Volume 10, Number 2 (January 2023):4129-4142, doi:10.15243/jdmlm.2023.102.4129 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 4129 Research Article
Mine void identification using Object-based Image Analysis (OBIA) of satellite imagery Sentinel 2 data
Leta Lestari1, Ginting Jalu Kusuma1*, Abie Badhurahman2, Sendy Dwiki1, Rudy Sayoga Gautama1
1 Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology, Indonesia
2 Center of Research Excellence (CoRE) in Mine Closure and Mining Environment, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology, Indonesia
*corresponding author: [email protected]
Abstract Article history:
Received 25 August 2022 Accepted 22 October 2022 Published 1 January 2023
Open pit mining is an extensively-used method in Indonesian coal mining. This method is characterized by the formation of mine void at the end of life-of-mine (LOM) due to insufficient material to backfill the mine-out areas. Mine voids are legally accepted as one of mine closure options and categorized as “Reklamasi Bentuk Lain” - a miscellaneous reclamation option (Decree of Minister of Energy and Mineral Resources/KepMen ESDM No.1827, 2018). However, unmanageable voids will exert negative impacts. The identification and mapping of mine voids spatially are imperative to give stakeholders ample information to construct viable mine voids management and benefit all stakeholders. In this research, Sentinel 2 satellite image data is used for land monitoring so that void can be mapped based on land cover classification. The land cover classification was carried out based on the Object-based Image Analysis (OBIA) method. This method has a good level of accuracy, ranging from 86.1 to 96.4%. Based on the land cover classification, potential voids are analyzed based on their shape, where potential voids have elongation values of 0.2- 1.0 and circularity of 0.1-0.8. In addition, potential voids are analyzed based on the location where they are found (referred to as the Mining License Area/WIUP data).
In 2018 there were 40 potential voids inside WIUP and 5 potential voids outside WIUP, while in 2020, 62 potential voids inside WIUP and 8 potential voids outside WIUP were identified in the study area.The final result of potential mine void, i.e.
mine void-1 could not further be distinguished between mine sumps, voids, or mine ponds without additional data and analysis. On the other hand, mine void-2 could not be further assigned as natural water bodies or mine void from illegal activities.
Subsequent studies using more elaborated data, processes, and analysis are important, to enhance the accuracy of void mapping using satellite images.
Keywords:
land cover mine void OBIA shape WIUP
To cite this article: Lestari, L., Kusuma, G.J., Badhurahman, A., Dwiki, S. and Gautama, R.S. 2023. Mine void identification using Object-based Image Analysis (OBIA) of satellite imagery Sentinel 2 data. Journal of Degraded and Mining Lands Management 10(2):4129-4142, doi:10.15243/jdmlm.2023.102.4129.
Introduction
Indonesia is the largest steam coal exporter and 4th largest coal producer, with proven reserves of coal in 6th place (BP, 2020). In 2019, 42% of those reserves were found in Kalimantan (Directorate General of Mineral and Coal, 2017; Hudaya and Madiutomo, 2019). Indonesian surface coal resources are four times bigger than underground resources (Ayuhati, 2018);
thus, open pit mining is more common than the
underground mining method. At the end of the life of the mine (LOM), a mine void could be formed. It is characterized as a mined-out depressed area that may be filled with water due to insufficient overburden material quantity during backfilling activities at the end of Life of Mine (LOM).
Mine void is legally accepted as one of mine closure options that is categorized as “Reklamasi Bentuk Lain”- a miscellaneous reclamation option.
This reclamation option is an alternative way to use the
Open Access 4130 post-mining area other than revegetation that can be
used for the benefit of the community, such as tourism areas, water resources, flood control, aquaculture, farm, and/or power plants (Ministry of Energy and Mineral Resources (2018). However, unmanageable voids will exert negative impacts. Therefore, existing mining voids need to be identified spatially to construct viable mine voids management and benefit all stakeholders.
