Landslides represent a major geological hazard and play an important role in the evolution of landscapes [1
]. Landslides can be caused by a variety of natural or human-induced triggers, such as extreme rainfall, earthquakes and engineering activity, and often result in tragic casualties, tremendous economic losses and ecological environment damage [3
]. In particular, numerous landslides occur in the hilly and gully regions of China’s Loess Plateau during the rainy season from July to September [6
]. In July 2013, prolonged heavy rain fell over an area of approximately 37,000 km2
in Yan’an in the central part of the Loess Plateau, and this rain induced 8135 landslides, destroyed approximately 10,000 cave dwellings, displaced 160,000 residents and killed 45 people according to the local government [7
]. Landslide monitoring and mapping are the most essential measures to protect against landslide hazards and improve risk management.
Landslide-related studies have examined various perspectives on landslide hazards in the geo-environmental sciences over the past two decades, such as landslide inventories, susceptibility and hazard zoning, landslide monitoring and early warning, and land risk assessment and management [8
]. Landslide monitoring and mapping remain research hotspots because they play a fundamental role in comprehensively examining landslide mechanics and are crucial to landslide hazard mitigation [1
]. Numerous efforts have been devoted to preparing landslide inventory maps to improve landslide risk assessment and management, reduce uncertainties and facilitate more reliable decision making [1
]. Visual interpretation and field investigations have conventionally been used to produce landslide inventories [14
]. This traditional method of landslide investigation is time consuming and costly, and the results are somewhat unreliable [15
]. Therefore, early studies often focused on detecting large or individual landslides, mostly for limited areas [7
]. Improvements in monitoring capability through the use of very high resolution imagery (e.g., 0.61 m QuickBird, 0.5 m WorldView, 2.5 m SPOT-5, 1 m Ikonos and 2 m GF-1) have enabled regional and relatively small landslide inventory mapping. Recent studies have increasingly utilized these high-resolution remote sensing images for landslide mapping and risk assessment [18
]. The availability of very high resolution images and the development of satellite image processing techniques can allow us to produce more reliable landslide inventory maps.
Several image processing techniques can be categorized into two groups: pixel-based and object–oriented approaches. Pixel-based methods classify each pixel in the image without considering its neighborhood. Previous studies have documented conventional pixel-based approaches, including parallelepiped, minimum distance, maximum likelihood, iterative self-organizing data analysis technique (ISODATA), K-mean, etc. [20
]. Recently, advanced pixel-based techniques, such as artificial neural networks (ANN) and support vector machines (SVM), have been applied to produce landslide inventory maps based on information-rich high-resolution imagery [22
]. However, the abilities of these methods to describe a landslide are somewhat limited. The landslide results are not suitable for identifying landslide boundaries [16
]. These classifications only rely on spectral signatures, which are not suitable for characterizing geomorphic processes such as landslides [24
]. In addition, the outputs from pixel-based methods are typically difficult to verify on the ground [14
]. Object-oriented approaches (OOAs) can incorporate a multitude of diagnostic landslide features, including its spectral, textural, morphological and topographic characteristics. Previous works have proven these methods’ abilities to successfully detect landslides by quantifying various diagnostic landslide features [14
]. OOAs have been used to detect landslides and have been shown to perform better than pixel-based methods [27
]. Classifications from OOAs can be 15%–20% better than those from pixel-based techniques [16
]. Rau et al. [15
] created a landslide inventory map using an OOA by combining optical ortho-images and a digital elevation model. Li et al. [28
] proposed a semi-automated approach for landslide mapping using change detection applied to bi-temporal aerial orthophotos. However, these approaches mainly depend on knowledge that has been developed by experts for the detection of landslides [15
]. The identification capacity for landslides must be further validated in other areas and requires a comprehensive understanding of all potentially useful landslide characteristics.
Landslide hazards in China have exhibited a sharp increase since the 1980s [17
]. The most serious landslides have occurred in western China and are always catastrophic because of their large affected areas and the number of fatalities [30
]. Numerous studies have investigated these large-scale landslides triggered by earthquakes together with prolonged rainfalls. Representative examples include the “Wenchuan” earthquake, a catastrophic earthquake that occurred on 12 May 2008 and measured Ms 8.0 in surface wave magnitude [32
], and the “Zhouqu” debris event, a catastrophic debris flow that occurred on 7 August 2010 and was triggered by a rainstorm with an intensity of 77.3 mm·h−1
]. Loess landslides comprise an area of 6.3 × 105
and have been extensively investigated; however, the loess landslide inventory is essentially based on a field-based method [7
]. Since the implementation of the “Grain to Green” project, which was launched by the Chinese government in 1999, soil erosion on sloping areas has been comprehensively controlled; however, gravitational erosion, such as loess landslides from extreme precipitation events of the Loess Plateau, occurs more frequently [36
]. The spatial distribution and mechanisms of loess landslides remain poorly understood because of inadequate investigation and limited knowledge of the nature of loess landslides [7
]. Thus, we must study how to more quickly obtain results for loess landslides over a large area with automated/semi-automated methods instead of manual approaches.
The purpose of this study is to provide an effective approach to recognize and map loess landslides by analyzing recognition indices to determine the parameters and their corresponding thresholds. We quantitatively assessed the ability of identification parameters, including spectral, textural and morphometric features, to distinguish landslides from their surroundings using GF-1 satellite imagery combined with a DEM.
