ul 2004 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004
Table 2. Suggested periods for taking the images
Season State assessment Control
1. March - April - assuming the hazard of flood and surplus water |- distinguishing the cultivated and
- study of soil facilities not disturbed areas
- Identification of fields of winter and perennial
crop (wheat, alfalfa, etc.)
- | surveying of the expansion of wetlands
2. April-Mai - assuming the hazard of flood and surplus water |- ^ protection of breeding habitats
- identification of fields of winter and perennial |- limitation of grazing and
crop (wheat, alfalfa, etc.) assignation of expansion of
- | identification of buffer zones filtering zones
3. July-September - evaluation of salinity processes - limitation of grazing and
- risk and damage estimation of drought assignation of expansion of buffer
- identification of fields of the vernal crop zones
(maize, sugar beet, etc.) - the expansion of stubble burning
(table - Soil status
: There are many rule points in NAKP, where these Running, S.W., Nemani, R. R., Peterson, D.L., 1989.
remotely sensed images can be applicable for Mapping regional forest evapotranspiration and photosyntesis
identification and control of the time and spatial frame by coupling satellite data with ecosystem simulation. Ecology
of natural phenomena. 70, pp. 1090-1101.
For example: Checking of growing areas of the plants
appropriate to the cropping plans (5-10 years), Running, S. W., Loveland, T. R., Pierce, L. L., Nemani, R.
distinguishing the small spots of plant associations from R., Hunt Jr., E. R., 1995. A remote sensing classification
the cultivated lands, etc. logic for global land cover analysis. Remote Sensing of
- A High resoluted and multispectral data can give spatial Environment, 51, pp. 39-48.
information for detecting the land cover and controlling
the cropping. RS data taken by airborn digital Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S.,
multispectral camera can be used for updating the Briggs, J.M., 1999. Relationships between Leaf Area Index
contents of GIS and Landsat TM Spectral Vegetation Indices across three
temperate zone sites: Remote Sensing of Environment, 70, pp.
52-68.
d use. REFERENCES
cation Zilinyi V., 1995. Természetes felszínek spektrális reflexiós
o the tulajdonságai, es hasznosításuk az optikai távérzékelés
ivated Angyan, J., Fésüs, L., Podmaniczky L., Tar F., Vajnáné, M., interpretaciojában. Egyetemi Doktori Ertekezes, DATE,
A., 1999. Nemzeti Agrár-kórnyeztvédelmi Program. FVM MTK, Debrecen, pp. 24-43.
agrár-kórnyeztgazdálkodási tanulmánykótetek. Budapest
cain Carpenter, G. A., Gopal, S., Macomber, S., Woodcock, C. E.,
F the Franklin, J., 1999. A neutral network method for eficcent
ng of vegetation mapping. Remote Sensing of Environment, 70. pp.
reases 326-338.
small DeFries, R. S., Townshend, J. R. G., 1994. NDVI-derived
d well land cover classification at global scales. International
large Journal of Remote Sensing, 15, pp. 3567-3586.
3 Friedl, M., Brodley, C., 1997. Decesion tree classification of
ar are land cover from remotely sensed data. Remote Sensing of
id the Environment, 61, pp. 399-409
ion of
ap can Floyd F. Sabins, 1996. Remote Sensing. Principles and
Interpretation. W. H. Freeman and Co. Los Angeles
L. L. F. Janseen, M. N: Jaarsma, E.T.M. van der Linden,
1990. Integrating Topographic data with Remote Sensing for
Land-Cover Classification. Photogrammetric Engineering &
Remote Sensing, Vol. 56, No. 11, pp. 1503-1506.
Martonchik, J. V., 1994. Retriaval of surface directional
reflectance properties using ground level multiangle
measurements. Remote Sensing of Environment, 50, pp. 303-
316.
89
