AUTOMATED DETECTION OF SMALL AND SHALLOW LANDSLIDES AFTER THE 2010 MADEIRA ISLAND FLASH-FLOODS IN VHR IMAGERY

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1 AUTOMATED DETECTION OF SMALL AND SHALLOW LANDSLIDES AFTER THE 2010 MADEIRA ISLAND FLASH-FLOODS IN VHR IMAGERY Sandra Heleno, Maura Lousada, Maria João Pereira, Pedro Pina CERENA, Instituto Superior Técnico, Av. Rovisco Pais 1, Lisbon, Portugal ABSTRACT In this paper we use very high spatial resolution images (GeoEye-1, 0.5m/pixel and orthophotos with 0.4m/pixel) acquired before and after the February 20, 2010 flash-floods in Madeira Island, to test several automated detection methods, seeking for a robust approach able to produce a detailed and complete identification of landslides. We perform an accurate segmentation (delineation of contours) of all the structures (objects) resolved in the remotely sensed images, aiming at landslides scars about tens of squared-metres in area. The best classification performance of segmented objects into landslides/nonlandslides is obtained through the combined use of watershed transform for segmentation and support vector machine for classification. We present our preliminary findings, supported and validated by in-situ measurements in different locations of Funchal basin. 1. BACKGROUND In recent years, semi-automated methods have been developed to map landslides from remote-sensed imagery. The number and variety of approaches is increasing (a recent review can be consulted in Guzzeti et al. (2012), but the performances reported on the literature, highly dependent on the spectral and spatial features of the images and also on landscape characteristics, are very uneven. This still is an open problem, where novel methodological contributions to automatize the detections with a high degree of confidence are welcome, particularly in regions where these mass movements are frequent and their number usually high. That is a common situation in Madeira Island (Portugal) (Fig.1) where small and shallow landslides are frequently triggered by heavy rainfall (Fragoso et al., 2012); Couto et al., 2012), resulting in fast transport of solid material that in more extraordinary situations may have tragic consequences (Baioni, 2012). Assessing its hazard through landslide inventory maps is therefore essential for risk management purposes. These numerous, small and shallow types of landslides present a challenge to image classifiers and this study addresses such difficulties, reporting with detail the successful and critical situations. Thus, in this paper we test and compare several automated methods for identification of landslides in very high resolution satellite images. The motivation for this work came from previous analysis of VHR images documenting a damaging flash flood in Madeira Island (Lira et al., 2013). On February 20, 2010, an exceptionally heavy and rainfall episode triggered numerous shallow landslides across Madeira Island, in the North Atlantic. Figure 1. Location map of Madeira Island (from Lira et al., 2013). The areas most affected areas by the flashfloods (Funchal and Ribeira Brava) are shown. These shallow landslides developed in many cases into debris flows, flowing down to the valley bottoms, dragging material along the way, and increasing the destructive impact of the floods, that claimed 45 lives. The most affected areas were Funchal and Ribeira Brava districts. At the inferior part of Fig. 2 we can see Funchal city, and the sediments transported downhill by the flood still flowing into the ocean. Three days after this event Geoeye-1 satellite acquired images over Madeira. This programming of the satellite was made possible through GMES SAFER project. After this event, a team was assembled by the local authorities in Madeira to, among other tasks, map in detail the landslides produced during this calamity, with the main motivation of assisting mitigation measures, such as the construction of barriers to retain the material flowing down towards the cities. A first attempt was made to use the VHR GeoEye-1 images for this mapping, and a semi-automatic classification of the landslides was tried to speed up the task, based on a supervised pixel-based maximum likelihood classifier, but the results were affected by a Proc. ESA Living Planet Symposium 2013, Edinburgh, UK 9 13 September 2013 (ESA SP-722, December 2013)

2 high percentage of commission errors and the final landslide mapping was heavily supported by visual interpretation of GeoEye-1 imagery and ortophotomaps (Lira et al., 2011). This study addresses such difficulties by testing several automatic methods, in the same images, seeking a robust and efficient approach to assist a detailed mapping of landslides in VHR imagery. 2. DATA AND STUDY AREA The remotely sensed images used are GeoEye-1 datasets acquired just immediately after the flash-flood events on February 23, These datasets are constituted by 4 multispectral bands (R-red, G-green, Bblue and NIRnear-infrared) with 2.0 m/pixel resolution and one PANpanchromatic band with 0.5m/pixel resolution. The area analysed in this study is a subset of the most affected region by landslides, north of Funchal city, delimited in Fig. 2 by the box with approximate dimensions of 3x5km 2. This dataset is used to compare and assess the results (and here the term ground-truth is used in a not strict way) of the different classifiers is shown in Fig. 3, was delineated in Geoeye-1 imagery but assisted by visual interpretation of orthophotomaps (taken before and after the event) and in-situ data (landslide locations by the Forestry Service and topographic surveys from DRIGOT). Figure 3. Landslides delineation on Geoeye-1 imagery (true-colour combination on the left) assisted with visual interpretation of orthophotomaps (centre). Comparison/validation with in-situ data (right). 3. METHODOLOGY The methodology built is mainly constituted by a segmentation phase to delineate the structures or objects available in the images, followed by their classification into landslide and non-landslide classes through a set of describing features of spectral, textural and spatial natures. The schematic sequence we have followed is represented in the diagram of Fig. 4. Figure 2. GeoEye-1 true colour composition in February 23, 2010 of Madeira Island with location of the city of Funchal (inferior part of the image), the study area used in this study (orange box) and the ground-truth dataset of landslides (in red). The inventory previously made with a semi-automatic strategy and based on a pixel-based classification procedure (Lousada et al., 2011) is used as ground-truth. Figure 4. Diagram of the landslide detection methodology.

