Showing papers in "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing in 2009"

Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

Abstract: The contribution of power production by photovoltaic (PV) systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the electricity grids and for solar energy trading. This paper presents an approach to predict regional PV power output based on forecasts up to three days ahead provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Focus of the paper is the description and evaluation of the approach of irradiance forecasting, which is the basis for PV power prediction. One day-ahead irradiance forecasts for single stations in Germany show a rRMSE of 36%. For regional forecasts, forecast accuracy is increasing in dependency on the size of the region. For the complete area of Germany, the rRMSE amounts to 13%. Besides the forecast accuracy, also the specification of the forecast uncertainty is an important issue for an effective application. We present and evaluate an approach to derive weather specific prediction intervals for irradiance forecasts. The accuracy of PV power prediction is investigated in a case study.

A Comparison of Evaluation Techniques for Building Extraction From Airborne Laser Scanning

Abstract: In this paper, different methods for the evaluation of building detection algorithms are compared. Whereas pixel-based evaluation gives estimates of the area that is correctly classified, the results are distorted by errors at the building outlines. These distortions are potentially in an order of 30%. Object-based evaluation techniques are less affected by such errors. However, the performance metrics thus delivered are sometimes considered to be less objective, because the definition of a ldquocorrect detectionrdquo is not unique. Based on a critical review of existing performance metrics, selected methods for the evaluation of building detection results are presented. These methods are used to evaluate the results of two different building detection algorithms in two test sites. A comparison of the evaluation techniques shows that they highlight different properties of the building detection results. As a consequence, a comprehensive evaluation strategy involving quality metrics derived by different methods is proposed.

Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program

Abstract: Recently, global biomass-burning research has grown from what was primarily a climate field to include a vibrant air quality observation and forecasting community. While new fire monitoring systems are based on fundamental Earth Systems Science (ESS) research, adaptation to the forecasting problem requires special procedures and simplifications. In a reciprocal manner, results from the air quality research community have contributed scientifically to basic ESS. To help exploit research and data products in climate, ESS, meteorology and air quality biomass burning communities, the joint Navy, NASA, NOAA, and University Fire Locating and Modeling of Burning Emissions (FLAMBE) program was formed in 1999. Based upon the operational NOAA/NESDIS Wild-Fire Automated Biomass Burning Algorithm (WF_ABBA) and the near real time University of Maryland/NASA MODIS fire products coupled to the operational Navy Aerosol Analysis and Prediction System (NAAPS) transport model, FLAMBE is a combined ESS and operational system to study the nature of smoke particle emissions and transport at the synoptic to continental scales. In this paper, we give an overview of the FLAMBE system and present fundamental metrics on emission and transport patterns of smoke. We also provide examples on regional smoke transport mechanisms and demonstrate that MODIS optical depth data assimilation provides significant variance reduction against observations. Using FLAMBE as a context, throughout the paper we discuss observability issues surrounding the biomass burning system and the subsequent propagation of error. Current indications are that regional particle emissions estimates still have integer factors of uncertainty.

Improving Curve Number Based Storm Runoff Estimates Using Soil Moisture Proxies

Abstract: Advances in data dissemination and the availability of new remote sensing datasets present both opportunities and challenges for hydrologists in improving flood forecasting systems. The current study investigates the improvement in SCS curve number (CN)-based storm runoff estimates obtained after inclusion of various soil moisture proxies based on additional data on precipitation, baseflow, and soil moisture. A dataset (1980-2007) comprising 186 Australian catchments (ranging from 51 to 1979 km 2 in size) was used. In order to investigate the value of a particular proxy, the observed S (potential maximum retention) was compared to values obtained with different soil moisture proxies using linear regression. An antecedent precipitation index (API) based on gauged precipitation using a decay parameter proved most valuable in improving storm runoff estimates, stressing the importance of high quality precipitation data. An antecedent baseflow index (ABFI) also performed well. Proxies based on remote sensing (TRMM and AMSR-E) gave promising results, particularly when considering the expected arrival of higher accuracy data from upcoming satellites. The five-day API performed poorly. The inclusion of soil moisture proxies resulted in mean modeled versus observed correlation coefficients around 0.75 for almost all proxies. The greatest improvement in runoff estimates was observed in drier catchments with low Enhanced Vegetation Index (EVI) and topographical slope (all intercorrelated parameters). The present results suggest the usefulness of incorporating remotely sensed proxies for soil moisture and catchment wetness in flood forecasting systems.

