A METHOD TO MAP LAND-USE CHANGE AND URBAN GROWTH IN NORTH RHINE-WESTPHALIA (GERMANY)

Transcription

1 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover A METHOD TO MAP LAND-USE CHANGE AND URBAN GROWTH IN NORTH RHINE-WESTPHALIA (GERMANY) Roland Goetzke, Martin Over and Matthias Braun University of Bonn, Center for Remote Sensing of Land Surfaces (ZFL), Bonn University, Walter-Flex-Str. 3, D Bonn, Germany; goetzke@uni-bonn.de ABSTRACT For the German federal state of North Rhine-Westphalia land use maps were generated for four time slices between 1975 and 2005 using LANDSAT satellite data. The determination of built-up areas was the main focus, since this study aims at raising public awareness on the problems related to urban sprawl and growing human induced consumption of land. Within this context a method was developed for cost- & time-efficient change detection in land cover classes based on prior classification results and newly acquired imagery. To cover an entire state with about km² and various time steps operational tools are required that enable the derivation of consistent land use and imperviousness maps. ISODATA clustering and knowledge-based decision trees were used to separate change and no-change pixels between a classification from 2001 and newly acquired imagery in The method was successfully tested on small subsets and subsequently applied to entire North Rhine-Westphalia. Independent accuracy assessment with 85.6 % proofed the quality of the approach. For the urban areas additional improvements and homogenization of all four classifications could be achieved by integrating results from regression analysis between NDVI and the degree of imperviousness. For the country, the derived products form an unprecedented base for visualization and quantification as well as for various applications as e.g. land use cover change modeling, hydrological applications or any other study with a regional coverage. INTRODUCTION The processes of urbanization have considerably changed the characteristics of landscape in Western and Central Europe in the past decades. This particularly includes the transformation from agricultural use into urban and built-up areas. One of the major problems related to those processes is the sealing of soil surfaces by urban areas and transportation infrastructure with its impacts on meso- and microclimate, soil, ground water, flora/fauna and urban hygiene (i,ii). In North Rhine-Westphalia (NRW) in the western part of Germany the population development is stagnating in recent years, while simultaneously the newly converted urban areas are increasing. The urban and infrastructure areas have doubled since the 1950s from about 10% to 21.76% of the total land area in Recent trends show that the per capita land consumption is increasing (iii). NRW includes highly industrial areas like the Ruhrgebiet with formerly intensive coal mining and steel industry but also considerable proportion of rural landscapes in the surroundings. As a consequence the situation in NRW can be seen as an exemplary case for agglomeration areas in Western and Central Europe. The work is embedded in a project aiming to raise public awareness of this topic by providing spatially explicit information based on multi-temporal satellite imagery. This paper focuses on the development of a semi-automated, knowledge-based method for land use/cover change (LULC) detection. It effectively uses existing land cover products in the same level of quality with newly acquired satellite images in order to derive updated land use classification and change products. Using Normalized Difference Vegetation Index (NDVI) regression with the degree of imperviousness three built-up classes of 0-40%, 40-80% and >80% were further distinguished. This approach proofed to be a suitable procedure to produce consistent classification results in urban areas with the required high precision over a 102

