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Agriculture/Soil
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Utilization Ofstatellite Remotely Sensed Data As An Information Source for a GIS In Agricultural Resource Management
Deriving Base Maps from Image Data
A very important application of high spatial resolution satellite data is their use as abase map, especially in those application where no other reliable data is available (Gastella-Etchegorrg 1989).
Digital satellite data have a number of advantages over conventional photo-grametric methods in terms of providing cartographic information (Swann 1988). Satellites provide regular, repeat coverage, and the required data can often be simply extracted from an archive. The data are inherently digital, and so can be used directly in digital cartographic production systems. Data costs are often much less than for an equivalent aerial photographic coverage, and the cost of establishing ground control is very much less when using near-nadir satellite imagery.
Both visual interpretation and digital image processing software's were used in the project. The below stages have been concerned;
- Visual image interpretation of TM quadrants of three different dates, covering whole province according to the legend approved by Project Manager; containing seven major classes (Forests, Cultivated area, Ranges, Wetlands, Bare lands, Water resources, Constructed area) divided into 24 subclasses and four mixed classes. In addition, another features such as Roads, Rivers, International and Provincial boundaries were mapped.
- Digital image processing
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Mosaicking of TM quadrants, covering the whole province and masking the interested area
- Image rectification, geometric correction of the mosaicked image, using 1.50,000 scale topomaps and ground control points (GCP) selection (image/map registration). Geometric correction of an image is necessary because in GIS applications the reference coordinate system is the foundation of the database. Without a coordinate system coverages could not be accurately overlayed compared or used for analysis. Once a coordinate system has been selected ground control points could be selected.
- Unsupervised classification of masked area to identify main agricultural regions and obtain the auxiliary information to be used in the further stages.
- Ground checking of unsupervised classification results to select the best areas for training sites.
- Concerning to the noticeable forest areas in the mentioned province, these areas were masked out to decrease the disk space and data processing time.
- Pre-precessing, enhancement and supervised classification of relative images. It worth to mention to raise the spectral separability, different kinds of data manipulation such as signature separability assessment, principal component analysis, feltering and the like were applied.
- Filed sample checking, using GPS facilities to determine the reliability of the classification and correct possible errors by statistical methods. In this step, the constant geodethic points were used as base stations for differential positioning with GPS.
- Plotting a hardcopy of classified image after all corrections.
By combination of the information derived from visual interpretation and digital image processing, the L/LC maps of the province in 1 : 100,00 scale sheets, with30' by 30' geographic coordinate were mapped.
Creation of the Geographic Information System
Two types of analyses that could use satellite imagery to create GIS coverages are 1 when polygon boundaries are defined by another process but attributes are determined from the imager. It is important to remember that the satellite data will need to be converted into an acceptable format by the Geographic Information System.
Satellite data generally have a raster data structure, which has considerable advantages during image processing operations. In contrast, the structure of data commonly used to represent geographical phenomena are vector based. At present, the majority of GIS use a vector data structure. Raster to vector data conversion is an essential step that can cause many problems to be resolved before digital image data relating to natural resources can be incorporated automatically into GIS. This is the generalization of classified digital image. Data to the mapping scales commonly encountered of classified digital image data to the mapping scales commonly encountered in GIS (Ehlers at et al. 1989). The remotely sensed data that offer the most benefits for natural resource mapping currently come from the Landsat TM or SPOT satellites, whose sensors have ground resolutions of 30 to 20 meter, respectively. For the purpose of inclusion in GIS, these image data must be generalized to homogeneous polygons that cover an area, for example, of 100 or more pixel data. The generalization is well defined when each of the features being mapped can be uniquely classified, with the classified image consisting of relatively large domain-areas of the image which comprise a single class, with the possible exception of a relatively few, isolated, pixels from other classes. Under these circumstances, an edge detection filter can be applied to define domain boundaries, a median or contextual filter used to generalize the data (e.g. Both et al. 1989) and the intersection of the edge and median filtered data used to generate the general image prior to raster-to-vector conversion.
The operational procedure to create the GIS is outlined below;
Input spatial data
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Digitizing basic geographic data of the province, using 1 : 2500,000 scale topomaps. 1 : 50,000 topomaps.
- Digitizing contour lines layer from 1 : 50,000 topomaps.
- Scanning Landuse/Landcover maps derived from satellite data, rester-to-vector data conversion, create LU/LC data layers.
- Editing and create topology of the data, gathered in the previous steps.
- Building the geographic database, input attribute data of each geographic feature and any relative information.
- Data manipulation, projection, transformation, map-joining, edge-matching.
Location of the Study area in North of Iron Gilan Province
A Scaled seet of Landuse/Landcovi maps; derived from satellite data
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