# Spatial analysis for the assessment of the environmental changes in the landscapes of Izmir surroundings

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Spatial analysis for the assessment of the environmental changes in the landscapes of Izmir surroundings. 10th International Conference on Environmental, Cultural, Economic and So- cial Sustainability, Jan 2014, Split, Croatia. pp.31, 10.6084/m9.fishshare.7435202.v1 . cel-01991180

HAL Id: cel-01991180

https://hal.science/cel-01991180

Submitted on 29 Jan 2019

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
Spatial analysis
for the assessment
of the environmental changes
in the landscapes of Izmir surroundings

Lemenkova Polina,

PhD student at Institute for Environmental Studies,
Charles University in Prague (Czech Republic)
# Study Area

The study region is located in western Turkey, Izmir surroundings:

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

Western Turkey, Izmir region. Landscapes from the aerial view. Source: Google Earth
# Research problem

* The region of Izmir is a *particular* part of Turkey: it has *unique* landscapes with variety of vegetation types, diverse relief and natural reserve areas.

* The vegetation within the Aegean region has very *complex* character, characterized by the variety, biogeographical *diversity* and *richness*.

* At the same time, Izmir, a *third large metropolis* of Turkey, is an industrial city of high importance and a key seaport harbor, strategic for the country and Mediterranean region.

* Therefore, the region has *strong anthropogenic pressure*: well developed transport network, intensive shipping and maritime constructions, industrial factories, plants, densely populated urban districts, intensive agricultural cultivation.
# Research Questions and Goals

* How (if) the landscapes within the test area of Izmir region have changed due to the anthropogenic effects in the given time scope of 13 years (1987-2000) ? - Visualize (Map it).

* If there are changes, what are the exact areas (in ha or km) occupied by every land cover type.
  - Calculate & Assess Accuracy.

* How can remote sensing (RS) data and GIS tools of Erdas Imagine be used for answering questions (1) and (2). - Demonstrate & Discuss.
Methods

* Data import and conversion

* Creating multi-band layer & color composite

* Selecting AOI (Area Of Interest)

* Clustering segmentation and classification

* GIS Mapping

* Verification via Google Earth

* Accuracy Assessment

* Analyzing results
<figure><img src="image_4.png" /><figcaption>METHODOLOGICAL FLOWCHART</figcaption></figure>
# I. Data Import

Selecting study area covered by Landsat TM scenes
on the GLCF website:

**Landsat Thematic Mapper (TM):**
Global Land Cover Facility (GLCF)
Earth Science Data Interface

<figure><img src="image_5.png" /></figure>

For selecting the target area,
a spatial mask of coordinates ranging
from 26°00′–26°00′ E to 38°00′–39°00′N was applied.
The target images were chosen on 1987 and 2000 year.

**Two images:**

- have a time span of 13-years (1987-2000)
  (change detection in land cover types)

- taken during summer (June):
  Analysis of vegetation types

<figure><img src="image_6.png" /></figure>
## II. Data Conversion

<figure><img src="image_7.png" /></figure>

Conversion of raw .TIFF Landsat TM images into Erdas Imagine “.img” format.
III. Creating multi-band color composite

<figure><img src="image_8.png" /></figure>
## IV. Test area: Izmir surroundings

* To illustrate ecological diversity, the chosen test area Manisa and Izmir provinces covers all types of the landscapes :

* urban built-up areas, narrow coastal zone, agricultural crop areas and hilly landscapes.

* The urban areas located on the coastal area of the Aegean Sea with ~ 4 million people

* People have impact on the environment (demographic, cultural & economic pressure).

* This is reflected in the environment: land cover types, landscapes patterns, heterogeneity.
# IV. Selecting AOI (Area Of Interest)

<figure><img src="image_9.png" /></figure>

**Left:** Selecting AOI area from the overlapping initial Landsat images.

**Center:** adjusting parameters, Erdas Imagine.

**Right:** AOI 1987 (above) and AOI 2000 (below).
# V. Clustering segmentation

* The logical algorithmic approach of clustering segmentation consists in merging pixels on the images into clusters.

* Grouping pixels is based on the assessment of their homogeneity / distinguishability from the neighboring pixel elements.

* Clusters enable to analyze spectral & textural characteristics of the land cover types, (i.e. to perform spatial analysis).

