Wednesday, January 14, 2009

Landscape Monitoring: Challenges to remote sensing production, distribution and archiving

Landscape Monitoring: Challenges to remote sensing production, distribution and archiving:
Lecture on Quantitative Remote Sensing for Terrestrial Monitoring

Archaeologists call the remote sensing technology the “eyes in the sky” as it sees archaeological sites without actually excavating it. In agricultural research, the same term is used to mean remote sensing (Senft, 1996). Hydrology researchers working with the U.S. Department of Energy are "seeing" through solid ground, using aerial remote sensing techniques, to create three-dimensional images of the flow of groundwater as deep as 1,000 feet below the earth's surface (Anna, 2003).

The so-called “eyes in the sky” has been remarkably useful to a large number of disciplines: geographers, cartographers, foresters, geologists, oceanographers, meteorologists, ecologists, coastal managers, urban planners, military, and professionals in a wide variety of other areas – all rely on remotely sensed data.

As more and more disciplines want remote sensing data; as additional remote sensing capabilities emerge; as the swift progression of online geospatial and location-based services make transmission and sharing of earth knowledge easier, key challenges become obvious as a result. Conventional remote sensing data production, distribution methods and archiving may not be adequate for various end users – novice and advance user communities – to effectively and efficiently use the technology’s products.

Key challenge 1: On Data Production
The potential of remote sensing lies in its ability to provide qualitative and quantitative information (Servilla, 1998). In lieu of this, the first challenge is to produce (that includes the process and integration) the physical data and metadata as individual data sources and as compound datasets for analysis. Obtaining data and imagery and producing an application is long and complex, involving individuals and organizations with diverse requirements and needs (NAS, 2001). For instance, a dataset consisting of both an optical image and a microwave image will require different analysis methods for each subset, to meet parallel goals.

Another constraint under data production is the swift advancements or the evolution of technology that renders equipment and remote sensing software obsolete in a short period of time. Even if equipment still performs the desired functions, it may no longer be supported by the company it was purchased from, whether due to mergers or companies going out of business (Lowenberg-DeBoer, 1999). Thus, the end user may be frustrated at the rate of change in technology, which can keep back the adoption of the technology as a whole. This is not unusual with new, evolving technologies; however, it has been widely recognized as a major drawback in the adoption of remote sensing (Johannsen et al., 2000).

Also, as data sources and data sets become available, maintaining the mounting volumes of data in forms that are, not just easily accessible, but comprehensible and that meet the needs of very varied user communities presents other intellectual challenges that are at least the equal of the challenges of building a hardware into outer space.

Behind the scenes activities of scientists are very crucial to producing and delivering information to professionals who utilize the data to conduct their work. Scientists spend much of their time analyzing the technical details of airborne and spaceborne sensors. They develop methodologies to process collected data. They also develop new techniques for building reconnaissance satellites that can be launched on short notice as needs arise. They continue to work on expanding the use of non-visible fractions of the electromagnetic spectrum (particularly infrared and microwave), and think of ways to increase the number of active sensors such as lidar and radar, and to improve the spatial and temporal sampling. All these and more are challenges that remote sensing specialists face in order to understand and model the earth system as accurately as possible.

Key challenge 2: On Data Distribution
Data and information distribution is as important as data production. According to Qu et all. 2002, remote sensing data distribution has a big challenge and cited the following crucial issues:
1. Huge data volumes;
2. Complex data formats, such as, HDF (Hierarchical Data Format) and HDF-EOS
(Hierarchical Data Format Earth Observing System);
3. Different map projections;
4. Geographic information system (GIS) applications;
5. Communication protocol and capability; and
6. Processing time.

Qu et all. 2002 added that customized real-time remote sensing data with GIS/Web-GIS compatible formats may become very essential for a lot of end users. End users need to acquire Earth observing remote sensing data in more useful forms. A more widely distributed data in different formats through diversified protocols will result in better usage of the observing satellite systems. To address the data distribution issues, data compressing and pre-processing (sub-setting and sub-sampling), data format conversing (easy accessing data format such as, GIS compatible format), GIS and Open GIS applications, and simple real time data processing are necessary.

It is foreseen that remote sensing data will soon be accessible to the ordinary citizen on the street. Someone with a handheld wireless device will be able to access satellite data from the Internet, overlay it with GIS coordinates, and obtain on-the-spot atmospheric information for any location on the planet.

Another challenge is to have users’ homes link up to atmospheric satellite data and monitor their own internal environments appropriately. The answer to the challenge lies on sensor networks that will be smart, upbeat, and able to act in response to environmental changes at lightning speed sans human help. The network will allow the information to be combined so as to support rapid decisions in complex situations. In this fashion, remote sensors will enable computers not only to view their environment, but also to shape their physical surroundings.

Key challenge 3: On Data Management and Archiving
Remotely sensed imagery has evolved a great deal since it was first introduced. The bulk of satellite data generated in a month now, which would have taken ten years to produce a decade ago, leads to the question of how the data are to be managed. Mark Gray, a senior programmer at NASA, says the terabyte of raw data NASA collects from satellites every day -- equal to one trillion bits of information -- is beginning to strain computational capacity. To improve its storage potentials, NASA is taking undue credit on commercially driven improvements by partnering with private-sector companies.

Data management and archiving ensure data preservation (Bewley et al., 1998). The only strategy for long-term preservation of remote sensing data is for them to be systematically collected and archived to preserve information whatever the medium on which the information is stored and in the end they be made accessible to users operating in diverse computing environments.
It is hoped that in the future, millions of low-cost devices embedded throughout the environment will add to the data management challenge. At the University of Southern California's Information Sciences Institute, researchers are working to resolve the problem by crafting systems that convey intelligence to the sensors themselves, a tactic based on a growing technology called "intelligent multitasking."

A computer scientist suggests that embedded intelligence would do away with the necessity for a centralized data processing facility as each sensor would hold a microcomputer and some other communications abilities, providing for collaborative signal processing and the ability to make "group decisions" about which data to send and when.

Finally, to monitor, understand and model the earth system using remote sensing technology is an exciting clash between the vision of what needs to be accomplished and the reality of the resources at hand and the commitment that comes along. The outcome of the confrontation has long-term implications for the remote sensing producers and users, for the society and the earth as a whole.

Anna, David. 2003. Remote sensing technology maps flow of groundwater from the air. National Energy Technology Laboratory (NETL).

Bewley, R., Donoghue, D., Gaffney, V., Leusen, M.V., Wise, A. 1998. Archiving Aerial Photography and Remote Sensing Data: A Guide to Good Practice. Section 5 - Archiving Your Dataset.

Johannsen, C. J., P. G. Carter, D. K. Morris, K. Ross, and B. Erickson. 2000. The real applications of remote sensing to agriculture. Pages 1–5 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.

Lowenberg-DeBoer, J. 1999. Risk management potential of precision farming technologies. J. Agric. Appl. Econ. 31:275–285.

National Academy of Sciences (NAS). 2001. Transforming remote sensing data into information and applications. Steering Committee on Space Applications and Commercialization Space Studies Board Division on Engineering and Physical Sciences and Ocean Studies Board, Division on Earth and Life Studies, National Research Council.

Qu, J.J., Kafatos, M. and Yang, R. 2002. New challenge of remote sensing data processing and distribution for future earth observing satellite system. Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings.


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