OOA approaches have proven to be effective and useful high-spatial-resolution imagery processing techniques for extracting landslides according to previous studies [15
]. More reasonable and accurate landslide classifications can be achieved with OOA approaches [16
]. In particular, spectral information from landslides and some topographical variables can be integrated with OOAs to greatly improve the identification of landslide. The current major issues of landslide identification with OOAs include the selection of training samples, determination of threshold values, and choice of diagnostic features and a semantic network for classification [15
]. Previous approaches for the recognition of landslides were mainly derived from knowledge that was developed by experts for the detection of landslides [14
]. In our study, we quantitatively analyzed the spectral, textural, and morphometric properties of loess landslides and topographic characteristics. Furthermore, an appropriate algorithm was developed for the extraction of loess landslides, which were determined from a few training samples (averaged for 3.62% of the total study area in Table 3
). The identification algorithm of landslides was validated in the hilly and gully loess regions, and the results represented a good assessment (i.e., the QP values were all greater than 0.80 and the kappa indices were all higher than 0.85 in Table 3
). These accuracy assessments indicated better landslide detection when using the proposed approach.
The NDVI was found to be a very useful method and was considered as an initial criterion in our study, although certain other diagnostic features can also discriminate loess landslides from vegetation cover and water areas (Figure 4
, Figure 5
and Figure 6
). The hill slopes were exposed after landslide events and exhibited lower reflectivity characteristics compared to surrounding areas covered with vegetation. These changes to the land covers could be best represented by NDVI, which has also been successfully used by previous studies [14
]. In our study, we used NDVI as a diagnostic feature to discriminate loess landslides from vegetation cover and water areas (Figure 4
a). The brightness, or the weighted average of the image intensity, has been used as a spectral diagnostic feature in previous studies. Martha et al. [14
] used the brightness as a diagnostic feature to remove shadows and rocky areas; shadows have lower brightness and rocky areas have higher brightness compared to landslides. Rau et al. [15
] believed that the brightness was sometimes more significant than the NDVI when filtering out vegetation areas and bare soil, except for removing shadows and clouds. In our study, we found that the brightness (intensity) can remove most of the vegetation areas (forest and grassland) and small parts of roads from landslide candidates (Figure 4
h). However, this diagnostic feature was not recommended in this study because the brightness for these land covers, such as forest and grassland, had substantially overlapping distributions with those of landslides (Figure 4
h) and had relatively poorer ability to distinguish landslides from vegetation areas compared to NDVI (Figure 4
a,h). In addition, clouds did not exist, and shadows were not obvious in the acquired GF-1 image, so the brightness feature was not used in the study.
The texture has seldom been applied in previous studies as a diagnostic feature for landslides and other types of land covers. Martha et al. [14
] used the texture means of the red band to discriminate terrace patterns, which exhibit unique textures (terraces are parallel to contours, and their width is largely uniform), in a Resourcesat-1 image with 5.8-m spatial resolution. Our results confirmed that the texture means in the near-infrared region can also be applied to distinguish terrace areas (0.24 to 0.31) from landslides (0.14 to 0.25) in high-spatial-resolution images (GF-1, 2 m), although they have minimal overlapping distributions (Figure 5
d). Furthermore, we found that the texture variances of NDVI proved to be suitable for distinguishing landslides from buildings (Figure 5
f), reflecting the larger heterogeneity of buildings compared to other land covers.
The topographic slope is usually used as a diagnostic indicator; however, a large overlapping distribution existed between loess landslides and terraces in this study (Figure 7
a). In these areas, gentle to moderate slopes (less than 25°) were often converted to terraces for agriculture purposes and orchards. Loess landslides often occurred between two sides of the slopes, and their materials were deposited into narrow gullies. When multi-scale segmentation was performed, the hillslope landslides and their deposited materials in the gully, which had smaller slopes, were often segmented as an object, thus creating a relatively large overlapping distribution of topographic slopes between loess landslides and terraces. High elongation and low roundness were found to be very helpful in identifying both major and minor roads (Figure 6
f,e). An alternative and similar diagnostic feature from previous studies, namely, the length/width ratio, can also be helpful to identify roads [14
]. Hill slope landslides were separated based on the topographic position (a comprehensive topographic feature of elevation, slope, etc.), which was derived from the DEM, to obtain more reasonable results and to identify and improve the recognition accuracy of loess landslides.
The mapping of loess landslides is an urgent and difficult task due to the large number of loess landslides occurring in regions with complex topographies and numerous gullies. By comprehensively analyzing the spectral, textural, morphometric and topographic properties of loess landslides, we can successfully develop a suitable algorithm to distinguish loess landslides, rationally assess landslide hazards and reduce landslide damage by adopting proper mitigation measures.
Landslide recognition is very important in loess areas. Our proposed method potentially provides a way to quantify parameters by analyzing recognition indices. However, at the present stage of development, several issues remain to be solved in the future, such as the limitations and drawbacks related to real application in other areas or for other landslide types. Thus, the parameters need to be adjusted according to the specific research situation when applied in other areas. Although the parameters and their thresholds for landslide recognition must be adjusted case by case, our method provides a concept and a similar procedure for determining parameters and their thresholds in different areas where different types of landslides can occur. Some common parameters and their thresholds can also be used in other landslide identification studies.