* Accurate cluster segmentation of the images is an important step for supervised classification.
# V. Differentiating patterns via DNs

* The image classification = assignation of all pixels into land cover classes in this study area.

* Classification is done using multispectral data, spectral pattern (signatures) of the pixels that represent land cover classes.

* Various land cover types and landscape features are detected using individual properties of digital umbers (DNs) of the pixels.

* The DNs show values of the spectral reflectance of the land cover features, and individual properties of the objects.
# V. Clustering: the procedure

* Clustering was performed to classify pixels into thematic groups, or clusters.

* The Nr. of clusters = 15, which responds to the selected land cover types in the study area.

* During clustering, each digital pixel on the image is categorized to the respecting cluster, to which the mean DN value of the given pixel is the closest.

* The process is repeated in an iterative way, until optimal values of the class groups and the pixels assigned to the corresponding classes are reached.

* Afterwards, the land cover types were visually assessed and identified for each land cover class.
V. Clustering (visualization)

<figure><img src="image_10.png" /></figure>
# VI. Mapping

* Final thematic mapping is based on the results of the image classification:

visualizing landscape structure and land cover in the study area.

* Final thematic maps are represented on the following two slides.
<figure><img src="image_11.png" /><figcaption>LAND COVER TYPES IN PROVINES MANISA AND IZMIR, WESTERN TURKEY.
Supervised classification applied to Landsat TM image, 2000.</figcaption></figure>

Final map:
1) classified Landsat TM 1987 image (above) and
2) thematic map of land cover types (below).

Auxiliary technical data:

Initial data: Landsat TM image acquired on 2000.06.16 by EarthSat. WRS-2, Path 180, Row 033. Distributed by GLCF in GeoTIFF.

Classification type:
Supervised classification,
Minimal Distance method

Land cover classes: 15 types
Area: Izmir and Manisa provinces, western Turkey.
<figure><img src="image_12.png" /><figcaption>LAND COVER TYPES IN PROVINES MANISA AND IZMIR, WESTERN TURKEY.
Supervised classification applied to Landsat TM image, 1987.</figcaption></figure>

Final map:

1) classified Landsat
TM 2000 image
(above)
and

2) thematic map of
land cover types
(below).
# VII. Verification via Google Earth

* The selected areas with the most diverse landscape structure and high heterogeneity of the land cover types, have been verified by the overlapping of the Google Earth aerial images.

* The function “**connect to Google Earth**” was activated that enabled to visualize the same region of the current study on the Google Earth in a simultaneous way.

* The functions “**Link Google Earth to View**” and “**Sync Google Earth to View**” enabled to synchronize the view areas between the Google Earth and the current view on the image.

* This enabled to check the difficult study areas where questions arose in which land cover type this site belongs.
## VII. Verification via Google Earth

<figure><img src="image_13.png" /></figure>
# VIII. Accuracy Assessment

The quality control and validation of the results has been performed using
accuracy assessment operations in Erdas Imagine menu

<figure><img src="image_14.png" /></figure>
VIII. Accuracy Assessment (continue)

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

Left: Correction of the assigned class values of the generated points according to the real values.

Right: Error matrix generated for each land cover class, Landsat TM classification 1987.
# Accuracy Assessment: Final Calculations

