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Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges
ISPRS Journal of Photogrammetry & Remote Sensing 57 (2003) 315 – 326 www.elsevier.com/locate/isprsjprs
Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges Manfred Ehlers *, Monika Ga¨hler, Ronald Janowsky Research Centre for Geoinformatics and Remote Sensing, University of Vechta, P.O. Box 1553, D-49364 Vechta, Germany Received 11 February 2002; accepted 12 July 2002
Abstract The advent of very high-resolution satellite programs and digital airborne cameras with ultra high resolution offers new possibilities for very accurate mapping of the environment. With these sensors of improved spatial resolution, however, the user community faces a new problem in the analysis of this type of image data. Standard classification techniques have to be augmented with appropriate analysis procedures because the required homogeneity of landuse/landcover classes can no longer be achieved by the integration effect of large pixel sizes (e.g., 20 – 80 m). New intelligent techniques will have to be developed that make use of multisensor approaches, geographic information system (GIS) integration and context-based interpretation schemes. The ideal goal should be that GIS ‘intelligence’ (e.g., object and analysis models) should be used to automate the classification process. In return, GIS objects can be extracted from a remote sensing image to update the GIS database. This paper presents the development of an automated procedure for biotope type mapping from ultra high-resolution airborne scanner data (HRSC-A). The hierarchical procedure incorporates a priori GIS information, a digital surface model (DSM) and multispectral image data. The results of this study will serve as a basis for a continuous environmental monitoring process in the tidally influenced region of the Elbe River, Germany. D 2003 Elsevier Science B.V. All rights reserved. Keywords: high resolution remote sensing; GIS, classification; biotope type mapping
1. Introduction The availability of remote sensing data applicable for global, regional and local environmental monitoring has greatly increased over recent years. New technologies such as Global Positioning System (GPS), digital photogrammetry and multi-source sat-
* Corresponding author. Tel.: +49-4441-15-423; fax: +49-444115-445. E-mail address: [email protected] (M. Ehlers).
ellite remote sensing are opening new application fields for remote sensing. With the advent of new (civilian) satellite programs such as IKONOS or Quickbird and, in addition, digital airborne cameras, image data are being acquired at higher spatial, spectral and temporal resolution than have been collected at any other time on Earth. There are many ways to distinguish between remote sensing systems. The most common one is probably the differentiation between space/satellite and airborne systems. Other possible taxonomies include spatial/spectral resolution, recording mode or spectral coverage (Table 1).
0924-2716/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0924-2716(02)00161-2
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Table 1 Taxonomy of remote sensing systems (modified after Ehlers et al., 2002) Recording platform
Satellite/shuttle
Recording mode
Passive (visible, reflected infrared, thermal infrared, thermal microwave) Analog (camera, video) Visible/ultraviolet Reflected infrared Panchromatic (1 band) Multispectral (2 – 20 bands) Low ( < 6 bit) Medium (6 – 8 bit)
Recording medium Spectral coverage Spectral resolution Radiometric resolution Spatial ground resolution
Very low (>250 m)
Aircraft/balloon
Low (50 – 250 m)
With very high-resolution sensors, the user community faces a new problem in the automated analysis of this type of image data. Simple pixel-based analyses are no longer applicable because of the difficulty of classifying high-resolution data where each pixel is related not to the character of an object or an area as a whole, but to components of it (Blaschke and Strobl, 2001). Gong et al. (1992), as well as Johnsson (1994), showed that spectral classification of higher resolution data does not automatically lead to more detailed classification results. Further, using only multispectral information for classification does not lead to accurate interpretation results because the differentiation between object classes is done not only with the help of spectral information, but also with spatial (contextual) information of the image data. For example, using only multispectral information, different objects like roofs and streets might not be separated into two object-classes because they are built of the same material (Hoffmann et al., 2000). Consequently, new intelligent techniques will have to be developed that make use of GIS integration, multisensor approaches and context-based interpretation schemes (Ehlers, 2000). In a pilot study, the Research Centre for Geoinformatics and Remote Sensing at the University of Vechta, Germany developed a procedure for a context-based hierarchical image analysis and classification scheme for an automated biotope type mapping from ultra high resolution airborne scanner data (HRSC-A). The procedure incorporates a priori GIS information, digital surface model (DSM) and multispectral image data.
Medium (10 – 50 m)
Stationary
Active (laser, radar) Digital (Whiskbroom, Line Array, 2D CCD) Thermal infrared Microwave Hyperspectral Ultraspectral (20 – 250 bands) (>250 bands) High (8 – 12 bit) Very high (>12 bit) High (4 – 10 m) Very high Ultra high (1 – 4 m) (< 1 m)
2. Ultra high resolution scanner data for monitoring of biotope types The reason for this study was the latest expansion of the fairway of the River Elbe in the region of Hamburg, Germany, due to requirements for the new generation of container ships (BfG, 2000). The precedent environmental impact assessment mandated continuous environmental monitoring after the end of the expansion project with focus on the tidally influenced riverside biotopes. Changes in composition and size of these biotopes should be documented over the long term to assess the impacts of hydraulic engineering measures. The Waterways and Shipping Office (WSA) in Hamburg charged the German Federal Institute of Hydrology (BfG) with the development of a generally applicable automated and cost-effective method for an operational, long term monitoring process. In cooperation with the Research Centre for Geoinformatics and Remote Sensing and the German Aerospace Centre (DLR) in Berlin, an integrative monitoring concept was developed using a combination of GIS, image analysis and modelling software. Usually, biotope type maps have been derived from extensive fieldwork supported by conventional aerial photographs. This task requires an expensive amount of manpower and often lacks the necessary geometric accuracy (Janowsky et al., 2001). In addition, despite every effort in giving accurately described key features for interpretation, inconsistencies occur among the persons analysing the photographs.