3 Geoeye-1 imagery, taken 3 days after the event, was used as input for the classifiers, specifically the bands R-G-B-NIR with 2m resolution and PAN with 0.5m resolution. Orthophotomaps and a digital terrain model were used to orthorrectify the Geoeye-1 imagery, a procedure described in Lira et al. (2013). We tested two separate classification procedures: pixelbased, which means that each pixel of the image is classified as belonging to the landslide class or not; and object-based, in which the image is first divided, or segmented, into objects or regions, based on the similarity of the pixels that comprise the object, and then the object itself is classified according to its attributes or features. In the latter case we have the advantage of using also spatial and textural features in the classification, and not only spectral attributes as in the pixel-based approach (see the list of the features used in the classification procedures on Table 1). In any case, the classifier needs a set of examples (pixels or objects) to learn to distinguish the landslides in the image. This is the training phase. Both pixel and object training sets were retrieved in a random way from the ground-truth. Then the entire image is classified using different learning algorithms. Finally the results are compared with the original mapping of the landslides, allowing assessing the performance of the automatic classification. The partition of the image into objects was performed with ENVI feature extraction module (preceded by frost filtering, segmentation is edge-based and the watershed algorithms used). Different parameters controlling the homogeneity of the pixels within the segmented regions were tried (Fig. 5), ending up choosing the parameters that although led to an oversegmentation (scale 10, merge 90), preserve the accuracy of the landslide borders. In future experiments we will repeat the procedure in a more exhaustive way in order to find the optimal scale and merge parameters to use for the segmentation procedure. Figure 5. Details on edge-based segmentation (watershed algorithm) applied on PAN and NDVI. Segments are subsequently merged according to spectral similarity of bands R, G, B, NIR and PAN. Fig. 6 shows a detail of the object training set: in red, examples of the landslide class and in green, examples of the non-landslide class. These examples were retrieved by random selection from the groundtruth mapping. The object training set represents less than 1% of the total objects to classify (represented in blue). The same procedure was made for the pixel training set: landslide example pixels and non-landslide example pixels were retrieved randomly from the ground-truth mapping and represent much less than 1% of the total pixels that will be classified by the algorithms. Figure 6. Training datasets of objects of positive or landslides examples (red) and negative or non-landslide examples (green), superimposed to a segmented image (blue).

4 The study area of about 15km 2 was divided into a training area of larger dimension, from which the examples for learning were taken randomly as shown above, and two smaller validation areas, which naturally do not contribute with examples for the training. Their geographic limits are shown in Fig. 7. Figure 7. Delimitation of disjoint training and validation regions in our study site. The information used by the classifiers is listed in Tables 1 and 2. It includes spectral information from the 5 bands of Geoeye-1, a computed vegetation index (NDVI), and some relevant topographic information derived from a 4m-resolution DTM. In the case of object-based classification, textural and morphological information were also used. Three different learning algorithms were tested using the same exact training sets. These are Support Vector Machine () (scheme in Fig. 8a), k-nearest Neighbour (k-nn) (Fig. 8b) and Maximum Likelihood (Fig. 8c). In all cases the landslide (in red) and nonlandslide (in green) examples are plotted into a feature space which, depending on the rules of the classifier, defines a decision boundary in this space. Next, for each pixel or object of the image, the classifier will map it into the feature space and, depending on which side of this decision boundary is located, will classify it accordingly. In brief, what distinguishes each of these learning algorithms is the way in which the decision boundary is placed in the feature space: chooses the boundary with the maximum safety margin to the closest training features (termed support vectors); mapping kernel allows linearization of decision boundary; k-nn class label corresponds to the predominant class within the k nearest neighbors in feature space and MaxLike assumes a statistical distribution for each class and calculates the probability of belonging to a class. Table 1 Layers used in the classifications. Spectral bands RED (2m) GREEN (2m) BLUE (2m) NIR (2m) Vegetation index NDVI Topgraphic index (only object-based classifications) Slope (4m) Aspect (4m) Curvature (4m) Table 2 Features used for object-based learning. Category Features Description Spectral Textural Spatial 1. Mean 2. Std Dev 3. Maximum 4. Minimum 1. Range 2. Variance 1. Convexity 2. Shape factor 3. Length 4. Major length 5. Minor length The spectral mean, stdev, max and min value of the pixels inside the segmented region are computed for each band and index. A 3x3 moving window computes range and variance of the pixels inside it, followed by averaging inside the segmented region. Spatial features are computed from the polygon defining the boundary of the segmented object. (a) (b) (c) Figure 8 Schematic definition of decision region borders by the classifier: (a) Support Vector Machine; (b) k-nearest Neighbour and (c) Maximum Likelihood. 4. RESULTS For assessment and comparison of the learning algorithms with the ground-truth data in the validation area 1, we used several standard measures, namely the overall accuracy (acc), kappa index of agreement (k), the omission (om) and commission (com) errors. Table 3 shows the results for the three classifiers for object and pixel based approaches. For the classifier we tested different types of kernel functions (linear, polynomial (degrees 3 to 6), sigmoid and radial basis function (RBF)). We can see that the object-based approach is clearly superior in performance compared to the pixel-based one, for every method and variant tested. For the methods, the algorithm clearly outperforms k-nn or MaxLike classifiers, being the RBF kernel the best of all.

