KEYWORDS: Earth observing sensors, Satellites, Landsat, Remote sensing, Spatial resolution, Satellite imaging, FDA class I medical device development, Geographic information systems, Vegetation, Data acquisition
Rapid urbanization has been an important social and economic phenomenon in the last 50 years. Our study analyzes the spatial–temporal landscape pattern in the National Capital Region (NCR) of Delhi, one of the most rapid urbanization areas in the world. Delhi metropolitan area and its surrounding satellite cities exhibit a soaring rate of landscape pattern change during the last two decades. A set of landscape metrics with supplementary ecological meaning was chosen to study the changes of landscape pattern in NCR. The results indicate that the rapid urbanization has brought enormous landscape changes in NCR, and consequently, substantial impacts on its landscape pattern. The most “active” landscapes are farmland and impervious surface, as the major landscape change (41.46%) is found in the transition from farmland landscape to impervious-surface landscape. Meanwhile, the landscape pattern is fragmented into a more heterogeneous pattern in both farmland and urban landscape with more irregularly shaped patches during urbanization. Our research confirms the effectiveness and applicability of a combination of remote sensing, geographic information systems, and landscape metrics in revealing spatial–temporal of landscape change throughout rapid growth periods.
Mapping plastic-mulched landcover (PML) is an important agricultural monitoring task. Because of its daily revisiting, imagery from moderate-resolution imaging spectroradiometer (MODIS) has been widely used to detect PML over a large area. However, the coarse spatial resolution and small field size make subpixel PML a problem for accurate PML mapping from MODIS. This study applies the improved spatial attraction model (ISAM), which estimates the spatial attraction of the central subpixel of a moving window by all subpixels in the window, to map large-scale subpixel PML from MODIS imagery. The linear spectral mixing model is used to obtain fractions of PML and three other landcover classes in each MODIS pixel as the inputs to ISAM for obtaining the hard subpixel PML maps at spatial resolution of 31.25 m. The accuracy evaluation, with validated landcover classification derived from Landsat-8 imagery as the ground truth, shows that overall accuracy, Kappa coefficients, producer accuracy, and user accuracy are 83.51%, 0.69, 90.97%, and 81.51%, respectively, indicating large-scale PML mapping at spatial resolution comparable to Landsat-8 can be derived from MODIS images with acceptable accuracy by ISAM. This study provides a practical and economic way for mapping PML for a large area at ∼30-m spatial resolution.
Plastic-mulched landcover (PML) is the land surface covered by thin plastic films. PML has been expanding rapidly worldwide and has formed a significant agriculture landscape in the last two decades. Large-scale PML may impact the regional and global climate, ecosystem, and environment because it changes the energy balance and water cycles of the land surfaces, reduces the biodiversity, deteriorates the soil structure, etc. To study its impact, the spatial and temporal distributions and dynamics of PML have to be obtained. This paper presents a threshold model (TM) for PML detection and mapping with moderate-resolution imaging spectroradiometer (MODIS) time series data. Based on the temporal-spectral features of PML in the early stage of a growing season after planting, a TM was designed with the number of days (d) when the normalized difference vegetation index (NDVI) value is larger than a threshold value (x) as the discriminator. The model has been successfully applied to map PML in southern Xinjiang, China, from the interpolated MODIS NDVI time series (from 90th to 125th day of each year). Results indicate that when TM parameter x is set to 0.2 and d to 8, the overall accuracy and kappa coefficient (κ) are >0.84 and 0.65, respectively. We believe this classification accuracy can meet the PML mapping for large geographic areas. Furthermore, visual comparison between the PML maps from TM classification of MODIS time series and that from the maximum likelihood classification of Landsat ETM+ and OLI images shows they are consistent both in the pattern and location of PML. Therefore, detection and mapping of PML by using MODIS time series with the TM method is feasible. The PML mapping in this study used a cropland mask derived from Landsat images using a maximum likelihood classifier to mask out non-cropland when applying the TM algorithm. The accuracy of such a mask is subject to further study. Because of frequent global coverages of MODIS data, the method presented in this paper could potentially be used for PML detecting and mapping at continental and global scales.
KEYWORDS: Web services, Sensors, Data modeling, Standards development, Systems modeling, Telecommunications, Dynamical systems, Data processing, Interfaces, Translucency
The Open Geospatial Consortium, Inc (OGC) Web Services (OWS) were initially primarily simple synchronous Web
services based on the HTTP transport protocol, which is perfectly valid in the case of simple geoprocessing of simple
data available from local sources. However, with the development of Web-based geospatial technologies, especially the
development of the Sensor Web, a number of limitations have been identified with using HTTP-GET/POST binding in
OGC OWS, which cannot meet the needs of asynchronous communication and operations between clients and services
or in OGC services chain. Asynchronicity in Web services could be achieved in different ways. Callback pattern is
widely supported in client asynchronous invocation. Message-based middleware often can be used together with the
asynchronous invocation alternatives. Web Notification Service (WNS) is designed to provide asynchronous messagebased
communication in OGC. This paper describes a mechanism for an asynchronous, message-based, event-driven,
dynamic geospatial Web system based on OGC Web services. The addition of asynchronicity in OGC Web services has
two components. One is the augmentation of OGC Web services with asynchronous message-based notification. The
other is asynchronous OGC Web service orchestration based on BPEL.