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Digital post-processing of analogously recorded data by means of scanning the aerial photographs and subsequent digital image processing is an alternative to a completely analogue processing. It has, however, still a number of disadvantages that aggravate its use for a completely automated procedure, namely, radiometric inconsistencies within and between the recorded photographic images. Digitised and rectified conventional aerial photographs can mainly be used for visualisation. The only way of deriving reliable information from scanned aerial photographs is the so-called on-screen-digitising. There, the same problems are encountered in deriving objective interpretations as in the analogue process. An essential improvement seems only possible by using appropriately recorded digital data (Hoffmann and Lehmann, 2000; Mo¨ller et al., 2001). The analysis process can then be automated employing a rule-based procedure. The present study used for the first time ultra-high resolution multispectral scanner data of the High Resolution Stereo Camera-
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Airborne (HRSC-A) for very accurate mapping of biotope types. 2.1. Study area The study area is located along the tidally influenced section of the Elbe River in the border area of the German states Lower Saxony, Schleswig-Holstein and Hamburg. Three representative sections of this area were selected (Fig. 1): (1) The small river island of Pagensand, about 20 km seaward from Hamburg shows both man-made and natural structures, such as reeds and willow forests, cultivated grassland and ruderal vegetation (i.e., vegetation characteristic of highly disturbed areas), sandy grassland and open sands, deciduous and coniferous forests. (2) The nature reserve ‘‘Heuckenlock’’ at the southeast edge of Hamburg contains large areas of reeds, some bayous and willow forests.
Fig. 1. Study sites in Germany.
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(3) An area around the confluence of the Ilmenau River in Lower Saxony, mostly dominated by various grassland and reed vegetation types. Most of this study was performed on data of Pagensand Island because of its excellent representation of nearly all important biotope types appearing along the whole lower reaches of the Elbe River. The Heuckenlock area was used for verification of the developed algorithm using a ‘‘blind testing’’ approach. For the other regions, ground truth was available in the form of older reference maps dated 1992 that were produced by photointerpretation and fieldwork. 2.2. The HRSC-A scanner system The HRSC-A (High Resolution Stereo CameraAirborne) is a narrow-angle digital multi-spectral
stereo scanner for photogrammetric and remote sensing applications (Lehmann et al., 2000; Wewel and Scholten, 1999). The camera was originally developed for the Russian Mars-96 space mission and later modified for airborne remote sensing applications. The peripheral digital electronics, in particular, were redesigned for use on aircraft. The system is mounted on a Zeiss T-AS stabilisation platform. The HRSC uses the ‘‘push-broom’’ principle. Nine CCD line detectors are mounted in parallel at the focal plane behind one single optics and perpendicular to the flight direction. Nine images of the same strip are recorded, almost simultaneously, as a result of the forward motion of the aircraft. Five of the nine CCD lines of the HRSC-A are arranged at specific viewing angles to provide stereo and photometric viewing capability; four of the nine CCD lines are covered with different filters for the acquisition of multispectral images (Fig. 2, Table 2).
Fig. 2. HRSC-A imaging principle (modified after Hoffmann and Lehmann, 2000).
M. Ehlers et al. / ISPRS Journal of Photogrammetry & Remote Sensing 57 (2003) 315–326 Table 2 Technical data for the high resolution stereo cameras-A and -AX HRSC-A Focal length Total field of view Number of CCD lines Sensors per CCD line Sensor size Radiometric resolution Spectral resolution/band numbers
Read-out frequency Stabilisation Data recording Georeferencing
HRSC-AX
175 mm 151 mm 38 12j 41 29j 9 9 5272 12,172 7 7 Am 6.5 6.5 Am 8 bit 12 bit Blue: Blue: 395 – 485 nm 450 – 510 nm Green: Green: 485 – 575 nm 530 – 576 nm ‘‘Red’’: Red: 730 – 770 nm 642 – 682 nm NIR: NIR: 920 – 1020 nm 770 – 814 nm 5 Nadir/stereo 5 Nadir/stereo (Panchromatic): (Panchromatic): 585 – 765 nm 520 – 760 nm 450 lines/s 1640 lines/s Zeiss T-AS platform Sony high speed data recorder Applanix POS/DG Navigation system with GPS and INS
The ‘‘red’’ band 3 of the HRSC-A sensor is in reality an infrared band. For consistency with the official HRSC nomenclature it is kept as red band.
A fully automatic photogrammetric and cartographic processing system was developed at the DLR, in cooperation with the Technical University
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of Berlin (Wewel and Scholten, 1999). Although this system can work without ground control points (GCPs), a few GCPs are usually required for validation purposes. The first step involves the synchronisation of the external inertial navigation system (INS) that provides a combination of gyro and accelerometer information augmented by carrier phase DGPS with the HRSC image data. Subsequently, the alignment between the camera axes and the gyro axes of the INS is determined on the basis of multi-stereo image information. For the generation of digital surface models (DSMs), a digital multiple correlation process is performed. The resulting identical image points are converted into object points by ray intersections. The DSM is derived by interpolation. Finally, based on the DSM digital orthoimages are generated and multispectral orthomosaics are produced. As a spin-off, an extended narrow-angle system (HRSC-AX) has been developed by the DLR providing higher resolution and re-designed multi-spectral bandwidths that are better suited for terrestrial applications (see Table 2; Neukum and HRSC-Team, 2001). This sensor, however, was not yet available for the case study. For this study, the DLR provided a complete HRSCA dataset (i.e., panchromatic and multispectral imagery, plus digital surface model). An example for
Fig. 3. Various views of the same area recorded with only one HRSC-A camera system: (a) panchromatic; (b) ‘true’ colour (‘R’GB); (c) colour infrared (CIR); (d) digital surface model (DSM) with red = high elevation, yellow and green = medium elevation and black = low elevation.
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various band combinations of the section ‘‘Heuckenlock’’ is presented in Fig. 3. The data were recorded from a flying height of 3000 m and delivered with 15cm ground pixel resolution. Absolute accuracies were given as F 20 cm in horizontal and F 30 cm in vertical direction.
3. Methodology 3.1. Hierarchical procedure design For the automated biotope type mapping from HRSC-A data, a hierarchical GIS-based procedure was developed. First, all HRSC-A data (i.e., multispectral images and DSM) were incorporated in a GIS database to allow integrative processing. In addition, textural measures and a synthetic red band (see Section 3.2) were calculated and added for increased information diversity. For this GIS/Remote Sensing database, a hierarchical classification procedure was
developed (Ehlers et al., 2000) (Fig. 4). The main steps of this procedure are: Level 1: Separation of information into semantic layers non-vegetation/sparse vegetation shadows low vegetation high vegetation Level 2: Image processing (individual classification of the semantic layers) ISODATA clustering supervised classification Level 3: GIS-based postprocessing defining minimum sizes of biotope types defining neighbourhood relations combining individual classifications eliminating shadow areas (refilling) Level 1 involves the use of vegetation indices (see Section 3.2) as masks to perform a semi-automated
Fig. 4. Flowchart of the hierarchical classification procedure.