Urban Expansion and Heat Island Dynamics in the Quanzhou Region, China

Abstract: Urban spatial expansion in the Quanzhou region of southeastern China has been accelerated over the past 20 years. This has caused land cover changes and, thus, has significant impacts on the local ecosystem and climate. To study the urban expansion and heat island dynamics of the region over the past 20 years, multitemporal Landsat TM images of 1987, 1996, and 2006 were used. The estimation of the urban expansion was assisted by the index-based built-up index (IBI) through enhancing built-up land features in the images. The urban-heat-island (UHI) effect was assessed using the urban-heat-island ratio index (URI). Multitemporal analysis indicates that the great increase in urban area has resulted in the development of UHIs in the region. Regression statistics reveal that built-up land has a positive exponential relationship with land surface temperature (LST). Therefore, the increase in built-up land percentage can exponentially accelerate the rise of LST.

Satellite Monitoring of Volcanic Sulfur Dioxide Emissions for Early Warning of Volcanic Hazards

Abstract: Satellite-based remote sensing measurements of volcanic sulfur dioxide (SO2) provide critical information for reducing volcanic hazards. This paper describes the use of SO2 measurements from the thermal infrared sounder IASI and the UV-VIS instrument GOME-2 in services related to aviation hazard and early warning of volcanic unrest. The high sensitivity of both instruments to SO2 allows the detection and global tracking of volcanic eruption plumes and makes them a valuable tool for volcanic aviation hazard mitigation. The GOME-2 and IASI SO2 data are produced in near-real time and distributed to the Volcanic Ash Advisory Centers (VAACS) to assist them in issuing alerts to airlines and air traffic control organizations. Examples of recent eruptions affecting air traffic are presented including Jebel al Tair (Yemen, September 2007), Mount Okmok (Alaska, July 2008), and Mount Kasatochi (Alaska, August 2008). In addition, GOME-2 can detect changes in the SO2 emissions from passively degassing volcanoes and, therefore, provide critical information for hazard assessment. The monitoring of pre-eruptive degassing by GOME-2 is used in early warning of volcanic activity by a mobile volcano fast response system in combination with numerous other parameters, such as seismicity, deformation, and thermal anomalies.

Discovery, Mediation, and Access Services for Earth Observation Data

Abstract: The international research community involved in the GMES, INSPIRE, and GEOSS initiatives is actively pursuing the specification of information and service oriented solutions for geospatial data interoperability A prominent interoperability issue pertains to discovery services From an information technology point of view, the challenge is to implement interoperable discovery services for data and processing resources that are collected and managed using multidisciplinary standards and tools We have designed and experimented a new, improved model and technology for the discovery of geospatial resources: an advanced catalog service featuring additional functionalities like mediation and asynchronous distribution Besides, the described solution addresses another well-recognized issue: the integration of discovery and access services for complex resources-such as EO datasets

Distributed Geospatial Data Processing Functionality to Support Collaborative and Rapid Emergency Response

Abstract: This paper presents a novel approach to integrate the latest generation very high-resolution earth observation imagery into the operational workflow of geospatial information support for emergency response actions. The core concept behind this approach is the implementation of an image pyramid structure that allows each image tile to be addressed separately. We propose a novel way to collate geospatial feature data from distributed sources and integrate them in visualization and image processing. The system components enable rapid collaborative mapping, support for in situ data collection, customized on-demand image processing, and geospatial data queries and near instantaneous map visualization. We adapt functional software modules that are available in the public and open source domain. The approach is demonstrated with a test case in a rapid damage assessment scenario using very high-resolution optical satellite QuickBird and IKONOS imagery over Southern Lebanon from 2006.