## ACCURACY TOTALS

<table><thead><tr><th>Class Name</th><th>Reference Totals</th><th>Classified Totals</th><th>Number Correct</th><th>Producers Accuracy</th><th>Users Accuracy</th></tr></thead><tbody><tr><td>Unclassified</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Water</td><td>3</td><td>2</td><td>2</td><td>66.67%</td><td>100.00%</td></tr><tr><td>Cropland 2 (barley, cereals)</td><td>18</td><td>14</td><td>14</td><td>77.78%</td><td>100.00%</td></tr><tr><td>Sparsely vegetated areas</td><td>42</td><td>41</td><td>39</td><td>92.86%</td><td>95.12%</td></tr><tr><td>Mixed forests</td><td>30</td><td>27</td><td>27</td><td>90.00%</td><td>100.00%</td></tr><tr><td>Wetlands</td><td>5</td><td>1</td><td>1</td><td>20.00%</td><td>100.00%</td></tr><tr><td>Broadleaf forests</td><td>29</td><td>31</td><td>26</td><td>89.66%</td><td>83.87%</td></tr><tr><td>Cropland 1 (wheat)</td><td>9</td><td>5</td><td>5</td><td>55.56%</td><td>100.00%</td></tr><tr><td>4</td><td>4</td><td>0</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Coniferous forest</td><td>21</td><td>23</td><td>19</td><td>90.48%</td><td>82.61%</td></tr><tr><td>Coppice</td><td>25</td><td>23</td><td>21</td><td>84.00%</td><td>91.30%</td></tr><tr><td>Pastures</td><td>21</td><td>18</td><td>18</td><td>85.71%</td><td>100.00%</td></tr><tr><td>Grasslands</td><td>6</td><td>2</td><td>2</td><td>33.33%</td><td>100.00%</td></tr><tr><td>Agricultural lands</td><td>5</td><td>2</td><td>2</td><td>40.00%</td><td>100.00%</td></tr><tr><td>Coniferous forest</td><td>34</td><td>34</td><td>31</td><td>91.18%</td><td>91.18%</td></tr><tr><td>1</td><td>1</td><td>0</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Shrubland</td><td>1</td><td>28</td><td>1</td><td>100.00%</td><td>3.57%</td></tr><tr><td>Urban areas</td><td>0</td><td>5</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Totals</td><td>256</td><td>256</td><td>208</td><td>---</td><td>---</td></tr></tbody></table>

Overall Classification Accuracy = 81.25%

----- End of Accuracy Totals -----

## KAPPA (K^) STATISTICS

Overall Kappa Statistics = 0.7923

Conditional Kappa for each Category.

<table><thead><tr><th>Class Name</th><th>Kappa</th></tr></thead><tbody><tr><td>Unclassified</td><td>0.0000</td></tr><tr><td>Water</td><td>1.0000</td></tr><tr><td>Cropland 2 (barley, cereals)</td><td>0.0000</td></tr><tr><td>Sparsely vegetated areas</td><td>0.9416</td></tr><tr><td>Mixed forests</td><td>1.0000</td></tr><tr><td>Wetlands</td><td>1.0000</td></tr><tr><td>Broadleaf forests</td><td>0.8181</td></tr><tr><td>Cropland 1 (wheat)</td><td>1.0000</td></tr><tr><td>Coniferous forest</td><td>0.0000</td></tr><tr><td>Coppice</td><td>0.9036</td></tr><tr><td>Pastures</td><td>1.0000</td></tr><tr><td>Grasslands</td><td>1.0000</td></tr><tr><td>Agricultural lands (cotton, etc)</td><td>1.0000</td></tr><tr><td>Coniferous forest (pines)</td><td>0.8983</td></tr><tr><td>Shrubland</td><td>0.0000</td></tr><tr><td>Urban areas</td><td>0.0000</td></tr></tbody></table>

----- End of Kappa Statistics -----

## ACCURACY TOTALS

<table><thead><tr><th>Class Name</th><th>Reference Totals</th><th>Classified Totals</th><th>Number Correct</th><th>Producers Accuracy</th><th>Users Accuracy</th></tr></thead><tbody><tr><td>Unclassified</td><td>0</td><td>0</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Water</td><td>3</td><td>0</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Grasslands</td><td>23</td><td>25</td><td>20</td><td>86.96%</td><td>80.00%</td></tr><tr><td>Urban areas</td><td>9</td><td>8</td><td>7</td><td>77.78%</td><td>87.50%</td></tr><tr><td>Coniferous forest</td><td>10</td><td>13</td><td>9</td><td>90.00%</td><td>69.23%</td></tr><tr><td>Coppice</td><td>4</td><td>3</td><td>3</td><td>75.00%</td><td>100.00%</td></tr><tr><td>Coniferous forest</td><td>23</td><td>19</td><td>18</td><td>78.26%</td><td>94.74%</td></tr><tr><td>Wetlands</td><td>3</td><td>1</td><td>0</td><td>0.00%</td><td>0.00%</td></tr><tr><td>4</td><td>6</td><td>0</td><td>0</td><td>---</td><td>---</td></tr><tr><td>Croplands 1 (barley, cereals)</td><td>25</td><td>30</td><td>24</td><td>96.00%</td><td>80.00%</td></tr><tr><td>Croplands 2 (barley, cereals)</td><td>17</td><td>17</td><td>14</td><td>82.35%</td><td>82.35%</td></tr><tr><td>Water</td><td>7</td><td>6</td><td>4</td><td>57.14%</td><td>66.67%</td></tr><tr><td>Mixed forests</td><td>32</td><td>41</td><td>31</td><td>96.88%</td><td>75.61%</td></tr><tr><td>Agricultural lands</td><td>16</td><td>10</td><td>10</td><td>62.50%</td><td>100.00%</td></tr><tr><td>Pastures</td><td>18</td><td>19</td><td>14</td><td>77.78%</td><td>73.68%</td></tr><tr><td>Sparsely vegetated areas</td><td>42</td><td>46</td><td>40</td><td>95.24%</td><td>86.96%</td></tr><tr><td>Broadleaf forests</td><td>10</td><td>14</td><td>9</td><td>90.00%</td><td>64.29%</td></tr><tr><td>Shrubland</td><td>3</td><td>4</td><td>3</td><td>100.00%</td><td>75.00%</td></tr><tr><td>Totals</td><td>256</td><td>256</td><td>206</td><td>---</td><td>---</td></tr></tbody></table>