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separation between different object types (non-vegetation/sparse vegetation, shadow and vegetation). In the next step, an integrated GIS/Remote Sensing database approach and the use of elevation thresholds made it possible to differentiate the vegetation layer into low vegetation and tall vegetation. Thus, biotope types that did not show a difference in their multispectral reflectance characteristics but were of different height (e.g., shrubbery vs. trees) could be separated. The separation of information step, therefore, permits the depth and accuracy of the classification to be improved. At Level 2 of the procedure, the separated layers were treated with appropriate classification algorithms (i.e., ISODATA clustering for the non-vegetation/ sparse vegetation layer and supervised classification for the herbaceous vegetation layers; see Fig. 4). With this approach, the level of detail in the biotope type classification could be significantly improved. It was possible to identify more than 20 different classes. Level 3 involved GIS-based postprocessing to produce the final classification result. GIS operations such as majority filtering, logical overlay and minimum area functions were used to estimate appropriate classes for shadow areas and to combine the individual information layers. The final output was a GIS layer with 21 biotope types for the study sites. 3.2. Preparation of HRSC-A data Unfortunately, the HRSC-A lacks a spectral band in the red domain that is very important for vegetation interpretation. This is due to the original design of the HRSC-A for a Mars mission. The difference in characteristics of the HRSC-A and a standard remote sensing systems such as Landsat ETM is presented in Fig. 5.
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Consequently, a synthetic red band was calculated using arithmetic operations involving the near infrared and panchromatic bands (see Table 2, Fig. 5). This artificial red band could be used for true colour and false colour infrared visualisation (see Fig. 6) and in a further step for the calculation of vegetation indices— even though the use of the standard vegetation indices yielded no satisfactory results (Ga¨hler, 2000). Consequently, vegetation indices based on the spectral bands of the HRSC-A were developed. The newly developed sensor, HRSC-AX, includes a true red band (see Table 2) which will allow the easy calculation of standard vegetation indices. Best results were obtained by the difference of the synthetic red band and Band 1 as well as the difference between Band 4 and Band 1 (see Table 2). The result of the first calculation (‘synthetic red’ minus Band 1) separates herbaceous vegetation from nonvegetation/sparse vegetation with shadow. The second calculation (Band 4 minus Band 1) separates nonvegetation/sparse vegetation from shadowed vegetation. By means of those vegetation indices, not only the vegetation and non-vegetation could be distinguished, but also shadow areas could be identified. In addition to band subtraction operations, textural measures were applied for further differentiation. The use of a variance filter (7 7 matrix) based on the panchromatic channel proved to be the most useful. Biotope types with relative smooth surfaces (e.g., water and grassland) could be distinguished from biotope types with relatively rough texture features such as tall reeds and trees. For classification and visualisation, the calculated texture layer and the synthetic red band were integrated (along with the DSM) into the original multispectral layerstack to form an expanded multiband image as
Fig. 5. Distribution of spectral bands for Landsat ETM and HRSC-A (pan = panchromatic, ms = multispectral).
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Fig. 6. Separation of information using vegetation index masks and altitude threshold values.
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input for the hierarchical classification procedure (see Fig. 4).
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Table 3 Classified biotope types and their required minimum sizes Group
Biotope type
Minimum size [m2]
4. Results of the hierarchical classification procedure
Tall vegetation
4.1. Level 1: separation of information
Low vegetation
Deciduous forest, general Lowland riparian willow forest Coniferous forest Single Trees Shrubs Silted zone with floating plants Trench with duckweed Reed (Phragmites) Scirpus Meadow, rich wet site Intensively used grassland, dry site Lawn, poor in species Semi-ruderal vegetation, humid site Semi-ruderal vegetation, medium site Ruderal vegetation, humid site Ruderal vegetation, dry site Water Meager sandy grassland with sparse vegetation Impervious/sand Mud/sand deposit 1 Mud/sand deposit 2
200 200 200 40 40 40 40 40 40 40 40
Using the calculated vegetation indices as masks, the multiband images were divided into two semantically meaningful information layers: (1) herbaceous vegetation without influences of shadows; and (2) nonvegetation/sparse vegetation without influences of shadows. In a further step, the vegetation areas were differentiated into high and low vegetation by means of a threshold value (>12 m above sea level) derived from the DSM. The resulting separated multiband images shown in Fig. 6 were treated with ISODATA clustering and supervised classification algorithms. 4.2. Level 2: individual layer classification The further classification of non-vegetation/sparse vegetation was performed by an ISODATA clustering algorithm while the discrimination of the vegetation layers (low and high) was performed using a supervised Maximum-Likelihood classification. The procedure is based on extensive testing of a number of alternatives in processing and error analyses employed to determine the most accurate procedure (Ehlers et al., 2000; Ga¨hler, 2000). Using this method, biotope types of Pagensand Island could be automatically distinguished into five types of non-vegetation, 12 types of herbaceous vegetation and four types of tree-vegetation (Table 3). 4.3. Level 3: GIS-based postprocessing The individually classified layers were combined in a hierarchical process considering a predefined priority order to obtain one final resulting layer. For example, biotope types of herbaceous vegetation had a higher priority than types of non-vegetation. Within this process a minimum size filter for each biotope type was used. The minimum biotope type sizes were determined together with experts from the WSA and BfG (Table 3). In addition, neighbourhood relations between the different biotope types were considered
Non-vegetation/ Sparse vegetation
40 40 40 40 40 40 70 70 70 70
(e.g., if a road is located between two water objects it is defined as a bridge). The shadow areas were eliminated by GIS based filter techniques, such as majority and altitude-based filtering. Shadow pixels/ regions which were higher than 12 m (derived from the DSM) were filled up only with biotope types of tree vegetation in the direct neighbourhood and not with biotope types of non-vegetation or low vegetation and vice versa. A flowchart based modeller tool was used to formalise and automate this hierarchical classification procedure and implemented in the ERDAS ImagineR software (Mo¨ller et al., 2001).