5 lin pol3 pol4 pol5 pol6 sigm RBF k-nn Max Like Table 3. Performance of the classifiers. Object-based Pixel-based acc k om com acc k om com % % % % % % The output for the overall study area, using again the best result for automatic landslide classification ( RBF), including training areas (of which only about 1% of the area was used for training) is presented in Fig. 10. The same thematic map is presented Fig. 11 with the overlap of the contours of the ground-truth dataset (in red), where a better perception of the good performance achieved with the object-based approach is achieved. Fig. 9 shows a detail of the validation area where the results (in pink) are compared with the groundtruth (red contours). It should be noticed that almost landslides are detected and that unconformities reside mainly in the transitions between the affected regions (the scars) and their neighbourhoods. It should be noted again that this is a testing area, thus it did not provide any examples to train the classifier. Figure 10. Results of RBF classification: objects classified as landslides in pink. Figure 11. Results of RBF classification: landslides detected (pink) compared to ground-truth (red) Figure 9. Detail of RBF ouptut: classified landslides (pink) and ground-truth contours (red). 5. CONCLUSIONS AND FUTURE WORK The major conclusion to be withdrawn from this study is that the object-based approaches outperformed all classifications with pixel-based methods. In particular, the machine learning algorithm with the best performance in this study was the Support Vector Machine with the Radial Basis Function as mapping kernel. Although this approach seems adequate to deal

6 with small and shallow landslides, like those of Madeira Island, there are several enhancements that still need to be done. Thus, future work should include the following activities: 1) To test different segmentation parameters; 2) To make a finer adjustment of RBF kernel parameters; 3) To test different feature space dimensions; 4) To refine selection of training data; 5) To test different training sets sizes; 6) To enlarge the study area in order to cover the whole Madeira Island. 6. REFERENCES Baioni D., 2011, Human activity and damaging landslides and floods on Madeira Island, Natural Hazards and Earth System Sciences, 11, Couto F.T., Salgado R., Costa M.J., 2012, Analysis of intense rainfall events on Madeira Island during 2009/2010 winter, Natural Hazards and Earth System Sciences, 12, Fragoso M., Trigo R.M., Pinto J.G., Lopes S., Lopes A., Ulbrich S., Magro C., 2012, The 20 February Madeira flash-floods: Synoptic analysis and extreme rainfall asessment, Natural Hazards and Earth System Sciences, 12, Guzzetti F., Mondini A.C., Cardinali M., Fiorucci F., Santangelo M., Chang K.-T., 2012, Landslide inventory maps: New tools for and old problem. Earth-Science Reviews, 112, Lira C., Falcão A.P., Gonçalves A., Heleno S., Lousada M., Matias M., Pereira M.J., Pina P., Sousa A.J., Oliveira R., Betâmio de Almeida A., 2013, The 20th February 2010 Madeira Island flash-floods: VHR satellite imagery processing in support of landslide inventory and sediment budget assessment, Natural Hazards and Earth System Sciences, 13, Lira C., Falcão A.P., Gonçalves A., Heleno S., Lousada M., Matias M., Pereira M.J., Pina P., Sousa A.J., Oliveira R., Betâmio de Almeida A., 2011, Automatic detection of landslide features with remote sensing techniques: Application to Madeira Island. In Proceedings of IGARSS IEEE Geoscience and Remote Sensing Symposium, pp Lousada M., Lira C., Pina P., Gonçalves A., Falcão A.P., Heleno S., Matias M., Sousa A.J., Pereira M.J., Oliveira R., Betâmio de Almeida A., 2011, A comparative assessment of supervised pixel based classification methods in the detection of landslide scars, Geophysical Research Abstracts, Vol. 13, EGU , 2011 EGU General Assembly 2011, Vienna.