2 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 period of three decades. For other studies on impervious surface estimation, in most cases based on spectral unmixing, see Civco et al. (2000, iv), Small (2001, v), Phinn et al. (2002, vi), Zhang et al. (2002, vii), Yang & Lo (2002, viii), or Lu & Weng (2006, ix). The relation between NDVI and vegetation fraction that is in this study used as an indicator for imperviousness, is a well established research topic. See e.g. Jensen (1996,x), Fung and Siu (2000,xi). METHODS Study Area and Data NRW is the most densely populated federal state in Germany with a population of about 18 Million people in an area of about km², resulting in a population of 530 inhabitants per km² (xii). It has undergone serious economic and structural transitions in the last decades, especially in the old industrial Ruhr area in the center of the state where decreasing coals mining activity and shrinking heavy industries have led to considerable transformation processes, partially shrinking population and unemployment. NRW is covered by 2-3 LANDSAT tracks with 2-3 rows each. Complete cloud-free coverage was available for the years 1975, 1984, 2001 and 2005 (Tab. 1). All scenes were radiometrically normalized using Pseudo invariant features determined by the iteratively reweighted multivariate alteration detection (IR-MAD) transformation (Nielsen et al., 1998 (xiii); Nielsen, 2006 (xiv); Canty et al., 2004 (xv); Canty and Nielsen, 2006 (xvi)). Geometric coregistration was below 1 pixel and absolute reference was provided by the national landscape information system ATKIS. The regression analysis for impervious surfaces was calibrated using aerial photographs for each time step (Photographs of high density, residential and low density urban areas in 8 test sites, covering an area of ~ 4 km² respectively). Tab. 1: LANDSAT-data used in this study. Sensor / Path/Row Landsat MSS Landsat-5 TM Landsat-7 ETM+ Landsat-5 TM /08/1975 (211-23) 10/08/1975 (211-24) 10/08/1975 (211-24) 29/08/1975 (212-24) 30/08/1975 (213-24) 29/08/1975 (212-25) 25/04/ /04/ /04/ /08/ /08/ /07/ /07/ /07/ /05/ /06/2001 ( shifted) 03/04/ /04/ /05/ /05/2005 Knowledge-based change detection The LANDSAT data from 1975, 1984 and 2001 was classified with 12 identified classes (Fig. 1) in a prior stage of the project using supervised procedures (Over et al., 2004 (xvii), Schöttker et al., 2004 (xviii)). Particular attention had been given to the 2001 classification where contemporary field surveys in 2002 were available. This time-slice with a respective high classification accuracy of 91.6 % was now used as a base for change detection with the 2005 data set since expected changes in this 4 year time difference would be small and changes would mainly result from classification errors rather than real transformations. 103

3 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover 2005 Figure 1: The 12 LULC classes represent the main land cover features in NRW. In a first step the 2005 data was intersected with the classification from Subsequently, an ISODATA clustering algorithm was applied to the 2005 data in the respective 2001 class area. The clusters were labeled using class-specific decision rules in order to assign them to a certain conversion or keep them as stationary in the class. As an example, to examine the changes in the forest between 2001 and 2005, the 2005 LANDSAT data was intersected with the forest -class of To identify the changes within this class, natural spectral clusters were built using the ISODATA unsupervised classification algorithm. Because the spectral range of a forest is quite narrow, areas that do not belong to the class forest any more can easily be observed at the borders of the spectrum (Fig. 2). A Change forest grassland/bare soil B Change forest water C 104

4 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 D Figure 2: (A) Scatter plot (band 4 against band 3) showing the spectral distribution of pixels (2005) within the borders of the class forest of Pixels that do not belong to the class forest any more are located at the edges of that scatter plot.(b) Classified image of 2001; the forest areas have been extracted and intersected with the multispectral image of 2005 (C). The cyan areas in (D) have been converted from forest to grassland/clearing, the blue areas are classified as water and the purple areas are uncertain and had to be reclassified by knowledgebased decision trees. The rest of the 2001 forest areas were unchanged. The unsupervised approach provided a high degree of, because the ISODATA algorithm uses minimum distance to assign a cluster for each candidate pixel (Yang & Lo, 2002). Where the ISODATA algorithm did not provide sufficient reliability in the separation, knowledge based decision trees containing NDVI and Tasseled Cap images were built to separate areas of change and no-change by thresholds (Fig. 3). Because of the problem of spectral similarity between bare soil, urban built-up structures and construction sites a manual correction of the classification was required in order to achieve a final product of adequate quality for the map service. Isodata clustering Visual inspection change Tass. Cap greeness < 30 AND Tass. Cap brightness > 130 not true Water (no change) No vegetation Vegetation Agricult. fields Gravel pits, etc. Forest Grassland Figure 3: Example flowchart for the discrimination of water change pixels into vegetated and non-vegetated areas. 105