5. Validation Comparison of the hierarchical classification results from this study with older maps of the same area that were produced by photointerpretation techniques show the tremendous advantages of the new method including richness of detail and geometric accuracy (Fig. 7). This comparison could easily be performed by over-
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Fig. 7. Comparison of the classification result (top left) derived from HRSC-A image data (top right) and a conventionally generated map (bottom left).
laying the different digital layers in the GIS environment. Differences between the classification result and conventionally generated maps are presented in Fig. 7 for a small test site located on Pagensand Island. The richness of detail of the classification results corresponds well with the structures in the original image. The visual interpretation result already shows the generalisation that was performed by the human operator. The older reference map shows the subset mapped with five polygons consisting of 93 vertices whereas the new classification result yields 32 better fitting polygons, described by 1488 vertices overall. Even single trees, shrubs or open forest areas smaller than 100 m2 can be detected over large areas. The biggest problem proved to be that the quality of the digital analysis of HRSC-A data seemed to be better than the ground truth measurements. In particular, the richness of classified detail from the HRSC-
A data proved the superiority of this method over ground-based analysis. Fig. 7 clearly indicates that the old reference map could not be used for accuracy checking. For a thorough field test, scientists of the BfG used the area of Heuckenlock. For this area, the classification was carried out by means of ‘blind testing’ without ground truth, just using the procedure, signatures and experiences attained in work with the Pagensand study area. For accuracy checking, about 200 samples of representative biotopes and their delineation were recorded and their geographic coordinates measured by DGPS. Additionally, their characteristics were described and documented by photographs. All data were registered to the GIS database (Fig. 8) and compared with the biotope layers from the HRSC-A classification. Almost all samples showed an excellent reproduction of the real situation. Structural differences were picked up in a very
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Fig. 8. Validation procedure: DGPS-based recording of samples (bottom right), integration of all data in a GIS-database (top right), comparison of ground-based map (top left) with the HRSC-A classification (bottom left).
detailed way. It was only rarely the case that mismatches were found and some biotope delineation did not correspond to reality. This was mostly true for herbaceous vegetation where in some cases the outlines were not correctly defined. The most important features to be monitored, however, such as reeds and willows were classified with an accuracy exceeding 95%. It was also evident that the richness of detail from the ultra high-resolution remote sensing data could not be matched by the recorded ground truth.
6. Conclusions The potential of high-resolution digital airborne scanner data with excellent geometric fidelity was
investigated in a pilot project. It could be shown that digital image data in combination with an integrated GIS/remote sensing processing environment allowed the development of an automated classification procedure for biotope mapping which proved to be highly accurate also in an independent test area. This automated hierarchical technique facilitates the documentation of dynamic processes in long-term environmental monitoring projects. The original images as well as the classification results can easily be integrated in a GIS environment. This allows operational analysis and measuring of changes over time. Of particular importance proved the integration with the DSM that is simultaneously produced from the HRSC-A sensor. With this, it was possible to differentiate between tall and low vegetation despite
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their similarity in spectral reflectance. Using GIS operators such as majority filtering and rule-based overlay techniques, shadows could be eliminated and individual classification layers combined. The hierarchical classification procedure could be formalised and stored in a flow chart environment. Future work will involve the use of the new HRSC camera, the extended model HRSC-AX. This camera will have a true red band that will be of great advantage for vegetation analysis. Also, testing this methodology in areas of higher relief will be needed for a thorough analysis of the DSM usability. Combining multispectral image data with accurate height information from laser scanners might be a way to avoid matching errors in areas of low reflectance values. As a result of this study, we see new possibilities and advantages for the integration of GIS, elevation data and high resolution remote sensing. Object and analysis models that are typically associated with GIS technology are needed for an automated extraction of geospatial information from remote sensing data. In return, GIS objects can be extracted from remotely sensed images to update the GIS database.
Acknowledgements The support of the Waterways and Shipping Office (WSA) Hamburg and the German Federal Institute of Hydrology (BfG), Koblenz, for the project is gratefully acknowledged. References BfG (Bundesanstalt fu¨r Gewa¨sserkunde), 2000. Computergestu¨tzte Klassifizierung von Biotoptypen auf Grundlage digitaler hochauflo¨ sender multispektraler Scannerdaten (HRSC-A), BFG, Koblenz, Final Project Report. 44 pp. Blaschke, T., Strobl, J., 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GISZeitschrift fu¨r Geoinformationssysteme (6), 12 – 17. Ehlers, M., 2000. Integrated GIS—from data integration to integrated analysis. Surveying World 9, 30 – 33.
