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

Issue 2 • Date Feb. 2014

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Displaying Results 1 - 25 of 36
  • Front cover

    Publication Year: 2014 , Page(s): C1
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  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing publication information

    Publication Year: 2014 , Page(s): C2
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  • Table of contents

    Publication Year: 2014 , Page(s): 373 - 374
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  • Nonlinear Dimensionality Reduction via the ENH-LTSA Method for Hyperspectral Image Classification

    Publication Year: 2014 , Page(s): 375 - 388
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3494 KB) |  | HTML iconHTML  

    The problems of neglecting spatial features in hyperspectral imagery (HSI) and the high complexity of Local Tangent Space Alignment (LTSA) still exist in the nonlinear dimensionality reduction with LTSA for classification. Therefore, this paper proposes an innovative ENH-LTSA (Enhanced-Local Tangent Space Alignment) method to solve the two problems. First, random projection is introduced to preliminarily reduce the dimension of HSI data. It aims to improve the speed of neighbor searching and the local tangent space construction. Then, the new method presents the similarity measure via the adaptive weighted summation kernel (AWSK) distance. The AWSK distance considers both spectral and spatial features in HSI data, and attempts to ameliorate the k-nearest neighbors (KNNs) of each pixel. Furthermore, the adaptive spatial window is proposed to automatically estimate the proper window size for the description of spatial features. After that, fast approximate KNNs graph construction via Recursive Lanczos Bisection is incorporated into the new method to reduce the complexity of KNNs searching. When finishing constructing each local tangent space, the new method uses a fast low-rank approximate singular value decomposition to speed up eigenvalue decomposition of the global alignment matrix that is constituted with local manifold coordinates. Five groups of experiments with two different HSI datasets are designed to completely analyze and testify the ENH-LTSA method. Experimental results show that ENH-LTSA outperforms LTSA, both in classification results and in computational speed. View full abstract»

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  • Comparison of Column-Averaged Volume Mixing Ratios of Carbon Dioxide Retrieved From IASI/METOP-A Using KLIMA Algorithm and TANSO-FTS/GOSAT Level 2 Products

    Publication Year: 2014 , Page(s): 389 - 398
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2099 KB) |  | HTML iconHTML  

    The ESA research project “Application of KLIMA Algorithm to CO2 Retrieval from IASI/METOP-A Observations and Comparison with TANSO-FTS/GOSAT Products” aims to develop a dedicated software, based on the KLIMA inversion algorithm (originally proposed by IFAC-CNR for the 6 ° cycle of ESA Earth Explorer Core Missions), suited for CO2 retrieval and integrated into the ESA grid-based operational environment Grid Processing On-Demand (G-POD) to process Level 1 data acquired by the infrared atmospheric sounding interferometer (IASI) and to perform a comparison with Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS), on board of the Greenhouse gases Observing SATellite (GOSAT), Level 2 data. In order to obtain a reasonable capacity to bulk processing IASI data, we choose to integrate the KLIMA code into the G-POD system. For this reason, we investigated an optimized version of the KLIMA algorithm, aiming at developing a nonoperational retrieval code with adequate features for the integration on the G-POD system. The optimized version of KLIMA retrieval code has been completed and integrated on the G-POD operational environment and is available for bulk processing of IASI data. Using the KLIMA inversion code integrated into the ESA G-POD, it was possible to perform an extensive comparison of a selected set of IASI measurements collocated with TANSO-FTS observations. We performed an extensive comparison of the column-average CO2 dry air mole fraction (XCO2) retrieved from IASI measurements by using the KLIMA/G-POD inversion code with the operational Level 2 SWIR products (Version 01.xx and Version 02.xx) from collocated TANSO-FTS observations. In this work, we describe the strategy adopted for the comparison and we show the results of this activity. View full abstract»

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  • Minimizing Measurement Uncertainties of Coniferous Needle-Leaf Optical Properties, Part I: Methodological Review

    Publication Year: 2014 , Page(s): 399 - 405
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1128 KB) |  | HTML iconHTML  

    Optical properties (OPs) of non-flat narrow plant leaves, i.e., coniferous needles, are extensively used by the remote sensing community, in particular for calibration and validation of radiative transfer models at leaf and canopy level. Optical measurements of such small living elements are, however, a technical challenge and only few studies attempted so far to investigate and quantify related measurement errors. In this paper we review current methods and developments measuring optical properties of narrow leaves. We discuss measurement shortcomings and knowledge gaps related to a particular case of non-flat nonbifacial coniferous needle leaves, e.g., needles of Norway spruce (Picea abies (L.) Karst.). View full abstract»

