DESIGNING FORECAST THEMATIC MAPS USING TIME SERIES REMOTELY 
SENSED IMAGES 
A.V. Zamyatin, N.G. Markov 
Tomsk Polytechnic University 
84, Sovetskaya street, Tomsk, 634034, Russia, 
e-mail: ZamyatinA V@prim.ce.cctpu.edu.ru 
KEY WORDS: Remote Sensing, Land Use Mapping, Prediction, GIS, Neural Networks 
ABSTRACT: 
In the paper a number of original algorithms implemented as the program system are considered. The algorithms allow to perform 
the automated forecasting of land use/cover change using time series remotely sensed (RS) images. In the framework of the proposed 
approach the forecasting performs in two stages. In the first stage time series RS images are classified by the original classification 
algorithm, which is based upon both statistical nonparametric and artificial neural network (ANN) classification and separate 
processing of spectral and spatial features of RS images. The complex classification allows significantly decreasing the “noisiness” 
of the final thematic maps and to increase the classification accuracy in comparison with classification methods of traditional 
processing, 
RS and mapping software. To make the interpretation of RS images more flexible and effective it is proposed to perform 
classification using ANN with original way of forming feature space. In the second stage, the designed raster thematic maps are 
processed by the enhanced algorithm of time series analysis, which is based upon Markov chains for transition matrix calculation 
and cellular automata application allowing to take into account not only probability of transition from one class to another but also 
spatial interclass correlation. 
The results of effectiveness investigation of the proposed algorithms, obtained with use of model RS images and real time series RS 
images for Uymon steppe area (Altay Region, Russia) obtained from satellite RESURS-O1 are discussed. 
1. INTRODUCTION 
Every year high accuracy and operative forecasting necessity of 
land cover change has been growing. At the same time the 
operative and accurate forecasting is not possible without joint 
pplication of the comprehensive technologies for time series 
(TS) RS data interpretation, geoinformation systems (GIS) 
performing complex spatial analysis of interpreted data, and 
comprehensive methods of spatial forecasting. 
lot of researchers are solving the task of designing 
forecast maps with use of simple classification methods and 
highly specialized models of land use/cover change. 
Application of simple methods and algorithms of classification 
lcads to inadequate thematic maps. Saying about highly 
pecialized models of land use/cover models with their 
idvantages and disadvantages, we may say that these models 
ften require a large amount of additional information (digital 
vation model, dependability of different land types change, 
of migration etc.) that makes their application field 
nited. The all mentioned shortcomings restricts the 
| of existing program facilities, that does not allow to 
At present a 
orecast maps with the appropriate accuracy using TS RS 
tioned above declare about the imperfections for using 
approaches and algorithms for RS images 
ntation in the task of forecasting land use/cover 
Also it says about urgent need for new methods and 
hms allowing to solve all mentioned problems and 
trictions more effectively 
492 
Let's consider the possibilities and restrictions of methods and 
algorithms applied for tasks of RS images interpretation and 
designing forecast maps more detailed. 
Now the task of interpretation of TS aerospace images, which 
in their turn can be used for forecasting, traditionally is solving 
with image processing, RS and mapping software such as ER 
Mapper (Earth Resource Mapping), ERDAS Imagine 
(ERDAS), Idrisi 32 (Clark University). This software is based 
upon either parametric statistical methods using assumption 
about normal distribution of features (traditional maximum 
likelihood algorithm) or nonparametric methods that can 
produce acceptable results in few cases only. Besides the actual 
image processing RS, and mapping software do not use in full 
measure spatial (texture) information about classes on 
aerospace images. Application of such simple methods and 
approaches allow to use RS information to some extent only. 
Therefore for obtaining more accurate interpretation results it is 
required new and significantly more complicated scheme of RS 
images classification. 
While forecasting the behaviour of complex systems such as 
mapping nature territorial complexes to which influence a lot of 
stochastic processes, basically have been modelled by 
stochastic forecasting methods (Baker W. L, 1989). The 
widespread among them are methods using Markov chains. 
Markov chains have been used in a variety of fields and have 
modelled changes on a variety of spatial scales (Baker W. L, 
1989). In order to Markov model considers spatial interaction 
between classes on thematic map cellular automata (CA) are 
often applied (e.g. Park, S. and Wagner, 
CA is the distance of the neighbourhood from the central grid- 
cell. In majority cases of CA application for land use/cover 
change modelling this parameter is taken equal for all types of 
1997). A parameter of 
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