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Lake Maggiore - Italy, 3-6 April 2000


R. Ressl, A. Ptichnikov, G. Kapustin, P. Reimov, D. Forstman.
DLR Oberpfaffenhofen Germany

Paper (pdf file, 212 k)

The ecological situation in the Aral Sea region changed dramatically in direction of catastrophe during last three decades. Ecosystems around Aral sea, including deltaic ones, are in a state of dynamic disequilibrium as they adjust to rapid and intense internal and external impact-factors. The aim of the project is to provide careful ecological assessment of current state, level and rate of degradation, possible seasonal and perennial trends of ecosystems dynamics of Amudarya and Syrdarya delta and dry bottom of the Aral sea, using integration of Remote Sensing data into existing layers of Aral GIS.

The research focuses on the development of GIS-project for the Amudarya delta of the Aral Sea region. GIS tools in association with computer processing and spatial analyses of digital satellite imagery of different resolution, has played an especially important role in meso- and macro scale land cover/landuse analysis, planning and decision making where the constantly changing land cover/landuse patterns require implementation of a flexible and fast response information/data entry, analysis and output system.

The important part of the presented research was evaluation of VEGETATION (VEG) data to support Aral sea GIS development. Particularly were investigated:

  • Approaches to use VEG data in classifications in comparison with NOAA AVHRR and other sensors
  • Possibilities to obtain accurate land cover/land use classifications using VEG data
  • Monitoring of NDVI index for natural ecosystems
  • Ranking natural ecosystems by different parameters
  • Detection of vegetation period length and growing differences between several agricultural crops.

After testing of VEG data we found that:

  • VEG data exceeds NOAA AVHHR data for classification of natural vegetation, detection of ecosystem parameters, monitoring and time/series analysis. It provides additional strength in application of specific algorithms, such as different indexes, tassled cup transformation, principal component analysis etc.

  • VEG data seems promising substitute of more expensive data (Landsat TM) for detection and fine-tuning of ecosystems dynamics, especially in between period of observation, based on more expensive data.

  • We didn’t found major advantages of VEG data in agricultural related analysis, except classification of crops.