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  • Minimizing Measurement Uncertainties of Coniferous Needle-Leaf Optical Properties. Part II: Experimental Setup and Error Analysis

    Publication Year: 2014 , Page(s): 406 - 420
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2600 KB) |  | HTML iconHTML  

    We present uncertainties associated with the measurement of coniferous needle-leaf optical properties (OPs) with an integrating sphere using an optimized gap-fraction (GF) correction method, where GF refers to the air gaps appearing between the needles of a measured sample. We used an optically stable artificial material simulating needle leaves to investigate the potential effects of: 1) the sample holder carrying the needles during measurements and 2) multiple scattering in between the measured needles. Our optimization of integrating sphere port configurations using the sample holder showed an underestimation of the needle transmittance signal of at least 2% in flat needles and 4% in nonflat needles. If the needles have a nonflat cross section, multiple scattering of the photons during the GF measurement led to a GF overestimation. In addition, the multiple scattering of photons during the optical measurements caused less accurate performance of the GF-correction algorithms, which are based on the assumption of linear relationship between the nonGF-corrected signal and increasing GF, resulting in transmittance overestimation of nonflat needle samples. Overall, the final deviation achieved after optimizing the method is about 1% in reflectance and 6% in transmittance if the needles are flat, and if they are nonflat, the error increases to 4%-6% in reflectance and 10%-12% in transmittance. These results suggest that formulae for measurements and computation of coniferous needle OPs require modification that includes also the phenomenon of multiple scattering between the measured needles. View full abstract»

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  • Monitoring Vegetation Moisture Using Passive Microwave and Optical Indices in the Dry Chaco Forest, Argentina

    Publication Year: 2014 , Page(s): 421 - 430
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1680 KB) |  | HTML iconHTML  

    Information about daily variations of vegetation moisture is of widespread interest to monitor vegetation stress and as a proxy to evapotranspiration. In this context, we evaluated optical and passive microwave remote sensing indices for estimating vegetation moisture content in the Dry Chaco Forest, Argentina. The three optical indices analyzed were the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Infrared Index (NDII) and, for the microwave region the Frequency Index (FI). All these indices are mainly sensitive to leaf area index (LAI), but NDWI and NDII, and FI are also sensitive to leaf water content (LWC) and Canopy Water Content (CWC) respectively. Using optical and microwave radiative transfer models for the vegetation canopy, we estimated the range of values of LAI, LWC and CWC that can explain both NDWI/NDII and FI observations. Using a combination of simulations and microwave and optical observations, we proposed a two step approach to estimate leaf and canopy moisture content from NDWI, NDII and FI. We found that the short variation of LWC estimated from NDWI and NDII present a dynamic range of values which is difficult to explain from the biophysical point of view, and it is partially related to atmosphere contamination and canopy radiative transfer model limitations. Furthermore, the observed FI short-term variations (~ 8 days) cannot be explained unless significant CWC variations are assumed. The CWC values estimated from FI present a short-term variations possibly related to vegetation hydric stress. View full abstract»

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  • Crop Leaf Area Index Observations With a Wireless Sensor Network and Its Potential for Validating Remote Sensing Products

    Publication Year: 2014 , Page(s): 431 - 444
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2081 KB) |  | HTML iconHTML  

    The collection of ground measurements for validating remotely sensed crop leaf area index (LAI) is labor and time intensive. This paper presents an automatic measuring system that was designed based on a wireless sensor network (WSN). The corn LAI was continuously observed from June 25 to August 24, 2012. Approximately, 42 in situ WSN measurement nodes were used in a 4 ×4 km2 area in the Heihe watershed of northwest China. The data were analyzed in three ways: 1) a comparison with LAI-2000, 2) a daily and 5-day aggregated time series analysis, and 3) a comparison with a Moderate Resolution Imaging Spectroradiometer (MODIS) LAI using both a ground LAINet LAI and a scaled-up LAI through inversion of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data. The preliminary results indicated that the measured LAI values from the LAINet were correlated with the values derived from LAI-2000 (R2 from 0.27 to 0.96 with an average of 0.42). When compared with the daily crop LAI growth trajectory, the performance of the measurement system was improved by using the data that were aggregated over a 5-day window. When compared with MODIS LAI, we found that the spatial aggregation values of the ground LAINet observations and the scaled-up ASTER LAI were identical or similar to the MODIS LAI values over time. With its low-cost and low-energy consumption, the proposed WSN observation system is a promising method for collecting ground crop LAI in flexible time and space for validating the remote sensing land products. View full abstract»