Overall Classification Accuracy = 80.47%

----- End of Accuracy Totals -----

## KAPPA (K^) STATISTICS

Overall Kappa Statistics = 0.7843

Conditional Kappa for each Category.

<table><thead><tr><th>Class Name</th><th>Kappa</th></tr></thead><tbody><tr><td>Unclassified</td><td>0.0000</td></tr><tr><td>Grasslands</td><td>0.7803</td></tr><tr><td>Urban areas</td><td>0.8704</td></tr><tr><td>Coniferous forest (pines)</td><td>0.6798</td></tr><tr><td>Coppice</td><td>1.0000</td></tr><tr><td>Coniferous forests (firs)</td><td>0.9422</td></tr><tr><td>Wetlands</td><td>0.0119</td></tr><tr><td>Croplands 1 (barley, cereals)</td><td>0.7784</td></tr><tr><td>Croplands 2 (barley, cereals)</td><td>0.8110</td></tr><tr><td>Water</td><td>0.6573</td></tr><tr><td>Mixed forests</td><td>0.7213</td></tr><tr><td>Agricultural lands (cotton, etc)</td><td>1.0000</td></tr><tr><td>Pastures</td><td>0.7169</td></tr><tr><td>Sparsely vegetated areas</td><td>0.8440</td></tr><tr><td>Broadleaf forests</td><td>0.6283</td></tr><tr><td>Shrubland</td><td>0.7470</td></tr></tbody></table>

----- End of Kappa Statistics -----
# Accuracy Results

* Accuracy results are computed as follows:

* The classification of the image 1987 was done with accuracy 81.25%,

* For the year 2000 – 80,47%.

* Kappa statistics shows following results:

* 0.7843 for the Landsat TM image 2000, and

* 0.7923 for the Landsat TM image 1987.
<table><thead><tr><th>Land Cover Class</th><th>No pixels, 1987</th><th>No of pixels, 2000</th><th>Area(ha), 1987</th><th>Area (ha), 2000</th></tr></thead><tbody><tr><td>Grasslands</td><td>411879</td><td>795422</td><td>2141</td><td>4134</td></tr><tr><td>Urban areas</td><td>1443590</td><td>1797360</td><td>7504</td><td>9342</td></tr><tr><td>Coniferous forest (pines)</td><td>1253627</td><td>1711708</td><td>6516</td><td>8898</td></tr><tr><td>Coniferous forests (firs)</td><td>871502</td><td>921977</td><td>4530</td><td>4793</td></tr><tr><td>Coppice</td><td>1058062</td><td>134659</td><td>5500</td><td>700</td></tr><tr><td>Wetlands</td><td>51041</td><td>27721</td><td>265</td><td>144</td></tr><tr><td>Croplands 1 (wheat)</td><td>458425</td><td>643684</td><td>2382</td><td>3345</td></tr><tr><td>Croplands 2 (barley, cereals)</td><td>220975</td><td>850931</td><td>1149</td><td>4423</td></tr><tr><td>Mixed forests</td><td>790421</td><td>242800</td><td>4109</td><td>1262</td></tr><tr><td>Agricultural lands (cotton, etc)</td><td>24344</td><td>925968</td><td>127</td><td>4813</td></tr><tr><td>Pastures</td><td>815377</td><td>283646</td><td>4238</td><td>1474</td></tr><tr><td>Sparsely vegetated areas</td><td>165250</td><td>1137789</td><td>859</td><td>5914</td></tr><tr><td>Broadleaf forests</td><td>1716519</td><td>1098428</td><td>8923</td><td>5710</td></tr><tr><td>Shrubland</td><td>1260254</td><td>350603</td><td>6550</td><td>1822</td></tr></tbody></table>
Explanation of Table

