COMPARING OBJECT-BASED LANDSLIDE DETECTION METHODS BASED ON POLARIMETRIC SAR AND OPTICAL SATELLITE IMAGERY – A CASE STUDY IN TAIWAN Simon Plank(1), Daniel Hölbling(2), Clemens Eisank(3), Barbara Friedl(2), Sandro Martinis(1), André Twele(1) (1)
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), 82234 Oberpfaffenhofen, Germany Email:
[email protected],
[email protected],
[email protected] (2) Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg, 5020 Salzburg, Austria, Email:
[email protected],
[email protected] (3) GRID-IT Gesellschaft für angewandte Geoinformatik mbH, 6020 Innsbruck, Austria, Email:
[email protected]
ABSTRACT
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Applied to a test site located in southern Taiwan, this study compares two object-based image analysis methods for post-failure landslide detection based on PolSAR and optical satellite imagery. With its day-andnight availability and almost complete weather independency, SAR has several advantages compared to optical imagery. Consequently, in most cases, SAR imagery for a dedicated area of interest is earlier available than the first cloud-free optical data. However, the high spatial and spectral resolution of multispectral optical Earth observation data may enable a more detailed and accurate landslide detection. The result of the novel object-based method based on PolSAR data reveals a certain potential for landslide detection, especially for rapid assessment of affected areas after landslide events.
An area of approximately 25 km² around the Baolai village located in the Huaguoshan catchment, southern Taiwan, has been selected for mapping landslides based on PolSAR and optical data (Fig. 1). The landslides within the study area are mainly triggered by heavy rainfalls, which are brought along by typhoons, especially during summer season. Moreover, the unstable geology and the mountainous terrain with deeply incised valleys make this area susceptible to landslides (Figs. 2-3).
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STUDY SITE AND DATA
INTRODUCTION
Object-based image analysis (OBIA) has proven its applicability in the field of remote sensing during the past decade [1,2]. OBIA supports the integration of different datasets (e.g. SAR data, optical images, DEMs) and offers an efficient framework for the semi-automated analysis of complex natural features such as landslides, for example by its ability to consider spectral, spatial as well as contextual properties. Two OBIA methods for post-failure landslide detection based on (I) polarimetric synthetic aperture radar (PolSAR) and (II) optical satellite imagery are compared. In general, SAR imagery is earlier available after triggering events such as heavy rainfalls than the first useful (cloud-free) optical imagery. However, the high spatial and spectral resolution of multispectral optical Earth observation (EO) data may enable a more detailed and accurate landslide detection. A comparison of both methods is feasible, as the polarimetric SAR image (TerraSAR-X) and the very high spatial resolution optical imagery (QuickBird) were acquired at a temporal baseline of only 20 days (cf. section 2). Thus, SAR and optical data show the same state of the environment.
Figure 1. Study area in Taiwan. Huaguoshan catchment (red polygon) and study area around Baolai village (white rectangle); background data: © ESRI basemap data
Figure 2. Landslides in the study area (Photo © Daniel Hölbling)
Following satellite data is used for the two landslide detection methodologies: - SAR: Dual-polarimetric (HH/HV) TerraSAR-X StripMap imagery, acquired on 8 November 2010 (Fig. 4). - Optical: QuickBird imagery, acquired on 28 November 2010 (Fig. 5). Additionally, a digital elevation model (DEM) with 5 m spatial resolution, which was compiled from orthophotos taken in 2003 and 2004, and therefrom derived products (e.g. slope) were used as ancillary data. 3. Figure 3. The river bed (Photo © Daniel Hölbling)
Figure 4. The calibrated HH channel of the TerraSAR-X imagery of the study area
METHODOLOGY
3.1. PolSAR based landslide detection The basic concept of the presented landslide detection methodology based on polarimetric SAR data is to make use of the different backscattering signals of the land cover classes to be distinguished. For instance, in the cross-polarized channel (HV) vegetated areas (especially forest) show a much higher backscatter than bare soil, which was assumed to be an indication for the occurrence of mass-movements and debris/sediment transport and deposition areas. The procedure of the PolSAR based landslide detection is a follows: After speckle filtering of the PolSAR data using the refined Lee filter and radiometric calibration (σ0), the intensity information of both polarization channels (HH and HV) was geocoded. Next, the features of interest (i.e. landslides, debris flows and river bed) were derived from the PolSAR imagery using a newly developed OBIA procedure, which makes use of the different backscattering behaviour of forest as compared to bare soil (mass-movement indicator). Using the Normalized Difference Standard Deviation (NDSD) of the calibrated intensities of both polarimetric channels, HH and HV (Eq. 1), the OBIA procedure considers (I) the higher variation of the backscattering intensities in forest areas and (II) the relatively higher backscattering of vegetated areas in the cross-polarized channel compared to bare soil areas (Fig. 6).
Std σ 0HH
− Std 0 σ HV dB dB NDSD = + Std 0 Std σ 0HH σ HV dB dB with standard deviation Std; calibrated intensity σ0.
Figure 5. Optical image (Quickbird) of the study area
(1)
shows the workflow of the optical imagery based landslide detection procedure.
