Using Carbon Sequestration as a Remote-Monitoring Approach for Reclamation’s Effectiveness in the Open Pit Coal Mine: A Case Study of Mae Moh, Thailand

Abstract

Reclamation is regarded as one of the mining processes that can lessen the environmental impact of its production, particularly for large-scale coal mines that emit significant quantities of greenhouse gases. However, the assessment and evaluation of the reclamation process primarily rely on qualitative methods. Utilizing LANSAT8 Operational Land Imager (OLI) remote sensing in conjunction with GIS, this study aimed to develop a quantitative method for validating the efficacy of a reclamation procedure applicable to the emerging trend of carbon reduction. The empirical formula utilized to compute the annual carbon sequestrations of the reclamation area in the Mae Moh mine exhibited the appropriate spatial relative standard deviation (S-RSD) at 98.25%. The findings indicate that the reclamation area reached its highest level of carbon sequestration in 2022, at 331.28 ± 11.89 ktCO₂e, surpassing the baseline of 126.53 ktCO₂e. Furthermore, the approach demonstrates significant potential in improving the standard method for assessing reclamation through reforestation.

1. Introduction

At present, coal production mainly supports electric power, which is consumed on a global scale. It is anticipated that global coal usage for electricity generation will decline to 22% by 2040, except in Southeast Asia, where it will remain the predominant fuel type at 39% [1]. In Thailand, approximately 33% of the overall electricity production is supplied by the Electricity Generating Authority of Thailand (EGAT), which utilizes lignite coal as its source material [2].

The Mae Moh mine (hereafter: MMM) is presently the most expansive open pit mine in Thailand, encompassing an approximate total mining area of 37.5 km² and a substantial disposal area of 41.5 km². The alteration of geographical characteristics, land use, and land cover within the expansive mining region has resulted in various environmental repercussions on safety, biodiversity, and ecological processes [3]. In order to address the existing consequences and proactively avert any adverse outcomes, MMM is committed to implementing reclamation efforts concurrently with mining operations over the entirety of the mine’s lifecycle, with the objective of establishing an environmentally sustainable upstream industrial process.

The reclamation of mines poses a significant environmental concern in the context of coal mining operations. In order to address the issues at hand, MMM has produced a comprehensive mine reclamation master plan, which was first established in 1981 and has been consistently implemented to this day. Currently, MMM employs the principles of forestry inventory to analyze the field data. The permanent sample plots have been stratified randomly based on the years of plantation, ranging from 1982 to 2021. A total of 140 plots are distributed throughout the eastern and western regions of the afforestation area in the MMM dumping area. Consequently, a greater number of areas need to be reclaimed within the designated time frame, requiring increased manpower and efficient processing time to handle the substantial volume of primary and secondary data.

The assessment of tree growth in mine reclamation areas serves as an indicator of the effectiveness of this reclamation approach. However, the process of monitoring tree growth in large open mines and dumping areas is resource-intensive, requiring significant investments of time, labor, and financial resources [2]. According to the study of McKenna et al., the utilization of remote sensing as opposed to field inventory has been proposed as a successful approach, complemented by systematic field observation [4]. The monitoring and comparison of afforestation and tree health in mine reclamation areas can be achieved by the utilization of satellite pictures and the calculation of the Normalized Difference Vegetation Index (NDVI) at various time intervals. However, it should be noted that relying just on NDVI may not provide a comprehensive assessment of the overall effectiveness of the reclamation process.

Forest productivity refers to the growth of forests’ capacity to mitigate carbon dioxide (CO₂) emissions in the atmosphere via their physiological processes. The assessment of forest productivity mostly pertains to the measuring of biomass in the form of potential sequestration of CO₂. Mine reclamation, mostly related to reforestation, is also the potential area that can absorb carbon in many forms, including plants, soil organic matter (SOM), and litter [5]. According to this characteristic, the account of carbon sequestration in the area can be one of the alternatives that can be used as the monitoring tools for the effectiveness of reclamation procedures; however, direct measurement of carbon sequestration for the vast area is merely possible.

