1 Introduction

The significance of mapping and monitoring the stability of slopes in open-cast mining operations lies in its role in validating coal mine designs. A lot of conventional methods are available to obtain the stability of slopes such as Bishop’s method, Fellinius method, and Iteration method [1]. The Digital Elevation Model (DEM) generated by contour maps are very essential and found helpful for slope monitoring and analysis [2]. The slope estimation was done using DEM; compared results from regional to global scale indicates different views whereas the most significant outcome was that slope depends on DEM grid size and varies inversely [3]. Singh [4] uses the limit equilibrium technique, the most common and widely acknowledged design tool to determine stability of slopes in opencast copper mines. A supervisory system for monitoring steep slopes in opencast mines was developed using the MSARMA method to construct analytical models [5]. Roy et al. (2006) [1] elucidated the methodology for stability analysis, with particular emphasis on the impact of mine floor inclination on the stability of backfilled dumps. Dakar coastline region's landslide hazard was mapped, utilizing a multi-method approach and slope stability was evaluated [6].

Osasan and Afeni [7] utilized a range of methodologies for monitoring movements in mine slope, encompassing laser scanners, crack meters, total stations, Sirovision, visual inspections and monitoring radar. Geographic Information System (GIS) skills are crucial for fundamental geomorphology studies, including slope analyses [8]. A DEM was derived from stereo Radarsat Synthetic Aperture Radar (SAR) data and utilized to determine slope angles, alongside key parameters such as soil cohesion, soil unit weight and internal friction angle. These factors were correlated with soil moisture information obtained from Landsat TM data to assess slope stability [9]. Rashid [10] utilized Interferometric Synthetic Aperture Radar (IFSAR) data from the Shuttle Radar Topographic Mission (SRTM, 2000), which had a spatial resolution of 90 m, to generate a slope map for the state of Jammu & Kashmir. Various equations were employed to analyze the three-dimensional surface characteristics. The landslide susceptibility assessment was conducted using a weighted overlay method, depend on 3-dimensional terrain visualization and analysis stereo satellite image from the Cartosat-1 high resolution imagery and aerial photographs scale 1:8000 [11]. Nithya and Prasanna [12] utilized a coordinated methodology of remote sensing and GIS for the landslide hazards zone to partition land surface in to different zones as per degrees of actual potential hazard caused by landslides.

Remote sensing data are invaluable in providing precise and timely information regarding terrain conditions. These data encompass a wide range of variables critical for understanding landscape dynamics, including but not limited to land use/land cover, topography, landforms, and soils [13, 14]. The evolution of stereo sensors deployed on space platforms has notably augmented its capabilities by capturing three-dimensional terrain data, facilitating comprehensive topographic analyses across relatively extensive areas for environmental studies. The wide accessibility of DEM information offers new open doors in structural terrain analysis of enormous regions [15]. High-resolution space-borne remote sensing data exhibit increased spatial detail and offer extensive opportunities for integration into various remote sensing applications [16,17,18]. Cartosat-1 sensor is competent to distribute panchromatic stereo data with high resolution and are utilized to create DEM to infer terrain parameters for aspect and slope and other geological studies [19]. The DEM not only furnishes a comprehensive depiction of the three-dimensional surface but also serves as the fundamental dataset for generating impressive three-dimensional visualizations of geographic data. Additionally, it establishes the groundwork for deriving various surface morphological parameters, including slope, aspect, curvature, slope profile, and catchment areas [3].

Limited studies in India have utilized Cartosat-1 satellite data for deriving terrain indices via DEM analysis. Scientifically, constructive studies have been conducted in various regions of India; however, these studies did not utilize satellite data for coal monitoring [20, 21]. The current study employs geospatial technology to map and evaluate slope stability at the Shatabdi opencast coal mine. Image analysis techniques are widely used for detecting and interpreting the land surface characteristics [22, 23]. Satellite remote sensing and GIS have significantly advanced the comprehension of information sources crucial for informed scientific planning in conservation efforts. This is achieved through the utilization of multi-thematic, climatic, and topographic databases [24,25,26]. Cartosat-1 stereo images typically include Rational Polynomial Coefficients (RPC), which describe the geometric relationship between the images and the objects in space [11]. The aims of study are (1) mapping of DEM, shaded relief, aspect and slope map of Shatabdi opencast region, Jharia. (2) slope stability analysis over Shatabdi opencast coal mine, Jharia. The analysis of slope stability is of critical importance due to the potential for loss of life and significant economic ramifications in case of failure.