Figure 6. Workflow for landslide detection based on dual-pol SAR imagery (TerraSAR-X) and DEM data 3.2. Optical image based landslide detection For the object-based detection based on the QuickBird image the Normalized Difference Vegetation Index (NDVI) was applied to detect the unvegetated areas, i.e. potential landslides (Eq. 2). NIR − RED NDVI = (2) NIR + RED with NIR and RED representing the near infrared and red channels of the multispectral sensor, respectively. Based on the NDVI layer a histogram-based threshold was computed to automatically distinguish nonvegetated from vegetated areas by dividing the image into two subsets. Therefore, the multi-threshold segmentation algorithm as implemented in eCognition (Trimble) software was applied using this NDVI threshold. To produce suitable image objects for the differentiation of landslide types, the areas potentially affected by mass-movements (landslide candidates) were resegmented using the multiresolution segmentation algorithm. Additionally to the QuickBird image the DEM and therefrom derived slope and plan curvature were integrated in the object-based analysis to support the classification. The distinction of landslides, debris flows and river beds was mainly based on morphological parameters derived from slope and plan curvature and only partly on spectral values (e.g. standard deviation of red band) as the purely spectral information caused class ambiguities. Finally, spatial and especially contextual properties (e.g. relative border to neighbouring objects), as well as growing and shrinking algorithms were employed to refine the classification and to remove false positives (e.g. built-up areas, agricultural fields). Fig. 7
Figure 7. Workflow for landslide detection based on optical imagery (QuickBird) and DEM data 4.
RESULTS AND DISCUSSION
The reference dataset for validation consists of landslides, debris flows and river beds represented by polygons, and was produced through manual digitization, performed by a local expert (Fig. 8). The classification results were compared to the reference data to assess the spatial overlaps. Corresponding user’s and producer’s accuracy values were calculated. Fig. 9 shows the results of the PolSAR based landslide detection procedure. Compared to the reference dataset, the PolSAR imagery based methodology achieves a user’s and producer’s accuracy of 60.3% and 27.9% for the landslides and debris flows combined, and 99.5% and 96.0% for the river bed, respectively. When focusing only on the detection of debris flows, its producer’s accuracy increases to 45.0% (while the user’s accuracy remains stable). The main challenge for the PolSAR based landslide detection methodology are the spatial distortions of SAR images caused by its ‘range-azimuth’ imaging geometry. Especially in foreshortening and layover areas, the detection of landslides and debris flows is complicated due to the strong backscatter of the SAR signal. Nevertheless, the result of the novel object-based method based on PolSAR data reveals a certain potential for landslide detection, especially for rapid assessment of affected areas after landslide triggering events.
Figure 8. Reference (manual digitalization)
hampers the differentiation of landslides and debris flows [3]. When combining landslides and debris flows into one class, the user’s and producer’s accuracies increase to 54.0% and 70.0%, respectively. Optical images are most commonly used for mapping landslides using different methods, whereby OBIA is one promising method for that. The QuickBird image is well suited for the identification of landslide affected areas, but the differentiation of landslide types is not feasible solely based on the optical image. Besides, the differentiation of landslide types is even very difficult for a visual interpreter. Thus, the obtained accuracy values have to be treated with caution as they strongly rely on the quality of the reference data set. Due to their variety in shape, size and colour, especially the differentiation of landslides and debris flows is a nontrivial task as there is no unique set of parameters and thresholds that is always applicable either for semi-automated or manual mapping. This is particularly true for Taiwan, where hardly any debris flow inventories are available. Anyway, a visual interpretation is often the only reference data available but cannot constitute a completely true reference as results of manual digitization strongly depend on the skills and perception of the interpreter, the knowledge about the study area, the mapping scale and purpose and the data used [4].
Figure 9. Result of the SAR image based semiautomated OBIA method for landslide detection The result of the optical imagery based landslide detection procedure is shown in Fig. 10. It achieves the following user’s and producer’s accuracy values: 22.9% and 58.8% (landslides), 51.1% and 26.4% (debris flows), 90.8% and 89.6% (river bed), respectively. Generally, the optical imagery based classification overrates the manual classification. Obviously, relatively small objects (particularly landslide objects) were detected through the semi-automated approach, whereas the manual mapping was rather general [3]. The results further show that the class landslide has been significantly overestimated, while less debris flows have been classified compared to the reference data. These results are influenced by the use of a DEM which is older than the QuickBird image, and thus, it does not reflect the same state of the environment. This fact particularly
Figure 10. Result of the optical image based semiautomated OBIA method for landslide detection 5.
CONCLUSIONS
Semi-automated object-based landslide mapping based on various remote sensing data deliver adequate results and can complement traditional manual mapping efforts. Polarimetric SAR-based mapping provides acceptable results for debris flows and river beds, whereas individual landslides could only be detected with optical images. In future, we will test the potential of combining the two object-based approaches (respectively, the
two data types, i.e. optical imagery and PolSAR) into one workflow for automated post-failure landslide mapping. Moreover, application at other test sites is planned to improve the presented landslide detection procedures. 6.
ACKNOWLEDGMENTS
The research leading to these results has received funding from the European Space Agency (ESA) through the project ASAPTERRA (ESA Contract No. 4060 112374/ 14/I-NB) and from the Austrian Science Fund (FWF) through the project iSLIDE (Grant P 25446-N29). 7.
REFERENCES
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