Ehlers, M., Mo¨ller, M., Janowsky, R., Ga¨hler, M., 2000. Entwicklung einer Methode zur automatisierten Biotoptypenerfassung auf der Grundlage von HRSC-A-Scannerdaten. University of Vechta, Final Project Report. 43 pp. Ehlers, M., Janowsky, R., Ga¨hler, M., 2002. New remote sensing concepts for environmental monitoring. In: Ehlers, M. (Ed.), Remote Sensing for Environmental Monitoring, GIS Applications, and Geology. Proc. SPIE, vol. 4545, pp. 1 – 12. Bellingham, WA. Ga¨hler, M., 2000. GIS-basierte Biotoptypenklassifikation auf Grundlage von multispektralen HRSC-A-Scannerdaten-Untersuchung der Einsatzmo¨glichkeiten am Beispiel der Elbinsel Pagensand. MSc Thesis, Geographic Programme, University of Osnabru¨ck. 101 pp. Gong, P., Marceau, D.J., Howarth, P.J., 1992. A comparison of spatial feature extraction algorithms for landuse classification with SPOT HRV data. Remote Sensing of Environment 40 (2), 137 – 151. Hoffmann, A., Lehmann, F., 2000. Vom Mars zur Erde-die erste digitale Orthobildkarte Berlin mit Daten der Kamera HRSC-A. Kartographische Nachrichten 50 (2), 61 – 71. Hoffmann, A., van der Vegt, J.W., Lehmann, F., 2000. Towards automated map updating: is it feasible with new digital dataacquisition and processing techniques? International Archives of Photogrammetry and Remote Sensing 33 (Part B2), 295 – 302. Janowsky, R., Skubski, T., Bo¨gelsack, E., Kastler, T., Ludowicy, C., Weidemann, D., 2001. GIS-gestu¨tztes Biotoptypenmonitoring – dargestellt am Beispiel der Insel Norderney. Vechtaer Studien zur angewandten Geographie und Regionalwissenschaft (VSAG), Vechta, No. 22, pp. 101 – 107. Johnsson, K., 1994. Segment-based land use classification from SPOT satellite data. Photogrammetric Engineering and Remote Sensing 60 (1), 47 – 53. Lehmann, F., Hoffmann, A., Renouard, L., van der Wegt, J.W., 2000. The high resolution stereo camera-airborne (HRSC-A). GIM International 14 (7), 12 – 17. Mo¨ller, M., Ehlers, M., Janowsky, R., Ga¨hler, M., Tcherepanov, L., 2001. Entwicklung einer Methode zum automatisierten Biotopmonitoring auf der Grundlage von HRSC-A-Scannerdaten. BfN (Bundesamt fu¨r Naturschutz)-Skripten, Bad Godesberg, No. 42, pp. 95 – 102. Neukum, G., HRSC-Team, 2001. The airborne HRSC-AX cameras: evaluation of the technical concept and presentation of application results after one year of operation. In: Fritsch, D., Spiller, R. (Eds.), Photogrammetric Week 2001. Wichmann Verlag, Heidelberg, pp. 117 – 130. Wewel, F., Scholten, F., 1999. High-resolution stereo camera (HRSC): multispectral data acquisition and photogrammetric data processing. Proc. 4th International Airborne Remote Sensing Conference and Exhibition, Ottawa, vol. 1, pp. 263 – 271.
Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges Manfred Ehlers *, Monika Ga¨hler, Ronald Janowsky Research Centre for Geoinformatics and Remote Sensing, University of Vechta, P.O. Box 1553, D-49364 Vechta, Germany Received 11 February 2002; accepted 12 July 2002
Abstract The advent of very high-resolution satellite programs and digital airborne cameras with ultra high resolution offers new possibilities for very accurate mapping of the environment. With these sensors of improved spatial resolution, however, the user community faces a new problem in the analysis of this type of image data. Standard classification techniques have to be augmented with appropriate analysis procedures because the required homogeneity of landuse/landcover classes can no longer be achieved by the integration effect of large pixel sizes (e.g., 20 – 80 m). New intelligent techniques will have to be developed that make use of multisensor approaches, geographic information system (GIS) integration and context-based interpretation schemes. The ideal goal should be that GIS ‘intelligence’ (e.g., object and analysis models) should be used to automate the classification process. In return, GIS objects can be extracted from a remote sensing image to update the GIS database. This paper presents the development of an automated procedure for biotope type mapping from ultra high-resolution airborne scanner data (HRSC-A). The hierarchical procedure incorporates a priori GIS information, a digital surface model (DSM) and multispectral image data. The results of this study will serve as a basis for a continuous environmental monitoring process in the tidally influenced region of the Elbe River, Germany. D 2003 Elsevier Science B.V. All rights reserved. Keywords: high resolution remote sensing; GIS, classification; biotope type mapping
1. Introduction The availability of remote sensing data applicable for global, regional and local environmental monitoring has greatly increased over recent years. New technologies such as Global Positioning System (GPS), digital photogrammetry and multi-source sat-
* Corresponding author. Tel.: +49-4441-15-423; fax: +49-444115-445. E-mail address: [email protected] (M. Ehlers).
ellite remote sensing are opening new application fields for remote sensing. With the advent of new (civilian) satellite programs such as IKONOS or Quickbird and, in addition, digital airborne cameras, image data are being acquired at higher spatial, spectral and temporal resolution than have been collected at any other time on Earth. There are many ways to distinguish between remote sensing systems. The most common one is probably the differentiation between space/satellite and airborne systems. Other possible taxonomies include spatial/spectral resolution, recording mode or spectral coverage (Table 1).
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Table 1 Taxonomy of remote sensing systems (modified after Ehlers et al., 2002) Recording platform
Satellite/shuttle
Recording mode
Passive (visible, reflected infrared, thermal infrared, thermal microwave) Analog (camera, video) Visible/ultraviolet Reflected infrared Panchromatic (1 band) Multispectral (2 – 20 bands) Low ( < 6 bit) Medium (6 – 8 bit)
Recording medium Spectral coverage Spectral resolution Radiometric resolution Spatial ground resolution
Very low (>250 m)
Aircraft/balloon
Low (50 – 250 m)
With very high-resolution sensors, the user community faces a new problem in the automated analysis of this type of image data. Simple pixel-based analyses are no longer applicable because of the difficulty of classifying high-resolution data where each pixel is related not to the character of an object or an area as a whole, but to components of it (Blaschke and Strobl, 2001). Gong et al. (1992), as well as Johnsson (1994), showed that spectral classification of higher resolution data does not automatically lead to more detailed classification results. Further, using only multispectral information for classification does not lead to accurate interpretation results because the differentiation between object classes is done not only with the help of spectral information, but also with spatial (contextual) information of the image data. For example, using only multispectral information, different objects like roofs and streets might not be separated into two object-classes because they are built of the same material (Hoffmann et al., 2000). Consequently, new intelligent techniques will have to be developed that make use of GIS integration, multisensor approaches and context-based interpretation schemes (Ehlers, 2000). In a pilot study, the Research Centre for Geoinformatics and Remote Sensing at the University of Vechta, Germany developed a procedure for a context-based hierarchical image analysis and classification scheme for an automated biotope type mapping from ultra high resolution airborne scanner data (HRSC-A). The procedure incorporates a priori GIS information, digital surface model (DSM) and multispectral image data.