2 About study area

The primary and largest coal reserves of prime coking coal in India are predominantly concentrated within the worldwide well-known, Jharia Coal Field (JCF) region. Renowned for its extensive mining heritage, the JCF is situated within the Dhanbad district of the Jharkhand state, positioned within the central expanse of the Damodar river valley. This coalfield is situated approximately 250 km NW of Kolkata and about 1150 km SE of Delhi. The Damodar River is the only major river crossing the Jharia coal field. Some smaller river and rivulets also exist in these coalfields. Dhanbad town is the district head quarter situated 10 km towards the northeast. The Shatabdi opencast coal mine (Fig. 1) situated within the Jharia region is positioned between latitudes 23°47′12"N and 23°48′52"N and longitudes 86°13′19"E and 86°15′16"E. This area has been chosen as the focal point of the study due to its extensive and varied mining history, spanning over a century, encompassing both opencast and underground mining operations.

Fig. 1
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Location map of study area

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3 Methodology

3.1 Flowchart of methodology

The datasets were initially collected through field visits employing DGPS observations. Ancillary data were integrated by utilizing toposheet information. Subsequent investigation utilized Cartosat-1 satellite data. The detailed flowchart of methodology is presented in Fig. 2.

Fig. 2
figure 2

Flowchart of methodology

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3.2 Data acquisition

The field data was collected using DGPS, Cartosat-1 satellite and toposheet information were used in the present study. A stereo pair of Cartosat-1 satellites images of August 2011 with path 0577 and row 0287 was used. The cartographic materials employed in this study encompass a topographic representation of the Jharia coalfield located in Dhanbad, India, characterized by a scale of 1: 50,000.

3.2.1 Field observation data

DGPS data points collected in field during field observation is applied to georeference of image and also for validation of the results produced by satellite images.

3.2.2 Satellite data product specifications

Cartosat-1 represents India's inaugural venture into remote sensing satellite technology, intended to offer high resolution imagery with along-track stereo functionality. Launched in May 2005 aboard the Polar Satellite Launch Vehicle (PSLV-C6), this satellite epitomizes India's stride in advancing its spaceborne observational capabilities. An emergent opportunity has presented itself, constituting an open avenue for the utilization of remote sensing technology in various planning and application domains. The Cartosat-1 mission's twin fixed panchromatic cameras, capable to offer in-orbit stereo images having resolution of 2.5 m nadir and a swath of 27 km [27]. The devices are positioned along the track with predetermined angles of inclination: + 26 degrees (Fore) and -5 degrees (Aft), configured for stereo functionality, each with 2.5 m ground resolutions [28]. The Cartosat-1 sensors, boasting a 10-bit per pixel radiometric resolution, significantly augment object discrimination capabilities, thereby improving their cartographic potential [27]. The integration time stands at 0.336 ms, while the nominal ground sampling distance (GSD) is recorded at 2.5 m [29]. The comprehensive description of Cartosat-1 data is provided in Table 1.

Table 1 Significant specifications of Cartosat-1 mission [27,28,29] are given as
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Considering the benefits of a long track stereo data availability by Cartosat-1 sensor, including systematic stereo coverage, uniform radiometric circumstance for both cameras, a consistent B/H ratio, and simultaneous operation of two cameras within a single orbit, stereo strip triangulation systems and CartoDEM s/w systems have been invented for routine operations. These systems aim to establish a repository of densely distributed control points referred to as secondary control points [27]. The Cartosat-1 satellite exhibits enhanced stability owing to its non-continuous alteration of its viewing orientation throughout the imaging process [29].