* The results indicate changes in land cover types affected by human activities, i.e. **increased** agricultural areas. For example, :

* - in 1987 croplands (wheat) covered 71% of the today's area (2000): 2382 vs. 3345 ha.

* - increase in barley cropland areas is noticeable as well: 1149 ha in 1987 vs. 4423 ha in 2000.

* Sparsely vegetated areas now also occupy more areas : 5914 ha in 2000 against 859 ha in 1987.

* The natural vegetation, **decreased**, which can be explained by the expansion of the agricultural lands:

* in 1987 coppice areas covered 5500 ha while later on there are only 700 ha in this land type.
# Conclusions

* The increased **human activities** (agricultural works, city spread and industrialization) affect environment, cause negative impacts on the ecosystems and make changes in vegetation coverage (land cover types).

* Climate change also affect land cover types, since there is a trend towards decrease of woody vegetation, which was typical earlier for these latitudes.

* As proven by various authors whose research papers were reviewed and discussed in the current work, drastic land use changes have been **recorded and detected** in diverse regions of Turkey, including coastal areas of Izmir surroundings.
# Résumé

* Monitoring land cover changes is necessary for maintaining environmental sustainability in the region. For this, up-to-date information and spatial analysis is a necessary tools.

* The paper aimed to demonstrate how landscapes changed in the selected study area at a 13-year time span (1987-2000).

* The data included Landsat imagery covering research area. The image processing was done by classification methods.

* The classification results detected changes in landscapes in 2000 comparing to 1987. This proved anthropogenic impacts on the landscapes which affect sustainable environmental development of the region.

* The results demonstrated successful combination of the RS data and methods of GIS spatial analysis, effective for monitoring of highly heterogeneous landscapes in the area of intensive anthropogenic activities.
Acknowledgements

* Current research has been supported by the TÜBİTAK: Türkiye Bilimsel ve Teknoloji Arastirma Kurumu

(The Scientific and Technological Research Council of Turkey) Research Fellowship for Foreign Citizens, No. 2216 for 2012.

* The research stay was done during 11-12.2012 at Ege University, Faculty of Geography, Izmir, Turkey.
Literature (the most important authors)

* Alrababah M.A., & Alhamad M.N. (2006) Land use/cover classification of arid and semi-arid Mediterranean landscapes using Landsat ETM. *International Journal of Remote Sensing*, 27 (13), 2703-2718.

* Ataberk E., and Baykal F. (2011). Utilization of natural and cultural resources of Dikili (Izmir) for tourism. *Procedia Social and Behavioral Sciences* 19, 173–180.

* Baskent E.Z., Kadiogullari A.I. (2007). Spatial and temporal dynamics of land use pattern in Turkey: A case study in Inegöl. *Landscape and Urban Planning* 81, 316–327.

* Benedetti R., Rossini P. & Taddei R. (1994) Vegetation classification in the Middle Mediterranean area by satellite data. *International Journal of Remote Sensing* 15 (3).

* Burak S., Dogan E., Gazioglu, C. (2004) Impact of urbanization and tourism on coastal environment. *Ocean & Coastal Management* 47, 515–527.

* Efe, R. (2000). The impact of land use changes on natural vegetation along the coastal zone between Edremit and Kucukkuyu. In: *International Symposium on Desertification, Turkey, Proceedings*, pp. 222–227.

* Hepcan S., Hepcan C.C., Bouwma I.M., Jongman R.H.G., Özkan M.B. (2009) Ecological networks as a new approach for nature conservation in Turkey: A case study of İzmir Province. *Landscape and Urban Planning* 90, 143–154.

* Kesgin B., Nurlu E. (2009) Land cover changes on the coastal zone of Candarli Bay, Turkey using remotely sensed data. *Environmental Monitoring Assessment* 157, 89–96.
DOI 10.1007/s10661-008-0517-x
Thank you for attention.

Questions ?