Large scale cartography and analyses of man-induced transformation in an urban area using

satellite imagery with very high resolution

E. Roumenina, V. Vassilev Space Research Institute

Bulgarian Academy of Sciences Sofia, Bulgaria

roumenina@space.bas.bg; vassilev_vas@yahoo.com

Kalin Ruskov EUROSENSE EOOD

83, Guieshevo Str., Business Centre Serdika Sofia, Bulgaria

kalin.ruskov@eurosense.bg

Abstract— A methodology for cartography and assessment of the degree of man-induced transformation on an urban territory based on extraction of thematic information from very high resolution multi spectral Quickbird image is presented in the present paper. The proposed methodology includes 10 work stages and it has been examined on a highly fragmented urban territory (including forested and agricultural lands) of the Gniliane district, city of Novi Iskar, municipality of Sofia, Bulgaria. A land cover classification scheme for the studied area was created and 11 land cover classes are established for the classification second level, depending on the differences in their spectral reflectance. Three levels for automatic identification of the land cover classes on the multi spectral image were applied in ERDAS Imagine: 1) Unsupervised classification; 2) Supervised classification with non-parametric rule of parallelepiped and a parametric rule of maximum likelihood; and 3) Fuzzy convolution filter. A large scale land use map is composed on the base of land cover classification as the land cover classes have been transformed to 7 land use categories depending on the possibilities for recovery of the anthropogenic changes on the environment. A rank of man-induced transformation, which values vary from 1 to 10, has been assigned to each category. A large scale assessment map of man-induced transformation is created. The calculated local index of the man-induced transformation in the study area is 466.96 and the highest relative shares in its formation have the categories permanent crop fields, build-up land for residential and industrial purposes, and the forest territories. This index for the study area is slightly higher than the calculated average index for the overall territory of Bulgaria (448.1). The methodology developed proposes an opportunity for quick and objective extraction of thematic information from multi spectral images in order to assess the man-induced transformations of the environment. This allows to take the right decisions in planning, and to conduct a regional policy which ensures a sustainable development of the environment.

Keywords- Quickbird; Image analysis; Large scale cartography; Man-induced transformation; Land cover classifications

I. INTRODUCTION (HEADING 1) A methodology for cartography and assessment of the

degree of man-induced transformation on an urban territory based on extraction of thematic information from very high resolution multi spectral images is presented in the present paper. The evaluation of the man-induced transformation was performed after Goffmann’s methods adapted for Bulgarian territory by Iliev and Ilieva, 1998[1]. This enables to perform accurate and objective analysis of the degree of man-induced transformation on the different land use types as a result of land resources exploitation. Such assessment of the territory is one of the main criteria used in the landscape planning for sustainable development of the environment.

II. RESULTS AND DISCUSSION

A. Methodology The proposed methodology includes 10 work stages:

1) Building-up a geodatabase with two sub modulus - informational and analytic; 2) Selection of appropriate satellite image; 3) Creation of a digital elevation model and ortorectification and georeferencing on the base of an aerial image; 4) Selection of methodology for assessing the degree of man-induced transformation of the territory; 5) Creating a land use classification scheme with hierarchical structure in two levels. Each land use class is formed as a result of considering the thematic classes area objects needed for assessment of the man-induced transformation of the territory, as well as the highest difference between their reflectance in n-dimensional feature space, which is defined by the bands of the satellite image used; 6) Selection of a method for automatic land cover identification on the multi spectral satellite image; 7) Conducting land cover classifications and evaluating their accuracy; 8) Accomplishment of Visual computer aided interpretation of the classified satellite images that show less than 80% accuracy assessment for the land cover classes; 9) Conducting a field check of the results; 10) Composing a large scale land use map on the base of land

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cover classification; 11) Composing a large scale assessment map of the man-induced transformation of the territory using the geodatabase.