3.3 Digital elevation model

The recorded data through most advanced remote sensing technology are always in digital format [25, 30]. Image analysis is conducted primarily to facilitate image interpretation and feature extraction. It is typically characterized by processes such as geometric correction, image enhancement (including spatial, radiometric, and spectral enhancement), band combination (utilizing color composite techniques), and contrast stretching [22, 31, 32]. A DEM is a digital portrayal of ground surface topography or terrain, presented either as a raster grid or a triangular irregular network. Unlike traditional elevation contours, a DEM comprises precise elevation values assigned to individual grid points. DEM data are essential for various applications including topographic feature extraction, slope stability analysis, and runoff modelling [11].

3.4 Image pre-processing

Satellite imagery pre-processing encompasses operations conducted prior to data analysis and information extraction, typically grouped as radiometric or geometric corrections [13, 33]. Radiometric distortions are because of errors in digital number of image and corrections include correction of the data for sensor irregularities and atmospheric noise [34]. The correction converts the data in such a form, which represents accurate reflection or emitted radiation measured by the sensor [20, 35].

3.4.1 Geometric correction

Raster information is obtained through scanned maps along with aerial photographs and satellite images to acquire the different information. The data in conjunction with other spatial data needs geo-referencing with a map coordinate system. Geometric corrections correct distortions caused by sensor-earth geometry variations and convert data to real-world coordinates [36].

3.4.2 Selection of a map coordinate system

The coordinate system employed in the study utilizes the Universal Transverse Mercator (UTM) projection with UTM zone 44, specifically Everest 1956. UTM-WGS 84 datum is widely accepted globally and remains highly popular in geographic and cartographic applications worldwide. India adopted the New Map Policy 2005 and the Survey of India initiated the production of Open Series Maps utilizing the UTM-WGS 84 datum [37, 38]. In Indian context, analysis has shown that the projections utilizing UTM WGS 84 are notably more accurate in area measurement compared to polyconic or other conical projections. This is primarily due to the fact that in conical projections, particularly towards the southern regions, longitudes are more widely spaced out, whereas UTM zones offer a more precise representation [39]. As none of the projected coordinate frameworks can inherently retain the geometric properties (such as direction, distance, and area) of the Earth's surface, individual countries have formulated their own coordinate systems, taking into account their geographical position and map scale. Topographic maps serve as fundamental references for geometric transformations in map presentations at moderate scales (1/50,000 to 1/250,000) or large scales (1/10,000 to 1/50,000).

3.4.3 Transformation of raster data utilizing a geometric model

Ground control points (GCPs) were mathematically transformed to compute their respective map coordinates, enabling accurate determination of map coordinate positions for each cell within the raster map. Field-collected GCPs are employed to solve the model equations, employing a first-order polynomial transformation to translate, scale, and rotate the raster map thereby producing and updated version. The polynomial model is applied for rectifying scanned maps or satellite images over planar surfaces, as it proves effective particularly for medium-resolution images covering flat terrain [40]. For evaluating a first-order polynomial model, a minimum of 3 GCPs is necessary, although collecting multiple points is advisable for enhanced accuracy [41]. In scenarios where images exhibit significant geometric distortions, orthorectification becomes imperative to mitigate distortion arising from relief or viewing angle variations.

3.5 The re-sampling methods

Following coordinate transformation, pixels are reassigned to new locations. The nearest neighbor algorithm allocates each pixel to its nearest neighbor within the new coordinate system, making it the quickest resampling technique suitable for thematic data. Conversely, bilinear interpolation and cubic convolution techniques utilize a weighted averaging approach, incorporating a multitude of nearby cells to calculate the value of the transformed cell [42, 43]. These techniques are specifically suitable for continuous data types such as elevation or slope.

3.6 Image enhancement

The visual interpretation and image appearance was improved by image enhancement for appropriate human viewing. Expanding contrast, eliminating blurring and noise, are the examples of major enhancement activities [44, 45]. The contrast enhancement increases the visual contrast between two areas with varying uniform densities.

3.7 Methodology for slope analysis

The stepwise methodology for slope analysis is give as follows:

3.7.1 Methodology for DEM generation

The DEM was generated utilizing Cartosat-1 stereo data in conjunction with RPC and employing RPC alongside GCPs. After importing the geo-reference satellite imagery in to ENVI 4.3, go to “Data Prep > Create Surface > 3D Surface” and then obtained a DEM of Shatabdi opencast coalmine in Jharia coalfield. The detailed methodology is described using different steps as:

  • Step 1: Import the satellite imagery

    To initiate imagery analysis, the user is required to launch the software and import the requisite imagery. In this study, the classic viewer within ERDAS Imagine 10.0 was chosen. This viewer provides access to all essential software functionalities pertinent to the study and represents the most commonly utilized user interface in ERDAS Imagine. The Cartosat-1 data was incorporated into the viewer as a raster layer. The imported raster data encompassed the panchromatic band.