Remote sensing is a method that can observe the condition of the earth's surface by utilizing the reflection or emission of surface materials through three sensors, namely optical sensors, thermal sensors, and radar sensors (Smith, 2012). Identification and mapping of mine voids as open areas can be made using the remote sensing method. It has the potential advantages of low cost and fast time in mapping mine voids (Soulard et al., 2016; Bürck, 2019; Werner et al., 2020). Optical sensors in remote sensing can distinguish objects on the earth's surface using different wavelengths (ultraviolet, visible, near- infrared, and thermal infrared) (Xie et al., 2008;
Hilker, 2017). One of the optical sensors which can be used to detect objects on the earth's surface is mounted in satellite Sentinel 2, and can be used for land monitoring and environmental planning (Heilman et al., 2002; Alberti, 2008).
The use of Sentinel 2 satellite imagery data in Indonesia to detect land cover changes has been widely carried out because it has a fairly good temporal resolution and wide area coverage (Awaliyan and Sulistyoadi, 2018; Heryadi and Miranda, 2020; Indarto et al., 2020; Arifin and Kartika, 2021). In addition, it is also utilized for large-scale and small-scale mining identification in the Amazon, Brazil (Lobo et al., 2018) and mapping the boundaries of mining areas in Amyntaio City, northwest Greece (Kotaridis and Lazaridou, 2021). However, studies on mine voids identification and mapping using Sentinel 2 image data have never been undertaken.
The initial step in mine voids mapping is land cover classification. There are several methods, one of which is spectral indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and MNDWI (Modified Normalized Difference Water Index). Spectral indices have several weaknesses, including the inability to distinguish different land cover classifications. For instance, NDVI and NDWI are often unable to distinguish grasslands and built-up areas due to those areas refracting similar artificial surface reflections (Szabó et al., 2016). Moreover, NDWI has a weakness in distinguishing built-up land and water objects which identified water bodies were often misidentified with built-up land, as many built-up land features possess positive values of the NDWI (Xu, 2006). Therefore, the spectral index cannot be utilized fully in land cover classification, so additional methods are needed to improve the quality of land cover class classification.
The Object-based Image Analysis (OBIA) method is an image classification approach that considers the spectral and spatial aspects of objects. It is a classification technique that observes the unity of objects based on the hue and texture of pixels. It has been widely used for land cover classification using Sentinel 2 image data and has an accuracy value of more than 80% (Csillik and Belgiu, 2017; Kaplan and Avdan, 2017; Marangoz et al., 2017; Gašparović and Jogun, 2018; Kolokoussis and Karathanassi, 2018). As a result, the method, alongside with shape and location parameters of land cover classification, was used for land cover classification in the study to attain a higher accuracy of void mining mapping.
This study aimed to explore the possibility of identification and mapping of mine void spatially using the OBIA of Sentinel 2 image data.
Materials and Methods Study area
This study was conducted in one of the sub-districts in East Kalimantan Province with a cover area of 221.29 km2 (Figure 1). Coal mines are extensively spread in this area, some of them are in active operation, and the others are in final condition; thus, the area is deemed appropriate for conducting the study. Satellite images of the area in the year 2018 and 2020 from the Copernicus Sentinel-2 Satellite launched by the European Space Agency were used by accessing and downloading freely through https://eartheexplorer.
usgs.gov/. Downloaded satellite images were in the form of Level 1C data, which were geometrically corrected and referenced into Universal Transverse Mercator or World Geodetic System 84 (UTM/WGS84). Values of each pixel were radiometrically processed into Top-of-Atmosphere (ToA) reflectance data (European Space Agency, 2015; Phiri et al., 2020). For further analysis, data pre- processing was needed to provide correct data in mine void mapping.
Methods
Methods applied in this study were data pre- processing, land cover classification using OBIA and mine void mapping. Each method is explained hereafter. The overall methods applied in this study are shown in Figure 2.
Data pre-processing
There were three stages of the pre-processing data such as radiometric correction, resampling and cloud masking.
