The use of remote sensing techniques and subsequent analysis by means of geographical information system (GIS) offers an effective method for monitoring temporal and spatial changes of landscapes. This work studies the urbanization processes and associated threats to natural ecosystems and resources in the metropolitan areas of Berlin and Erlangen-Fürth-Nürnber Schwabach (EFNS). To compute the land use/cover (LULC) of the study areas, a supervised classification of “maximum likelihood” using Landsat data for the years of 1972, 1985, 1998, 2003, and 2015 is used. Results show that the built-up area is the dominant land use in both regions throughout the study period. This land use has increased at the expense of green and open areas in EFNS and at the expense of agricultural land in Berlin. Likewise, 5% of forest in EFNS is replaced with urban infrastructure. However, the amount of forest in Berlin increased by 3%. While EFNS experienced relatively big changes in its water bodies from 1972 to 1985, changes in water bodies in Berlin were rather slight during the last 40 years. The overall accuracy of our remotely sensed LULC maps was between 88% and 94% in Berlin and between 85.87% and 87.4% for EFNS. The combination of remote sensing and GIS appears to be an indispensable tool for monitoring changes in LULC in urban areas and help improving LU planning to avoid environmental and ecological problems.
LULC change is a major issue of global environment change. In addition, LULC mapping is an essential component where required parameters are integrated on the requirement basis to drive various developmental indexes for land and water resource. LU refers to man’s activities and the varied uses carried on, such as natural vegetation, water bodies, rock, soil and artificial covers. Land cover (LC) is defined as the assemblage of biotic and abiotic components on the earth’s surface is one of the most crucial properties of the earth system. LC is that which covers the surface of the earth and land use (LU) describes how the LC is modified. LC includes water, snow, grassland, forest, and bare soil. LU includes agricultural land, built up area, recreation area, wildlife management area etc. The LC reflects the biophysical state of the earth’s surface and immediate subsurface, thus embracing the soil material, vegetation and water. LU refers to man’s activities on land, which are directly related to the land. LULC are dynamic. Changes may involve the nature or intensity of change but may also include spatial and temporal aspects [
Urban LC types and their spatial distributions are fundamental data required for a wide range of studies in the physical and social sciences, as well as by municipalities for land planning purposes [
Visible and shortwave infrared (SWIR) bands of Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data have been extensively used for forestry and agricultural LC analysis since the Landsat program began in 1972 [
The majority of studies relied on remotely sensed information in order to classify LC types. Geological and biological applications have also used band ratio techniques to accentuate the spectral features of specific surficial materials, biomass, and vegetation health [
Yet another successful technique for improving classification accuracy is incorporating post-classification in a supervised mode classification (expert system) [
The present work applies a supervised classification (expert system) approach for the classification of the urban LC in metropolitan regions. The main purpose of the present study is to examine the possibilities of using remote sensing data to analyze the nature and extent the LULC changes in the metropolitan city of Berlin and the metropolitan region of Nürnberg-Fürth-Erlangen-Schwabach in the past 40 years. The objective of this research would be identifying the main forces behind these changes. This study attempts to establish the relationships between the ground truth and the remotely sensed images, maps and reports.
This study has chosen the metropolitan city of Berlin and the metropolitan region of Nürnberg-Fürth-Erlangen-Schwabach as the study areas. The metropolitan city of Berlin presents the central area of the European metropolitan region of Berlin Brandenburg. Thus, in its function as federal capital and metropolitan center of the region, Berlin is surrounded by a dense suburban space with a partial rural character [
city as well as numerous lakes and forests. Due to its location in the North European Plain, Berlin is influenced by a temperate seasonal climate. Around one third of the city’s area is composed of forests, parks, gardens, rivers and lakes. Berlin is surrounded by the state of Brandenburg. The city is located in the temperate climate zone with humid continental climate. The average annual temperature in Berlin-Dahlem is 9.5˚C and the average annual precipitation is 591 mm. The warmest months are July and August, with average temperatures of 19.1˚C - 18.2˚C while the coldest month is January, with average temperature of 0.6˚C. Berlin’s built-up area creates a microclimate, where heat is stored by the city’s buildings. The temperatures can be up to 4˚C higher in the city than in the surrounding areas.
The Metropolitan region of Nürnberg-Fürth-Erlangen-Schwabach represents the metropolitan center of the European Metropolitan Region of Nürnberg. This region is located in the southern part of the Federal state of Bavaria in the south east of the Federal Republic of Germany. It belongs to the Middle Franconian basin (
To depict the history of past spatial and temporal changes of LULC in study areas, archive remote sensing images has been chosen. To achieve the results, several multispectral Landsat data such as Landsat 4 (MSS), Landsat 5 (TM) and 8 (OLI) were downloaded for Berlin (path 193, row 23) and Nürnberg-Fürth- Erlangen-Schwabach (path 193, row 26). Images acquired in five specific years (1972, 1985, 1998, 2003 and 2015).
All the images used in this study have been systematically corrected. Geometric corrections were performed by using ground control points (GCPs) from the digital topographic maps, LU maps and GPS control points. Identical control points were used for all five scenes to obtain a good geometric accordance. This is very important for change analysis based on Geodata. The goal of image enhancement is to improve the visual interpretability of an image by increasing the
Data source (Sensor) | Month of Observation | Spatial Resolution (m) |
---|---|---|
LANDSAT4 (MSS) | August 1972 | 60 |
LANDSAT5 (TM) | May 1985, April 1998 and June 2003 | 30 |
LANDSAT8 (OLI) | May 2015 | 30 |
Map | Scale | Year |
---|---|---|
Land use plan | 1:35,000 | 1965, 1984, 1994 |
1:50,000 | 1998, 2004, 2009, 2015 | |
Soil map | 1:50,000 | 1990, 1998, 2005, 2013 |
Soil evaluation map | 1:50,000 | 2001, 2005, 2010 |
Topographic map | 1:5000 | 2001, 2003, 2006, 2008, 2010, 2012, 2014 |
General map Berlin | 1:50,000 | 2015 |
Green and open spaces map | 1:50,000 | 1990, 2000, 2001, 2005, 2010 |
Maps and data base | Scale | Year | Study Area |
---|---|---|---|
Land use plan | 1:10,000 | 1970, 2006, 2015 | Fürth |
1:5000 | 1985 | Schwabach | |
1:10,000 | 2015 | ||
1:10,000 | 2015 | Erlangen | |
1:10,000 | 1969 | Nürnberg | |
1:2000 | 2006 | ||
Soil map | 1:25,000 | 2015 | Fürth, Schwabach, Erlangen, Nürnberg |
Soil evaluation map | 1:25000 | 2001, 2012 | Fürth, Schwabach, Erlangen, Nürnberg |
Topographic map | 1:25,000 | 1966, 1976, 1987, 1994 | Fürth |
1:25,000 | 1969, 1977, 1988, 2004 | Schwabach | |
1:25,000 | 1971, 1983, 1992, 1997 | Erlangen Nord | |
1:25,000 | 1962, 1982, 1988, 1993, 1998 | Erlangen Süd | |
1:25,000 | 1962, 1978, 1987, 1994, 1998 | Nürnberg | |
Statistical yearbook and statistics of the Bavarian State Office for statistics and data processing | 2014, 2015 | Fürth | |
2015 | Schwabach | ||
2012, 21013, 2014, 2015 | Erlangen | ||
1965, 2013, 2014, 2015 | Nürnberg |
apparent distinction between the features. The process of visually interpreting digitally enhanced imagery attempts to optimize the complementary abilities of the human mind and the computer. The mind is excellent at interpreting spatial attributes on an image and is capable of identifying obscure or subtle features [
The LC classes are typically mapped from digital remotely sensed data through the process of a supervised digital image classification [
Three methods of data analysis are adopted in this study:
・ Calculation of the area in hectares of the resulting LULC types for each year for each study area is based on Landsat-Classification.
The first task is to create a table showing the area of LULC classes in hectares and its percentage for each year (1972, 1985, 1998, 2003 and 2015) measured against each LULC type. The Percentage change was then calculated to determine the trend of change of each LU.
・ Classification accuracy of each LULC class of used Landsat-images is then calculated using of reference maps and statistical data in order to evaluate the overall accuracy and then to determine the quality of information derived from the data of Landsat-Classification.
・ The LULC statistics of both study areas are compared to assist in identifying the percentage change, trend, and rate of change of LULC in metropolitan areas.
high proportion. The residential use is followed by commercial and industrial use. In contracts, a large part of the suburban area was covered with green, open area, forest and agriculture showing the various types of non-built-up proportions as well as their distribution. Interestingly, green and open areas have been increased through the years. The amount was 13.3% in 1972, 13.5% in 1985, 19.9% in 1998, 18.38% in 2003 and 14.3% in 2015 (
Class name | 1972-August | 1985-May | 1998-April | 2003-June | 2015-May | |||||
---|---|---|---|---|---|---|---|---|---|---|
ha | % | ha | % | ha | % | ha | % | ha | % | |
Built-up | 45,956.84 | 51.6 | 46,316.84 | 52 | 47,953.26 | 53.85 | 47,908.73 | 53.8 | 54,498.41 | 61.2 |
Green and open areas | 11,845.46 | 13.3 | 12,024.56 | 13.5 | 17,720.89 | 19.9 | 16,634.48 | 18.68 | 12,734.11 | 14.3 |
Agricultural land | 14,072.06 | 15.8 | 11,757.35 | 13.2 | 2342.01 | 2.63 | 3241.41 | 3.64 | 2190.62 | 2.46 |
Forest | 12,023.59 | 13.5 | 13,805.98 | 15.5 | 15,361.07 | 17.25 | 15,779.61 | 17.72 | 14,497.29 | 16.28 |
Water bodies | 5165.69 | 5.8 | 5166.11 | 5.8 | 5672.47 | 6.37 | 5485.46 | 6.16 | 5129.26 | 5.76 |
Overall | 89,063.64 | 100 | 89,063.64 | 100 | 89,063.64 | 100 | 89,063.64 | 100 | 89,063.64 | 100 |
It is noteworthy that in the last 40 years almost 84.5% of the agricultural land has been converted into built up area, forest, green and open spaces. The proportion of agricultural land was reduced sharply between 1985 and 2015, compared to the previous period. This can be found in the outer city districts of Pankow, Marzahn-Hellersdorf and Lichtenberg. The agricultural areas, which cover 2.46% of the LU of the urban area in 2015, are now located in the northeastern of Berlin (Pankow and Weißensee). The water bodies contain lakes, rivers, canals and ponds. The proportion of water bodies was 5.8% in 1972, 5.8% in 1985, 6.37% in 1998, 6.16% in 2003 and 5.76% in 2015. The fluctuation of the mentioned percentages could be attributed to the rainfall and conversion of LU in the study area.
The estimation of classification accuracy is an integral part of the thematic evaluation of remote sensing data. Since the classification errors have influence on the accuracy of the change detection, the overall accuracy assessment must be considered. Additionally, the classification of the LULC and a reference map are compared with one another. In this regards, the basis of random raster samples [
Class name | Producer’s accuracy (%) | |
---|---|---|
2003 | 2015 | |
Built-up | 97 | 93 |
Green and open areas | 81 | 93 |
Agricultural land | 75 | 97 |
Forest | 98 | 94 |
Water bodies | 88 | 95 |
Overall | 87.8 | 94 |
both years of 2003 and 2015. Whereas, the lowest classification accuracy is for the class “agricultural land” for the year 2003. This could be explained by the fact that this class is spectrally confused with the “green and free areas”. In addition, the temporal difference between the Landsat classified image 2003 and the reference map of 2002 could be the reason to this cause. The accuracy for the same class in 2015 was 97%. This is probably due to the integration of the visual interpretation of LU map (2015) with the Landsat classified image (2015). The information obtained from the topographic map could be an addition to previous ones.
