baseline of vegetation conditions in the northern Great Plains of North America. An environmental normal of vegetation vigour was created from NDVI means and standard deviations calculated over 22 years for each 10-day period during the growing season. Significant vegetation vigour anomalies - differences from the normal - were subsequently identified and associated with concurrent temperature and precipitation data. Growing season vegetation vigour anomalies were found to be most dependent on weather patterns from the previous spring, and in some cases, from the preceding summer. Regions with the densest and most diverse vegetation covers were impacted the most by temperature and precipitation. Statistically significant increases in vegetation vigour over the 22-year period were measured across the entire study area, with the exception of the vegetation communities with the sparsest ground covers. This increase was matched by a similarly significant rise in annual NDVI variability for all of the phenologies. The changes in vegetative cover leading to the increase in NDVI values may be related to warmer winter temperatures. View full abstract»

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  • Mapping Impervious Cover Using Multi-Temporal MODIS NDVI Data

    Publication Year: 2011 , Page(s): 303 - 309
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1522 KB) |  | HTML iconHTML  

    Mapping impervious surfaces over regional or continental scale study areas with high spatial resolution imagery is difficult due to the cost and time involved in processing the large number of images required. This study investigated the benefits of using the coarse spatial resolution, high temporal resolution MODIS sensor to produce impervious surface maps. MODIS NDVI data for multiple years were analyzed with two multi-temporal image analysis methods: the Sequential Maximum Angle Convex Cone and Linear Spectral Unmixing. Impervious surface maps were generated and compared with a set of reference data and a Landsat-derived impervious cover map. The mapping accuracies for the algorithms used were generally good, particularly for the LSU approach, which was able to identify areas with 50-60% impervious cover at 77% accuracy and areas with a cumulative impervious cover of 50% or greater at 80% accuracy. The methods presented in this paper have potential for mapping impervious cover over large areas where the use of higher spatial resolution data is impracticable. View full abstract»

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  • TimeStats: A Software Tool for the Retrieval of Temporal Patterns From Global Satellite Archives

    Publication Year: 2011 , Page(s): 310 - 317
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4045 KB) |  | HTML iconHTML  

    TimeStats is a free tool for the analysis of multitemporal equidistant georeferenced remote sensing data archives, such as MODIS, AVHRR, MERIS and SPOT-Vegetation. Key features include parametric and non-parametric methods for trend detection, generalized-least square regression, distributed lag models, cross spectra analysis, windowed trend and frequency analysis, continuous wavelet transform, empirical mode decomposition and extraction of phenological indexes (peaking times and magnitudes). The intension of this paper is to demonstrate how these methods can be used for data mining in long-term remote sensing data archives to retrieve transient, cyclic and stochastic components and to regress autocorrelated series in a statistical meaningful way to each other. TimeStats is programmed in the Interactive Data Language® (IDL) and freely distributed with the IDL virtual machine®. Generated raster output files are saved in the standard ENVI® format with appropriate header files and are portable to common geospatial satellite imaging processing software packages. Software binaries and an extended user manual can be obtained from the author. View full abstract»

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  • Development and Application of Multi-Temporal Colorimetric Transformation to Monitor Vegetation in the Desert Locust Habitat

    Publication Year: 2011 , Page(s): 318 - 326
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1265 KB) |  | HTML iconHTML  

    The Desert Locust (Schistocerca gregaria) is the most feared of all the locusts worldwide. Satellite imagery can provide a continuous overview of ecological conditions (i.e., vegetation, soil moisture) suitable for the Desert Locust at the continental scale and in near real time. To monitor green vegetation, most remote sensing techniques are based on vegetation indices (e.g., NDVI). However, several limitations have been observed for this index based approaches in sparsely vegetated areas. To guarantee a more robust and reliable image-independent discrimination between vegetation and non-vegetated surface types, an innovative multi-temporal and multi-spectral image analysis method was developed based on a combination of MIR, NIR and Red reflectance measurements. The proposed approach is based on a transformation of the RGB color space into HSV that decouples chromaticity and luminance. A complete automatic processing chain combining the daily observations of MODIS and SPOT VEGETATION, was designed to provide user-friendly vegetation dynamic maps at 250 m resolution over the entire locust area every 10 days. This new product informs users about the location of green vegetation and its temporal evolution. The methodology is currently implemented at the Vlaamse instelling voor technologisch onderzoek (VITO) to provide vegetation dynamic maps every dekade to the Desert Locust Information Service at FAO. View full abstract»