Contemporary methodologies in remote sensing and integrated spatial analysis are crucial in harnessing vital data pertaining to topography, land area, and existing vegetation. The method has the capacity to evaluate the extent of vegetation restoration, therefore offering a notable opportunity to better understand mine site reclamation and its potential for carbon sequestration. In the past few decades, there has been an increasing adoption of remote sensing methods for the purpose of detecting carbon sequestration and mine revegetation [6].

The research conducted by Badreldin N. and Sanchez-Azofeifa demonstrates the application of an integrated spatial analysis approach, using LIDAR and LANDSAT data, to estimate the temporal changes in forest biomass inside a mining zone in Canada. It suggests that the method effectively demonstrates the measurable increase in biomass within the reforestation area [7].

Furthermore, this approach can also serve as a crucial tool for assessing the carbon sequestration potential in diverse reforestation areas, including post-mining sites. Thiteja et al. employed a spatial regression model to monitor the carbon storage levels of plantation forests in the reclaimed area located in the Northern Region of Thailand. The findings suggest that the reforestation efforts have resulted in a significant increase in aboveground carbon storage (AGV) biomass, reaching 75.55% of the biomass found in the nearby natural forest after a period of 18 years [8]. Yang et al. integrated field data and remote sensing techniques to generate maps that depict the landscape at the tree level. The researchers found that carbon storage exhibits an increase during the initial and stable stages but drops throughout the development stage, characterized by mining expansion and reclamation activities [9].

The evaluation of carbon sequestration via remote sensing, on the basis of its capabilities and technical support, may be one of the instruments that prioritize mine reclamation monitoring in the form of quantifiable data while reducing the investment and consumption of mine resources. The objective of this research was to utilize LANDSAT8 OLI remote sensing to illustrate the forest productivity and effectiveness of the MMM reclamation area.

To achieve this objective, ten empirical formulas pertaining to the capabilities of remotely analyzing aboveground carbon storage (AGC) are compared using the spatial relative standard deviation (S-RSD) of their results from the satellite image of the MMM in 2023. The formula, which contains the highest suitability in reflecting the complexity in the study area, was additionally utilized to calculate yearly carbon sequestration in the reclamation area of MMM during the peak growth every August from 2013 to 2023. The carbon sequestration in the area is analyzed to determine their baseline and is compared to the calculation’s results for retrieving the effectiveness of their reclamation procedures.

2. Materials and Methods

Figure 1 presents a graphical illustration of the comprehensive methodologies employed in the present study. The collection and analysis of field survey data establish the boundaries of the study area. By conducting a review of the relevant literature, it is possible to ascertain which estimation formulas are suitable for delineating the AGC in the current investigation. The equation is chosen based on its strong correlation with the reforestation of mines in Thailand concerning geographical, ecological, and topographical aspects. The spatial accuracy of these equations is assessed through a comparative analysis utilizing spatial statistics in a GIS, with the images obtained from LANDSAT8 OLI. In order to ascertain the AGC calculation for reforestation in MMM, the formula with the appropriate spatial precision was chosen. The forest productivity, as measured by carbon sequestration, was subsequently computed utilizing this equation and satellite imagery captured during the peak of vegetation growth in August of each year between 2013 and 2023 [10]. An assessment is conducted on the productivity of each monitoring year through a comparison with the baseline value of the monitoring area.

2.1. Site Description

The Mae Moh mine, located in Lampang, Thailand, is operated under the opencast method; the operation area of the mine consists of 37.5 km² of operating area and approximately 41.5 km² for the outside overburden dumping, as can be followed in Figure 2. The study area is located in the northeastern region of the mine, where reforestation has been conducted since the 2010s. The area contains various divisions of species, including giant crepe-myrtle, Indian cork tree, laza wood, teak, mixed shrub, and grassland.