KEYWORDS: Sensors, Data modeling, Process modeling, Surface plasmons, Web services, Performance modeling, Data processing, Environmental sensing, Flame detectors, Computer security
A common Sensor Web data service framework for Geo-Processing Workflow (GPW) is presented
as part of the NASA Sensor Web project. This framework consists of a data service node, a data
processing node, a data presentation node, a Catalogue Service node and BPEL engine. An abstract
model designer is used to design the top level GPW model, model instantiation service is used to
generate the concrete BPEL, and the BPEL execution engine is adopted. The framework is used to
generate several kinds of data: raw data from live sensors, coverage or feature data, geospatial
products, or sensor maps. A scenario for an EO-1 Sensor Web data service for fire classification is
used to test the feasibility of the proposed framework. The execution time and influences of the
service framework are evaluated. The experiments show that this framework can improve the quality
of services for sensor data retrieval and processing.
Google Earth, as one of most popular geospatial data visualization environment, has been used to augment the research
value of Earth science data at NASA Goddard Earth Science Data and Information Service Center. The solutions of how
to use Google Earth to facilitate the sharing and interaction of geospatial data are described and summarized in this
paper first. Some of solutions are applied to two-dimensional mapped data to render the data into Google Earth via
Earth science-specific software and keyhole markup language. A 3D model based innovative method is proposed here
to visualize and display the three-dimensional atmospheric vertical profiles derived from A-Train constellation satellites
in the form of 3D orbit curtain in Google Earth. This visualization capability extends awareness and visibility of NASA
Earth science data to massive Google Earth user groups, including the general public. The availability of many scientific
results in Google Earth enables easy and convenient synergistic research, advancing collaborative and globalized
scientific research on a virtual platform.
Successful data sharing is important to integrating information between the Earth Science and geographic information
communities for knowledge discovery. The Open-source Project for a Network Data Access Protocol (OPeNDAP) is a
data transport architecture and protocol widely used by earth scientists. However, because the DAP and OGC protocols
are incompatible. OPeNDAP lacks effective tools to access the OGC data. To solve this problem, middleware between
the OGC and DAP protocols, must perform two functions--data discovery and data access. This paper discusses
implementation of the data discovery component. This data discovery component first converts an OGC catalog to a
THREDDS catalog encoded in XML, and then provides the XML file to the OPeNDAP client. By developing a catalog
component of the middleware, the implementation enables OPeNDAP clients to discover data cataloged in OGC data
catalogs.
Daniel Mandl, Rob Sohlberg, Chris Justice, Stephen Ungar, Troy Ames, Stuart Frye, Steve Chien, Daniel Tran, Patrice Cappelaere, Linda Derezinski, Granville Paules, Don Sullivan, Liping Di, Stephan Kolitz
KEYWORDS: Sensors, Satellites, Surface plasmons, Web services, Internet, Web 2.0 technologies, Standards development, Data processing, Received signal strength, MODIS
This paper describes the work being managed by the NASA Goddard Space Flight Center (GSFC) Information System
Division (ISD) under a NASA Earth Science Technology Office (ESTO) Advanced Information System Technology
(AIST) grant to develop a modular sensor web architecture which enables discovery of sensors and workflows that can
create customized science via a high-level service-oriented architecture based on Open Geospatial Consortium (OGC)
Sensor Web Enablement (SWE) web service standards. These capabilities serve as a prototype to a user-centric
architecture for Global Earth Observing System of Systems (GEOSS). This work builds and extends previous sensor
web efforts conducted at NASA/GSFC using the Earth Observing 1 (EO-1) satellite and other low-earth orbiting
satellites.
The NOAA/NASA Pathfinder AVHRR Land (PAL) product is one of important data products for studying global vegetation. It contains global NDVI time series derived from NOAA-AVHRR at 8-km and10-day resolutions for the past two decades. Many studies have used the PAL product to analyze the global vegetation dynamics and trends in the past two decades with an assumption that either PAL has no systematic temporal errors or the errors are much smaller than the signals of the global vegetation changes. However, this study finds the significant systematic temporal errors in PAL due to the orbital drifting of NOAA POES satellites that increases the global NDVI value with aging of a satellite. Another source of NDVI errors is the volcanic aerosols, which have large negative effects on global NDVI value in the product. The aerosols brought into atmosphere by June 1991 eruption of Mt. Pinatubo reduced the means of the annual global maximum NDVI by 0.03, 0.067, and 0.027, for 1991, 1992, 1993, respectively. The aerosols from the eruption of El Chichon in 1982 reduced the mean of global maximum NDVI 0.016 NDVI unit for 1982 and 0.022 for 1983. Those errors are larger than signals of global vegetation change due to the climate change. After removing the errors by a statistics-based algorithm, it is found the mean of the annual global maximum NDVI increases at the rate less than 0.005 NDVI units per year.
KEYWORDS: Data modeling, Data archive systems, Geographic information systems, MODIS, Standards development, Data processing, Binary data, Remote sensing, Data centers, Satellites
This paper describes a Web-based server software that provides subsetting, resampling, georectificationon, and reformatting for standard data products of the Earth Observing System (EOS) program initiated by U.S. National Aeronautics and Space Administration (NASA). Designed upon standardized interface specifications, the server allows clients to access EOS data in interoperable, personalized, on-demand manners, facilitating the use of NASA EOS data in research and application communities.
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