Volume 9, Number 4 (July 2022):3695-3702, doi:10.15243/jdmlm.2022.094.3695 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 3695 Research Article
Development of a land stability index for land damage assessment: the case of a nickel mine, North Konawe, Indonesia
Baba Barus1,2*, Suria Darma Tarigan1, Reni Kusumo Tejo1, Yuri Ardhya Stanny2
1 Department of Soil Science and Land Resource, IPB University, Bogor 16680, Indonesia
2 Center of Regional Development and Planning, IPB University, Bogor 16127, Indonesia
*corresponding author: [email protected]
Abstract Article history:
Received 13 April 2022 Accepted 21 May 2022 Published 1 July 2022
Assessment of land damage has been included in several Indonesian government policies, but it tends to have zero dimensions or only one point in the year. This study tried to propose an inter-time assessment of land damage with a land stability index by including the development of knowledge and technology at nickel mining sites in the easternmost part of North Konawe Regency.
Orthophoto and Digital Elevation Model (DEM) images from drones in 2020 were taken in a field survey and used as primary data. While the SPOT 5 Imagery in 2014 and National DEM were collected as secondary data. The developed method combining slope, soil, TRI (Terrain Ruggedness Index), and land cover factor has been considered moderately accurate. Applying the method between different periods has produced a temporal land stability index where a positive value means more unstable, zero means unchanged, and a negative value means more stable. The results showed that after six years, the largest area due to nickel mining in the area has not changed much or had zero value. This is because the area tends to remain a natural forest. The more stable area is located in the southern part of the study site. However, the increasingly unstable area is located in the northern part of the study site. If no reclamation action is taken, the potential for further damage will occur.
Keywords:
DEM ore orthophoto slope TRI
To cite this article: Barus, B., Tarigan, S.D., Tejo, R.K. and Stanny, Y.A. 2022. Development of a land stability index for land damage assessment: the case of a nickel mine, North Konawe, Indonesia. Journal of Degraded and Mining Lands Management 9(4):3695-3702, doi:10.15243/jdmlm.2022.094.3695.
Introduction
Ecosystem degradation can be caused by an imbalance between human and technological interactions with natural life, such as construction, development, and exploitation (Sholihah and Sjarmidi, 2014; Maurya et al., 2020; Mentis, 2020). Exploitation in mining areas has caused a number of social conflicts, changes in the physicochemical and biological environment, and severe ecological problems, namely soil fertility destruction or extensive soil damage, fauna displacement, vegetation suppression, and triggers erosion and landslides (Sheoran et al., 2010; Kumar, 2013; Herdiansyah et al., 2018; Nadalia and Pulunggono, 2020; Ramadhan et al., 2020; Hengkai et al., 2020; Wang et al., 2021; Martins et al., 2022). The destruction of soil fertility leads to land damage. Based
on Republic of Indonesia Law No. 37 of 2014 regarding Soil and Water Conservation, land damage can be defined as land that can no longer function as a production medium to grow cultivated or non- cultivated plants.
Although mining provides a large economic contribution to the resource-producing country, land exploitation due to mining can occur and refer to land damage (Mensah et al., 2015; Okyere et al., 2021).
Constraints experienced by the soil at mining sites include low organic matter content, poor pH, and low water holding capacity (Kumar, 2013). Prematuri et al.
(2020) added that post-nickel mining soils have lower total N, total carbon (TC), available P, cation exchange capacity (CEC), exchangeable Ca, and Na compared to natural forest soils. While the surrounding environment at mining sites will have subsidences,
Open Access 3696 deformations, the forced flow of surface and
underground water, emission of soil waste, water, and air pollution (Vu et al., 2012; Ignacy, 2021).
