96 Knowledge horizons – Volume 5, no. 2/2013

DYNAMICS OF BUILT AREAS AND OF AGRICULTURAL USE IN SIBIU AND SURROUNDING AREAS THROUGH

REMOTE SENSING

Senior Lecturer Teodor TODERAŞ, PhD. “Dimitrie Cantemir” Christian University The Faculty of Tourism Geography Sibiu

Email: teotoderas@yahoo.com

Lecturer Traian CRĂCEA, PhD. “Dimitrie Cantemir” Christian University The Faculty of Tourism Geography Sibiu

Email: traiancracea@yahoo.com

Abstract: This paper highlights the essential aspects

regarding the dynamics of certain components of the geographical landscape (agricultural use, industrial use, town site), which has increased greatly, especially after 2000. There have been extensions of built surfaces especially for the western and eastern sectors of Sibiu site, designed for industrial or service area, at the expense of the agricultural use. These changes have been revealed by analyzing cartographic documents and satellite images for various areas within Sibiu and the surrounding areas, at scales large enough to emphasize certain details, being made in different time periods. The study focused on processing and comparing maps and satellite images for the same areas, captured in different moments, in order to identify similarities and differences, and to show the evolution of the town site (especially of the industrial and service constructions) and the evolution of the agricultural use.

Key words: geographical landscape

dynamics, land use, agricultural use, remote sensing, satellite images, industrial areas

JEL Classification: Q56

Introduction

The geographical landscape represents the real, visible aspect of a territory and it should be understood and analyzed from systemic perspective. Thus, it is made up of various components among which there are established interdependence relationships. When acting on a component, the functionality of the entire system is affected. .

The geographical landscape consists of three relevant components: abiotic component (relief, river, soil), biotic component (vegetation, fauna) and anthropic component (town site, land use, communication network etc.).

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Aspects of selective landscape elements dynamics

From the three components, the lowest dynamics is recorded by abiotic components, especially relief, while the greatest and fastest changes appear within the anthropic component. In this respect, the human impact on landscape is reflected through mutations recorded in the site, evidenced by its expansions in case of increased anthropic interventions, or restrictions when the demographic element1 does not show a significant impact on the landscape.

Likewise, land use represents an element which can prove a more or less increased dynamics in the landscape. At present, for Sibiu and surrounding areas, the economic restructuring in the last two decades led to remarkable landscape changes, shown through the analysis of cartographic documents and satellite images.

Thus, the built areas expanded considerably in the western and eastern extremities of the town site, by construction of an industrial park and of a large shopping center.

The industrial park2 situated on the west of the town site, lies on a surface of 123 123 acres and includes over 30 companies, the most representative being Polisano Pharmaceuticals and Continental German Corporation. The area of Sibiu International Airport has also registered increased landscape dynamics in the last years, due to the works for modernizing and extending the runaway to allow large airplane access.

In the south-east of the town site, starting with 2006, a large shopping center appeared, composed of Real and Carrefour hypermarkets, followed by Baumax and Altex stores, and Promenada Mall and Shoping City Sibiu shopping galleries. After 2008, one can also add not only Takko Fashion, New Yorker and Deichmann, specialized in textiles and shoes, but also the furniture store Mobexpert, Proges, and Flanco World. Penny Market XXL hypermarket and Cytröen representative in Sibiu are also remarkable. Although from the administrative-territorial point of view they belong to the commune of Selimbar,                                                             

1 The anthropic impact decreases considerably where the depopulation phenomenon appears, as a consequence of natural factors (low birth rate, migrations) or anthropic factors (industrial pollution, military conflicts, deportations etc.)

2 The first constructions that belong to some companies in the actual western industrial park were built starting with 2003.

all these built perimeters represent, in fact, eastern extensions of Sibiu site, and, together with Selimbar, they can represent in a near or distant future areas or neighborhoods included3 in the site of the town.

An important element, which reflects the anthropic impact on the landscape, is represented by the ring road that by-passes the town site in the north, with a total length of 21 km of which 17 km are highway scheme.

Under the circumstances, the agricultural use recorded a regress, especially in the surrounding areas of the town, mainly in the case of the natural pastures and hayfields, and of the potato crop lands4, in the north-west of the town.

Characteristics of the satellite images and used cartographic material

An important aspect that must be taken into consideration when obtaining the source material for analyzing the dynamics of the built areas and the land use is related to the characteristics of the satellite images and used topographic maps. The main characteristics taken into consideration when selecting the satellite images, and when processing and analyzing them, were: image resolution, brightness and shade of grey, image contrast, sensing capacity, spatial coverage, geometric properties, and processing level. For multi-temporal analysis of the built areas and industrial areas dynamics, there have been purchased panchromatic satellite images, and for the qualitative analysis of the agricuural crops there have been purchased LANDSAT 7 ETM+ multispectral satellite images in the 7 spectral bands, radiometrically and geometrically corrected (1G processing level).

The topographic map, scale 1:25000, trapezoids L-35-73-C-a and L-35-73-C-b, was used as main cartographic support. LEOWorks software was used for processing and analyzing the multispectral satellite images, and Quantum GIS software was used for vectorizing and making the geographical database.                                                             

3 The localities of Gusterita and Turnisor had an almost identical evolution, and at present they are included in Sibiu, representing peripheral districts.

4 The potato represents the main crop for the sub-mountain depressions in the southern Transylvania (Făgăras Depression, Sibiu Depression, Săliste Depression and Apold corridor).

