A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal

Yu, Bo; Chen, Fang

doi:10.1016/j.cageo.2016.12.007

Cited by 33 publications

(21 citation statements)

References 48 publications

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“…Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from multispectral imagery are commonly used to differentiate rock-, soil-, and mud-covered landslides from vegetation and water bodies (Chen et al 2013;Singh and Singh 2016;Chen and Lin 2018). However, Landsat-like imagery alone usually resulted in moderate classification accuracies (Barlow et al 2003;Yu and Chen 2017). Even when geomorphological parameters derived from a digital elevation model (DEM) were used as ancillary data, the commission error and omission error for landslide detection can be as high as 30% (Hong et al 2016;Yu and Chen 2017).…”

Section: Introductionmentioning

confidence: 99%

“…However, Landsat-like imagery alone usually resulted in moderate classification accuracies (Barlow et al 2003;Yu and Chen 2017). Even when geomorphological parameters derived from a digital elevation model (DEM) were used as ancillary data, the commission error and omission error for landslide detection can be as high as 30% (Hong et al 2016;Yu and Chen 2017). The major challenge related to the use of Landsat-like multispectral imagery comes from the separation between landslides and the surrounding environment, as spectral signatures from landslide occurrences and anthropogenic landscapes are often similar.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Chen

Prishchepov

Fensholt

et al. 2019

Remote Sensing of Environment

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Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km 2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p<0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Chen

Prishchepov

Fensholt

et al. 2019

Remote Sensing of Environment

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show abstract

“…Landsat 8, with a spatial resolution of 30 m, has been found effective in mapping larger landsides for rapid assessment in Nepal [60][61][62][63]. The launch of Sentinel-2, with a spatial resolution of 10 m in 2015, has also increased the availability of free high-resolution optical imagery and enabled landslide detection at finer scales than what was possible with previous open source satellite imagery from Landsat and ASTER.…”

Section: Introductionmentioning

confidence: 99%

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal

2019

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The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

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“…The objected-based high resolution image classification takes into account the geometry and texture of partitioned objects, and it can achieve high overall accuracy (above 85%) in landslide detection [21,23]. However, the spatial resolution of these images is usually less than 1 m (Quickbird and GeoEye images), and different sequences of applying the multiple rules may lead to different segmentation results, which are hard to define [24]. For a large-scale area, the number of images to be processed is large and time-consuming [25], and that makes the object-based method impractical to apply [26].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, repetitive observations with dense satellite time-series, such as Landsat (30 m) imagery, are favored by researchers in pixel-based landslide detection [27,28]. Although the shortcomings that the Landsat-like imagery usually lead to a moderate classification accuracy [24,29], this can be effectively overcome by the applications of multi-temporal imagery, which extracts time-series trajectories; incorporating multiple seasonal features, it is used as auxiliary information. For instance, the Normalized Difference Vegetation Index (NDVI) time-series allows separation between permanently non-vegetated and post-event landslide areas in different geographic settings [29][30][31], and the Normalized Difference Water Index (NDWI), derived from multi-spectral imagery, is commonly used to differentiate landslides from water bodies of mountainous areas [27,32].…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China

et al. 2019

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The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples.

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A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal

Cited by 33 publications

References 48 publications

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal

Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China

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