Radiometric correction
The radiometric correction was applied to reduce disturbances caused by the sensor’s internal configuration and interaction of the electromagnetic spectrum with the atmosphere (European Space
Open Access 4131 Agency, 2015; Lamquin et al., 2019; Pancorbo et al.,
2021) and performed using the Sen2Cor280 plugin provided by Sentinel Application Platform (SNAP) launched by ESA/European Space Agency (Drusch et al., 2012; Lantzanakis et al., 2016; Gascon et al., 2017;
Main-Knorn et al., 2017; Pflug et al., 2020). The plugin only needs specific date acquisition of Sentinel 2 Satellite image as input, without entering other specific parameters such as type of sensors, height of sensors, climate, etc.
Figure 1. Study area.
Figure 2. The workflow of methods used in this study.
Open Access 4132 Resampling
Satellite Imaginary of Sentinel 2 consists of 13 bands with different spatial resolutions of 10 m, 20 m, and 60 m (Drusch et al., 2012; van der Meer et al., 2014;
Gascon et al., 2017). Resampling using the nearest neighbour (NN) method was applied to increase spatial resolution to 10-m resolution. The advantage of this method is a simple calculation and the ability to avoid any changes in values at any given pixels/point (Baboo and Devi, 2010; Porwal and Katiyar, 2014).
Cloud masking
Satellite imaging data containing a cloud-cover area of less than 10% is preferred in land cover monitoring activities (Zhu and Woodcock, 2014; Candra et al., 2016; Gómez-Chova et al., 2017; Lopes et al., 2020).
Satellite images containing a cloud cover of more than 10% could be enhanced using the cloud-masking approach. The cloud masking approach was made by detecting cloud and cloud shadow by calculating difference reflectance values (Hagolle et al., 2010;
Hollstein et al., 2016; Coluzzi et al., 2018) and removing those areas using the image analysis toolbar and the masking function provided by ArcGIS. At least two satellite images taken from closely-acquired time intervals and different cloud cover characteristics and areas were used to produce a compounded or stacked satellite image with a lesser cloud cover area than the parent satellite image (Sinabutar et al., 2020).
Land cover classification method using OBIA The land cover classification used the OBIA method.
It generally consists of two stages segmentation and classification.
Segmentation
Segmentation divides satellite images into homogenous, contiguous, and meaningful objects related to the appearance of the texture or spatial pattern of land cover (Castillejo-González et al., 2009;
Blaschke, 2010; Whiteside et al., 2011). In this study, the multiresolution segmentation method based on the Fractal Net Evolution Approach (FNEA) is applied (Baatz and Schäpe, 2000; Ouattara et al., 2011; Cote and Saeedi, 2014). FNEA classifies objects iteratively based on object dimension parameters, namely scale, shape, and compactness. These parameters are determined by various factors, including data type, the nature of satellite images and the range of land use classes (Espindola et al., 2006).
The trial-and-error method is often used to determine these parameters aiming for overall good object segmentation (Burnett and Blaschke, 2003;
Zhou et al., 2008; Blaschke, 2010). Shape and compactness parameters are valued between 0-1, whilst the scale parameter depends on the allowable heterogeneity of objects. The larger the scale value, the greater segments resulting from those objects. In this study, the shape value is set at 0.3 to detect smaller
objects. The colour parameter value is automatically set to 0.7 to further distinguish objects based on colour since all objects are mainly classified by the appearance (red-green-blue bands) of satellite images.
Image interpretation, classification process and class selection are much easier if the colour parameter is prioritized (Pei et al., 2017). The compactness parameter value is set to 0.7 to group similar and uniform objects. The scale parameter controls the size of segmentation; thus, a scale parameter value of 50 was chosen to identify and classify objects in a detailed manner.
Spectral indices classification
The classification process was subsequently carried out based on segmentation results, which utilized a rule-based classification method. This method aims to interpret images based on human experiences and transfer them into computer software/applications (Widayani, 2018). The classification process was arranged based on a two-level classification hierarchy.
Vegetation and non-vegetation were defined at the first hierarchical level. In the second hierarchy, the non- vegetation class was further classified into water bodies, open-land areas and built-up land areas.