The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

Abstract: In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.

Fog Over Indo-Gangetic Plains—A Study Using Multisatellite Data and Ground Observations

Abstract: Every year, fog formation over the Indo-Gangetic Plains (IGP) of Indian region during the winter months of December-January is believed to create numerous health hazards, economic loss, and cross-country transportation of aerosols. It has attracted the global scientific community's attention to address the uncertainties pertaining to its formation and physico-chemical properties. In this paper, we made an attempt to study the fog conditions that occurred over the north Indian region and long-range transport of aerosols from the fog region towards the southern region during November 2008, using multisatellite data sets and ground-based observations on aerosol properties and solar irradiance in the urban region of Hyderabad, India. Ground measurements showed a considerable increase in aerosol optical depth (AOD) at 500 nm ( ~ 30%) and a decrease in total solar irradiance ( ~ 7%) over Hyderabad, India, during the fog period compared to a normal day corresponding to 04 November 2008.

The Impact of Phenological Variation on Texture Measures of Remotely Sensed Imagery

Abstract: Measures of image texture derived from remotely sensed imagery have proven useful in many applications. However, when using multitemporal imagery or multiple images to cover a large study area, it is important to understand how image texture measures are affected by surface phenology. Our goal was to characterize the robustness to phenological variation of common firstand second-order texture measures of satellite imagery. Three North American study sites were chosen to represent different biomes. At each site, a suite of image textures were calculated for three to four dates across the growing season. Texture measures were compared among dates to quantify their stability, and the stability of measures was also compared between biomes. Interseasonal variability of texture measures was high overall (mean interseasonal coefficient of variation = 0.79), indicating that care must be taken when using measures of texture at different phenological stages. Certain texture measures, such as first-order mean and entropy, as well as second-order homogeneity, entropy, and dissimilarity, were more robust to phenological change than other measures.

Lake Level Variations Monitored With Satellite Altimetry Waveform Retracking

Abstract: Over lake shores, altimetric waveforms are generally contaminated by lands, rough lake surfaces, and lag effects of the altimeter's automatic gain control. To improve altimeter ranging accuracy and in turn to get better surface height measurement, contaminated waveforms should be retracked against geophysical corrections. In this paper, an improved threshold retracker (ITR) is developed to retrack waveforms over lakes. ITR considers not only the physical characteristics of the reflecting surface, but also the stochastic feature of waveform, and two new retrackers, the N-Beta function model, and the N-5-Beta function model, are also put forward to develop the waveform retracking program of this study. TOPEX/POSEIDON waveforms over Hulun Lake in the North China are retracked to monitor the temporal lake level variations. A comparison with the in situ hydrological data indicates ITR is very efficient to monitor the lake level variations with the retracked altimetric data. The result of our study shows accurate seasonal level variations and the descending trend of Hulun Lake.

Analysis on the Use of Multiple Returns LiDAR Data for the Estimation of Tree Stems Volume

Abstract: Small footprint Light Detection and Ranging (LiDAR) data have been shown to be a very accurate technology to predict stem volume. In particular, most recent sensors are able to acquire multiple return (more than 2) data at very high hit density, allowing one to have detailed characterization of the canopy. In this paper, we utilize very high density ( >8 hits per m2) LiDAR data acquired over a forest stand in Italy. Our approach was as follows: Individual trees were first extracted from the LiDAR data and a series of attributes from both the first, and non-first (multiple), hits associated with each crown were then extracted. These variables were then correlated with ground truth individual estimates of stem volume. Our results indicate that: (i) non-first returns are informative for the estimation of stem volume (in particular the second return); (ii) some attributes (e.g., maximum at the power of n) better emphasize the information content of returns different from the first respect to other metrics (e.g., minimum, mean); and (iii) the combined use of variables belonging to different returns slightly increases the overall model accuracy. Moreover, we found that the best model for stem volume estimation (adj - R2 = 0.77, P < 0.0001, SE = 0.06) comprised four variables belonging to three returns (first, second, and third). The results of this analysis are important as they underline the effectiveness of the use of multiple return LiDAR data, underling the connection between LiDAR hits different from the first and tree structure and characteristics.

