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Applications of Remote Sensing in Brief

Applications

Natural resource management is a broad field covering many different application areas as diverse as monitoring fish stocks to effects of natural disasters (hazard assessment).

Remote sensing can be used for applications in several different areas, including:

q       Geology and Mineral exploration
q       Hazard assessment
q       Oceanography
q       Agriculture and forestry
q       Land degradation
q       Environmental monitoring,…

(Each sensor was designed with a specific purpose. With optical sensors, the design focuses on the spectral bands to be collected. With radar imaging, the incidence angle and microwave band used plays an important role in defining which applications the sensor is best suited for.)

Each application itself has specific demands, for spectral resolution, spatial resolution, and temporal resolution.

For a brief, spectral resolution refers to the width or range of each spectral band being recorded. As an example, panchromatic imagery (sensing a broad range of all visible wavelengths) will not be as sensitive to vegetation stress as a narrow band in the red wavelengths, where chlorophyll strongly absorbs electromagnetic energy.

Spatial resolution refers to the discernible detail in the image. Detailed mapping of wetlands requires far finer spatial resolution than does the regional mapping of physiographic areas.

Temporal resolution refers to the time interval between images. There are applications requiring data repeatedly and often, such as oil spill, forest fire, and sea ice motion monitoring. Some applications only require seasonal imaging (crop identification, forest insect infestation, and wetland monitoring), and some need imaging only once (geology structural mapping). Obviously, the most time-critical applications also demand fast turnaround for image processing and delivery - getting useful imagery quickly into the user's hands.

(Let as consider an application, in concrete the use of remote sensing in the forest inventory. Forest inventory is a broad application area covering the gathering of information on the species distribution, age, height, density and site quality.)

For species identification, we could use imaging systems or aerial photos.
For the age and height of the trees, radar could be used in combination with the species information assessed at a first stage.
Density is achieved mainly by an optical interpretation of aerial photos and/or high-resolution panchromatic images.
As for site quality, is one of the more difficult things to assess. It is based on topological position, soil type and drainage and moisture regime. The topological position can be estimated using laser or radar. However, the soil type and drainage and moisture regime could be more profitably collected using ground data.

The use of Remote Sensing in Crop monitoring (real case)

The countries involved in the European Communities (EC) are using remote sensing to help fulfill the requirements and mandate of the EC Agricultural Policy, which is common to all members. The requirements are to delineate, identify, and measure the extent of important crops throughout Europe, and to provide an early forecast of production early in the season. Standardized procedures for collecting this data are based on remote sensing technology, developed and defined through the MARS project (Monitoring Agriculture by Remote Sensing).
The project uses many types of remotely sensed data, from low resolution NOAA-AVHRR, to high-resolution radar, and numerous sources of ancillary data. These data are used to classify crop type over a regional scale to conduct regional inventories, assess vegetation condition, estimate potential yield, and finally to predict similar statistics for other areas and compare results. Multisource data such as VIR and radar were introduced into the project for increasing classification accuracies. Radar provides very different information than the VIR sensors, particularly vegetation structure, which proves valuable when attempting to differentiate between crop types.

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