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  • Improving LAI Mapping by Integrating MODIS and CYCLOPES LAI Products Using Optimal Interpolation

    Publication Year: 2014 , Page(s): 445 - 457
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2243 KB) |  | HTML iconHTML  

    Leaf area index (LAI) is an important land surface biophysical variable used to characterize vegetation amount and activity. Current satellite LAI products, however, do not satisfy the requirements of the modeling community due to their large uncertainties and frequent missing values. There is an urgent need for advanced methods to integrate multiple LAI products to improve the product's accuracy and integrity for various applications. To meet this need, this study proposes a method based on Optimal Interpolation (OI) to integrate MODIS true LAI and CYCLOPES effective LAI retrievals. Multiple years' LAI means and variances (LAI climatology) are pre-calculated and used as the baseline for data integration. The locally adjusted cubic-spline capping algorithm is used to smooth the climatology data. An LAI normalization scheme, based on a linear measurement error model, is developed to account for the systematic difference between the two products and to generate the LAI predictions. This integration process removes the unrealistically large temporal variations of the original LAI products. The data gaps are filled with information from adjacent pixels and the prior knowledge acquired from multiyear climatology. A spatially and temporally continuous true LAI data set is generated. High resolution reference maps of true LAI are collected to validate the two true LAI data sets: MODIS LAI product and the integrated LAI values. The validation results suggest that the integrated results agree better with the LAI reference maps than the MODIS LAI product, showing higher R2, smaller bias and root mean squared error. View full abstract»

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  • Inter-Comparison and Validation of the FY-3A/MERSI LAI Product Over Mainland China

    Publication Year: 2014 , Page(s): 458 - 468
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1933 KB) |  | HTML iconHTML  

    Leaf area index (LAI) is a key surface parameter that describes the structure of vegetation and plays an important role in Earth system process modeling. In this paper, a new set of LAI products (MERSI GLOBCARBON LAI) has been developed based on the GLOBCARBON LAI algorithm and one year of FY-3A/MERSI land surface reflectance data. MERSI GLOBCARBON LAI has been inter-compared and validated over mainland China against MODIS land surface reflectance (LSR) derived LAI (using the same algorithm) and field LAI measurements. MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI show continuous and smooth LAI distributions at the start and end of the growing season. For most areas in China, the two LAI products agree well. The temporal variation in MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI consistently follows the growing season. The largest LAI difference occurs during July, when MERSI shows a much higher frequency of retrievals than does MODIS. Through validation of LAI retrievals with field measurements, our study demonstrates that LAI derived from MERSI and MODIS land surface reflectance products have comparable accuracy. MODIS top-of-atmosphere simple ratio (MODIS TOA SR) is related to MERSI TOA SR with linear correlation coefficients greater than 0.6. After atmospheric correction, the correlation coefficient increases from 0.69 to 0.75 over cropland and from 0.82 to 0.93 over grassland. However, atmospheric correction can still give rise to substantial differences in the reflectance data between the two sensors. Furthermore, different land cover types and different terrain relief have contrasting influences on the atmospheric correction, and these influences reduce the agreement between the two LAI products. This study shows the great potential of FY-3A/MERSI data for global LAI retrieval. View full abstract»

    Open Access
  • Rice Biomass Estimation Using Radar Backscattering Data at S-band

    Publication Year: 2014 , Page(s): 469 - 479
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2196 KB) |  | HTML iconHTML  

    This paper presents an inversion method based on neural networks (NN) to estimate rice biomass in a paddy rice field with fully polarimetric (HH, HV, VH, VV) measurements at S-band. The backscattering coefficients are measured by a ground-based scatterometer system during the rice growth period from May to September 2010. The rice growth parameters including biomass, leaf-area index (LAI) and canopy structure are collected by random sampling at the same time. Data analyses show that the multi-temporal backscattering coefficients are very sensitive to the changes of biomass, LAI, canopy height and stem density. We also find that multi-temporal observations are suitable for paddy detection in the early growth period, and co-polarimetric observations perform well for monitoring rice status in the late growth period. According to the field measurements, a rice growth model was established as the function of rice age. The model made the parameters more representative and universal than limited random measurements over a given rice field. The scatter model of rice fields was simulated based on Monte Carlo simulations. The input parameters in the scatter model were generated by the rice growth model. The simulation results of the scatter model were composed as the NN training dataset, which was used for training and accessing the NN inversion algorithm. Two NN models, a simple training model (STM) and a related training model (RTM), were applied to estimate rice biomass. The obtained results show that the root mean square error (RMSE = 0.816 Kg/m2) of the RTM is better than that of the STM (RMSE = 1.226 kg/m2). The results suggest that the inversion model is able to estimate rice biomass with radar backscattering coefficients at S-band. View full abstract»