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  • Unsupervised Land Cover Change Detection: Meaningful Sequential Time Series Analysis

    Publication Year: 2011 , Page(s): 327 - 335
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1398 KB) |  | HTML iconHTML  

    An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short term Fourier transform coefficients computed over subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. The method was evaluated on real and simulated land cover change examples and obtained a change detection accuracy exceeding 76% on real land cover conversion and more than 70% on simulated land cover conversion. View full abstract»

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  • Sub-Pixel Mapping of Tree Canopy, Impervious Surfaces, and Cropland in the Laurentian Great Lakes Basin Using MODIS Time-Series Data

    Publication Year: 2011 , Page(s): 336 - 347
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1645 KB) |  | HTML iconHTML  

    This research examined sub-pixel land-cover classification performance for tree canopy, impervious surface, and cropland in the Laurentian Great Lakes Basin (GLB) using both time-series MODIS (Moderate Resolution Imaging Spectro radiometer) NDVI (Normalized Difference Vegetation Index) and surface reflectance data. Classification training strategies included both an entire-region approach and an ecoregion-stratified approach, using multi-layer perceptron neural network classifiers. Although large variations in classification performances were observed for different ecoregions, the ecoregion-stratified approach did not significantly improve classification accuracies. Sub-pixel classification performances were largely dependent on different types of MODIS input datasets. Overall, the combination of MODIS surface reflectance bands 1-7 generated the best sub-pixel estimations of tree canopy (R2 = 0.57), impervious surface (R2 = 0.63) and cropland (R2 = 0.30), which are considerable higher than those derived using only MODIS-NDVI data (tree canopy R2 = 0.50, impervious surface R2 = 0.51, and cropland R2 = 0.24). Also, sub-pixel classification accuracies were much improved when the results were aggregated from 250 m to 500 m spatial resolution. The use of individual date MODIS images were also examined with the best results being achieved for Julian days 185 (early July), 217 (early August), and 113 (late April) for tree canopy, impervious surface, and cropland, respectively. The results suggested the relative importance of the image data input selection, spatial resolution, and acquisition dates for the sub-pixel mapping of major cover types in the GLB. View full abstract»

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  • Pixel-Based Invariant Feature Extraction and its Application to Radiometric Co-Registration for Multi-Temporal High-Resolution Satellite Imagery

    Publication Year: 2011 , Page(s): 348 - 360
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2449 KB) |  | HTML iconHTML  

    Here we present a robust fully automated method for relative radiometric co-registration. First, a new low dimensional feature-point descriptor, called the Expanded Haar-Like Filter (EHLF) descriptor, is introduced. The EHLF has many desirable properties like flexible design, fast computation, and multi-scale description, while also being insensitive to variations in image quality. Next, two spatial matching schemes are proposed for increasing the percentage of correctly matched feature points. The first is based on a global affine model and the second utilizes dynamic local template fuzzy distance matching. Finally, precise pixel-to-pixel invariant feature points are extracted from a diversity of image locations centered at matched local extrema points. Experimental results show that for high-resolution multi-temporal imagery, the EHLF descriptor can obtain matched feature points with accuracies equivalent to that using a higher dimensional descriptor. In addition, the EHLF descriptor produces a larger number of correctly matched feature points. The spatial matching methods significantly improve feature-point matching, especially for image pairs with large geometric distortions. Radiometric co-registration quality based on the pixel-based invariant features was tested using four different evaluation datasets, and the results demonstrate that the proposed approach produces the lowest normalized root-mean-square error compared with six other automated methods. The proposed method depends on successful extraction of feature points, which may not be available for the scenes that are fully undeveloped (e.g., forest areas). Nonetheless, Monte Carlo simulations show that 30 to 50 correctly matched feature points will provide relatively stable radiometric calibration coefficients. View full abstract»