The proposed methodology has been examined on a highly fragmented urban territory (including forested and agricultural lands) of the Gniliane district, city of Novi Iskar, municipality of Sofia, Bulgaria. A geodatabase with two sub modules (informational and analytic) is built for the purpose of this study. It has been chosen a multi spectral Quickbird image acquired on 31.05.2008. For orthorectifying of the QuickBird image (from Digital Globe) were used Digital Elevation Model (DEM) and Rational Polynomial Coefficients (RPC) geometric correction model in ERDAS IMAGINE. A DEM with 40-meter cell size was used in the rectification. For adjusting of the RPC coefficient values were used ground control points selected from orthophoto images with 0.5 meter resolution. The RPC model uses cubic polynomials for transformation from ground surface coordinates to image coordinates.

B. Land cover classifications A land cover classification scheme for the studied

area was created using the information in the geodatabase and initial visual interpretation of the image. For this purpose the first field check was also conducted, ground control points (GCP) were taken with GPS for some typical land cover classes, and test regions were evaluated as area of interest for the supervised classification. As a result, 11 land cover classes are established for the classification second level, depending on the differences in their spectral reflectance, as follows: coniferous forest; deciduous forest; grassland and scrubland; grass land; crop 1; crop 2; crop 3; mixed land cover in urban territory; water bodies; paved roads and roofs of buildings.

Three levels for automatic identification of the land cover classes on the multi spectral image were selected: 1) Unsupervised classification 2) Supervised classification with non-parametric rule of parallelepiped and a parametric rule of maximum likelihood; and 3) Fuzzy convolution filter. The areas of interest (AOI) for the supervised classification were digitalized using a visual interpretation of the image in different band combinations, NDVI image composed in ERDAS Imagine, as well as in situ information. Visual interpretation of the image was used to identify the differenced of the land cover classes in hue, shape, size, structure, texture, shade, associations between them as the most common combination of bands was 4, 3 and 2. The panchromatic image was used to support the visual interpretation of the multispectral image. Two to five AOI were appointed for each class in the signature file. A Contingency matrix for assessing the accuracy of AOI in the signature file was performed for all the classes. The report shows accuracy between 92-100% for all land cover classes. The supervised classification uses a non-parametric rule of parallelepiped and a parametric rule of maximum likelihood. In order to deal with the mixed pixel problem a Fuzzy Convolution filter was applied on the supervised classification of the image using the same areas of interest. Accuracy assessment for the Fuzzy convolution filtered scene is computed using a set of referenced pixels representing geographical locations. 200 points are randomly

spread upon the whole classified scene to avoid bias. The accuracy assessment report shows that from 190 actual points 176 are classified correctly. The overall accuracy of the classification is 92.63% and the Kappa coefficient is 0.9112. A Visual computer aided interpretation of the land cover classes that show accuracy less than 80% in the accuracy reports has been accomplished and these classes were appointed for a field check to evaluate their difficulty in the classification process. Field check of the results was conducted in order to compare the accomplished results from the accuracy report with the actual situation on the field. The purpose of the second field check is to get the real picture of the quality of the work and to evaluate the methodology presented in this paper. It was established that all land cover classes were well distinguished and represent the real distribution of land cover classes in the study area.

C. Land Use map A large scale land use map is composed on the base of land

cover classification (Fig. 1). For that purpose, the above mentioned 11 land cover classes have been transformed to 7 land use categories depending on the possibilities for recovery of the anthropogenic changes on the environment as follows: forest territories; natural meadows; pastures; permanent crop fields; water areas; transport and infrastructure territories; and build-up land for residential and industrial purposes. The statistical method majority from focal statistic in ArcGIS 9.2 software was applied with the purpose of additional cleaning of the mixed pixels on the map. The thematic data obtained have been integrated into the geodatabase.