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  • Extracting Structural Vegetation Components From Small-Footprint Waveform Lidar for Biomass Estimation in Savanna Ecosystems

    Publication Year: 2014 , Page(s): 480 - 490
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2715 KB) |  | HTML iconHTML  

    Measurement of vegetation biomass accumulation is critical for ecosystem assessment and monitoring, but doing so typically involves extensive field data collection that yields relatively crude structural outputs, e.g., plot- or site-level metrics. This study assessed the utility of airborne light detection and ranging (lidar) waveform features to explain structural and biomass variation in a savanna ecosystem across a land-use gradient. The ability of aboveground waveform lidar features to model field-based woody and herbaceous biomass measurements was evaluated statistically by regression models using forward variable selection. Waveform features explained 76% of the variation in woody biomass in a regulated communal land use area (RMSE = 29.0 kg). The waveform features were also correlated to herbaceous measurements in the same land-use area, with increased correlations at higher biomass levels. These results indicate that small-footprint waveform lidar data potentially can be used as a single modality to describe heterogeneous woody cover in a savanna environment; however, further research is warranted during the full growing season to fully evaluate its performance. View full abstract»

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  • A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

    Publication Year: 2014 , Page(s): 491 - 502
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2753 KB) |  | HTML iconHTML  

    Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments. Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDAR data. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial features are extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach. View full abstract»

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  • Integration of LiDAR Data and Orthophoto for Automatic Extraction of Parking Lot Structure

    Publication Year: 2014 , Page(s): 503 - 514
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2123 KB) |  | HTML iconHTML  

    To overcome the challenges of parking lot structure extraction using optical remote sensing images, this study proposes an automatic method for the extraction of parking lot structure by integrating LiDAR data and orthophoto, which consists of three steps. The first step is to extract vehicles from LiDAR data and then to identify the corresponding central axes for each vehicle. In the second step, orientations of the identified vehicle central axes are used as principle orientation constraints for parking lines extraction from orthophoto. The third step is the determination of parking lot structure with vehicle central axes and parking lines, in which parking lot parameters are calculated and an adaptive growth method is used for parking lot structure determination. In this method, vehicle central axes identified from LiDAR data and parking lines extracted from orthophoto are integrated for the extraction of parking lot structures. The main novelty of this study lies in two new algorithms: an algorithm on parking lines extraction with principal orientation constraints and an algorithm on parking lot structure determination based on parameter solution and adaptive growth. The experiment shows that the proposed method can effectively extract parking lot structure with high correctness, high completeness, and good geometric accuracy. View full abstract»

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  • A Dynamic Observation Capability Index for Quantitatively Pre-Evaluating Diverse Optical Imaging Satellite Sensors

    Publication Year: 2014 , Page(s): 515 - 530
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2955 KB) |  | HTML iconHTML  

    Choosing a capable satellite sensor from a mass of homogeneous sensors to meet the requirements of observation tasks in various application scenarios is one of the basic challenges faced by the collaborative observation in an Earth Observation Sensor Web environment. This paper analyzed five main factors affecting the observation capability of optical imaging satellite sensors. This study proposed the concept of dynamic observation capability index (DOCI), which denotes the continuously changing observation performance of diverse sensors in various applications. A higher DOCI demonstrates stronger observation capability. The DOCI model consists of five subcapabilities: spatial-temporal covering capabilities (Coverage), thematic observation capability (Theme), environmental capability (Radiation), attribute capability (SpaceTime), and quality capability (Accuracy). We discussed the assessment methods on the basis of the DOCI model. To verify the proposed DOCI method, seven sensors (AVHRR/3, BGIS-2000, Hyperion, MERSI-1, MODIS, OLI, and SeaWiFS) were used in four different observation task scenarios: normalized difference vegetation index measurement, snow cover monitoring, oil spill detection, and vegetation-type mapping. The results showed that the changes in the observation capability of different sensors in different scenarios can be effectively assessed and modeled using the DOCI index, thus aiding in the scientific pre-evaluation of homogeneous optical sensors. DOCI can also be used as a quantitative, comprehensive, and all-purpose prior assessment method in web-based sensor planning. View full abstract»