  • Step 2: Reproject the image

    The Cartosat-1 imagery was given in to the projection Geographic (Lat/Long), we will change this projection in to UTM (Everest 1956). Open the imagery in to Classic Viewer of ERDAS Imagine 10.0 and reproject this imagery in to projection UTM (Everest 1956).

  • Step 3: Geo-reference of image

    Geo-referencing refers to the method involved with assigning map coordinates or GCPs collected during field visit to raster imagery. Initially, the satellite data underwent spatial geo-referencing to a UTM projection employing 25 GCPs. Subsequently, the image underwent resampling utilizing a nearest neighbor algorithm, employing a first-order transformation and achieving a pixel size of 2.5 m. The root mean square error (RMSE) associated with the resampling process was recorded to be below one.

  • Step 4: Subsetting of the image

    The region of interest (ROI) for this project encompassed the Shatabdi opencast coal mine area and its surrounding. To isolate relevant data for this study, a subset of the JCF region was generated, eliminating extraneous information. The initial ROI selection procedure commenced by accessing the "AOI > Tools" menu, where a polygon was delineated around the designated study area. Subsequently, the "Data Prep" menu was employed to generate a subset of the JCF region image, employing previously defined ROI.

  • Step 5: Ground control point generation

    The GCP’s are taken from the field. After collecting ground control points, upload them onto both images.

  • Step 6: Tie point generation

    The epipolar geometry is established through tie points, which are subsequently utilized to generate epipolar images essential for DEM extraction. The tie points are generated here by matching points in both the images.

  • Step 7: DEM generation

    DEM`s are utilized frequently, and are most well-known to produce digital maps. Taking GCPs and generating tie points process in ENVI software and DEM will be generated.

4 Results and discussion

4.1 Digital elevation model

DEMs are informational datasets containing terrain elevation data, usually sampled at regular grid intervals across the ground surface topography. These grid points are consistently referenced to a specific geographical coordinate system, commonly either latitude–longitude or UTM coordinate systems. The spatial resolution of the DEM, determined by the proximity of grid points, directly influences the level of detail present in the dataset [46]. A DEM with enhance resolution was produced utilizing Cartosat-1 data. Subsequently, aspect, shaded relief, and slope maps were derived to facilitate the assessment and monitoring of slope stability analysis. The DEM proves advantageous for its detailed height information. DEM data generated from Cartosat-1 satellite imagery were stratified into height ranging from 156 to 320 m, with the predominant extent falling within the 181 to 260 m height (Fig. 3). Results were validated using DGPS collected field data in Shatabdi opencast region [47].

Fig. 3
figure 3

DEM of Shatabdi region

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4.2 Shaded relief map

The shaded relief map over the region displays mostly smooth regions with few features, while regions with steep slopes in mountains seems rougher [48]. The computer simulates the landscape’s appearance before sunset, assuming the sun is low in the western sky. West-facing slopes, being exposed to direct sunlight, exhibit higher brightness compared to east-facing slopes, which are shaded from direct sunlight. Shaded relief maps accurately depict three-dimensional landforms within the constraints of two-dimensional map representation. The maps utilize colors to show variety in elevation over the Shatabdi region, explicitly height above sea level. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades (Fig. 4). The main relief map of Shatabdi region represents black color for low elevations, brown color for moderate elevations, and white color for the maximum elevations of the region.