5 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover Determination of the imperviousness classes Special attention was paid on the differentiation of the three impervious classes. It has been frequently reported that a negative correlation between degree of imperviousness and NDVI exists (vi, xix). In a first step the 2001 classification results were updated by the method explained below, using an NDVI/imperviousness regression model. The results for the impervious classes using post-classification change detection as e.g. two products of a Maximum Likelihood Classifier were discarded in favor of this method that maps minimum changes in classification results between time steps. Often errors in classification results are higher than the actual change, especially in small time steps, when only small changes occur. Therefore a method was needed that minimizes the discrepancies between time steps. 1,0 0,8 Degree of imperviousness 0,6 0,4 0,2 y = -1,1141x + 1,1548 R 2 = 0,7046 0,0 0,0 0,2 0,4 0,6 0,8 1,0 NDVI Figure 4: Correlation between NDVI (LANDSAT-TM 2001) with the degree of imperviousness (50 random points spread over an aerial image of the city of Essen). The degree of imperviousness was estimated by separating impervious/non-impervious surfaces in aerial photographs and projecting the means to a 30m raster. For 8 test sites (approximately 4 km² each) aerial photographs were classified in the two classes impervious and not impervious. The test sites covered a representative selection of urban, suburban and rural areas. Afterwards a 30m raster was created and superimposed on the classified aerial photographs. The mean degree of the impervious/not impervious surface calculated for each 30m cell was derived. A linear regression model was built between NDVI and the degree of imperviousness based on the raster cells. Fig. 4 shows example results in the city of Essen of a correlation between NDVI and degree of imperviousness. In a subsequent step the continuous impervious degrees of the imperviousness images were broken down into 3 discrete classes of imperviousness (0-40%, 40-80% and >80%). An example for the procedure is given in Fig. 5. Since NDVI is also influenced by phenology and general vegetation status the correlations need to be set up for each acquisition date separately. This results in different NDVI-thresholds corresponding with the three imperviousness classes for every path/row. 106

6 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 A B C Figure 5: estimation of the degree of imperviousness in the city of Essen-Altenessen (A), extract of the classified aerial photograph (red impervious, green not impervious), 30m raster of the classified aerial image superimposed with the NDVI image on the right (B), classified NDVI image in 3 impervious classes (C) In order to reduce the time-consuming classification of aerial photographs the procedure was run for one LANDSAT scene per acquisition date. In overlapping regions of two scenes the regressions were matched using the overall amount of the imperviousness areas for several administrative units as matching criteria. The NDVI for the second scene was then adjusted until the amount of the imperviousness classes for those regions fitted up to 0.2% to the foremost classified one. In a subsequent step the change detection for the 2005 time step was realized. To reduce errors occurring by misclassification only the regions that were classified 2001 as impervious were selected from the 2005 image. Assuming a relative persistence of urban structures, the NDVI thresholds for the 2005 image was adjusted iteratively until a match of 95% between the two images was achieved. This NDVI threshold was also used for the classification of the new built-up areas detected by the change detection method explained above and by visual inspection. For the two acquisition dates in 2005 (April and May) this procedure was performed separately. The same method was applied for the impervious areas of the 1984 and 1975 images guaranteeing continuous results. RESULTS & DISCUSSION Table 2 shows the distribution of the land cover classes and their changes. For the impervious surfaces an increase of 23.7% between 1984 and 2005 could be detected. The increase between 1975 and 1984 adds up to 64%, but because of the lower spatial resolution of the MSS-Sensor small built-up areas could not be detected, which lead to an underestimation of the impervious classes. Hence this rate is not very significant. In large parts the increase in impervious surface is in account of arable land. This development is quite obvious, since settlements in this region in most cases are directly surrounded by agricultural areas and new built up areas are mostly established in vicinity to already existing settlements. But the loss in well arable land must be considered problematic. 107