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  • Mapping of Central Africa Forested Wetlands Using Remote Sensing

    Publication Year: 2014 , Page(s): 531 - 542
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2373 KB) |  | HTML iconHTML  

    Wetlands represent 6% of the Earth's land cover surface. They are of crucial importance in the global water cycle and climatic dynamics. Nowadays, wetlands are the most threatened land cover type, nevertheless their spatial distribution and ecological functions are poorly documented. Despite the need for more detailed information, wetland mapping is a rare activity. Few data are available mainly because of the complexity of obtaining good field data. We therefore propose a method based on multisensor imagery analysis to characterize land cover patterns of the second largest wetland area of the world (The Cuvette Centrale of the Congo River Basin). The time series of moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) images are used to map land cover types based on their phenological differences. Flooded areas in the Congo basin have been mapped during different seasons using L-band synthetic aperture radar (PALSAR) imagery. The associated model has been improved upon by the addition of elevation data as well as mean canopy heights acquired with light detection and ranging (LIDAR) data. The result of this study is the first detailed spatial distribution of four forested wetland types within the Cuvette Centrale of the Congo River Basin. This study demonstrates that the spatial organization of the floodplain landscape depends on the extent of flooding. The results also show that land cover phenology is closely related to the time period of flooding and solar intensity for this region, similarly to what is observed in the extensive floodplain of the Amazon basin. View full abstract»

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  • Classification of Inland Waters Based on Bio-Optical Properties

    Publication Year: 2014 , Page(s): 543 - 561
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3306 KB) |  | HTML iconHTML  

    Multiple bio-optical measurements of optically active substances were conducted in Taihu Lake, Chaohu Lake, Three Gorges Reservoir, and Dianchi Lake (China). A hierarchical cluster analysis was applied to remote sensing spectra (Rrs(λ)), by which those waters were clustered into three optically distinct types (Type I, Type II, and Type III). Absorption coefficients of phytoplankton were simulated using the linear function and the coefficients of the functions varied in different water types. The slope of colored dissolved organic matter (CDOM) absorption could be clustered into two classes, one class of high slope for Types I and II, whereas the other class of low slope for Type III. A uniform model was used for parameterization of the nonalgal particles absorption coefficients spectral between different water types. The power-law function was used to parameterize the scattering coefficient and the slope of scattering spectra could be classified into two groups of high slope for Types I and II, low slope for Type III. The composition of particles, particulate scattering, and nonalgal particulate absorption are the factors controlling the variability in Rrs(λ) for corresponding type waters. Those results support such a fact that it is indispensable to classify waters before developing a model to estimate water quality parameters for multi-type optical waters. View full abstract»

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  • Flood Mapping With TerraSAR-X in Forested Regions in Estonia

    Publication Year: 2014 , Page(s): 562 - 577
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3974 KB) |  | HTML iconHTML  

    In this study, an extensive flood in Estonia during spring 2010 was mapped with TerraSAR-X data acquired over both open and forested areas. This was the first time when a large scale flooding area was mapped in Estonia by means of spaceborne remote sensing. This was also the first time when X-band SAR images were successfully used for flood mapping under the forest canopy in the temperate forest zone. The tree height in the study region was 15-25 meters on average, and main tree species were birch (leaf-off condition), pine and spruce. The results were compared with ALOS PALSAR and Envisat ASAR images of the same flooding event. In the study area, TerraSAR-X provided on average 3.2 dB higher backscatter over mixed forest flooded areas compared to non-flooded areas. In deciduous and coniferous forests the difference in average backscatter between flooded and non-flooded forests was even greater, 6.2 dB and 4.0 dB, respectively. A supervised classification algorithm was developed to produce high resolution maps of the flooded area from the TerraSAR-X images to demonstrate the flood mapping capability at X-band. Our results show, that spaceborne X-band SAR data, which currently has the highest resolution among the SAR instruments in space, can be used to map floods under forest canopy in temperate zone despite its short wavelength and high attenuation. View full abstract»

