Special UNISPACE III volume

Marsteller, Deborah

Bibliographic data

Monograph

Persistent identifier:: 856485039

Author:: Marsteller, Deborah

Title:: Special UNISPACE III volume

Sub title:: including: ISPRS Workshop on "Resource Mapping from Space", ISPRS-EARSeL Workshop on "Remote Sensing for the Detection, Monitoring and Mitigation of Natural Disasters", ISPRS-NASA Seminar on "Environment and Remote Sensing for Sustainable Development", July 1999, Vienna, Austria

Scope:: IV, 170 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856485039

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: ISPRS Workshop on "Resource Mapping from Space"

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: SATELLITE REMOTE SENSING APPLICATION IN AGRICULTURECROP MONITORING, YIELD FORESCASTING AND ESTIMATION. Cs. Ferencz, J. Lichtenberger, D. Hamar and P. Bognár

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing. Vol. XXXII Part 7C2, UNISPACE III, Vienna. 1999 53 I5PR5 UNISPACE III - ISPRS Workshop on Resource Mapping from Space” 9:00 am -12:00 pm, 22 July 1999, VIC Room B Vienna, Austria ISPRS temporal profiles for wheat in different counties of Hungaiy in different years can be seen. □ General crop canopy monitoring □ (l.r. data). □ Local crop canopy monitoring □ (h.r. data). □ Crop yield estimation and forecasting □ (l.r. data; or l.r.+kr. □ Investigation and monitoring of land cover (forests, meadows, crops, man-made areas) □ (h.r. data; or h.r.+l.r. data). The CORINE international land cover project produces good examples of this application □ Monitoring of forests and meadows □ (l.r.+h.r. data). □ Monitoring of natural stress and man-made effects on the whole canopy □ (l.r. data; h.r. data). c) Soil investigations; □ Soil moisture monitoring, irrigation assistance □ (kr data in most cases). D Desertification, waste land mapping □ (l.r. data; h.r. data). □ Monitoring of sodification □ (kr. and hyperspectral data). □ Monitoring of soil degradation and destruction □ (h.r. data; or h.r.+l.r. data). d) Agricultural water monitoring: □ Flood monitoring □ (h.r. data in most cases). □ Surface water monitoring (irrigation reserves etc.) □ (kr. data in most cases). At the present time the satellite data bases used in these tasks are mainly the data measured by optical (visible and infrared) sensors. The first cause of this is that at the beginning of satellite remote sensing (meteorology, reconnaissance and the first Landsat satellites) optical sensors (scanners. TV cameras) were used onboard. Therefore the development of the application methods preprocessing of raw satellite RS data: geometric and atmospherio- correction (e.g. Ferencz et al, 1993, Lichtenberger et al, 1995) incorporating ground reference data is some cases; development of calibration functions (i.e. crop filters) only one times for a given crop using ground reference data and satellite RS- data together; derivation of tire temporal profiles of remote sensed parameters (at the given moment) of the investigated crops on the interesting territory, calculation of special agricultural RS indeces for the given application, stress (e.g. drought) and disaster (e.g. flood, fire) detection; yield forecasting (at the given moment); refreshing of producting areas (acreage); final estimation of crop yields at the harvesting. From the point of view of the final accuracy of the whole process the preprocessing of the RS data lias a key role. The main phases of the preprocessing (Ferencz et al, 1993, Lichtenberger et al, 1995) are the following independently from the resolution of the data; data) □ see in the next chapter. □ Analysis of changing in the whole agricultural production-system on country, regional and global level. b) Monitoring of the canopy in general: was optical-band oriented. The second cause is the radiation characteristic of the canopy, especially tire radiation characteristic of the chlorophyll. The living plant absorbs the light in the visible band, especially in the red band. So the visible reflectance of a living canopy is small, but the reflectance of a living canopy in the near infrared band is high. This is an important characteristic of the plant canopy. Therefore the importance of optical RS data will not decrease in the future in agricultural applications. However, in the near future the role of microwave (MW) remote sensed data will increase for two reasons. First, the atmospheric attenuation of the MW radiation is much smaller than the optical one. so the MW radiation of Earth's surface remain detectable by satellite instruments even if the whole surface is covered by clouds. Second, the surface MW radiation (the radiation temperature of tire surface) and the surface MW reflectance is sensible to soil moishtre and water content of plant. (See e.g. the Radarsat or ERS results.) 2. THE MAIN STEPS OF DATA PROCESSING IN AGRICULTURAL APPLICATIONS The agricultural application of satellite remote sensing means an accurate quantitative processing of remote sensed data. The values and the geographical positions of the processed data □ i,e. the pixel values and the spacial coordinates of the pixels □ must have an exact and accurate relation to the physical characteristics and the geodetical coordinates of the investigated surface. Therefore the methods used in this field have the following main phases of data processing: The original digital numbers transmitted from satellite must be recalculated into physical values using the calibrational curves of satellite instruments before applying remote sensing radiation models. A strict filtering of cloudy pixels from data set. Correction of atmospherical effects on remaining (i.e. "non-cloudv") pixels and derivation of surface characteristic values (spectral surface reflectances, spectral surface radiances etc.). The full correction of atmospherical effects in satellite remote sensed data is essentially important. We use a high accuracy pixel-by-pixel atmospherical correction (the ACAB A algorithm □ Lichtenberger et al, 1995). □ At the end of this process the calculation of canopy characteristics (vegetation indices, such as the tasseled cap greenness, the normalized difference index etc. □ e.g. Ferencz et al, 1993). The next step is the geometrical transformation of the satellite data sets (images) into a commonly used map-projection. After this the data set is compatible to GIS. the geographical data and contours can be used to remove the pixels of the non-agricultural or non- investigated territories decreasing the whole data mass in the next

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