7 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover Table 2: LULC change for NRW as extracted from a time series of LANDSAT images, Classification map 1975 (%) 1984 (%) 2001 (%) 2005 (%) High imperviousness (> 80%) 1,94 2,37 3,04 3,24 Middle imperviousness (40-80%) 3,60 5,26 6,58 6,22 Low imperviousness (< 40%) 3,14 3,67 4,46 4,71 Open brown coal pit mining 0,17 0,23 0,26 0,26 Gravel pits & quarries 0,16 0,23 0,24 0,32 Military training areas 0,47 0,47 0,47 0,23 Agricultural fields 38,06 37,30 34,82 34,61 Grassland 28,94 26,94 23,87 23,65 Coniferous forest 9,81 9,62 11,13 10,77 Mixed forest 6,09 4,54 5,26 6,11 Deciduous forest 7,10 8,62 8,94 8,95 Water bodies 0,52 0,74 0,93 0,94 Between 2001 and 2005 an increase in the impervious surface classes of 0.6% could be detected. As an example Fig. 6 shows classification results of an area 15km west of Cologne with recultivation sites of open brown coal pit mining areas and some new construction sites Kilometers 2005 Figure 6: Sample of the classification results based on the knowledge based change detection. The area is located ~15km west of the city of Cologne and contains open brown coal pit mining sites that were partially recultivated in the past 5 years. The small black arrows indicate new construction sites. 108

8 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 The ongoing loss in agricultural areas and grassland is surprising inasmuch half of the military training areas were abandoned, some open pit mining areas have been recultivated and some urban shrinkage processes could be detected in the last five years. For the accuracy assessment of the 2005 classification 450 stratified random points were generated that showed an overall accuracy of 85.6%. As reference the existing ground truth data from 2002 and additional high-resolution aerial photography were available. For most of the classes the change detection worked very well. But a manual correction was necessary, especially where built-up areas emerged on former bare agricultural fields. Also difficult to detect was the change from crop in an early stage to grassland and reverse. For those two cases multitemporal datasets would have been essential, but were unfortunately not available for the whole region. The use of the NDVI/imperviousness regression model showed good results. An exact comparison of the Maximum Likelihood with the NDVI-regression results by an accuracy assessment of only the impervious classes has yet to be carried out. This will shed light on the fact, which of the two methods is the one with the higher classification accuracy. But what is already obvious by visual inspection and by comparison with aerial photographs is the circumstance that a high consistency between time steps is granted by using the NDVI-regression and transferring the results for the calibration of the NDVI thresholds of other time slices. Despite that some problems had to be faced. What this study showed and what was also described by Braun & Herold (2004) is the fact that the low degree of imperviousness usually is underestimated. To get a good match for the high and middle degree of imperviousness, too many low imperviousness pixels are misclassified, so that the overall correctness of the classification decreases. By changing the NDVI thresholds to get better results for the low degree of imperviousness the other two impervious classes lose a lot of their accuracy. It was decided that classifying more pixels correctly within the whole urban class was preferred to the best match for one impervious class. For a further study it is recommended to improve the calculation of the imperviousness for example by adding the Normalized Difference Built-up Index (NDBI) (xx) or by using Spectral Mixture Analysis (SMA) (Small, 2001; xxi). Small (2001) prefers SMA to the use of the NDVI as an indicator for imperviousness because of the non-linearity of the NDVI. But this study was focused on finding an easy and comprehensible way of updating a statewide land use map. The consistency between time steps and a rational and reproducible way to estimate imperviousness were the most important issues. Finding representative spectral endmembers for a SMA for a large research area covering 4 LANDSAT scenes with different acquisition dates is very complex. A possible loss in accuracy was in this case accepted in favor of comprehensible classification results and an easy and updating of the LULC classes. CONCLUSIONS An effective approach for updating a statewide land use map, especially taking the consistence between time-slices into account was presented here. Without using a complex methodology a reasonable and comprehensible updating of the classification could be assured. The use of the NDVI to derive different imperviousness classes could be applied with good results. It was recognized, that with a multitemporal dataset the results could be improved, because still a considerable amount of manual correction and a good knowledge of the research area and its characteristics was needed. For further work on this topic the use of multitemporal data is regarded as essential. Especially dividing bare agricultural fields from built-up areas and grassland from crop in an early stage would be much easier and would prevent a lot of manual corrections that have been necessary in this case. A further improvement of the NDVI-related estimation of the imperviousness could be given by including the NDBI or by changing the concept in favor for the use of SMA, although finding the correct endmembers for a large and diverse study area like NRW might lead to a much more complex methodology and not necessarily to much better results. 109