The built-up area has been the dominant LU between the years of 1972 to 2015. This amount increased from 44.65% to 44.69% between the years of 1972 to 1988, a slight decrease between the years of 1998 and 2003 and then an increase from 42.61% to 51.49% in the years of 2003 to 2015. The expenses of increase of built-up area were on the shoulders of green and open area as well forested area, especially in the largest cities of Nürnberg and Erlangen. Around 40% to 50% of the area in the region was unoccupied by settlements and traffic during the investigation period. These were predominantly agricultural areas or forest areas, which were found in the outskirts of the region. Larger connected farmland were found in the western part of Fürth and in the western Northwestern part of the city of Erlangen.
Class name | Landsat 2003 | Environmental Atlas 2002 | Landsat 2015 | Environmental Atlas 2015 | ||||
---|---|---|---|---|---|---|---|---|
ha | % | ha | % | ha | % | ha | % | |
Built-up | 47,908.73 | 53.8 | 49,024.85 | 55.00 | 54,498.41 | 61.20 | 52,325 | 58.74 |
Green and open areas | 16,634.48 | 18.68 | 13,504.12 | 15.15 | 12,734.11 | 14.30 | 13,686 | 15.36 |
Agricultural land | 3241.41 | 3.64 | 4323.10 | 4.85 | 2190.62 | 2.46 | 2251 | 2.52 |
Forest | 15,779.61 | 17.72 | 16,044.50 | 18.00 | 14,497.29 | 16.28 | 15,437 | 17.32 |
Water bodies | 5485.46 | 6.16 | 6239.53 | 7.00 | 5129.26 | 5.76 | 5397 | 6.06 |
Overall | 89,063.64 | 100 | 89,136.10 | 100 | 89,063.64 | 100 | 89,096 | 100 |
They spread out to the outskirts as well. The forest areas had almost 20% of the area in 1972. Nevertheless, the forested areas were largely reduced due to the settlement development in the last decades.
Class name | 1972-August | 1985-May | 1998-April | 2003-June | 2015-May | |||||
---|---|---|---|---|---|---|---|---|---|---|
ha | % | ha | % | ha | % | ha | % | ha | % | |
Built-up | 16,429.68 | 44.65 | 16,226.29 | 44.10 | 16,439.17 | 44.69 | 15,672.24 | 42.61 | 18,938.88 | 51.49 |
Green and open areas | 4235.04 | 11.51 | 4902.66 | 13.32 | 5590.89 | 15.10 | 7227.97 | 19.65 | 4419.72 | 12.01 |
Agricultural land | 8677.80 | 23.58 | 8116.74 | 22.06 | 7794.00 | 21.19 | 7581.42 | 20.61 | 7086.42 | 19.26 |
Forest | 6994.08 | 19.01 | 6537.33 | 17.77 | 6051.06 | 16.45 | 5569.65 | 15.14 | 5553.45 | 15.10 |
Water bodies | 461.52 | 1.25 | 1009.35 | 2.74 | 907.25 | 2.46 | 731.09 | 1.99 | 784.00 | 2.13 |
Overall | 36,798.12 | 100 | 36,792.37 | 100 | 36,782.37 | 100 | 36,782.37 | 100 | 36,782.47 | 100 |
15.19% during the years of 2003 to 2015. Results (
With the adoption of the Landscape Protection Area Ordinance in 28th June 2000, many of open areas located at the protected area were preserved. Thus, the open areas were significantly expanded on one hand. However, on the other hand there is a clear decline in the green area. The decrease in the proportion of green areas could be attributed to the following factors: extend in cemeteries, realization of sports grounds and conversion of children’s playgrounds into built-up areas. It is expected that according to the published reports, the region is experiencing a significant expansion of green areas from 2014 onward. The transformed areas of the region-such as the areas that are located in the city of Nürnberg-currently offer the largest development and construction rates within the green and open area in the inner city. The proportion of the water surface shows a relatively large change in the investigation area. These proportions of water surface were 1.25% in 1972, 2.74% in 1985, 2.46% in 1998, 1.99% in 2003 and 2.13% in 2015. These changes of water surface could be attributed to changes the rainfall, the conversion of the LU and the ecological change of the several lakes and ponds in the western urban area. Last but not least, the consumption of land in the region, i.e. the conversion of agricultural and forestry land but also natural areas (uncultivated areas) into the settlement and traffic area, essentially results from the use of land for construction activities.
