Lenz & Stary (1995) point out the need to use clear concepts in planning processes. The Geographic Information System (GIS) techniques are useful in this respect as they allow for multi-disciplinary approaches and complex data to be used in landscape planning in a comprehensible way (Cengiz, 2009; Cengiz et al., 2011; Ozyavuz & Yazgan, 2010; Tixerant et al., 2010). While generic classifications that lacked in depth and detail were used widely in forming landscape typologies (Antrop, 2000, as cited in Van Eetvelde & Antrop, 2007), recent practices make it possible to obtain detailed landscape typologies, such as zoning which demonstrates different uses in the selected areas (Clark, 1996) and the typologies based on GIS techniques like overlay and spatial analysis. A landscape classification depends on aspects such as the aim of classification, the way of defining the landscape through typology or chorology, the method chosen between holistic and parametric methods, the data quality and the hierarchical scales (Van Eetvelde & Antrop, 2007).
Use of GIS in landscape planning brings substantial advantages. Content and technical requirements and standards are necessary which enable reciprocal data exchange so that the data of other sectoral administrations can be used for landscape planning and so that the results of landscape planning can be incorporated in the information systems and planning of other technical disciplines. Consideration of the relevant existing standards (e. g. ISO standard 19115 for documentation using metadata) in GIS-assisted landscape planning makes it easier, or indeed makes it possible in the first place, to forward and make use of the data and information acquired within the scope of landscape planning in a relatively uncomplicated way. In addition, it would be important to increase the use of standard methods, classifications and structuring in order to merge information from different landscape planning, e. g. within the scope of an SEA or Environmental Impact Assessment (EIA). The use of GIS supports the integration of landscape planning content during the planning process is made easier (Haaren et al., 2008):
• The plan produced is no longer a comprehensive data packet which remains unchanged until it is updated. The use of GIS enables the plans to be updated as needed with little effort. Independent of this, the nature conservation concept must be evaluated at suitable intervals and changed if necessary.
• The data on which landscape planning is based can be directly evaluated for pending planning tasks and if necessary linked with other information. This makes it easier to use landscape planning for other planning, because the planning authorities can specifically retrieve the contents of landscape planning according to their requirements.
“Remote sensing — by satellites such as LANDSAT (USA) and SPOT (France)-is very helpful in planning process. The high spatial resolution of multiband radiometers on LANDSAT and SPOT, well proved for land survey, also works moderately well for shallow-water survey (where waters are clear and cloud cover is low). Remote data have their best use in coastal zone planning and management when coupled to digital mapping and GIS technology (Salm, et al, 2000)."
“Remote Sensing Platforms: Light reflectance-based remote sensing technologies can generally be grouped according to the resolution (pixel size) of the resulting data. This resolution is affected by both the altitude of the platform from which data are collected and the design of the instrument or camera. Low-resolution satellite platforms such as NASA’s SeaWIFS (Sea Viewing Wide Field-of-View Sensor) and NOAA’s AVHRR (Advanced Very High Resolution Radiometer) produce images where each pixel represents an area of 1 to 10 sq. km. Moderate-resolution satellite platforms such as LandSat, SPOT, and human-occupied spacecraft (e. g., the Space Shuttle or International Space Station) produce images where each pixel represents an area of 10 – 30 sq. meters. Instruments mounted on fixed wing aircraft and helicopter platforms produce images where each pixel represents an area of 1 – 5 sq. meters. Classified remote sensing platforms from the National Technical Means (NTM) Programme produce images where each pixel represents an area of less than 1 sq. meter (Salm, et al, 2000)."