Medium (10 – 50 m)
Stationary
Active (laser, radar) Digital (Whiskbroom, Line Array, 2D CCD) Thermal infrared Microwave Hyperspectral Ultraspectral (20 – 250 bands) (>250 bands) High (8 – 12 bit) Very high (>12 bit) High (4 – 10 m) Very high Ultra high (1 – 4 m) (< 1 m)
2. Ultra high resolution scanner data for monitoring of biotope types The reason for this study was the latest expansion of the fairway of the River Elbe in the region of Hamburg, Germany, due to requirements for the new generation of container ships (BfG, 2000). The precedent environmental impact assessment mandated continuous environmental monitoring after the end of the expansion project with focus on the tidally influenced riverside biotopes. Changes in composition and size of these biotopes should be documented over the long term to assess the impacts of hydraulic engineering measures. The Waterways and Shipping Office (WSA) in Hamburg charged the German Federal Institute of Hydrology (BfG) with the development of a generally applicable automated and cost-effective method for an operational, long term monitoring process. In cooperation with the Research Centre for Geoinformatics and Remote Sensing and the German Aerospace Centre (DLR) in Berlin, an integrative monitoring concept was developed using a combination of GIS, image analysis and modelling software. Usually, biotope type maps have been derived from extensive fieldwork supported by conventional aerial photographs. This task requires an expensive amount of manpower and often lacks the necessary geometric accuracy (Janowsky et al., 2001). In addition, despite every effort in giving accurately described key features for interpretation, inconsistencies occur among the persons analysing the photographs.
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Digital post-processing of analogously recorded data by means of scanning the aerial photographs and subsequent digital image processing is an alternative to a completely analogue processing. It has, however, still a number of disadvantages that aggravate its use for a completely automated procedure, namely, radiometric inconsistencies within and between the recorded photographic images. Digitised and rectified conventional aerial photographs can mainly be used for visualisation. The only way of deriving reliable information from scanned aerial photographs is the so-called on-screen-digitising. There, the same problems are encountered in deriving objective interpretations as in the analogue process. An essential improvement seems only possible by using appropriately recorded digital data (Hoffmann and Lehmann, 2000; Mo¨ller et al., 2001). The analysis process can then be automated employing a rule-based procedure. The present study used for the first time ultra-high resolution multispectral scanner data of the High Resolution Stereo Camera-
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Airborne (HRSC-A) for very accurate mapping of biotope types. 2.1. Study area The study area is located along the tidally influenced section of the Elbe River in the border area of the German states Lower Saxony, Schleswig-Holstein and Hamburg. Three representative sections of this area were selected (Fig. 1): (1) The small river island of Pagensand, about 20 km seaward from Hamburg shows both man-made and natural structures, such as reeds and willow forests, cultivated grassland and ruderal vegetation (i.e., vegetation characteristic of highly disturbed areas), sandy grassland and open sands, deciduous and coniferous forests. (2) The nature reserve ‘‘Heuckenlock’’ at the southeast edge of Hamburg contains large areas of reeds, some bayous and willow forests.
Fig. 1. Study sites in Germany.
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(3) An area around the confluence of the Ilmenau River in Lower Saxony, mostly dominated by various grassland and reed vegetation types. Most of this study was performed on data of Pagensand Island because of its excellent representation of nearly all important biotope types appearing along the whole lower reaches of the Elbe River. The Heuckenlock area was used for verification of the developed algorithm using a ‘‘blind testing’’ approach. For the other regions, ground truth was available in the form of older reference maps dated 1992 that were produced by photointerpretation and fieldwork. 2.2. The HRSC-A scanner system The HRSC-A (High Resolution Stereo CameraAirborne) is a narrow-angle digital multi-spectral
stereo scanner for photogrammetric and remote sensing applications (Lehmann et al., 2000; Wewel and Scholten, 1999). The camera was originally developed for the Russian Mars-96 space mission and later modified for airborne remote sensing applications. The peripheral digital electronics, in particular, were redesigned for use on aircraft. The system is mounted on a Zeiss T-AS stabilisation platform. The HRSC uses the ‘‘push-broom’’ principle. Nine CCD line detectors are mounted in parallel at the focal plane behind one single optics and perpendicular to the flight direction. Nine images of the same strip are recorded, almost simultaneously, as a result of the forward motion of the aircraft. Five of the nine CCD lines of the HRSC-A are arranged at specific viewing angles to provide stereo and photometric viewing capability; four of the nine CCD lines are covered with different filters for the acquisition of multispectral images (Fig. 2, Table 2).
Fig. 2. HRSC-A imaging principle (modified after Hoffmann and Lehmann, 2000).
M. Ehlers et al. / ISPRS Journal of Photogrammetry & Remote Sensing 57 (2003) 315–326 Table 2 Technical data for the high resolution stereo cameras-A and -AX HRSC-A Focal length Total field of view Number of CCD lines Sensors per CCD line Sensor size Radiometric resolution Spectral resolution/band numbers
Read-out frequency Stabilisation Data recording Georeferencing
HRSC-AX
175 mm 151 mm 38 12j 41 29j 9 9 5272 12,172 7 7 Am 6.5 6.5 Am 8 bit 12 bit Blue: Blue: 395 – 485 nm 450 – 510 nm Green: Green: 485 – 575 nm 530 – 576 nm ‘‘Red’’: Red: 730 – 770 nm 642 – 682 nm NIR: NIR: 920 – 1020 nm 770 – 814 nm 5 Nadir/stereo 5 Nadir/stereo (Panchromatic): (Panchromatic): 585 – 765 nm 520 – 760 nm 450 lines/s 1640 lines/s Zeiss T-AS platform Sony high speed data recorder Applanix POS/DG Navigation system with GPS and INS
The ‘‘red’’ band 3 of the HRSC-A sensor is in reality an infrared band. For consistency with the official HRSC nomenclature it is kept as red band.
A fully automatic photogrammetric and cartographic processing system was developed at the DLR, in cooperation with the Technical University
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of Berlin (Wewel and Scholten, 1999). Although this system can work without ground control points (GCPs), a few GCPs are usually required for validation purposes. The first step involves the synchronisation of the external inertial navigation system (INS) that provides a combination of gyro and accelerometer information augmented by carrier phase DGPS with the HRSC image data. Subsequently, the alignment between the camera axes and the gyro axes of the INS is determined on the basis of multi-stereo image information. For the generation of digital surface models (DSMs), a digital multiple correlation process is performed. The resulting identical image points are converted into object points by ray intersections. The DSM is derived by interpolation. Finally, based on the DSM digital orthoimages are generated and multispectral orthomosaics are produced. As a spin-off, an extended narrow-angle system (HRSC-AX) has been developed by the DLR providing higher resolution and re-designed multi-spectral bandwidths that are better suited for terrestrial applications (see Table 2; Neukum and HRSC-Team, 2001). This sensor, however, was not yet available for the case study. For this study, the DLR provided a complete HRSCA dataset (i.e., panchromatic and multispectral imagery, plus digital surface model). An example for
Fig. 3. Various views of the same area recorded with only one HRSC-A camera system: (a) panchromatic; (b) ‘true’ colour (‘R’GB); (c) colour infrared (CIR); (d) digital surface model (DSM) with red = high elevation, yellow and green = medium elevation and black = low elevation.