Particulate Matter at Surface: Northern Italy Monitoring Based on Satellite Remote Sensing, Meteorological Fields, and in-situ Samplings

Abstract: Growing attention has been paid over recent years to the possibility of monitoring surface particulate matter (PM) concentrations through the use of satellite observations. Satellite remote sensing of both aerosol and trace gas constituents can be usefully employed in air quality monitoring (AQ). The MODIS (Terra-Aqua/NASA platforms) aerosol optical properties were used here in a semi-empirical approach to estimate PM2.5 content at ground level. PM2.5 samplings were employed to convert aerosol optical depth AOD into PM estimates, considering meteorological fields simulated by MM5. Thus, daily maps of satellite-based PM2.5 concentrations over Northern Italy were derived. Comparison with daily PM2.5, sampled on the ground during the QUITSAT project over six validation sites of the Po valley, showed good agreement (R2 ? 0.68 and R2 ? 0.59 for MODIS/Terra and MODIS/Aqua, respectively), with the satellite-based concentrations tending to underestimate the values by at most ~20%. Monthly averaged values were also compared providing good agreement, with R2 ? 0.7 for each platform.

An Efficient Multiscale SRMMHR (Statistical Region Merging and Minimum Heterogeneity Rule) Segmentation Method for High-Resolution Remote Sensing Imagery

Abstract: Multiscale segmentation is an essential step for higher level image processing in remote sensing. This paper presents a new multiscale SRMMHR segmentation method integrating the advantages of Statistical Region Merging (SRM) for initial segmentation and the Minimum Heterogeneity Rule (MHR) for object merging. The high-resolution (HR) QuickBird imageries are used to demonstrate the SRMMHR segmentation method. The SRM segmentation method not only considers spectral, shape, and scale information, but also has the ability to cope with significant noise corruption and handle occlusions. The MHR used for merging objects takes advantage of its spectral, shape, scale information, and the local and global information. Compared with the Fractal Net Evolution Approach (FNEA) that eCognition adopted and SRM methods, the results show that the proposed method wipes off small redundant objects existed in traditional SRM methods, avoids the phenomena where the big homogeneity region has lots of small similar regions existed in the FNEA method, and gets more integrated and accurate objects. Therefore, the proposed SRMMHR segmentation method is an efficient multiscale segmentation method for HR imagery.

Impact of Radiometric Calibration and Specifications of Spaceborne Optical Imaging Sensors on the Development of Operational Automatic Remote Sensing Image Understanding Systems

