Self-organizing GIS for solving problems of ecology and

landscape studying

Self-organizing GIS for solving problems of ecology and

landscape studyingNikolay G. Markov, Alexandr A. Napryushkin

Tomsk Polytechnical University,

GIS laboratory, Tomsk, Russia

e-mail: markov@ce.cctpu.edu.ru

Self-organizing vector-raster GIS (SOVR GIS) solves the following tasks:

Preliminary processing of the received remote sensing (RS) data (solving tasks of projection transforming, geo-referencing, linear and nonlinear filtration, spectral and geometrical transformation)

Thematic processing of the processed RS data (automated interpretation)

Spatial analysis of the extracted thematic information represented in a vector format (complex quantitative estimations of the researched objects and phenomena)

Subsystem of preliminary processing

Subsystem of self-organizing

Subsystem of vector data visualization

Interface shell of SOVR GIS

Subsystem of interpretation and

vectorization

Subsystem of spatial analisys

Subsystem of raster data visualization

Data input-output

subsystem

Subsystem of 3D visualization

Raster component Vector component

Fig. 1. General structure of SOVR GIS

Thematic processing - the stage of extracting the geometric

information from preliminary processed aerospace images.

SOVR GIS provides the facilities for automatized extraction of thematic information

from aerospace images.

Fig. 2. Automatized extraction of thematic information from aerospace images by means of SOVR GIS

Kohonen’s neuronet classifier

Preliminary processed aerospace image

Vectorizing procedure

Recognition procedure

Self-organizing procedure

Textural analysis

procedure

Cartographic sources

Extended feature space

Training data

Vector thematic layers

Spatial analysis

Decisions

Self-organizing procedure

(decision making algorithm)

Non-parametric classifiers

Advanced Bayesian classifier

Extended feature space aerospace image

Recognized landscape

objects

Fig.3 Self-organizing procedure

Fig. 4. Initial aerospace image of Tomsk-city (satellite RESURS-0, MSU-E scanner)

Fig. 5. Obtaining training data from a map

Fig. 6. Result of recognition. Red areas show the zones polluted with

radioactive contaminants

Fig. 6. Mapping forest types of Tomsk region with SOVR GIS (satellite RESURS-0, MSU-E scanner)

Initial aerospace image Map

Cedar Pine tree Cedar+Fir

Classified image

Self-organizing GIS for solving problems of ecology and

landscape studying

Self-organizing GIS for solving problems of ecology and

landscape studyingNikolay G. Markov, Alexandr A. Napryushkin

Tomsk Polytechnical University,

GIS laboratory, Tomsk, Russia

e-mail: markov@ce.cctpu.edu.ru