The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B7. Beijing 2008 
downloaded from GLCF (Global Land Cover Facility) - 
University of Maryland server (Table 2). 
Year 
Scale 
1964 
1:23 000 
1975 
1:29 500 
1984 
1:25 000 
1995 
1:26 000 
2004 
1:26 000 
Table 1. Date and scale of aerial photos 
Sensor 
Spectral 
range 
Bands 
Pixel 
resolution 
Registration 
date 
LANDSAT 
MSS 
0.50-1.10 
4 
60 
31.05.1979 
LANDSAT 
ETM+ 
0.45-2.35 
6 
30 
13.06.2000 
Table 2. Sensor characteristic 
Thematic vector maps (geological, hydrogeological, 
geomorphological, water ecosystems, soils, forest and non 
forest communities, fauna, flora, water resources and other) and 
tabular descriptive database (the MS Access format) concerning 
particular elements of the SNP environment refer to each vector 
layer. Also topographic maps of the Park area, scale 1:10 000, 
were included in the data set. 
2.2 Methods 
The aerotriangulation procedure was difficult due to poor 
quality (both radiometric and geometric) of processed photos 
(those dating back to 1964, 1975, 1984). Therefore, modem 
methods of digital image processing, such as filtering, image 
enhancement, or preliminary colour balancing were applied to 
upgrade the quality of those photogrammetric materials, and to 
improve the possibilities of their photointerpretation. The 
ImageStation Automatic Triangulation photogrammetric 
software was used to perform measurements, necessary for 
adjustment of the aerotriangulation block. In the first stage, 
interior and relative orientations were performed for all photo 
blocks. Main difficulties in the relative orientation stage were 
related to the identification of Grand Control Points (GCP) 
because of specific character of the SNP area (70% of its 
surface area covered by active dunes, lakes and the Baltic Sea). 
For all aerialtriangulation projects the signa nought was 
between 4-6 m 
The Digital Elevation Models (DEM) were produced using 
DEPHOS (Mapper Stereo) digital photogrammetric workstation, 
with 0.9-1.2 m accuracy. DEMs for the whole investigated area 
from each epoch were converted from vector format to a grid 
with a 3-metre resolution. DEMs derived from aerial 
photography provided relief data, which were used to indicate 
selected features changes of the SNP landscape. 
In the next step orthophotomaps (scale 1:5000 and 0.5 m 
ground resolution) were generated from the aerial photos for 
each period of time with ORTHO ENGINE, the PCI Geomatica 
module. 
On the basis of stereoscopic observation of air photos, contour 
maps of dunes that occur within the SPN area were generated 
for all year group photos. Contours of particular dune forms 
were created through digitalisation of dune skeleton lines. To 
verify the correctness of vectorized dune forms, curvature maps 
were prepared, as well as elevation maps of the 0.5 m isolines. 
Layers showing lines and water course lines were generated on 
the basis of DTM, with the help of Geomedia Grid function, 
which determines the curvature area in a given point along the 
slope inclination direction. 
The processing of satellite data was done with the use of ENVI 
(Environment for Visualizing Image) IDRISI and PCI 
Geomatica software. 
In order to minimize the impact of atmosphere on the values of 
reflection recorded on images, the images were atmospherically 
corrected, with the use of ATCOR 2 (Richter, 1996), module of 
PCI Geomatica. 
Based on the unsupervised classification, colour composite 
images and orthophotomaps (derived from aerial photos), the 
supervised classification (The Maximum Likelihood) was 
performed (Fig.L). Seven classes of the land cover were 
defined: 
1. Dune 1 
2. Dune 2 
3. Water 
4. Forest complex 
5. Meadows and pastures 
6. Agricultural 
7. Coastal dune forest 
Next, Normalized Difference Vegetation Index (NDVI) was 
completed for LANDS AT temporal data (Fig. L). 
Figure 1. Classification and NDVI results. 
3. RESULTS 
Multi-temporal aerial photography, DEMs, orthophotomaps, 
thematic vector layers and satellite images have been integrated 
for the changes detection of the unique environment of the 
Slowinski National Park. Full integration of multi-temporal data 
made it possible to conduct an analysis in GIS environment and 
to compute maps quantifying the features of landscape changes. 
All processed images, integrated with thematic vector maps 
enabled to get complete information about environmental 
components revealed in raster and vector data. 
The differential maps, which had been prepared in GIS 
environment, made it possible to perform quantitative analysis 
of horizontal shift of selected dunes, as well as observation of 
changes in land occupied by them in consecutive time intervals 
(Fig. 2. and Fig. 3). Based on the observation of the dune’s