Abstract: The development of operational automatic remote sensing (RS) image understanding systems (RS-IUSs) represents a traditional goal of the RS community. Unfortunately, to date, the transformation of huge amounts of multisource, multiresolution Earth observation (EO) imagery into information still remains far below reasonable expectations. The original contribution of this work to existing knowledge on the subject of automating the quantitative analysis of EO images is fourfold. In primis this paper moves from existing literature to consider the radiometric calibration of RS images a necessary, although not sufficient, condition for implementation of operational automatic RS-IUSs. This requirement complements the traditional perception of calibration and validation (Cal/Val)-related activities as crucial in achieving harmonization and interoperability of multisource EO data and derived information products generated at all scales as envisaged under: (i) the Global Monitoring for the Environment and Security (GMES) project, led by the European Union (EU), and (ii) the Global Earth Observation System of Systems (GEOSS) program, conceived by the Group on Earth Observations (GEO) whose space arm, the Committee of Earth Observations (CEOS), recently delivered a Quality Assurance Framework for Earth Observation (QA4EO) data. The second objective of this paper is to solicit the RS community to further investigate the calibration quality and uncertainty of the well-known Satellite Pour l'Observation de la Terre (SPOT) and Indian Remote sensing Satellite (IRS) imaging sensor series whose zero-value offset parameters appear questionable based on experimental evidence. Third, this work provides a quantitative assessment of the spectral information loss that, in comparison with the ongoing SPOT-4/-5 optical sensors, may affect future planned European EO satellites such as Pleiades-1/-2 and the follow-on missions Astrium SPOT-6/-7. Finally, this work shows that, in several recent or ongoing scientific applications of EO images acquired across time, space, and sensors, the mandatory radiometric calibration preprocessing stage appears to be surprisingly ignored or underestimated by EU space agencies and research institutions that have been members of the CEOS for more than twenty years and should be eager to transform the new QA4EO initiative into RS common practice.

Generation of a Species-Specific Look-Up Table for Fuel Moisture Content Assessment

Abstract: This study involved the generation of a species-specific Look-Up Table (LUT) for the retrieval of Fuel Moisture Content (FMC) in natural areas dominated by Quercus ilex (Holm oak). Parameter combinations observed in drying Q. ilex samples were used as inputs into the linked PROSPECT and SAILH Radiative Transfer Models (RTM) to avoid unrealistic simulated spectra in the LUT. Terra/MODIS reflectance data, extracted over five plots dominated by Q. ilex, were used to carry out the LUT inversion. This inversion was based on the search for the minimum relative root mean square error (RMSErho*) between observed and simulated reflectance found in the LUT. Different inversion options were tested in order to search for the optimal spectral sampling necessary for accurately estimating FMC. The minimum number of solutions required for the calculation of the estimated FMC was also investigated. The retrieval performance was evaluated with FMC values measured at the five study plots. The most accurate FMC estimation was obtained when using the normalized difference infrared index (NDII6 ) and selecting the ten best cases as the solution (RMSE=26.28%). Finally, a non-oak-specific LUT (generic LUT) was used in the same way to evaluate whether or not the species-specific LUT retrieved FMC more accurately. The results showed that the species-specific LUT provided more accurate FMC estimations than the generic LUT. Only when the number of solutions was higher than 35 was the accuracy of the two LUT similar. Future work will focus on the possibility of generating a LUT adapted to a wider range of species based on data extracted from field measurements and literature.

Estimating Particle Sulfate Concentrations Using MISR Retrieved Aerosol Properties

Abstract: Understanding the spatial distribution of fine particle sulfate (SO4 2-) concentrations is important for optimizing emission control strategies and assessing the population health impact due to exposure to SO4 2-. Aerosol remote sensors aboard polar orbit satellites can help expand the sparse ground monitoring networks into regions currently not covered. We developed a generalized additive model (GAM) using MISR fractional aerosol optical depths (AODs) scaled by GEOS-Chem aerosol profiles to predict ground-level SO4 2- concentrations. This advanced spatial statistical model was compared with alternative models to evaluate the effectiveness of including simulated aerosol vertical profiles and adopting an advanced statistical model structure in terms of improving the AOD- SO4 2- association. The GAM is able to explain 70% of the variability in SO4 2- concentrations measured at the surface, and the predicted spatial surface of annual average SO4 2- concentrations are consistent with interpolated contours from ground measurements. Comparisons with alternative models demonstrate significant advantages of using model-scaled lower-air fractional AODs instead of their corresponding column values. The nonlinear association between SO4 2- concentrations and fractional AODs makes the GAM a more suitable model structure than conventional linear regressions.