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  • Near Real-Time Flood Volume Estimation From MODIS Time-Series Imagery in the Indus River Basin

    Publication Year: 2014 , Page(s): 578 - 586
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1926 KB) |  | HTML iconHTML  

    Satellite images have been widely applied in near real-time flood inundation maps in many cases. Such images have significant potential to predict the time, place and scale of a flood event, and can be very useful in emergency response efforts. The detection of floodwaters and the estimation of flood volumes are important to determine a hazard in flood risk. In this study, we conducted surface water detection based on the spatial distribution of the 2010 Indus River flood, which affected the entire Pakistan area. A modified surface water index derived from near-real-time Moderate Resolution Imaging Spectrometer (MODIS) images coupled with a digital elevation model (DEM) was used. We also developed and applied a simplified algorithm to extract the 3D volume of floodplain surface water considering surface heights. The results found that the MODIS-DEM combined approach was feasible for automatic, instant flood detection. This approach shows a methodological possibility as an integrated algorithm for producing flood maps at local to global scales. View full abstract»

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  • Proof of Concept of an Altimeter-Based River Forecasting System for Transboundary Flow Inside Bangladesh

    Publication Year: 2014 , Page(s): 587 - 601
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3027 KB) |  | HTML iconHTML  

    Recent work by Biancamaria (Geophysical Research Letters, 2011) has demonstrated the potential of satellite altimetry to forecast incoming transboundary flow for downstream nations by detecting river levels at locations in upstream nations. Using the Ganges-Brahmaputra (GB) basin as an example, we assessed the operational feasibility of using JASON-2 satellite altimetry for forecasting such transboundary flow at locations further inside the downstream nation of Bangladesh by propagating forecasts derived from upstream (Indian) locations through a hydrodynamic river model. The 5-day forecast of river levels at upstream boundary points inside Bangladesh were used to initialize daily simulation of the hydrodynamic river model and yield the 5-day forecast river level further downstream inside Bangladesh. The forecast river levels were then compared with the 5-day-later “nowcast” simulation by the river model based on in-situ river level at the upstream boundary points in Bangladesh. Results show that JASON-2 retains good fidelity at 5-day lead forecast with an average RMSE (relative to nowcast) ranging from 0.5 m to 1.5 m and a mean bias (underestimation) of 0.25 m to 1.25 m in river water level estimation. Based on the proof-of-concept feasibility, a 4 month-long capacity building of the Bangladesh flood forecasting agency was undertaken. This facilitated a 20-day JASON-2 based forecasting of flooding during Aug 1, 2012 to Aug 20, 2012 up to a 5 day lead time in a real-time operational environment. Comparison against observed water levels at select river stations revealed an average error of forecast ranging from -0.4 m to 0.4 m and an RMSE ranging from 0.2 m to 0.7 m. In general, this study shows that satellite altimeter such as JASON-2 can indeed be an efficient and practical tool for building a robust forecasting system for transboundary flow. View full abstract»

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  • River Surface Water Topography Mapping at Sub-Millimeter Resolution and Precision With Close Range Photogrammetry: Laboratory Scale Application

    Publication Year: 2014 , Page(s): 602 - 608
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1184 KB) |  | HTML iconHTML  

    High precision and high density measurements of surface water topography in laboratory rivers are useful in hydro-morphodynamic studies to analyze energy, momentum, and mass balances in the river system. A close range photogrammetry (CRP) method is developed to provide instantaneous sub-mm vertical and horizontal resolutions of surface water topography along a flowing laboratory river reach, which is infeasible with typical mechanical and point-based river stage measurements. The CRP method uses wax powder seeding of the river surface and two synchronized nonmetric digital cameras to capture stereo-pair images and derive digital elevation models (DEMs) of the river surface water topography. The river water surface DEM accuracy was limited by extent and density of wax coverage, control point accuracy, lighting conditions, as well as camera and lens specifications. Two Nikon D5100 16.2 MP CMOS digital SLR cameras with 20 mm prime lenses were mounted 1.3 m above the river water surface, generating DEM horizontal resolution of 0.3 mm. Control point elevation was surveyed using ultrasonic sensors with 0.3 mm vertical accuracy. The sub-mm overall DEM accuracy was based on ultrasonic sensor measurements at check points and overall DEM precision was based on differences between repeated images. We demonstrate how the CRP method can generate river surface water topography and bathymetry in a laboratory meandering river and suggest methods for successfully applying CRP in natural river surface water topography mapping. View full abstract»