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  • An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data

    Publication Year: 2011 , Page(s): 361 - 371
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (816 KB) |  | HTML iconHTML  

    An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: (a) original TIMESAT algorithm with original MODIS VI, (b) original TIMESAT algorithm with pre-processed MODIS VI, and (c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates. View full abstract»

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  • Monitoring Temperate Glacier Displacement by Multi-Temporal TerraSAR-X Images and Continuous GPS Measurements

    Publication Year: 2011 , Page(s): 372 - 386
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3692 KB) |  | HTML iconHTML  

    A new generation of space-borne SAR sensors were launched in 2006-2007 with ALOS, TerraSAR-X, COSMO-Sky-Med and RadarSat-2 satellites. The data available in different bands (L, C and X bands), with High Resolution (HR) or multi-polarization modes offer new possibilities to monitor glacier displacement and surface evolution by SAR remote sensing. In this paper, the first results obtained with TerraSAR-X HR SAR image time series acquired over the temperate glaciers of the Chamonix Mont-Blanc test site are presented. This area involves well-known temperate glaciers which have been monitored and instrumented i.e. stakes for annual displacement/ablation, GPS for surface displacement and cavitometer for basal displacement, for more than 50 years. The potential of 11-day repeated X-band HR SAR data for Alpine glacier monitoring is investigated by a combined use of in situ measurements and multi-temporal images. Interpretations of HR images, analysis of interferometric pairs and performance assessments of target/texture tracking methods for glacier motion estimation are presented. The results obtained with four time series covering the Chamonix Mont-Blanc glaciers over one year show that the phase information is rarely preserved after 11 days on such glaciers, whereas the high resolution intensity information allows the main glacier features to be observed and displacement fields on the textured areas to be derived. View full abstract»

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  • Monitoring Duration and Extent of Storm-Surge and Flooding in Western Coastal Louisiana Marshes With Envisat ASAR Data

    Publication Year: 2011 , Page(s): 387 - 399
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3758 KB) |  | HTML iconHTML  

    Inundation maps of coastal marshes in western Louisiana were created with multitemporal Envisat Advanced Synthetic Aperture (ASAR) scenes collected before and during the three months after Hurricane Rita landfall in September 2005. Corroborated by inland water-levels, 7 days after landfall, 48% of coastal estuarine and palustrine marshes remained inundated by storm-surge waters. Forty-five days after landfall, storm-surge inundated 20% of those marshes. The end of the storm-surge flooding was marked by an abrupt decrease in water levels following the passage of a storm front and persistent offshore winds. A complementary dramatic decrease in flood extent was confirmed by an ASAR-derived inundation map. In nonimpounded marshes at elevations <;80 cm, storm-surge waters rapidly receded while slower recession was dominantly associated with impounded marshes at elevations >;80 cm during the first month after Rita landfall. After this initial period, drainage from marshes-especially impounded marshes-was hastened by the onset of offshore winds. Following the abrupt drops in inland water levels and flood extent, rainfall events coinciding with increased water levels were recorded as inundation re-expansion. This postsurge flooding decreased until only isolated impounded and palustrine marshes remained inundated. Changing flood extents were correlated to inland water levels and largely occurred within the same marsh regions. Trends related to incremental threshold increases used in the ASAR change-detection analyses seemed related to the preceding hydraulic and hydrologic events, and VV and HH threshold differences supported their relationship to the overall wetland hydraulic condition. View full abstract»

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  • Introduction to the Special Section on Temporal Change Observation for Bio-Geophysical Parameters