The Added Value of a Proposed Satellite Imager for Ground Level Particulate Matter Analyses and Forecasts

Abstract: Monitoring aerosols over wide areas is important for the assessment of the population's exposure to health relevant particulate matter (PM). Satellite observations of aerosol optical depth (AOD) can contribute to the improvement of highly needed analyzed and forecasted distributions of PM when combined with a model and ground-based observations. In this paper, we evaluate the contribution of column AOD observations from a future imager on a geostationary satellite by performing an Observing System Simulation Experiment (OSSE). In the OSSE simulated imager, AOD observations and ground-based PM observations are assimilated in the chemistry transport model LOTOS-EUROS to assess the added value of the satellite observations relative to the value of ground-based observations. Results show that in highly polluted situations, the imager AOD observations improve analyzed and forecasted PM2.5 concentrations even in the vicinity of simultaneously incorporated ground-based PM observations. The added value of the proposed imager is small when considering monthly averaged PM distributions. This is attributed to relatively large errors in the imager AODs in case of background aerosol loads coupled to the fact that the imager AODs are column values and an indirect estimate of PM. In the future, model improvements and optimization of the assimilation system should be achieved for better handling of situations with aerosol plumes above the boundary layer and satellite observations containing aerosol profile information. With the suggested improvements, the developed OSSE will form a powerful tool for determining the added value of future missions and defining requirements for planned satellite observations.

Statistical Characteristics of the Global Surface Current Speeds Obtained From Satellite Altimetry and Scatterometer Data

Abstract: Near-real time ocean surface currents derived from satellite altimeter (JASON-1, GFO, ENVISAT) and scatterometer (QSCAT) data on 1deg times 1deg resolution for world oceans (60deg S to 60deg N) are available online as ldquoOcean Surface Current Analyses-Real Time (OSCAR).rdquo The probability distribution function (PDF) of the current speeds (omega), constructed from global OSCAR data from 1992 to 2008, satisfies the two-parameter Weibull distribution reasonably well, and such a PDF has little seasonal and interannual variations. Knowledge on PDF of omega will improve the ensemble horizontal flux calculation, which contributes to the climate studies.

Geometric Processing of QuickBird Stereo Imageries for Urban Land Use Mapping: A Case Study in Shanghai, China

Abstract: This paper developed an approach for urban land use mapping with QuickBird stereo imageries. The accuracy of ground point determination was first improved by RPCs regeneration using the second-order polynomial model for bias correction in the image space. In urban land use mapping, DEM was generated from the stereo pair using the refined RPCs and then an orthoimage was generated. Urban features such as roads, greenbelts, and waters were digitized from the orthoimage, and buildings were extracted using an approach of stereo positioning. The result demonstrated that a planimetric mapping accuracy of 0.8 m could be achieved.

Satellite Remote Sensing and Mesoscale Modeling of the 2007 Georgia/Florida Fires

Abstract: During April and May 2007, several hundred fires burned uncontrollably in Georgia and Florida. The smoke from these fire events were visible throughout the Southeastern United States and had a major impact on particulate matter (PM) air quality near the surface. In this study, we show the strength of polar orbiting and geostationary satellite data in capturing the spatial distribution and diurnal variability of columnar smoke aerosol optical depth from these fires. We quantitatively evaluate PM air quality from satellites and ground-based monitors, near and far away (> 300 km) from fire source regions. We also show the changes in organic carbon concentrations (a tracer for smoke aerosols) before, during and after these fire events. Finally, we use fire locations and emissions retrieved and estimated from satellite observations as input to a regional mesoscale transport model to forecast the spatial distribution of aerosols and their impact on PM air quality. During the fire events, near the source regions, total column 550 nm aerosol optical thickness (AOT) exceeded 1.0 on several days and ground-based PM2.5 mass (particles less than 2.5 mum in aerodynamic diameter) reached unhealthy levels ( > 65.5 mug m-3). Since the aerosols were reasonably well mixed in the first 1-2 km (as estimated from meteorology), the column AOT values derived from both geostationary and polar orbiting satellites and the surface PM2.5 were well correlated (linear correlation coefficient, r > 0.7). Several hundred miles away from the fire sources, in Birmingham, AL, the impact of the fires were also seen through the high AOT's and PM2.5 values. Correspondingly, PM2.5 mass due to organic carbon obtained from ground-based monitors showed a three fold increase during fire events when compared to background values. Satellite data were especially useful in capturing PM2.5 air quality in areas where there were no ground-based monitors. Although the mesoscale transport model captured the timing and location of aerosols, when compared to observations, the simulated mass concentrations are underestimated by nearly 70% due to various reasons including uncertainties in fire emission estimates, lack of chemistry in the model, and assumptions on vertical distribution of aerosols. Satellite products such as AOT, fire locations, and emissions from space-borne sensors are becoming a vital tool for assessing extreme events such as fires, smoke, and particulate matter air quality.