Vegetation and non-vegetation were classified based on visible brightness and vegetation spectral index, namely the Normalized Different Vegetation Index (NDVI). NDVI was used since it is the most widely used and well-established method to compute vegetation index from satellite images to monitor global vegetation cover for the last two decades (Defries and Townshend, 1994; Jiang et al., 2006;
Verhoeven and Dedoussi, 2022).
The water body was identified based on the Normalized Different Water Index (NDWI), which is one of the most common methods for open water feature extraction from satellite images (Li and Sheng, 2008; Szabó et al., 2016). NDWI method has the disadvantage of not efficiently suppressing spectral indices on the built-up land area; thus, water body features still might be mixed with noises that come up from built-up lands. To minimize this drawback, new methods were proposed using a longer wavelength (Shortwave Infrared/SWIR) instead of NIR in the index’s equation, called modified normalized difference water index/MNDWI (Xu, 2006). Open land and built-up land exhibit spectral index values between water and vegetation. As a result, the ruleset was determined based on the spectral index and visible brightness for these two objects. Each spectral index is formulated as shown in Table 1.
Accuracy of land cover classification
The Kappa coefficient shows the accuracy value of land cover classification (Kvålseth, 2015) since this value represents prevalence and is time-sensitive to changes in prevalence (Congalton, 1991; Rwanga et al., 2017).
Open Access 4133 Table 1. Spectral index equation.
Spectral Indices Equation Author/Sources
NDVI
Normalized Difference Vegetation Index NIR − RED NIR + RED
(Defries and Townshend, 1994; Jiang et al., 2006; Szabó et al., 2016b) NDWI
Normalized Difference Water Index GREEN − NIR GREEN + NIR
(Li and Sheng, 2008; Szabó et al., 2016 MNDWI
Modified Normalised Difference Water Index
GREEN − SWIR GREEN + SWIR
(Szabó et al., 2016)
Kappa scores consist of 3 (three) categories, namely user accuracy, producer accuracy, and overall accuracy (Foody, 2002; Roberts et al., 2002; Kitada and Fukuyama, 2012; Lillesand et al., 2015). The Kappa coefficient was calculated based on the confusion matrix (Figure 3), which depicted a simple cross-tabulation of rows and columns showing a number of sample units between the classification results from remote sensing data and sample or reference data (Foody, 2002). Sample/reference data is acquired from random taking points of known objects RGB imaginary of Sentinel 2 Level 2 A.
Actual Class
A B C D ∑
Predicted Class
A nAA nAB nAC nAD nA+
B nBA nBB nBC nBD nB+
C nCA nCB nCC nCD nC+
D nDA nDB nDC nDD nD+
∑ n+A n+B n+C n+D n Figure 3. Confusion matrix.
The Kappa coefficient was formulated based on Figure 3 as follows:
User’s Accuracy = × 100 (1)
Producer’s Accuracy = × 100 (2)
Overall Accuracy =∑ n
n × 100 (3)
Kappa value =∑ n . ∑ n n
n . ∑ n n × 100 (4) Kappa value of more than 80% is rendered as sufficient in the accuracy of land cover classification.
Void mapping
The assessed land cover classification was then further analyzed for mapping the presence of mine voids. The first step was to evaluate all detected water bodies by their 2-dimension geometrical properties, i.e.
elongation and circularity. Elongation is defined as the ratio between the longest (major) and shortest
(minor/secondary) 1-dimensional (line) parameter of a shape. These two lines should usually be perpendicular to each other; on the other hand, circularity is the ratio of the area of a shape to the area of a circle that has the same circumference as the basin (Wentz, 2010; Jiao et al., 2012; Zygmunt et al., 2022). The range of elongation and circularity values is 0-1; if elongation is equal to 1 then it is square or circular, while if it is less than 1 it is elongated. If the circularity is equal to 1 then it is circular, whereas if it is less than 1 then it is non-circular. The elongation and circularity formulas used in this study are shown in Table 2.