Concerning the land damage caused by mining, land damage assessment can be done by identifying degraded land using IRS-IB satellite data as well as research conducted by Soni and Loveson (2003). Other researchers conducted an assessment of the soil quality index on the reclaimed land using exploratory methods and field surveys (Noviyanto et al., 2017). Besides that, a land damage assessment has already existed in Indonesia's government policies, such as the Regulation of State Minister of Environment No. 7 of 2006 regarding Measurement Procedure for Standard Criteria of Soil Damage for Biomass Production tends to be based on the zero dimension (point). The standard criteria used to determine the status of soil degradation for biomass production are based on the key parameters of the basic properties of the soil, which include physical properties, chemical properties, and biological properties of the soil. This land damage assessment should be in line with the development of knowledge and technology in two or three dimensional. The use of sensing technology such as drones will produce more detailed and up-to-date assessment results, making it easier to access public data. Based on the reason for updating the existing assessment, a new measure is needed in the land
damage assessment based on-time performance, so a land stability index is proposed. Hence, this study aimed to develop a land stability index for land damage assessment on nickel mining areas in the easternmost part of North Konawe Regency, as a case study.
Materials and Methods Research location and time
The research (AOI) was conducted in the location of mining areas in the easternmost part of North Konawe, one of the regencies in Southeast Sulawesi Province, Indonesia. Astronomically, North Konawe Regency is located between 02°97' and 03°86' South Latitude and 121°49' and 122°49' East Longitude. The geographical boundaries of North Konawe Regency are North- Morowali Regency and Konawe Regency, East- Morowali Regency and Banda Sea, South - Konawe Regency, and West - Konawe Regency and North Kolaka Regency. Orthophoto images and Digital Elevation Model (DEM) images from drones were taken in a field survey in December 2020 and used as primary data. While the SPOT 5 Imagery in 2014 and National DEM were collected as secondary data. All the data were then analyzed in the Laboratory of Remote Sensing and Spatial Information, Department of Soil Science and Land Resource, IPB University.
Figure 1. Research location.
Method of analysis Land cover analysis
Orthophoto images in 2020 and SPOT 5 images in 2014 were manually digitized to obtain a land cover map. Year selection is based on the most visible changes. The 2020 land cover map consists of three main classes, namely natural forest, non-natural forest (disturbed forest), and open land. Furthermore, open
land is divided into roads, open land, concave open land, and land clearing, which is defined as open land where there are still many pieces of wood from felling trees (forest clearing). Hence, six classes were identified in the 2020 land cover map. While the 2014 land cover map consists of two classes, namely forest and open land. These land cover maps, both 2014 and 2020, were used to identify soil conditions, where natural forest and non-natural forest are categorized as
Open Access 3697 original land (non-fill soil). Open land around the "ore"
sample location is categorized as stockpiled soil because there has been a mixture of soil from the surrounding location. Meanwhile, other open lands are categorized as having original land (not stockpiles).
Terrain Ruggedness Index (TRI) analysis
Assessment of land surface roughness can be assessed using the Terrain Ruggedness Index (TRI) which can provide a fast and objective measure of terrain heterogeneity. TRI is derived from the USGS digital elevation model (DEM) using the terrain analysis function implemented in a geographic information system (GIS) (Riley et al., 1999). In this analysis, TRI
can be defined as the difference between the pixel value and the average of the 8 pixels around it.
DEMNas and DEM data from drones were used to analyze TRI. Processing of maximum focal statistics and minimum focal statistics with a window size of 3x3 in ArcGIS software was conducted to generate TRI. Then, TRI results were classified into seven classes using natural breaks in ArcGIS, namely level (flat), nearly level (wavy), slightly rugged, intermediate rugged, moderately rugged, highly rugged, and extremely rugged. TRI calculation using the formula (1).
SquareRoot(Abs((Square(“3x3 max”) – Square(“3x3 min”)))) (1) Land stability analysis
Land stability is analyzed using land-related factors, one of which is the slope. Slope instability can be explained as a resulting product of local geomorphic, hydrological, and geological conditions that are influenced by several natural processes such as geodynamic processes, vegetation, rainfall frequency and intensity, as well as human interactions and activities such as land use practices (Soeters and Westen, 1984). Baharuddin et al. (2016) noted that the method of slope instability uses spatial data such as geology, topography, land use, slope, and elevation.