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various band combinations of the section ‘‘Heuckenlock’’ is presented in Fig. 3. The data were recorded from a flying height of 3000 m and delivered with 15cm ground pixel resolution. Absolute accuracies were given as F 20 cm in horizontal and F 30 cm in vertical direction.
3. Methodology 3.1. Hierarchical procedure design For the automated biotope type mapping from HRSC-A data, a hierarchical GIS-based procedure was developed. First, all HRSC-A data (i.e., multispectral images and DSM) were incorporated in a GIS database to allow integrative processing. In addition, textural measures and a synthetic red band (see Section 3.2) were calculated and added for increased information diversity. For this GIS/Remote Sensing database, a hierarchical classification procedure was
developed (Ehlers et al., 2000) (Fig. 4). The main steps of this procedure are: Level 1: Separation of information into semantic layers non-vegetation/sparse vegetation shadows low vegetation high vegetation Level 2: Image processing (individual classification of the semantic layers) ISODATA clustering supervised classification Level 3: GIS-based postprocessing defining minimum sizes of biotope types defining neighbourhood relations combining individual classifications eliminating shadow areas (refilling) Level 1 involves the use of vegetation indices (see Section 3.2) as masks to perform a semi-automated
Fig. 4. Flowchart of the hierarchical classification procedure.
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separation between different object types (non-vegetation/sparse vegetation, shadow and vegetation). In the next step, an integrated GIS/Remote Sensing database approach and the use of elevation thresholds made it possible to differentiate the vegetation layer into low vegetation and tall vegetation. Thus, biotope types that did not show a difference in their multispectral reflectance characteristics but were of different height (e.g., shrubbery vs. trees) could be separated. The separation of information step, therefore, permits the depth and accuracy of the classification to be improved. At Level 2 of the procedure, the separated layers were treated with appropriate classification algorithms (i.e., ISODATA clustering for the non-vegetation/ sparse vegetation layer and supervised classification for the herbaceous vegetation layers; see Fig. 4). With this approach, the level of detail in the biotope type classification could be significantly improved. It was possible to identify more than 20 different classes. Level 3 involved GIS-based postprocessing to produce the final classification result. GIS operations such as majority filtering, logical overlay and minimum area functions were used to estimate appropriate classes for shadow areas and to combine the individual information layers. The final output was a GIS layer with 21 biotope types for the study sites. 3.2. Preparation of HRSC-A data Unfortunately, the HRSC-A lacks a spectral band in the red domain that is very important for vegetation interpretation. This is due to the original design of the HRSC-A for a Mars mission. The difference in characteristics of the HRSC-A and a standard remote sensing systems such as Landsat ETM is presented in Fig. 5.
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Consequently, a synthetic red band was calculated using arithmetic operations involving the near infrared and panchromatic bands (see Table 2, Fig. 5). This artificial red band could be used for true colour and false colour infrared visualisation (see Fig. 6) and in a further step for the calculation of vegetation indices— even though the use of the standard vegetation indices yielded no satisfactory results (Ga¨hler, 2000). Consequently, vegetation indices based on the spectral bands of the HRSC-A were developed. The newly developed sensor, HRSC-AX, includes a true red band (see Table 2) which will allow the easy calculation of standard vegetation indices. Best results were obtained by the difference of the synthetic red band and Band 1 as well as the difference between Band 4 and Band 1 (see Table 2). The result of the first calculation (‘synthetic red’ minus Band 1) separates herbaceous vegetation from nonvegetation/sparse vegetation with shadow. The second calculation (Band 4 minus Band 1) separates nonvegetation/sparse vegetation from shadowed vegetation. By means of those vegetation indices, not only the vegetation and non-vegetation could be distinguished, but also shadow areas could be identified. In addition to band subtraction operations, textural measures were applied for further differentiation. The use of a variance filter (7 7 matrix) based on the panchromatic channel proved to be the most useful. Biotope types with relative smooth surfaces (e.g., water and grassland) could be distinguished from biotope types with relatively rough texture features such as tall reeds and trees. For classification and visualisation, the calculated texture layer and the synthetic red band were integrated (along with the DSM) into the original multispectral layerstack to form an expanded multiband image as
Fig. 5. Distribution of spectral bands for Landsat ETM and HRSC-A (pan = panchromatic, ms = multispectral).
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Fig. 6. Separation of information using vegetation index masks and altitude threshold values.
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input for the hierarchical classification procedure (see Fig. 4).