D. Large scale assessment map of the man-induced transformation

A large scale assessment map of the man-induced transformation of the territory was composed using the geodatabase (Fig. 2). The evaluation of the man-induced transformation was performed after Goffmann’s methods adapted for Bulgarian territory by Iliev and Ilieva, 1998 [1] where the number of land use categories is reduced to 10, and each category is assigned an appropriate man-induced transformation rank (r), which values vary from 1 to 10. The values of the man-induced transformation ranks (r) for the respective land use categories are as follows: Protected territories (protected natural territories, archaeological sites, sanitary-protected areas etc.) - 1; Forest territories – 2; Natural meadows – 3; Pastures – 4; Perennial plants (vineyards, orchards) – 5; Fields – 6; Water areas (rivers, dams, gullies, canals, etc.) – 7; Transport and infrastructure territories – 8; Build-up land for residential and industrial purposes – 9; Disturbed lands (mines, quarries, landfills etc.) – 10. The man-induced transformation index (Uam) is equal to the product of multiplication of the value of the man-induced transformation rank (r) of the respective land use category and the share of respective category area in the whole territory (in %). The sum of the man-induced transformation indices of the individual categories represents the local index (Ual) [1]. Within the boundaries of the examined territory 7 land use categories were identified as the Protected territories, Perennial plants

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and Disturbed lands were not identified. The categories have been formed under the influence of both natural and social-economic factors [2].

Forest territories are of great importance as a factor maintaining natural sustainability. The great portion of forest territories are situated in the north of the study area. This category occupies 26.53% of the territory and the Uam index is 53.06, which is close to the average value for Sofia Municipality (58.4). It’s relative share ranks third for the formation of Ual index. Natural meadows occupy 13.69% of the region’s territory. They are characteristically lying in the territories with low elevation which are close to the river in the south part of the study area. Their Uam index is 41.07, which is more than three times higher than the average value for Sofia municipality (11.7). This high value is due to the fact that this category is very much suitable and intensively used for stock-breeding.

The pastures’ category occupies 8.22% from the territory of Gniliane district and is located generally in the large negative erosion forms of the relief in the foot of the Balkan mountain. Their Uam index is 32.88, which is close to the average for the Sofia municipality (37.00).

The permanent crop fields’ category has the biggest share of the study area with 40.95% and it is situated mainly in the elevation zone between 500 and 700 m. The Uam index is 245.7, which is the highest value for the studied territory and it is vastly over the average value of Sofia municipality (152.4). It’s relative share ranks first in the row of Uam values and has biggest importance for the formation of Ual index value. The water areas’ category occupies 0.44% of the territory of the Gniliane district. The Uam index is 3.08, which is many times lower than the average value for the Sofia municipality (23.1). This can be explained with the lower area share for this category in the Gniliane district. The transport and infrastructure category also occupies very small share of the study area of only 0.35% and its Uam index is 2.88. This is also relatively much lower value than the average for the Sofia municipality (12.00).

The build-up land for residential and industrial purposes category covers only 9.81% of the territory, however the high man-induced transformation rank assigned to the category puts it second in the row of Uam values in the studied area. Nevertheless, its Uam value of 88.29 is twice lower than the average value for the Sofia municipality (163.8). This can be explained with the type of constructions in the Gniliane district, where one- and two-storied buildings with courtyards and farmyards are predominated.

III. CONCLUSION The local index (Ual) of the man-induced transformation in the study area is 466.96 and the highest relative shares in its formation have the categories permanent crop fields, build-up land for residential and industrial purposes, and the forest territories. The Ual index for the study area is lower than the calculated Ual index value for Sofia municipality (512.66) and slightly higher than the average index for the overall territory of Bulgaria (448.1). This can be explained with the relatively slow process of urbanization on the study territory at present.

The methodology developed proposes an opportunity for quick and objective extraction of thematic information from multi spectral images in order to assess the man-induced transformations of the environment. This allows to take the right decisions in planning, and to conduct a regional policy which ensures a sustainable development of the environment.

ACKNOWLEDGMENT The study is implemented within the framework of scientific-research contract NZ – No.1507/05 concluded between the SRI-BAS and the Scientific Research Fund at the Bulgarian Ministry of Education and Science.

REFERENCES

[1] Iliev, I., M. Ilieva. 1998. Assessement of man-induced transformation on the territory of Bulgaria. International scientific conference “100 years Geography at Sofia University”, Sofia, 1998, 523-531. (in Bulgarian)

[2] Roumenina, E., G. Jelev, R. Nedkov, V. Naydenova, G. Kanev. 2007. A spatial model to evaluate man-induced transformation using geoinformation technologies. Aerospace Research in Bulgaria, 21: 35-47.

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