Table 2. Formula elongation dan circularity.
Shape
Index Formula Author/ Source
Elongation L/L’ Jiao et al. (2012); Wentz (2010); Zygmunt et al.
(2022); Miller (1953) Shape
Index 4πA/P2
Note: A = Area; P = Perimeter; L = longest (major); L’ = shortest/secondary (minor).
The value of elongation and circularity for mine void is discussed more thereafter. The second step of void mapping was evaluating the water body based on location. In general, they are expected to be located in the Mining License Area (WIUP). Therefore, the results of the mine voids analysis based on the shape were overlaid with the WIUP which was obtained from the ESDM (Ministry of Energy and Mineral Resources) one map Indonesia website. ESDM one map Indonesia is a web-based information system capable of displaying various information on the thematic maps of the ESDM online (webGIS) that can be accessed at https://geoportal.esdm.go.id (Setyowati et al., 2018).
Results and Discussion Preprocessing
All satellite images from Sentinel 2 of the year 2018 and 2020 in this study area were firstly radiometric corrected and resampled into 10-m resolution. Sentinel 2 satellite images in the year 2018 have a cloud cover
Open Access 4134 of more than 10%, and thus cloud masking process was
implemented in order to provide stacked/compounded cloudless satellite images. Two satellite images acquired on 07 June 2018 and 31 August 2018 were used; nevertheless, all cloud cover could not be removed, as some cirrus clouds were still detected. The overall cloud cover was reduced; thus, previously cloud-covered objects could be identified and then image data can be used for the land cover classification mapping process and mine voids mapping. Cloud masking results can be seen in Figure 4. Satellite images of the year 2020 are covered by a cloud cover of less than 10%; thus cloud masking process was not required.
Land cover classification
The land cover classification results and the ruleset developed to classify each object are shown in Figure 4. Spectral indices values varied from -1 to +1. In this study, 4 land cover classifications were used, as described in Table 3.
Table 3. Land cover classification.
Classification Example of objects
Vegetation (1) Area vegetated, including sparse ly-vegetated areas, a
Built-up Land (2) Developed land area, buildings, road
Open Land (3) Bare ground, unvegetated area, dry mining area
Water (4) Lakes, streams, rivers and water -filled mine voids
A water body is represented with a negative NDVI value due to electromagnetic wave absorption by water (Defries and Townshend, 1994). Vegetation has relatively low reflectance in the red and blue wavelengths with minor peaks in the green spectrum since vegetation absorbs energy at the beforementioned wavelengths. Red wavelength is absorbed by vegetation to be used for photosynthetic activity in leaves (Mather and Koch, 2010). Water produces positive NDWI and MNDWI values; on the other hand, less than zero NDWI and MNDWI values related to other land covers (vegetation, soil, etc.) (Li and Sheng, 2008; Szabó et al., 2016). The water body reflects almost none of the infrared wavelengths (NIR and SWIR) because almost all of the energy at these wavelengths is absorbed by the hydroxyl bond of water (Mather and Koch, 2010). Open land and built-up land have indices’ values between those values of vegetation and water. From the above-mentioned descriptions, spectral indices classification/ruleset for each object/land cover classification needs to be generated. The ruleset of land cover classification in 2018 and 2020 produced different threshold values for each spectral index and brightness value. This resulted from the different quality of the image data utilized since satellite images of the year 2018 have more cloud
cover than those of the year 2020, and yet cloud cover could not be removed completely by cloud masking, especially those of thin-layer of clouds (Figure 5).
Lillesand et al. (2015) showed that the reflection of an object and another object is never the same; even the spectral reflection of a tree of the same species is not exactly identical. Therefore, the range of spectral index values for each object/land cover classification in the years 2018 and 2020 is different (Figure 5 and Figure 6).