Fig. 4
figure 4

Shaded relief map of Shatabdi region

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4.3 Aspect map

An aspect map provides a detailed representation of the angle of inclination of coal or rock seams relative to the horizontal plane. This geological feature is crucial in understanding the orientation and structure of subsurface strata. These maps provide spatial information about the orientation of slopes, categorizing slope directions into distinct quadrants based on azimuthal angles. After DEM generation in ENVI import in the arc-GIS process and classify the aspect map will be generated. Aspect map prepared for the further slope analysis displays the direction and steepness of terrain slopes or consistent surface over the region [49]. Aspect categories are represented through the use of various hues (such as red, orange, yellow, etc.). Aspect map was designed between 0 and 360 degrees of direction, categorized and presented by different colours. The prevailing south-west to south-east azimuth of recurrent precipitation significantly influences slopes oriented from south to north-west. South-west-facing hill slopes exhibit a heightened susceptibility to landslides, with north-west-facing slopes showing a similar vulnerability in close proximity [50]. The aspect map of Shatabdi region is shown below in Fig. 5.

Fig. 5
figure 5

Aspect map of Shatabdi region

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4.4 Slope map

Slope and aspect are two of the morphological factors that are used in GIS applications maybe the most frequently. One of the important factors in slope stability study is a slope map. Slope is recognized as a fundamental morphological attribute crucial for analyzing the topographic configuration of terrain surfaces. If the slope is higher than they are usually more unstable. DEM has been used for the preparation of slope map. After preparation of DEM in ENVI import in arc-GIS process and classify the slope map will be generated. The slope map was stratified into categories based on degrees of inclination, comprising very gentle (0–5 degrees), gentle (6–10 degrees), moderate (11–15 degrees), moderately steep (16–25 degrees), steep (26–35 degrees), and very steep slopes (> 35 degrees). The maps were ground validated with DGPS collected data points in the study region [47]. Some of the locations have very steep slopes indicating about the maximum possibilities of slope failure. Scientifically, landslides are typically not anticipated with gentle slopes because of lower sheer stress. As slope steepness increases, probably landslide susceptibility will increase, reaching its maximum in the steep slopes ranges between 30 and 55 degrees [51]. Present investigation evaluated and indicated that the maximum risk occurs at slopes exceeding 35 degrees. The heavy and prolonged rainfall as well as coal fire from decades over the mining region has major causes of dump failure and it has the concerns of loss of lives and economy. Singh et al. [52] previously identified and analyzed the overall slope angles of the faces and final pit, determining them to be very steep, which increases the risk of slope failures at any time. Due to dump failure a total of 14 peoples were died in 2013 and made major issues for the coal mining industry at Basundhara mines of Mahanadi Coalfields Limited, Odisha [53]. The slope map of the Shatabdi region is depicted in Fig. 6.

Fig. 6
figure 6

Slope map of Shatabdi region

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5 Conclusion

The study aims to provide safety and analysis of slope stability. The Cartosat-1 data from 2009 was utilized to generate DEM, Shaded relief, aspect and slope map of the Shatabdi opencast coal mine, in Jharia for analysis of the slope stability. The data used is 2.5 m spatial resolution having panchromatic band. DEM generated using high resolution Cartosat-1 satellite imagery, categorized in to 156–320 m of height and the maximum study region lies in the range of 181–260 m of height. Here DEM shows the elevation of Shatabdi region. Shaded relief map presents different shaded portion of earth terrain of Shatabdi region in 3 different classes as low, moderate and high. Aspect map indicates orientation of coal/rock seams in different directions. The slope map delineated varying degrees of slope across different elevations, classified as: very gentle slope (0–5 degrees), gentle slope (6–10 degrees), moderate slope (11–15 degrees), moderately steep slope (16–25 degrees), steep slope (26–35 degrees), and very steep slope (> 35 degrees). So, it was found from analysis that the slope 0–10 degrees is stable slope, 11–35 degrees is unstable slope and slope > 35 degrees is most unstable. Several limitations and challenges arises during field data collection. The findings of this research discussed herein represent preliminary insights derived from the conducted work thus far. These preliminary results indicate the potential utility of remote sensing data, alongside supplementary datasets, for modeling slope stability within the study area. Slope analysis holds significant importance in civil engineering endeavors, notably in the design phases of infrastructure projects such as highways, railroads, canals, surface mining operations, waste disposal facilities, earth embankments, and dams. Additionally, it plays a pivotal role in various human activities entailing construction and excavation processes. Slope monitoring equipment allows early detection of changes in slope stability, facilitating prompt intervention to mitigate potential disasters.