In this paper, there are four main criteria for land stability index, namely slope, TRI, soil, and land cover land. The data are obtained from the analysis using DEMNas and DEM data from drones. The analytical process used to obtain the land stability index is the weighted sum, which is one of the simplest and most easily applicable methods of multi-criteria decision- making (Kolios et al., 2016). Multi-criteria decision was chosen based on traceable logic and offers a large selection of methods and pixels, and produces a good display. The weighted sum method in this study uses the formula (2).
35%* Slope + 30%* TRI + 20%* Soil + 15%* Land cover (2) The percentage value in each criterion in the formula
reflects the amount of its weight. Besides, every criterion has a score. All criteria used in this analysis are presented in Table 1.
Table 1. Land stability criteria.
No Criteria Weight Score
1 Slope
35
0-3% 0
3-8% 1
8- 15% 2
15-30% 3
30-45% 4
>45% 5
2 TRI (Terrain Ruggedness Index)
30
Level 0
Nearly level 0
Slightly Rugged 1
Intermediate rugged 2
Moderately rugged 3
Highly rugged 4
Extremely rugged 5
3 Soil
Non-fill/native land (forest) 20 1
Land not embankment/original (open land) 3
Landfill (open land around ore) 5
4 Landcover
Forest (natural/non natural) 15 1
Open land (land clearing-there are still logs left) 3
Open land (roads, open land, concave open land) 5
Open Access 3698 The result of the weighted sum method is still a certain
value; then all these values are classified into five classes using natural breaks in ArcGIS. The results of the classification are then subtracted between the 2020
and 2014 maps to validate or determine the level of change in land stability. The subtraction process is carried out in ArcGIS using a map calculator from a spatial analyst using the formula (3).
2020 land stability map - 2014 land stability map (3) The result of this formula will describe: (a) Positive
value indicates the changes that occur are unstable; (b) Negative value indicates that the changes that occur
are stable, and (c) Value of 0 indicates no change (conditions remain the same). All analytical procedures in this study are presented in Figure 2.
Figure 2. Research analysis procedures.
Results and Discussion
Nickel mining has been carried out in the research site for a long time. This does not rule out the possibility that the land is damaged. The process of digitizing and analyzing land cover maps, slopes, and TRI resulted in a map of the land stability index for 2020 and 2014, which will eventually map the stability of the land at
the research site. The land stability index in Figure 3 consisting of 5 classes, namely 1 (very stable), 2 (stable), 3 (moderate), 4 (unstable), and 5 (very unstable). The five classes are marked with a green to red color. The redder the color, the more unstable the land. Besides, the yellow dots indicate the nickel ore.
While the area of each class of land stability index in 2014 and 2020 is presented in Table 2.
Table 2. Area of land stability index in 2014 and 2020
Class Index Description 2014 2020
Area (ha) (%) Area (ha) (%)
1 Very stable 337.99 19.85 316.58 18.53
2 Stable 575.74 33.81 523.76 30.66
3 Moderate 307.06 18.03 528.32 30.93
4 Unstable 342.93 20.14 285.90 16.74
5 Very unstable 139.21 8.17 53.64 3.14
Total 1,702.95 100.00 1,708.20 100.00
The land stability index in 2014 was dominated by light green color, which is class 2 (Figure 3), with an area of 575.74 ha or 33.81% of total areas (Table 2).
While other classes, namely classes 1, 3, and 4 were
almost evenly distributed, with the percentage of the area being 19.85%, 18.03%, and 20.14%, respectively (Table 2). Very stable land can be interpreted as soil that has stable aggregates and soil structure produced
Open Access 3699 from certain types of clay and organic matter so that
erosion and land damage are minimal and create a good physical environment (Harahap et al., 2012). The more unstable soil is, the more vulnerable it is to erosion which will occur in the next 1-2 years (Hermon, 2019).