Applications of Satellite-Based Sulfur Dioxide Monitoring

Abstract: Sulfur dioxide is emitted by volcanoes, produced by combustion of fossil fuels or smelting of ores, and is an intermediate product from organic sources in the ocean. It is rapidly oxidized to sulfuric acid, which causes acidic pollution of lakes and streams and forms an aerosol that is important in climate change. Volcanic sulfur dioxide is a useful marker for ash clouds that are a hazard to aircraft. Satellites offer the best platform to monitor SO2 sources and to track volcanic clouds. UV remote sensing instruments have measured eruption plume masses since 1978. Newer instruments are sensitive enough to also measure volcanic degassing, emissions from power plants, refineries, smelters, and heavy air pollution episodes. New retrieval algorithms have improved the data quality. The observations are used to constrain models of eruption processes and to monitor activity of all volcanoes in a consistent manner. The practical applications of the satellite data include aviation safety, air quality, environmental control, climate modeling, and atmospheric dynamics modeling.

The Geospatial Service Infrastructure for DLR's National Remote Sensing Data Library

Abstract: This paper describes the motivation, requirements, and challenges of integrating a geospatial infrastructure, based on standardized web services, into an earth observation (EO) data library The design of harmonized data and information models of the EO and geospatial community is a precondition for interoperability at metadata, data and semantic levels A major challenge arises from raising the awareness that interoperability is essential for an interdisciplinary use of EO data in Geographic Information System (GIS) and value-adding services

A Web-Based Subsetting Service for Regional Scale MODIS Land Products

Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor has provided valuable information on various aspects of the Earth System since March 2000. The spectral, spatial, and temporal characteristics of MODIS products have made them an important data source for analyzing key science questions relating to Earth System processes at regional, continental, and global scales. The size of the MODIS product and native HDF-EOS format are not optimal for use in field investigations at individual sites (100 × 100 km or smaller). In order to make MODIS data readily accessible for field investigations, the NASA-funded Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics at Oak Ridge National Laboratory (ORNL) has developed an online system that provides MODIS land products in an easy-to-use format and in file sizes more appropriate to field research. This system provides MODIS land products data in a nonproprietary comma delimited ASCII format and in GIS compatible formats (GeoTIFF and ASCII grid). Web-based visualization tools are also available as part of this system and these tools provide a quick snapshot of the data. Quality control tools and a multitude of data delivery options are available to meet the demands of various user communities. This paper describes the important features and design goals for the system, particularly in the context of data archive and distribution for regional scale analysis. The paper also discusses the ways in which data from this system can be used for validation, data intercomparison, and modeling efforts.