To improve the overall accuracy of Landsat image classification of the study area, the LU reference map of 2015 (edition 2015, scale 1:10,000) has been used. The reference map were derived from the official land register information system and are available from the Bavarian Agency for Surveying and Geoinformation (ALKIS). In addition, “Bavarian state office for statistics and data pro- cessing” provided this research with the LU statistical data for the year of 2015. These data were used to improve the overall accuracy assessment of the study area. However, the statistical data for three classes of agriculture, forest and water bodies has been provided. Nevertheless, the results were used to compare to estimate the classification errors and the quality of the 2015 Landsat image classification.
The LU accuracy assessment based on the statistical data for the three mentioned classes are as the following: Agricultural land 77.53%, forest 81.18% and water bodies 98.9%. The overall accuracy assessment for these three classes were 85.87%.
In this study, the Landsat archive images were used to detect the history of LULC changes in the metropolitan city of Berlin and the metropolitan region of Erlangen-Nürnberg-Fürth-Schwabach. In addition, the historical data from the maps and the statistical data have been used to check the accuracy of this detection.
Built-up area proved to occupy the largest space in both study area in the period of current study. The percentages of the built-up area were 51% to 61% for the metropolitan city of Berlin and 45% to 52% for the metropolitan region of Erlangen-Nürnberg-Fürth-Schwabach during the past 40 years. A dramatic change has been seen from agriculture to built-up, green and open as well as forested area, especially within the period of 1985 to 2015 in Berlin. Until 2015, the agricultural area occupied a relatively large part, but remained stable, of the metropolitan region of Erlangen-Nürnburg-Fürth-Schwabach. There was an increase in forested and green and open area from the period of 1972 to 2003 for both study area on one hand. On the other hand, from 2003 to 2015 there is a dramatic change from forested and green and open land into other LU covers, in
Class name | Producer’s accuracy (%) |
---|---|
Built-up | 82.20 |
Green and open areas | 94.74 |
Agricultural land | 79.65 |
Forest | 81.48 |
Water bodies | 98.80 |
Overall | 87.4 |
Class name | Landsat 2015 | ALKIS 2015 | ||
---|---|---|---|---|
ha | % | ha | % | |
Built-up (settlement, road) | 18,938.88 | 51.49 | 16,062.153 | 43.71 |
Green and open areas | 4419.72 | 12.01 | 4194.882 | 11.45 |
Agricultural land | 7086.42 | 19.26 | 8887.808 | 24.18 |
Forest | 5553.45 | 15.10 | 6808.435 | 18.51 |
Water bodies | 784 | 2.13 | 792.722 | 2.15 |
Overall | 36,782.47 | 100 | 36746 | 100 |
particular the built-up area in both study areas. In the last decade, the open area in the two areas of study has been significantly expanded to ensure the nature and soil balance and for the recuperation under protection. The increase in forest and green areas is an inviting trend in the two study areas to preserve the natural ecosystems and its biological diversity in the urban areas of these regions.
The results show that the overall accuracy of remote sensing data is between 88% and 94% for Berlin and from 86% to 87.5% for Erlangen-Nürnburg-Fürth- Schwabach. This could indicate that integrating remotely sensed and GIS data are useful to monitor and mapping the LULC for both urban areas. However, better spatial and temporal resolution data enables us with improved results to avoid environmental and ecological problems.
This research was supported and funded by the Alexander von Humboldt Foundation. The work was carried out at the Department of Geomorphology and Soil Geography at the Geographical Institute at the Humboldt-Universität Berlin.
Mohamed, M.A. (2017) Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS. Natural Resources, 8, 353-369. https://doi.org/10.4236/nr.2017.85022