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Georeferencing of satellite images

To make the georeferencing of satellite images there have been used at least 6 points with geodetic coordinates determined in the geodetic system of the topographic map (Stereo 70). Georeferencing was made based on the polynomial transformation relationships, which have been accessed by selecting the polynomial

transformation method in the menu of the used software.

To georeference images there have been used, as photogrammetric reference, points of the topographic details visible on the image, whose coordinates were determined in the field through GPS technology, coordinates necessary for the transformation and resampling of the images (Fig. 1).

The georeferencing process involves selecting

and opening the satellite image, selecting cartographic projection parameters, identifying and measuring the reference points on the image,

introducing the geodetic coordinates of these points and running the georeferencing process (Fig. 2).

Analysis of industrial areas evolution based on satellite images, at the expense

of the agricultural land

LANDSAT panchromatic satellite images (band 8) from 2000, 2007 and 2012 were used to analyze the dynamics of the industrial areas.

These satellite images were georeferenced and were subjected to the differencing process in order

to select the differential details that appeared between the used satellite recordings. The images resulted after the differencing process were then imported to Quantum GIS, where the topographic details selected through differencing were vectorized and introduced in the database, the result being the digital map with the evolution in time of the area built at the expense of the agricultural lands.

Fig. 2. Window for interpolation method selection and Warp image function selection to run the process 

Fig. 1. Window for measuring and introducing reference coordinates values.

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The case study was made on the western industrial area of Sibiu (Fig. 3) and on the area

built in the north-west of Sibiu (Fig.4).

As we can see on the map presented at Figure

3, the industrial area (painted in purple) has a spectacular evolution, lying on 123 acres, at the expense of the agricultural land of the town. More

than 30 company, most of them foreign-owned, function in this industrial area, and they had an impact upon the economy of Sibiu and made direct investment of over 250 million euro.

Analysis of land use based on satellite images

Satellite images, especially the multispectral ones, can be successfully used for thematic analysis of land use, resorting, for this, to the

methods for classifying the informational content of the satellite images and to the method of the normalized differencing vegetation index (NDVI) in order to determine the consistency and quality of vegetation, this process involving a good theoretical training of the operator.

Fig. 4. Dinamica zonei construite în nord-vestul Municipiului Sibiu în perioada 2000-2012

Fig. 3. Dynamics of the western industrial area of Sibiu from 2000-2012

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Analysis of the agricultural crop types through thematic classification of the

information in the satellite images

In literature, there are two types of thematic classification of the satellite image informational content: supervised classification and unsupervised classification, which are implemented in the specialized software for spectral processing and analysis.

Supervised classification involves analyzing and extracting the topographic details from the image by grouping the pixels in known classes. In supervised classification the classes of objects on Earth are known beforehand on restricted areas in the images (areas called test areas or sites). In other words, the user identifies some areas on the image that are characteristic to each class of established details. By image analysis, each pixel in the image is classified into one of these indicated classes.

After applying the supervised classification method, the types of crops in the studied area were automatically selected, and a new image of the selected and classified crops was obtained, this image being them imported to Quantum GIS and vectorized.

Unsupervised classification of multispectral images involves creating pixel groups that represent geographic characteristics, without knowing beforehand what is classified. The pixels are organized in cluster classes, based on the spectral answer, and then it is verified if the clusters have or do not have a meaning in the studied digital image.

After applying the unsupervised classification method, there were automatically selected, in a new image, the types of crops in the studied areas, obtaining an image of these selected crops that was then imported to Quantum GIS and vectorized.

Analysis of the quality of the agricultural crop types based on

normalized differencing vegetation index

To analyze the quality and consistency of the agricultural crop types there was used the normalized differencing vegetation index (NDVI). This is a graphic indicator, a non-linear transformation, of two spectral bands obtained in the near infrared (NIR) and visible (red) spectrum.

NDVI calculation can be done with the

function of the used software, based on multispectral images. For this, 4 (near infrared) and 3 (red) spectral bands are used. NDVI values are between -1 (lack of vegetation) and 1 (dense vegetation).

The calculation formula is based on the evidence of spectral signatures of vegetation in the near infrared (the spectral area where the chlorophyll has the highest reflectance), compared to that from the red area (where light rays are

absorbed by chlorophyll, which makes it reflect the green color of healthy vegetation).

Following the analysis of the NDVI calculation results, it resulted that, in the studied area, the crops outside the built-up areas vary according to the calculated NDVI value. Thus, NDVI high values were close to 0.70 (represented by dark green shades), and moderate vegetation by light green color (corn, rape, hay etc). The lands with low vegetation are characterized by low values of 0.25 and shown in yellow.

Fig.5. Calculation of normalized differencing vegetation

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Conclusions

To make a quality spectral analysis of the information content of satellite images it is necessary that one takes into consideration their characteristics, like: image resolution, brightness and shade of grey, image contrast, sensing capacity, spatial coverage, geometric properties, and processing. For quality georeferencing of satellite images, the values of geodetic coordinates of the photogrammetric reference points must be determined din the field, with professional GPS equipment.

During the georeferencing process, it is recommended that one choose as georefrencing method the polynomial transformation method, as this ensures a more rigorous transformation, eliminating a number of errors the Helmert transformation methods, affine or other, can’t eliminate.

The process of multispectral processing of the satellite images involves that the images in the used spectral bands be first brought in the same coordinate system through the registration system. It is also important that, depending on the analysis that is made and the type of searched topographic details, the adequate classification method be used.

The analysis of the quality and consistency of the agricultural crop types based on the normalized differencing vegetation index (NDVI) can me made with good results if the characteristics of the multispectral images are within the standard values.

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