    Publication Year: 2011 , Page(s): 400 - 401
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  • Unsupervised Full-Polarimetric SAR Data Segmentation as a Tool for Classification of Agricultural Areas

    Publication Year: 2011 , Page(s): 402 - 411
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1888 KB) |  | HTML iconHTML  

    Versatile, robust and computational efficient methods for radar image segmentation, which preserve the full polarimetric information content, are of importance as research tools, as well as for practical applications in land surface monitoring. The method introduced here consists of several steps. The first step is a (reversible) transform of the full polarimetric radar information content into nine backscatter intensity values. The next steps relate to unsupervised clustering encompassing a simple region-growing segmentation (incomplete and over-segmented) followed by model-based agglomerative clustering and expectation-maximization on the pixels of these segments. Classification is achieved by Markov random field filtering on the original data. The result is a series of segmented maps, which differ in the number of (unsupervised) classes. For a (compatible) supervised approach, only the first and last step have to be applied. Results are discussed for the agricultural areas Flevoland in The Netherlands (AirSAR data) and DEMMIN in Germany, using the NASA/JPL AirSAR system and the DLR ESAR system, respectively. The applications include the use of groundtruth for legend development, the check for groundtruth completeness, and the construction of a bottom-up hierarchy of the characteristics that can be distinguished in the radar data. The latter gives important insights in physics of polarimetric radar backscattering mechanisms. Moreover, the relative importance of crop differences, (full-polarimetric) incidence angle effects and sub-classes (related to factors such as crop varieties, row direction or development stage) may be assessed. The overall classification results range between 84.3% and 98.0%, depending on number of observations dates and radar band(s) used, with higher values for the supervised approach, and substantially more thematic detail for the unsupervised approach. View full abstract»

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  • First Results of Rice Monitoring Practices in Spain by Means of Time Series of TerraSAR-X Dual-Pol Images

    Publication Year: 2011 , Page(s): 412 - 422
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3064 KB) |  | HTML iconHTML  

    Time series of dual-pol TerraSAR-X images have been acquired during the whole cultivation period over a rice site in Spain. The objective of this paper is to investigate the coherent co-polarized behavior of rice plants during the growing stages and to explore their information content for rice monitoring at high frequencies recently available through new SAR satellite missions. Among different observations, the backscattering coefficients at HH and VV channels and the HH/VV ratio have confirmed to show a temporal variation that has a significant correlation with the development of the plants during the vegetative and reproductive phenological phases. A physical interpretation in terms of the scattering mechanisms and extinction has been provided for this response. In addition, the information content of the HHVV complex coherence and a dual polarimetric target decomposition is investigated and discussed. All the information layers investigated are contributing to the discrimination of rice fields from other crop types. Apart of polarization, also the effect of high spatial resolution imaging for rice monitoring is of high interest for any kind of growth disturbances that may occur within one field for yield production. View full abstract»

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  • Crop Classification Using Short-Revisit Multitemporal SAR Data

    Publication Year: 2011 , Page(s): 423 - 431
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2264 KB) |  | HTML iconHTML  

    Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisit time acquired by the German ESAR system during the ESA-campaign, AgriSAR 2006, has been used to assess the performance of different polarization modes for crop classification. Both C-and L-band SAR data were acquired over the Demmin agricultural test site in North Eastern Germany on a weekly basis during the growing season. Single-and dual-polarization, and fully polarimetric data have been used in the analysis (fully polarimetric data were only available at L-band). The main results of the analysis are, that multitemporal acquisitions are very important for single-and dual-polarization modes, and that cross-polarized backscatter produces the best results, with errors down to 3%-6% at the two frequencies. There is a trade-off between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information. If only a few acquisitions are available, the polarimetric mode may perform better than the single-and dual polarization modes. View full abstract»

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  • Study of Physical Phenomena of Vegetation Using Polarimetric Scattering indices and Entropy

    Publication Year: 2011 , Page(s): 432 - 438
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1266 KB) |  | HTML iconHTML  

