Tapas R. Martha

Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis

To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.

Landslide Volumetric Analysis Using Cartosat-1-Derived DEMs

The monitoring of landscape changes can lead to the identification of environmental hot spots, improve process understanding, and provide means for law enforcement. Digital elevation models (DEMs) derived from stereoscopic satellite data provide a systematic synoptic framework that is potentially useful to support these issues. Along-track high-resolution stereoscopic data, provided with rational polynomial coefficients (RPCs), are ideal for the fast and accurate extraction of DEMs due to the reduced radiometric differences between images. In this letter, we assess the suitability of data from the relatively new Cartosat-1 satellite to quantify large-scale geomorphological changes, using the volume estimation of the 2007 Salna landslide in the Indian Himalayas as a test case. The depletion and accumulation volumes, estimated as 0.55 × 106 and 1.43 × 106 m3, respectively, showed a good match with the volumes calculated using DEMs generated only with RPCs and without ground control points (GCPs), indicating that the volume figures are less sensitive to GCP support. The result showed that these data can provide an important input for disaster-management activities.

Effect of Sun Elevation Angle on DSMs Derived from Cartosat-1 Data

Along-track stereoscopic satellite data are increasingly used for automatic extraction of digital surface models (DSM) due to the reduced radiometric variation between the images. Problems remain with the quality of such DSMs, especially in steep terrain. This paper explores the accuracy of DSMs extracted from Cartosat-1 data acquired under high and low sun elevation angle conditions in High Himalayan terrain. The metric accuracy of the DSM was estimated by comparing it with check points obtained with a differential GPS . Additionally, we used spatial discrepancy of drainage lines to estimate errors in the DSM due to spatial auto- correlation. For valleys perpendicular to the satellite track, the DSM extracted from a low sun elevation angle data showed 45 percent higher spatial accuracy than the DSM extracted from high sun elevation angle data. The results indicate that the sun elevation angle and valley orientation affect the spatial accuracy of the DSM, though metric accuracy remains comparable.

Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas

Landslide susceptibility is the likelihood of a landslide occurrence in an area predicted on the basis of local terrain conditions. Since last few years, researchers have attempted to analyse the probability of landslide occurrences and introduced different methods of landslide susceptibility assessment. The objective of this paper is to assess the landslide susceptibility in parts of the Darjeeling Himalayas using a relatively simple bivariate statistical technique. Seven factor layers with 24 categories, responsible for landslide occurrences in this area, are prepared from Cartosat and Resourcesat — 1 LISS-IV MX data. Each category was given a weight using the Information Value Method. Weighted sum of these values were used to prepare a landslide susceptibility map. The result shows that 8% area was predicted for high, 32% for moderate and remaining 60% for low landslide susceptibility zones. The high value (0.89) of the area under the receiver operating characteristic curve showed the high accuracy of the prediction model.

A Tool Assessing Optimal Multi-Scale Image Segmentation

Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this study, we developed an optimum scale parameter selector (OSPS) tool for objective determination of multiple optimal scales in an image by MRS using eCognition software. The ready to use OSPS tool consists of three modules and determines optimum scales in an image by combining intrasegment variance and intersegment spatial autocorrelation. The tool was tested using WorldView-2 and Resourcesat-2 LISS-IV Mx images having different spectral and spatial resolutions in two areas to find optimal objects for ground features such as water bodies, trees, buildings, road, agricultural fields and landslides. Quality of the objects created for these features using scale parameters obtained from the OSPS tool was evaluated quantitatively using segmentation goodness metrics. Results show that OSPS tool is able determine optimum scale parameters for creation of representative objects from high resolution satellite images by MRS method.

