METHODOLOGICAL QUESTIONS THE LAND COVER CHANGE OF THE URBAN AREAS IN SZEGED (HUNGARY) CASE STUDY
L. MucsiUniversity of Szeged Dept. of Physical Geography and Geoinformatics
AIMS of the STUDY
Spectral information
Textural information
SMA NDVI
Segmentation
Statistical parameters
Land cover map
Land use map
VISmodel
Land cover change detection (1987‐2011)
UHI (1987‐2011)
Condition of green areas
Demography
DATA, METHODS AND APPLICATIONS
• Long term land use and land cover change– Landsat TM images– Pixel based and subpixel classification– Application: Urban heat island mapping
• Annual change– Vegetation mapping
• Large scale mapping– Rapideye image– segmentation
Functional districts of Szeged
Land use and land cover change
Landsat TM, ETM+ 453 (RGB)
1986. 06.19. 1991.06.17. 2002.06.23. 2006.09.30.
RATIO MAPS FOR LAND COVER CLASSES USING NSMA METHOD
• 3 endmembers (impervious surface, vegetation, soil) spectral unmixing(10th July 2011, Landsat TM image)
Accuracy assessment
• 250 samples 90 m × 90 mRMSE=17 és SE=‐4,1
38,3% 41,4%
N
)VV̂(RMSE
N
1i
2ii
N
)VV̂(SE
N
1iii
Connection between land cover and land use
1 – Forest, 2 – Rare vegetation 3‐ Soil with vegatation cover, 4 – Bare soil surface, 5 – Grassland, 6 – low density build area,
7 – medium density build area, 8 – high density build area,
9 – commercial zone
Impervious surface‐Vegetation‐Soil compositeModified VIS diagram
PSAK XI. Konferencia, Szeged 2012.11.07-09.
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Annual change of NDVI values
Vegetation
Soils
Impervious surfaces
ND
VI v
alue
s
No. of days in a year
Standard deviation map made of 8 NDVI images from the time series of 1986
Fraction rate map of artificial surface taken from the spectral partition of a Landsat TM satellite
image (24th July 1987)
Relation between the standard deviation of NDVI values and the rate of impervious surface
Fraction map of artificial surface calculated from the deviation map
8 days air temperature measurement
‐2‐10123456789101112131415161718192021
1 15 29 43 57 71 85 99 113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
393
407
421
435
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463
477
491
505
519
533
547
561
575
589
603
617
631
645
659
673
687
701
715
729
743
Hőm
érsé
klet
(°C)
5037 ‐Baktói kiskertek
5047 Radnóti Miklós kísérleti Gimnázium
5049‐Tömörkényi István Gimnázium
5066 Alsóváros
THE SPATIAL DISTRIBUTION OF AVERAGE MAXIMUM UHI INTENSITY VALUES IN THE NON‐HEATING SEASON
IN 1986 and 2011The maximum UHI intensity (∆T) can described as a function of build
up ratio (B) with the best fit regression line in the non heating period of the year (from April to October) with the following equation:
∆T = 0,018B + 0,716
RapidEye satellite image in the range of visible light recorded March 24, 2011
Figure 1.
PSAK XI. Konferencia, Szeged 2012.11.07-09.
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Result of object based classification
• Segment (object) was classified according to the majorityof classified pixels inside the segment
Average Shape Index and Pattern Size of Funtional Districts
23
Downtown, inner residential and housing estate districts in the plane spanned by mean patch size and mean shape index
Conclusions• After radiometric and atmospheric correction, the time
series analysis of LTM images can give usable statistical parameter for assessment of impervious surfaces in each pixel in urban region
• Standard deviation of annual NDVI values is in strong correlation with the fraction values of impervious surfaces in NSMA
• Intensity of average annual maximum UHI values can be calculated from the fraction of impervious surfaces, so from the SD of annual NDVI values
• Combined geometrical and spectral parameters can be used for characterize segments and for supervised classification which resulted pixel based thematic map
• The thematic map is the input for segment object based classification, which resulted urban land cover map of high accuracy
Thank you for your attentions!