Accuracy assessment
The accuracy assessment using confusion matrices of the land cover classification in 2018 and 2020 are shown in Table 4 and Table 5. Based on the confusion matrices, the land cover classification in 2020 had a higher Kappa coefficient value than the land cover classification in 2018. Land cover classification in 2018 showed the best accuracy value of the vegetation class (user accuracy value was 95.8% and producer accuracy was 97.3%); on the other hand, the open land class gave the lowest user accuracy of 70.3%, and built-up land had the lowest producer accuracy 70.7%.
This is due to the misinterpretation between built-up land and open land, which is caused by the spectral index values of open land and built-up land similar and overlapping each other.
Water body shows moderate user accuracy of 89.4%. Land cover classification in 2020 gives better accuracy of more than 90% in user accuracy and producer accuracy for all land cover classes. The lower accuracy of land cover classification in 2018 is due to the thin layer of clouds; nevertheless, the overall accuracy value is more than 85%. According to Sim and Wright (2005), Kerr et al. (2015a; 2015b), the percentage of the Kappa coefficient of 80%-99%
belongs to great accuracy, and the minimum accuracy value that can be accepted by land cover classification is 85%. Thus, the results of land cover classification for the years 2018 and 2020 can be used to analyze the mapping of mine voids in the study area.
Mapping void
Mine voids are the mined-out depressed area that may be filled with water; thus, in this study, mine voids were identified as water bodies. Land cover classification of mine water in the year 2018 and 2019 was extracted and used as input for subsequent analysis. The land cover classification by the OBIA method was operated based on the segmentation result.
In Sentinel 2 image data, water bodies smaller than 1 hectare cannot be segmented properly according to the geometrical outline of the object, whilst water bodies more than 1 hectare can be categorized or segmented properly, as shown in Figure 7a. This result is due to the spatial resolution of the Sentinel 2 satellite image, which limits the segmentation process for objects with areas less than 1 hectare.
Open Access 4135 Figure 4. Cloud masking result (Sentinel-2 07 June 2018 and 31 August 2018).
Figure 5. Land cover classification using OBIA of the year 2018.
Open Access 4136 Figure 6. Land cover classification using OBIA of the year 2020.
Table 4. Confusion matrix of land cover classification using OBIA of year 2018.
Classification OBIA PA (%) UA (%) OA (%) K (%)
1 2 3 4 Total
Vegetation(1) 575 5 20 0 600 97.3 95.8
90 86.1
Built-up Land (2) 14 211 75 0 300 94.2 70.3
Open Land (3) 0 5 295 0 300 70.7 98.3
Water (4) 2 3 27 270 302 100 89.4
Total 591 224 417 270 1502
Note: PA = Producer’s Accuracy UA = User’s Accuracy; OA = Overall Accuracy; K = Kappa value.
Table 5. Confusion matrix of land cover classification using OBIA of year 2020.
Classification OBIA (2020) PA (%) UA (%) OA (%) K (%)
1 2 3 4 Total
Vegetation (1) 595 5 0 0 600 98.5 99.2
97.4 96.4
Built-up Land (2) 8 276 16 0 300 97.5 92
Open Land (3) 0 1 299 0 300 92.9 99.7
Water (4) 1 1 7 291 300 100 97
Total 604 283 322 291 1500
Note: PA = Producer’s Accuracy UA = User’s Accuracy; OA = Overall Accuracy; K = Kappa value.
Based on statistics of segmentation area of water bodies shown in Figure 7b, it is evident that 14.24% of segmentation was less than 1 hectare in area, and most of the segmentation areas varied from 1.00-2.50 ha (37.31% of segmentation area). Thus for mapping void, a segmentation area of more than 1 ha was used since the water body could be segmented properly and most of the water body detected (85.76% of water body segment) were more than 1 ha in area. Water bodies with areas of more than 1 hectare were further classified as potential mine voids or river/riverine by their geometry. Potential mine voids are characterized as the mine-out area (pits) of the coal mine, which extends along the strike of coal seams and expands towards the dip of the coal seam. These characteristics
result in relatively elongated shapes. In contrast to potential mine voids, rivers are characterized by greater elongated and curving shapes. The form of potential voids and rivers was distinguished from each other based on the elongation and circularity values (Table 2).