    A polarimetric model has been developed to study the temporal growth of different vegetation canopies, and their architecture. Eigen decomposition and coherency matrices are analyzed for completely polarimetric Radarsat-2 data. Polarimetric indices have been formulated using co and cross polarized backscattering coefficients, eigen values and eigen vectors. The polarization indices are used to completely understand the difference between polarized scattering signatures of vegetation in HH and VV polarizations. In this study, two decomposition techniques have been used like Freeman-Durden and H/A/α and their volume scattering and entropy components in conjunction with co and cross polarized indices are analyzed. This qualitative evaluation of vegetation parameters and growth stage are found to work better with polarimetric complex SAR data rather than using amplitude imagery. View full abstract»

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  • Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops

    Publication Year: 2011 , Page(s): 439 - 450
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1845 KB) |  | HTML iconHTML  

    This paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisiting time (1-2 weeks), to map temporal changes of surface soil moisture content (mv) underneath agricultural crops. The analysed data consist of a new ground and SAR data set acquired on a weekly basis from late April to early August 2006 over the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during the European Space Agency 2006 AgriSAR campaign. The paper firstly investigates the main scattering mechanisms characterizing the interaction between the SAR signal and crops, such as winter wheat and rape. Then, the relationship between backscatter and soil moisture content temporal changes as a function of different SAR bands and polarizations is studied. Observations indicate that rationing of the multi-temporal radar backscatter can be a simple and effective way to decouple the effect of vegetation and surface roughness from the effect of soil moisture changes, when volume scattering is not dominant. The study also assesses to which extent changes in the incidence angle between subsequent radar acquisitions may affect the radar sensitivity to soil moisture content. Finally, an algorithm based on the change detection technique retrieving superficial soil moisture content is proposed and assessed both on simulated and experimental data. Results indicate that for crops relatively insensitive to volume scattering in the vegetation canopy (as for instance winter wheat at C-band or winter rape and winter wheat at L-band), mv can be retrieved during the whole growing season, with accuracies ranging between 5% and 6% [m3/m3]. We also show that low incidence angles (e.g., 20-35 ) and HH polarization are generally better suited to mv retrieval than VV polarization and higher incidence angles. View full abstract»

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  • Monitoring Rice Agriculture in the Sacramento Valley, USA With Multitemporal PALSAR and MODIS Imagery

    Publication Year: 2011 , Page(s): 451 - 457
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (713 KB) |  | HTML iconHTML  

    Rice agriculture is an important crop that influences land-atmosphere interactions and requires substantial resources for flood management. Multitemporal acquisition strategies provide an opportunity to improve rice mapping and monitoring of hydroperiod. The objectives of this study were to 1) delineate rice paddies with Phased Array L-band Synthetic Aperture Radar (PALSAR) fine-beam single/dual (FBS/D) mode measurements and 2) integrate multitemporal, ScanSAR Wide-Beam 1 (WB1)- and Moderate Resolution Imaging Spectroradiometer (MODIS)- observations for flood frequency mapping. Multitemporal and multiscale PALSAR and MODIS imagery were collected over the study region in the Sacramento Valley, California, USA. A decision-tree approach utilized multitemporal FBS (HH polarization) data to classify rice fields and WB1 measurements to assess paddy flood status. High temporal frequency MODIS products further characterized hydroperiod for each individual rice paddy using a relationship between the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI). Validation found the PALSAR-derived rice paddy extent maps and hydroperiod products to possess very high overall accuracies (95% overall accuracy). Agreement between MODIS and PALSAR flood products was strong with agreement between 85-94% at four comparison dates. By using complementing products and the strengths of each instrument, image acquisition strategies and monitoring protocol can be enhanced. The results highlight how the integration of multitemporal PALSAR and MODIS can be used to generate valuable agro-ecological information products in an operational context. View full abstract»

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Aims & Scope

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) addresses current issues and techniques in applied remote and in situ sensing, their integration, and applied modeling and information creation for understanding the Earth.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Jocelyn Chanussot
Grenoble Institute of Technology