After six years, the land stability index of the nickel mining areas in the research site changed. This change is caused by the mining process by stripping and excavating the topsoil, which leads to land damage (Erthalia et al., 2018). It can also be seen from Table 2 that the land stability class was originally dominated by the stable class in 2014 to become dominant in class 3 or the moderate class in 2020. The area of class 3 in 2020 is 528.32 ha or 30.93% of total areas. From the color map in Figure 3, the dominant color of the land stability index in 2020 is light yellow. However, the
area of class 5 in 2020 is less than in 2014, which is 53.64 ha (Table 2). It can be interpreted that after six years, the area of the very unstable class is getting smaller in 2020 so that it can reduce the chance of land damage that occurs in 2020. However, soils that were already in the very stable class in 2014 decreased in 2020. So maintenance is needed to keep land stability and reduce the impact of land damage.
After obtaining a land stability index, the next important step is to perform an accuracy test or validation data. The approach using ore is an operational form to test the accuracy of the method.
The amount of ore is obtained from field surveys which are generally on stable land. Then the ores in each land stability index are calculated as a percentage of the total number of ores available. The validation results are presented in Table 3.
Figure 3. Land stability index of nickel mining areas in 2014 (a) and 2020 (b) in North Konawe.
Open Access 3700 Table 3. Validation results.
Class Index Description 2014 2020
Number of Ore % Ore Number of Ore % Ore
1 Very stable 80 16.70 2 0.42
2 Stable 139 29.02 46 9.58
3 Moderate 137 28.60 187 38.96
4 Unstable 52 10.86 197 41.04
5 Very unstable 71 14.82 48 10.00
Total 480 100.00 479 100.00
Table 3 shows that the number of ores in the first class (very stable) in 2020 was reduced from 2014, namely from 80 ores to 2 ores. The highest number of ore was in classes 3 and 4 in 2020, while in 2014, the most were in classes 2 and 3 (Table 3). The percentage of ore is very stable until the moderate class varies between 50 - 75%. This shows the assumption that the ore placed in the dominant field is on a stable land which means the developed method has moderate accuracy. The comparison of land stability maps for 2014 and 2020 was used to assess performance. The more the same and stable values, the better the land management after
mining. Meanwhile, the difference shows that there is a change in land stability. The changes in the land stability index between 2014 and 2020 are shown in Figure 4, following the areas of each description in Table 4. The results show that the area which has become more stable after nickel mining for a period of 6 years is located in the southern part of the research location (Figure 4). It covers 465.19 ha or 27.38% of the total areas (Table 4). The most dominating area is the area with a value of 0, which means that the area has not changed over six years and it tends to be natural forest cover. The total area is 934.98 ha (Table 4).
Table 4. Area of land stability index.
Value Description Changes between 2020 and 2014
Area (ha) (%)
Negative (-) More stable 465.19 27.38
0 Unchanged 934.98 55.03
Positive (+) More unstable 298.79 17.59
Total 1,698.96 100.00
Figure 4. Changes of land instability index of nickel mines in North Konawe.
Apart from these two criteria, the next area is a more unstable criterion which covers 17.59% of the total area or about 298.79 ha (Table 4). This unstable area is an indication of continuous mineral exploitation which will have implications for easy erosion and
landslides. This is supported by the statement of (Xiao et al., 2014) that the continuous exploitation of minerals can cause land and ecological environment destruction such as land subsidence, solid waste, and geological disasters. The amount of sediment on the
Open Access 3701 beach is a real example in the field, which shows the
land is unstable. In general, the more unstable area is located in the north of the research site, which is closer to the shoreline, while further south, the area is getting closer to stability.
Conclusion
Land stability models can be developed easily because of the availability of easily accessible public data such as DEMnas data and satellite imagery. The process of testing the land stability index in this study carried out at the nickel mining site in North Konawe was going well, but it can still be improved and modified by adding other parameters such as soil depth. The measure of land stability in this study can also be used as a new parameter in the assessment because it can quantify changes in the index over time. From the results of the study using the developed method, it is evident that at the North Konawe nickel mining location, there are more unstable areas after additional activity. If no reclamation action is taken, the potential for further damage will occur.
Acknowledgements
The authors would like to express their profound gratitude to the Ministry of Environment and Forestry (KLHK) and the Criminal Investigation Agency of the Indonesian National Police (Bareskrim Polri), who have facilitated the activities and writing of this research.
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