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  • Numerical Simulation and Forecasting of Water Level for Qinghai Lake Using Multi-Altimeter Data Between 2002 and 2012

    Publication Year: 2014 , Page(s): 609 - 622
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2882 KB) |  | HTML iconHTML  

    Satellite radar altimetry has effectively been used for monitoring the water level change in recent years. In this study, Qinghai Lake was taken as an example to simulate and forecast water level using the multi-altimeter data from Envisat/RA-2, Cryosat-2/Siral, and Jason-1/Poseidon-2. First, using the robust least square method and system bias correction algorithms, abnormal water levels and the system bias were eliminated, and an accurate lake-level time series was obtained. Then, singular spectrum analysis (SSA) algorithms were used to extract the effective fluctuation signal from the accurate lake-level time series, and the accuracy of the altimetry data was improved. Based on an analysis of SSA algorithms' characteristics, comparison of the SSA-extracted fluctuation signal, and in-situ gauge measurements of Qinghai Lake, the accurate lake-level time series was affected by white noise of zero-mean and 0.5-m variance and colored noise of 0.2202-0.2473-m mean and 0.252-0.2800-m root-mean-square difference. After eliminating the white noise, the accuracy of the altimeter data reached the decimeter level in inland lake monitoring. Next, the SSA-extracted fluctuation signal was decomposed into linear composition, periodic components, and a residual component, and a combined linear-periodic-residual model was established using simple regression, a trigonometric function, and autoregressive-moving-average models. Using the model, the water level change of Qinghai Lake was simulated and forecasted to 2 years, with its accuracy reaching the decimeter level. The experiences of this study can provide an effective reference for the other lakes. View full abstract»

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  • Early Results of Simultaneous Terrain and Shallow Water Bathymetry Mapping Using a Single-Wavelength Airborne LiDAR Sensor

    Publication Year: 2014 , Page(s): 623 - 635
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2684 KB) |  | HTML iconHTML  

    In this paper we present results obtained with a new single-wavelength LiDAR sensor which allows seamless sub-meter mapping of topography and very shallow bathymetry in a single pass. The National Science Foundation supported National Center for Airborne Laser Mapping (NCALM) developed the conceptual design for the sensor that was built by Optech Inc. The new sensor operates at a wavelength of 532 nm and is fully interchangeable with an existing 1064 nm terrain mapping sensor operated by NCALM, connecting to the same electronics rack and fitting into the same aircraft mounting assembly. The sensor operates at laser pulse repetition frequencies (PRFs) of 33, 50 and 70 kHz, making it possible to seamlessly map shallow water lakes, streams, and coastal waters along with the contiguous terrain, including rural and urban areas. This new sensor has been tested in a wide variety of conditions including coastal, estuarine and fresh water bodies, with water depths ranging from 20 centimeters to 16 meters, with varying benthic reflectivity and water clarity. Observed point densities range from 1-4 points/m2 for terrestrial surfaces and 0.3-3 points/m2 for sub water surfaces in a single pass, and double these values when the data are collected with 50% side swath overlap, a minimum standard for NCALM's airborne LiDAR surveys. The seamless high resolution data sets produced by this sensor open new possibilities for geoscientists in fields such as hydrology, geomorphology, geodynamics and ecology. View full abstract»

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  • Improved Understanding of Suspended Sediment Transport Process Using Multi-Temporal Landsat Data: A Case Study From the Old Woman Creek Estuary (Ohio)

    Publication Year: 2014 , Page(s): 636 - 647
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1831 KB) |  | HTML iconHTML  

    We used historical water quality data, continuous in situ water quality monitoring data, and multi-temporal Landsat-7 ETM+ imagery for the period of September 1999-April 2003 to study the distribution of total suspended sediments (TSS) in Old Woman Creek (OWC), a freshwater coastal wetland adjacent to Lake Erie. A multiple linear regression model was developed to describe the relationship between turbidity and atmospherically corrected reflectance from Landsat-7 ETM+ bands 2 and 4 (R2 = 0.65). Turbidity was then converted to total suspended sediments (TSS), based on in situ historical data. Mapped spatial patterns of TSS provided useful information on key physical drivers affecting the transport process of suspended sediment. This study demonstrates the potential and limitations of using medium- spatial scale multispectral data, such as Landsat, to understand important factors that control suspended sediment transport processes within an estuary. 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.

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Meet Our Editors

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