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Table 3 Classified biotope types and their required minimum sizes Group
Biotope type
Minimum size [m2]
4. Results of the hierarchical classification procedure
Tall vegetation
4.1. Level 1: separation of information
Low vegetation
Deciduous forest, general Lowland riparian willow forest Coniferous forest Single Trees Shrubs Silted zone with floating plants Trench with duckweed Reed (Phragmites) Scirpus Meadow, rich wet site Intensively used grassland, dry site Lawn, poor in species Semi-ruderal vegetation, humid site Semi-ruderal vegetation, medium site Ruderal vegetation, humid site Ruderal vegetation, dry site Water Meager sandy grassland with sparse vegetation Impervious/sand Mud/sand deposit 1 Mud/sand deposit 2
200 200 200 40 40 40 40 40 40 40 40
Using the calculated vegetation indices as masks, the multiband images were divided into two semantically meaningful information layers: (1) herbaceous vegetation without influences of shadows; and (2) nonvegetation/sparse vegetation without influences of shadows. In a further step, the vegetation areas were differentiated into high and low vegetation by means of a threshold value (>12 m above sea level) derived from the DSM. The resulting separated multiband images shown in Fig. 6 were treated with ISODATA clustering and supervised classification algorithms. 4.2. Level 2: individual layer classification The further classification of non-vegetation/sparse vegetation was performed by an ISODATA clustering algorithm while the discrimination of the vegetation layers (low and high) was performed using a supervised Maximum-Likelihood classification. The procedure is based on extensive testing of a number of alternatives in processing and error analyses employed to determine the most accurate procedure (Ehlers et al., 2000; Ga¨hler, 2000). Using this method, biotope types of Pagensand Island could be automatically distinguished into five types of non-vegetation, 12 types of herbaceous vegetation and four types of tree-vegetation (Table 3). 4.3. Level 3: GIS-based postprocessing The individually classified layers were combined in a hierarchical process considering a predefined priority order to obtain one final resulting layer. For example, biotope types of herbaceous vegetation had a higher priority than types of non-vegetation. Within this process a minimum size filter for each biotope type was used. The minimum biotope type sizes were determined together with experts from the WSA and BfG (Table 3). In addition, neighbourhood relations between the different biotope types were considered
Non-vegetation/ Sparse vegetation
40 40 40 40 40 40 70 70 70 70
(e.g., if a road is located between two water objects it is defined as a bridge). The shadow areas were eliminated by GIS based filter techniques, such as majority and altitude-based filtering. Shadow pixels/ regions which were higher than 12 m (derived from the DSM) were filled up only with biotope types of tree vegetation in the direct neighbourhood and not with biotope types of non-vegetation or low vegetation and vice versa. A flowchart based modeller tool was used to formalise and automate this hierarchical classification procedure and implemented in the ERDAS ImagineR software (Mo¨ller et al., 2001).
5. Validation Comparison of the hierarchical classification results from this study with older maps of the same area that were produced by photointerpretation techniques show the tremendous advantages of the new method including richness of detail and geometric accuracy (Fig. 7). This comparison could easily be performed by over-
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Fig. 7. Comparison of the classification result (top left) derived from HRSC-A image data (top right) and a conventionally generated map (bottom left).
laying the different digital layers in the GIS environment. Differences between the classification result and conventionally generated maps are presented in Fig. 7 for a small test site located on Pagensand Island. The richness of detail of the classification results corresponds well with the structures in the original image. The visual interpretation result already shows the generalisation that was performed by the human operator. The older reference map shows the subset mapped with five polygons consisting of 93 vertices whereas the new classification result yields 32 better fitting polygons, described by 1488 vertices overall. Even single trees, shrubs or open forest areas smaller than 100 m2 can be detected over large areas. The biggest problem proved to be that the quality of the digital analysis of HRSC-A data seemed to be better than the ground truth measurements. In particular, the richness of classified detail from the HRSC-
A data proved the superiority of this method over ground-based analysis. Fig. 7 clearly indicates that the old reference map could not be used for accuracy checking. For a thorough field test, scientists of the BfG used the area of Heuckenlock. For this area, the classification was carried out by means of ‘blind testing’ without ground truth, just using the procedure, signatures and experiences attained in work with the Pagensand study area. For accuracy checking, about 200 samples of representative biotopes and their delineation were recorded and their geographic coordinates measured by DGPS. Additionally, their characteristics were described and documented by photographs. All data were registered to the GIS database (Fig. 8) and compared with the biotope layers from the HRSC-A classification. Almost all samples showed an excellent reproduction of the real situation. Structural differences were picked up in a very
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Fig. 8. Validation procedure: DGPS-based recording of samples (bottom right), integration of all data in a GIS-database (top right), comparison of ground-based map (top left) with the HRSC-A classification (bottom left).
detailed way. It was only rarely the case that mismatches were found and some biotope delineation did not correspond to reality. This was mostly true for herbaceous vegetation where in some cases the outlines were not correctly defined. The most important features to be monitored, however, such as reeds and willows were classified with an accuracy exceeding 95%. It was also evident that the richness of detail from the ultra high-resolution remote sensing data could not be matched by the recorded ground truth.
6. Conclusions The potential of high-resolution digital airborne scanner data with excellent geometric fidelity was
investigated in a pilot project. It could be shown that digital image data in combination with an integrated GIS/remote sensing processing environment allowed the development of an automated classification procedure for biotope mapping which proved to be highly accurate also in an independent test area. This automated hierarchical technique facilitates the documentation of dynamic processes in long-term environmental monitoring projects. The original images as well as the classification results can easily be integrated in a GIS environment. This allows operational analysis and measuring of changes over time. Of particular importance proved the integration with the DSM that is simultaneously produced from the HRSC-A sensor. With this, it was possible to differentiate between tall and low vegetation despite
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their similarity in spectral reflectance. Using GIS operators such as majority filtering and rule-based overlay techniques, shadows could be eliminated and individual classification layers combined. The hierarchical classification procedure could be formalised and stored in a flow chart environment. Future work will involve the use of the new HRSC camera, the extended model HRSC-AX. This camera will have a true red band that will be of great advantage for vegetation analysis. Also, testing this methodology in areas of higher relief will be needed for a thorough analysis of the DSM usability. Combining multispectral image data with accurate height information from laser scanners might be a way to avoid matching errors in areas of low reflectance values. As a result of this study, we see new possibilities and advantages for the integration of GIS, elevation data and high resolution remote sensing. Object and analysis models that are typically associated with GIS technology are needed for an automated extraction of geospatial information from remote sensing data. In return, GIS objects can be extracted from remotely sensed images to update the GIS database.
Acknowledgements The support of the Waterways and Shipping Office (WSA) Hamburg and the German Federal Institute of Hydrology (BfG), Koblenz, for the project is gratefully acknowledged. References BfG (Bundesanstalt fu¨r Gewa¨sserkunde), 2000. Computergestu¨tzte Klassifizierung von Biotoptypen auf Grundlage digitaler hochauflo¨ sender multispektraler Scannerdaten (HRSC-A), BFG, Koblenz, Final Project Report. 44 pp. Blaschke, T., Strobl, J., 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GISZeitschrift fu¨r Geoinformationssysteme (6), 12 – 17. Ehlers, M., 2000. Integrated GIS—from data integration to integrated analysis. Surveying World 9, 30 – 33.
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