Figure 8 shows an example of a calculation for elongation and circularity values. Elongation is the ratio between the length and width of an object, while circularity is calculated as the ratio of area to the perimeter in order to determine whether the object is circular or non-circular. Based on elongation and circularity, water bodies >1 ha were divided into 3 categories, namely, potential voids, rivers, and other water bodies (Table 6 and Figure 9).
Open Access 4137
(a) (b)
Figure 7. Area of object segmentation (a) water body area <1 ha and >1 ha; (b) histogram of the average area of segmentation of water body.
Figure 8. Shape analysis of potential water body.
37.31%
14.24%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
5%
10%
15%
20%
25%
30%
35%
40%
0.05 0.10 0.20 0.50 1.00 2.50 5.00 10.00 Cummulative Frequency (%)
Frequency (%)
Segmentation Area (hectares)
Open Access 4138 Other water bodies are defined as objects that cannot
be classified based on elongation and circularity. This problem results from the resampling and segmentation process, which breaks linear and continuous objects, such as rivers and roads, into discontinuous objects. To resolve this problem, additional classification was done by examining the position of objects. Objects are classified as rivers if the objects are located between pre-classified river objects; on the other hand, objects are included in potential mine voids if the objects are located inside open land areas based on land cover classification (Figure 10). The previously analyzed potential voids were categorized further based on their location using the WIUP map. Using WIUP, potential voids were divided into 2, namely potential void-1 (potential voids inside WIUP/from legal mining) and potential void-2 (potential voids outside WIUP) (Figure 10). In 2018, 40 potential voids 1 and 5 potential voids-2 were identified, whilst in 2020, 62 potential voids-1 and 8 potential voids-2 were identified. It can be concluded that the number of potential voids both inside and outside WIUP has increased. The potential void-1 increased by 12 potential voids, while the potential for void 2 increased
by 3 potential voids in two years. This is because the study was not able to identify mine voids with an area
<1 ha, where usually, the potential voids outside the WIUP have a smaller area than inside the WIUP. The final result of potential mine void, i.e., mine void-1 could not further be distinguished between mine sumps, voids, or mine ponds without additional data and analysis. On the other hand, mine void-2 could not be further assigned as a natural water body or mine void from illegal activities. Subsequent studies using more elaborated data, processes, and analysis are important, to enhance the accuracy of void mapping using satellite images.
Table 6. Water bodies categories.
Categories Description Potential Void elongation 0.2-1
circularity 0.1-0.8.
River elongation <0.2 circularity <0.1 Other water
bodies elongation <0.2, and circularity
>0.1 or elongation >0.2, and circularity <0.1.
Figure 9. Void analysis based on shape/geometry.
Open Access 4139 Figure 10. The distribution map for potential void-1 and potential void-2 for the years 2018 (upper) and 2020
(lower) (blue areas cover potential void-1, red areas cover potential void-2, yellow polylines denote WIUP).
Open Access 4140 Conclusion
Based on the data and analysis above-mentioned, it can be concluded that void mapping using the OBIA method by Sentinel 2 satellite imaginary data yields good accuracy. The shape of object classification further shows that potential voids have elongation values of 0.2-1 and circularity of 0.1-0.8. WIUP polygons are further classifying the voids into two classes, namely potential void-1 (potential voids originating inside WIUP) and potential voids-2 (potential voids outside WIUP), yet obstacles are still found, including; the unable to identify voids with an area <1 ha. The final result of potential mine void, i.e., mine void-1 could not further be distinguished between mine sumps, voids, or mine ponds without additional data and analysis. On the other hand, mine void-2 could not be further assigned as a natural water body or mine void from illegal activities. Subsequent studies using more elaborated data, processes, and analysis are important, to enhance the accuracy of void mapping using satellite images.
Acknowledgements
The authors would like to thank the Ministry of Education, Culture, Technology and Research for financially supporting this study through P3MI Scheme-Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung. The authors would also like to thank all parties who helped during the data acquisition, processing and manuscript preparation.
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