The PROMOTE UV Record: Toward a Global Satellite-Based Climatology of Surface Ultraviolet Irradiance

Abstract: This paper describes the PROMOTE UV Record, which aims to provide a global long-term record of the surface UV radiation. The algorithm developed takes as input cloud information from the International Satellite Cloud Climatology Project (ISCCP) and a recently developed multisensor assimilated record of the total ozone column. Aerosols and surface albedo are based on climatologies. Here, first validation results of the PROMOTE UV Record are presented through comparison against ground-based measurements of daily erythemal UV doses at eight European stations. The validation shows that the method is working reasonably, although there is a clear tendency toward overestimation. Typically, the median bias as compared to measurements is 3%-10% and 56%-68% of the daily doses are within plusmn20% from the ground-based reference. The prototype version of the PROMOTE UV Record included in this paper covers the period from July 2002 to June 2005. The time series will later be extended to start in 1983.

Operational Delivery of Hydro-Meteorological Monitoring and Modeling Over the Australian Continent

Abstract: The Australian Water Availability Project (AWAP) is a system that operationally delivers weekly estimates of soil moisture stores and water fluxes at continental scale over Australia. The highly modularized system implements a miniature spatial data infrastructure by exploiting a simple data format standard and metadata scheme to enable the flexible ingestion of a variety of input data types, including gridded meteorological fields, land surface parameterizations and, optionally, remote sensing data. The use of these standards, together with a client-server architecture and portable coding, enable the system to function across multiple interchangeable computers, leading to a robust system with a high degree of redundancy. Through a well-defined interface, the framework supports the development and testing of multiple models. Thorough model and data version-control and log file capture also allows automated operational runs in the same environment as that in which models are built and tested. The system includes a web portal (http://www.csiro.au/awap) that provides a variety of ways for data users to dynamically explore and examine output (which currently includes over a century of data for the Australian continent at monthly intervals, in addition to weekly near-real-time products) in summary or extended forms.

Fast Coherent Signal Subspace-Based Method for Bearing and Range of Buried Objects Estimation in the Presence of Colored Noise

Abstract: In this paper, a new method for simultaneous range and bearing estimation for buried objects in the presence of an unknown colored noise is proposed. We propose a method based on multiple signal classification (MUSIC) with a new source steering vector which includes both the reflection and the refraction of wave at water-sediment interface at each sensor. The bearing and the range objects at each sensor are expressed as a function of those at the first sensor. To reduce the computational load we propose the fixed-Point algorithm, which it is faster than singular value decomposition (SVD) for MUSIC, to compute only the leading eigenvectors. A novel iterative denoising algorithm based on the noise subspace spanned by the eigenvectors associated with the smallest eigenvalues is developed when the noise spectral matrix is one unknown band matrix. The bilinear focusing operator is used to decorrelate the received wideband signals and to estimate the coherent signal subspace. The performance of the proposed algorithms is evaluated by computer simulations. Finally,we test the proposed algorithms on experimental data recorded during underwater acoustic experiments.

Spatial Analysis and Temporal Simulation of Landscape Patterns in Two Petroleum-Oriented Cities

Abstract: Rapid urbanization has been recognized as a critical process in urban areas. This research focuses on the comparison of landscape dynamics in two petroleum-based cities: Houston, TX, and Daring, Heilongjiang Province, China. Based on the multitemporal satellite images, a general trend of landscape change was revealed in these two cities: natural landscapes such as grassland and wetland were degraded into a more heterogeneous pattern, while the human landscapes such as residential area expanded and replaced other natural classes gradually. To further investigate the spatio-temporal process in these two petroleum-oriented cities, the Markov stochastic model was adopted to simulate their landscape dynamics. The results of this study provide evidence of the threatened natural landscapes and environment deterioration during the urbanization processes in two petroleum-oriented cities. This research also demonstrates that the integration of remote sensing and GIS model is an effective approach for comparing spatial pattern and temporal process of urban landscapes.

Foreword to the Issue on Remote Sensing of Regional Land Use and Land Cover

Abstract: This issue showcases some good work from Chinese authors who attended the International Workshop of Earth Observation and Remote Sensing Applications (EORSA), held in Beijing, China, June 30-July 2, 2008. The five articles in this issue focus on remote sensing of regional land use and land cover.