Landslide Inventory, Hazard and Risk Assessment in India

The recent census in India revealed that India is now housing 17% of the world’s population, and India is on the way to become the most populated country. Landslides are an increasing concern in India due to the rapid population expansion in hilly and mountainous terrain. Landslides affect vast areas within India, in particular in the Himalayan chain in the North and Eastern part of the country and the Western Ghats in the Southwest. The Geological Survey of India (GSI) has been designated as the nodal agency for landslides by the Indian government, and they are responsible for landslide inventory, susceptibility and hazard assessment. Until recently their landslide susceptibility assessment was based on a heuristic approach using fixed weights or ranking of geofactors, based on guidelines of the Bureau of Indian Standards (BIS). However, this method is disputed as it doesn’t provide accurate results. This paper gives an overview of recent research on how the existing methods for landslide inventory, susceptibility and hazard assessment in India could be improved, and how these could be used in (semi)quantitative risk assessment. Due to the unavailability of airphotos in large parts of India, satellite remote sensing data has become the standard data input for landslide inventory mapping. The National Remote Sensing Center (NRSC) has developed an approach using semi-automatic image analysis algorithms that combine spectral, shape, texture, morphometric and contextual information derived from high resolution satellite data and DTMs for the preparation of new as well as historical landslide inventories. Also the use of existing information in the form of maintenance records, and other information to generate event-based landslide inventories is presented. Event-based landslide inventories are used to estimate the relation between temporal probability, landslide density and landslide size distribution. Landslide susceptibility methods can be subdivided in heuristic, statistical and deterministic methods. Examples are given on the use of these methods for different scales of analysis. For medium scales a method is presented to analyze the spatial association between landslides and causal factors, including those related to structural geology, to select the most appropriate spatial factors for different landslide types, and combine them using the multivariate methods. For transportation corridors a method is presented for quantitative hazard and risk assessment based on a landslide database. Deterministic methods using several dynamic slope-hydrology and slope stability models have been applied to evaluate the relation between land use changes and slope stability in a steep watershed. The paper ends with an overview how the susceptibility maps can be combined with the landslide databases to convert them into hazard maps which are subsequently used in (semi) quantitative risk assessment at different scales of analysis, and how the results can be used in risk reduction planning.

Spatial Potential Analysis of Earthquakes in the Western Himalayas Using b-value and Thrust Association

In this study, we have analysed the spatial variation of b-values (from frequency-magnitude distribution (FMD)) in the western Himalayas as an indicator to demarcate the potential zones of earthquake occurrences. This is done under the acceptance of interpretation that decrease of b-values is correlated with a stress increase in the epicentral region of an approaching earthquake event. In addition to this, the spatial association of the earthquake epicenters with the major thrusts in the region using weights of evidence method, to identify potential zones of earthquake occurrences have also been analysed. Both analyses were carried out using a historical earthquake (Mw> 4) database of the1900-2015 period. Finally, based on the spatial variation of b-values and ‘contrasts’ derived from weights of evidence method (thrust associations), the derived map information was geospatially combined to prepare a “spatial earthquake potential” map of the western Himalayas. This map demarcates the western Himalayas into 3 zones - high, medium and low potential for future earthquake occurrences.

Mapping of hydrothermally altered zones in Aravalli Supergroup of rocks around Dungarpur and Udaipur, India, using Landsat-8 OLI and spectroscopy

We explored the utilization of Landsat-8 Operational Land Imager (OLI) data for mapping of hydrothermal alteration zones. The region in and around the cities of Dungarpur and Udaipur of Rajasthan state in India was selected for this study. The rock types of Dungarpur and Udaipur are serpentinites, talc-carbonate, talc-schist, and quartzite of the Aravalli Supergroup. Hydrothermally altered zones and resultant hydrous minerals play an important role in the genesis of these rocks. We aimed to identify possible locations of hydrothermally altered zones in regional context around Dungarpur and Udaipur using Landsat-8 OLI data. False-color composite maps and band ratios were prepared from Landsat-8 bands. Band ratios such as band 6/band 7 (short-wave infrared 1 (SWIR1)/short wave infrared 2 (SWIR2)), band 4/band 3 (red/green), and band 5/band 6 (near infrared (NIR)/SWIR1) and visual interpretation techniques were used to identify the hydrothermally altered zones. Spectroscopic analyses of field rock samples were done to validate the hydrothermal alteration zones delineated from the analysis of Landsat-8 data. We present the combined results of Landsat-8 and field spectroradiometer analysis which brings out the hydrothermal alteration zones associated with hydrous minerals (antigorite, lizardite, montmorillonite, vermiculite, talc, and saponite). The study demonstrates the utility Landsat-8 OLI (with field spectroradiometer data) in the mapping of hydrothermally altered zones as a key in understanding geological processes.