9 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover ACKNOWLEDGEMENTS The study was part of the project Visualisierung von Landnutzung und Flächenverbrauch in Nordrhein-Westfalen mittels Satelliten- und Luftbildern, funded by the Ministry of Environment and Nature Conservation, Agriculture and Consumer Protection of the federal state of NRW, Germany. The project was carried out close cooperation with the the Geography section at the PH Karlsruhe, the Wissenschaftsladen Bonn e.v., Chromgrün GmbH (Essen) and the Institut für Flächennutzung und Umweltkommunikation (Giessen). Project results and further information is available on i Heber, B & I Lehmann, Beschreibung und Bewertung der Bodenversiegelung in Städten. In: IÖR-Schriftenreihe 15, edited by Institut für ökologische Raumentwicklung e.v., Dresden. ii Arnold, C L & C J Gibbons, Impervious surface coverage: The emergence of a key environmental indicator. Journal of the American Planning Association, 62/2: iii Clarke, K C & L J Gaydos, Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12/7: iv v Civco, D L, Hurd, J D, Arnold, C & S Prisloe, Characterization of Suburban Sprawl and Forest Fragmentation through Remote Sensing Applications. Proceedings of 2000 ASPRS Annual Convention, Washington D.C. Small, C, Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22/7: vi Phinn, S, Stanford, M, Scarth, P, Murray, A T & P T Shyy, Monitoring the composition of urban environments based on vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. International Journal of Remote Sensing, 23/20: vii Zhang, Q, Wang, J, Peng, X, Gong, P & P Shi, Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. International Journal of Remote Sensing, 23/15: viii Yang, X & C P Lo, Using a time series of satellite imagery to detect land use and land cover change in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23/9: ix Lu, D & Q Weng, Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 102: x Jensen, J R, Introductory to digital image processing: a remote sensing perspective. Prentice Hall Series in Geographic Information Science, 316pp. xi Fung, T & W L Siu, Environmental quality and its changes, an analysis using NDVI. International Journal of Remote Sensing, 22: xii Destatis, xiii Nielsen, A A, Conradsen, K, and Simpson, J J, Multivariate alteration detection (MAD) and MAF processing in multi-spectral, bitemporal image data: New approaches to change detection studies. Remote Sensing of Environment, 64,

10 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 xiv Nielsen, A A, The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. Submitted for publication. xv Canty, M J, Nielsen, A A, and Schmidt, M, Automatic radiometric normalization of multitemporal satellite imagery. Remote Sensing of Environment, 91(3-4), xvi Canty, M J and Nielsen, A A, Visualization and un-supervised classification of changes in multispectral satellite imagery. International Journal of Remote Sensing. In Press. xvii Over, M, Moll, A, Schöttker, B, Braun, M & G Menz, Remote Sensing as a tool to visualise the land use/-cover and land use/-cover change of the last 25 years in North Rhine-Westphalia (Germany). Corine Landcover Workshop Berlin, Jan xviii Schöttker, B, Over, M, Braun, M, Siegmund, A & G Menz, Statewide land use change detection using remote sensing. Proceedings of SPIE Remote Sensing Europe, September 2003, Barcelona, Spain. xix Braun, M & M Herold, Mapping imperviousness using linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany). SPIE Remote Sensing Europe, September 2003, Barcelona, Spain. xx Zha, Y, Gao, J & S Ni, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24/3: xxi Rashed, T, Weeks, J R, Gadalla, M S & A Hill, Revealing the Anatomy of Cities through Spectral Mixture Analysis of Multispectral Imagery: A Case Study of the Greater Cairo Region, Egypt. Geocarto International, 16/4: