Landslides Detection using Aerial Ortho-Images and LiDAR Data

Jiann-Yeou Rau1, Yi-Chen Shao2, Liang-Chien Chen3, 1Specialist, 2Ph.D Candidate, 3Professor

Center for Space and Remote Sensing Research, National Central University (NCU), Jhong-Li, Taoyuan, TAIWAN

{jyrau, ycshao, lcchen}@csrsr.ncu.edu.tw

Chi-Chung Lau4 & Kuo-Hsin Hsiao4 4Researcher, Energy and Environment Lab.,

Industrial Technology Research Institute (ITRI), Chu-Tung, Hsinchu, TAIWAN {cclau, HKS }@itri.org.tw

Abstract: Traditionally the landslide areas were manually measured from aerial stereo-pair by visual interpretation or on-site surveying. Since those two approaches are tedious and cost, researches focus on automatic method for efficient and effective detection for landslides area. For the purpose of automatic landslide detection, its surface and spectral characteristics should be analyzed at first. In the paper, base on a pre-compiled landslide map, we utilize the airborne LiDAR and color aerial ortho-images to analyze the terrain slope, canopy texture, spectral greenness for the landslides. Experimental results indicate that the greenness is an important index to discriminate the bare soil from vegetation, it is better to use a radiometric calibrated near-infrared image for detecting non-vegetation area automatically. In this preliminary study, the proposed logical intersection method can get a correct detection rate up to 86%, but a large commission error will be introduced. A further modification of the thresholds considering the landslides that are covered by trees, a more reasonable result could be achieved. Keywords: Landslides Detection, Surface Analysis, Spectral Analysis. 1. Introduction

USGS (Highland, 2004) classify the landslides into seven classes according to the method of movement, i.e. Rotational Slides, Translational Slides, Falls, Topples, Flows, Lateral Spreads, and Complex. The reasons that introduce landslides are mostly due to geological, morphological, and human USGS (Highland, 2004). For forest or land management, the surveying of landslides area is an important work. A direct on-site surveying and measurement for landslides is a time-consuming and tedious work. Although this is the most accurate and reliable method, but somewhere due to steep terrain, weather condition or transportation problem, the on-site surveying is not possible to accomplish. Remote sensing and aerial photogrammetry thus provide another approach with in-direct measurement remotely. It is suitable for large area and efficient surveying.

There are lots of researches in landslides detection that utilize satellite images or airborne sensors, such as SPOT, IKONOS, QuickBird, SAR, Aerial Photo, Airborne LiDAR, and Terrestrial LiDAR. Considering the coverage, accuracy and reliability, the best choice is utilizing the aerial photo stereo-pair with manual interpretation and measurement. However, in case of disaster has happened, an efficient data acquisition with automatic approach is necessary for decision support.

Figure 1 demonstrates four typical landslides occurred in Taiwan. From Figure 1, one may notice that the surrounding of landslides is mostly dense forest, while the surface of the landslides is bare soil with steep terrain. Since the purpose of this study is to develop a suitable method for automatic landslides detection, the characteristics of landslides have to be analyzed at first. Thus, in the paper

the airborne LiDAR data and color aerial photo are utilized for landslides surface and spectral analysis. The LiDAR data provides high density of three-dimensional surface point clouds, while the color aerial photo could provide high-resolution land cover information. Since the airborne vehicle can provide a large area of ground coverage with high-resolution and high-accuracy remote sensing data, it is also suitable for disaster damage assessment.

In the paper, the aerial photo has been ortho-rectified and a pre-compiled landslides map is used for ground truth was produced by visual interpretation and manual stereo measurement. The LiDAR data has been classified as TERRAIN and SURFACE classes and interpolated as equal grid DTM and DSM, respectively. We can thus analyze the surface and spectral characters of the landslides, such as the terrain slope, surface texture, and greenness, etc.

Figure 1, Four typical landslides in Taiwan

2. Methodology 1) Spectral Analysis

In the field of remote sensing, for the purpose of analyzing the vegetation coverage on the ground there is a number of vegetation index could be used, such as NDVI (Normalized Difference Vegetation Index)(Jackson et al., 1983), EVI (Enhanced Vegetation Index)(Miura et al., 1998), LAI (Leaf Area Index)(Chen et al., 1997), etc.. In this preliminary study, the color aerial imagery is used for providing land-cover information. The color aerial imagery may be digitized from color analogue aerial photography and mostly do not consist of near-infrared band. In case the aerial photo contains near-infrared and visible red bands, the NDVI method could be used. However, in case only visible red, green, and blue spectral bands, an alternative greenness index will be utilized. The formula is shown in equation 1 which is similar to NDVI but the green band was adopted instead of near-infrared band.

edreenedreen

RGRGGreeness

+−

= (1)

According to equation 1, the range of greenness value is between -1 to +1. Since the utilized aerial images may not be radiometric calibrated, the estimated greenness index will not be an absolute value but a relative one. However, it could be a useful index to distinguish between vegetation and bare soil. A high greenness index will contain more green vegetation comparing to low greenness index. 2) Terrain Slope

In the paper, the DTM was interpolated and manually edited from airborne LiDAR data. There are lots of methods that could be used for terrain slope estimation. Most of them utilized a fixed window size to estimate the terrain slope (O’ Callaghan and Mark, 1984; Unwin, 1981; Sharpnack and Akin, 1969). Since the adopted high-resolution DTM can describe high-frequency of terrain height variation, a small mask may contain man-made artificial objects. Thus, a flexible window size denoting multiple scale of terrain feature would be suitable for the characterization of landslides. Parker (1997) proposed to use the derivative of Gaussian function to convolute with the DTM in x and y direction and combine together to estimate the slope. For example, equation 1

illustrates a one-dimensional Gaussian function. In equation 1, the sigma (σ) value represents the scale of the terrain feature. Equation 2 is the result of derivative of the Gaussian function on x direction. The terrain slope can thus be calculated using equation 3. For the case of raster DTM, an odd number of Gaussian kernel is adopted with a suitable scale factor (σ). For example, Figure 2 illustrates a 5x5 Gaussian kernel in x-direction with a scale factor of 0.8. A larger scale factor with larger window size will introduce smoother effect to the result of slope calculation.

2

22( )x

f x e σ−

= (2)2

222'( )

x

xxf f x e σ

σ

⎛ ⎞−⎜ ⎟⎜ ⎟⎝ ⎠⎛ ⎞= = −⎜ ⎟

⎝ ⎠ (3)

( )221tan yx ffslope += − (4)

0.137 0.715 0.000 -0.715 -0.137

0.137 0.715 0.000 -0.715 -0.137

0.137 0.715 0.000 -0.715 -0.137

0.137 0.715 0.000 -0.715 -0.137

0.137 0.715 0.000 -0.715 -0.137

Figure 2, An example of x-direction Gaussian kernel with 5x5 window size and a scale factor of 0.8.

3) Object Height Model (OHM) The object height model describes the height of above ground object in raster form. Since the

airborne LiDAR data can provide height information of the terrain and the canopy, the object height model can be derived simply by the subtraction of DSM with DTM. For most of the landslides, there is no vegetation exists on the ground. The OHM is thus an essential estimator for bare soil. 4) Texture Analysis

In the paper we assume the landslides are mostly appear as bare soil which has a smoother surface compare to forest. Thus, surface roughness is an index used for distinguishing bare soil from forest area. In order to estimate surface roughness, the surface height information is the best choice for reference. However, the terrain variation is high in mountainous area that may introduce improper estimation of surface roughness. Thus, we utilized the OHM for the calculation of surface roughness due to the terrain variation has been removed. In this preliminary study, a local height “Standard Deviation” (Std. Dev.) is adopted as surface roughness index. 5) Automatic Landslide Detection

In this preliminary study, we utilize the logical “intersection” operation to detect landslides based on its spectral and surface characteristics. We will estimate a set of suitable thresholds for automatic detection and compare the result with the ground truth to analyze any exception that may introduce. 3. Case Study 1) Study Site and Preprocessing

A 1:5,000 base map, i.e. 9622-1-094, was adopted as study site with coverage of 2.62 x 2.86 km2. The color aerial photo was taken after a serious typhoon, i.e. Typhoon AERE, at Oct., 2004, while the LiDAR was scanned on Dec. 16, 2005. It means that the data acquisition dates are different with about one year. During this period some areas may have change due to vegetation rehabilitation or other natural disasters. Figure 3 depicts the color aerial photo of the study site. In which the ground truth of landslides was overlaid with red color. The orthophoto has a ground sampling distance of 0.5 meters. Figure 4 demonstrates the grid DSM interpolated from LiDAR data which originally has a point density of 1.6 points/m2. Utilizing the DSM and DTM data we can thus subtract them to create the object height model, i.e. OHM (Figure 5). From Figure 5 we can found that the bare soil and river were display as black color meaning that there is no object above them. Additionally, a 4 meters resolution of OHM is used for texture index calculation, i.e. Std. Dev., which utilized a 3x3 window size (Figure 6). In the meantime, the DTM is applied for terrain slope map generation using

the Derivative Gaussian convolution method with 5x5 window size as described in Equations 2~4. An example that using 4 meters resolution of DTM is shown in Figure 7. From the legend one may find out the study site is dominate by steep terrain within the range around 45~60 degree. Finally, the greenness index map, as shown in Figure 8, was created from the color orthophoto using Equation 1. Since the study site is located at mountainous area that is mostly covered by the forest, the green color dominated the whole area.

350

400

450

500

550

600

650

700

750

800

850

900

950

1000

1050

1100

1150

0246810121416182022242628303234

Figure 3, Orthophoto with

landslide ground truth Figure 4, DSM Figure 5, OHM

-1.0

-0.8

-0.6

-0.4

-0.2

-0.0

0.2

0.4

0.6

0.8

1.0

Figure 6, Texture index.

(Std. Dev.) Figure 7, Terrain slope map. Figure 8, Greenness index

map. 2) Distribution Analysis

Based on the ground truth of landslide areas, we adopt the pixel-base overlay analysis to calculate the histogram for each index. Although the terrain slope and texture Std. Dev. have spatially relation to its neighborhood, in this preliminary study only 4 meters DTM is used to analyze its characters. Figure 9~12 illustrate the histogram and the Cumulative Probability Distribution (CPD) for terrain slope, texture Std. Dev., greenness, and OHM, respectively.

Figure 9. Histogram & CPD for terrain slope.

Figure10. Histogram & CPD for Std. Dev.

Figure 11. Histogram & CPD for greenness.

Figure 12. Histogram & CPD for OHM.

3) Landslides Detection In this preliminary study, we adopt the logical “intersection” operation to detect the landslides

automatically. We assume the result of automatic landslides detection will has a 95% of confidence, thus we can decide a set of thresholds to discriminate the landslides from others. Except for the terrain slope we choose 5% of the CPD as the threshold, the other thresholds are determined at the value that has 95% of the CPD. The determined thresholds are shown in the first column of Table 1. That means, in case one pixel has greenness index small than 0.55, terrain slope greater than 8 degree, OHM small than 11 meters, and texture Std. Dev. small than 11 meters, it is considered as a landslide. The result has a correction detection rate up to 86.1 %, but a large commission error has also introduced. In Table 1, we fix the thresholds for terrain slope and OHM, but changing the greenness and texture index to detect landslides. The detection results for case 1~3 are illustrated in Figure 13~15 for comparisons. Those figures depict the greenness index is sensitive to the results. In the meantime, in case 4~6 we assume the texture index is not functioning by assigning a big threshold. In case 7~9 we assume the aerial photo is not utilized for landslides detection. It demonstrates that utilizing only the texture index is not enough to filter out vegetation area.

From case 1~9 we noticed that all of them have large commission error. The major reason is that we adopt the pre-compiled landslide map as reference and choose a set of thresholds base on 95% confidence interval. Since the landslide map was created from aerial photo stereo-pair that have a good distinguish power around 30cm ground resolution, small landslides with dense forest surrounding or rehabilitated landslides can be recognized and recorded. It means that the 95% confidence interval is too large. Thus, we setup another three case studies, as shown in case 10~12 of Table 1. The results are reasonable but the correction rate and commission error rate are both reduced. Scrutinize the error evaluation map (Figure 16) and compare with ground truth (Figure 17) for case 12, we notice that most of the omitted pixels are located at or around the forest area (as shown with red color in Figure 16). One reason has just explained due to the visual interpretation has more distinguished power. The other possible reason is because the ortho-image is generated using the DTM not DSM, the relief correction does not applied on trees. From the automation point of view, it is not easy to detect landslides under the trees. The correction rate could be increased if we don’t consider those areas as landslides.

Table 1. Thresholds and evaluation for each case studies. Case Study 1 2 3 4 5 6 7 8 9 10 11 12

Greenness < 0.55 0.4 0.3 0.55 0.4 0.3 1 1 1 0.4 0.35 0.3

Slope (Degree) > 8 8 8 8 8 8 8 8 8 12 14 16

OHM (M) < 11 11 11 11 11 11 11 11 11 6 3 2

Texture Std. Dev. 3x3 (M) < 11 11 11 800 800 800 11 10 9 10 9 8

Correction Rate ( C/T ) 86.1% 72.7% 60.2% 88.6% 74.3% 61.5% 91.2% 89.2% 86.3% 69.2% 60.9% 51.8%

Omission Error Rate ( OE/T ) 13.9% 27.3% 39.8% 11.4% 25.7% 38.5% 8.8% 10.8% 13.7% 30.8% 39.1% 48.2%

Commission Error Rate ( CE/D ) 92.7% 81.5% 76.8% 94.7% 82.7% 77.5% 96.1% 95.3% 94.4% 74.27 63.4% 54.9%

4. Conclusions In the paper, we utilized a pre-compiled landslide map, color ortho-image and LiDAR data for the

study of landslide’s spectral and surface characters. The pre-compiled landslide map was created by visual interpretation that some landslides under or surround the trees were considered as landslides but not easy to be detected in an automatic way. We utilize the cumulative probability distribution to estimate thresholds for detecting landslides automatically. Considering the situation that landslides may covered by trees, we thus modify the thresholds and a reasonable result can be

achieved. A further classification method is necessary based on the spectral and surface characteristics of landslides. Finally, the greenness is an important index to discriminate the bare soil from vegetation, it is better to use a radiometric calibrated near-infrared image for detecting non-vegetation area automatically.

Figure 13. Result of case 1. Figure 14. Result of case 2. Figure 15. Result of case 3.

Figure 16. Error evaluation map.

(Blue: Correct, Red: Omitted, Green: Committed ) Figure 17. Ground truth.

Acknowledgement This study was funded by the Council of Agriculture, Republic of China, under the grants of

95AS-12.1.1-ST-a1. References Chen J.M., Rich P.M., Gower S.T., Norman J.M. & Plummer S., 1997. “Leaf area index of boreal

forests: Theory, techniques, and measurements”, J. Geophys. Res. 102, 29429-29443. Highland, L., 2004, “Landslide Types and Processes”, USGS Report No. 2004-3072, Available:

http://pbs.usgs.gov/fs/2004/3072/ Jackson, R. D., P.N. Slater, and P.J. Pinter, “Discrimination of growth and water stress in wheat by

various vegetation indices through clear and turbid atmospheres,” Remote Sensing of the Environment, vol. 15, 1983, pp.187-208.

Miura, T., Huete, A.R., van Leeuwen, W.J.D., and Didan, K., 1998, “Vegetation detection through smoke-filled AVIRIS images: an assessment using MODIS band passes”, J. Geophys. Res. 103:32,001-32,011.

O'Callaghan, J.F., and Mark, D.M., 1984, “The extraction of drainage networks from digital elevation data”, Computer Vision, Graphics, and Image Processing, Vo.28, pp.323-344.

Parker, J. R., 1997, “Algorithms for Image Processing and Computer Vision”, Wiley Computer, New York..

Sharpnack, D. A. and Akin, G., 1969. ”An algorithm for computing slope and aspect from elevations”. Photogrammetric Engineering, Vol.35, No.3, pp.247-248.

Unwin, D., 1981. “Introductory Spatial Analysis”. Methuen, London and New York, 212 pages.