2.2. Data Acquisition and Preparation

The data were processed and organized to serve as input for subsequent analysis within a geographic information system (GIS) application. The satellite imagery with a resolution of 30 m from the LANDSAT 8 OLI was acquired from the United States Geological Survey (USGS). The aforementioned photographs were carefully chosen and captured during the month of August, spanning from 2013 to 2023. The requirements were carefully managed to capture the period of maximum vegetation growth accurately. Additionally, a cloud coverage threshold of less than 20% was implemented to eliminate any potential disruptions in the computation process.

2.3. Satellite Image Analysis

In this work, the utilization of the Normalized Difference Vegetation Index (NDVI), a widely employed metric for monitoring forestry and vegetation, was explored. The calculation of NDVI using satellite imagery is based on Equation (1).

NDVI = NIR − R/NIR + R

(1)

The channels or bands of LANDSAT8 OLI, denoted as the NIR and R, correspond to the reflectance of the near-infrared wave (λ = 0.85–0.88 um; BAND 5) and visible red wave regions of reflectance, respectively. The factor is determined based on satellite imagery captured in August of 2023. The image that was analyzed and acquired as a raster layer is subsequently assigned for calculation within the geographic information system (GIS). The analysis must yield a number within the range of −1 to 1. Given the primary focus of this work on carbon sequestration resulting from active mine reforestation, the inputs for subsequent calculation were limited to areas where the Normalized Difference Vegetation Index (NDVI) was over 0.2, indicating the presence of vegetation [11].

2.4. Selection of Estimation’s Formula

The selection of an appropriate empirical methodology for measuring carbon sequestration in the reclamation area of MMM relies on the ecological factors and the specific species chosen for afforestation. Carbon sequestration estimation can be accomplished by utilizing certain formulas associated with remote sensing techniques. This study aims to assess and compare 10 NDVI-based formulas that have been previously published in the scholarly literature. These formulas are specifically relevant to reforestation initiatives in Thailand and similar climate zones. The formulas are used in combination with a predetermined NDVI layer to calculate the layers for assessment. Every formula in the studied field has both benefits and restrictions. Therefore, Table 1 provides an overview of their qualities, while Table 2 demonstrates the comparison between their advantages and limitations in application with the MMM area.

To specify the appropriate formula for the estimation, the formulation choices in this research are selected based on parameters that can support and control the similar biodiversity and species of the plant: mean temperature, mean intensity of rainfall, prevailing vegetation type, and altitude. The coefficient of determination, or R², of each formula is corporately considered to investigate their goodness when applying the formula to their data [12]. X refers to NDVI from the remote sensing analysis, while Y is the results in terms of aboveground carbon stock (${AGC}_i$) with a unit of stock ton/ha/cell.

Table 1. Significant characteristics of the selected formula used in the estimation of reforestation in Thailand.

Eq.	Formula	Region	Average Temperature (°C)	Altitude (MSL)	Average Rainfall Intensity (mm)	Vegetation Type	Source
The Properties of the Study Are in MMM		Thailand	28	340	1100	Deciduous Forest with Agroforestry
(2)	y = 12.019x − 8.6442	Indonesia	27.00	460.00	2500.00	Mixed orchard	[13]
(3)	y = 30,827x − 1587	Cameroon	31.00	667.00	1500.00	Agroforestry	[14]
(4)	y = 0.507 e9.933x	India	26.00	160.00	1170.00	Mangrove and near-shore forest	[15]
(5)	y = 0.2836 e0373x	Thailand	27.50	287.00	1200.00	Mixed orchard	[16]
(6)	y = 537.598x	Thailand	27.50	287.00	1200.00	Dense deciduous forest	[17]
(7)	y = 79.029x − 16.215	Thailand	27.50	287.00	1200.00	Post-mining reforestation	[8]
(8)	y= 204.37x − 102.1	Indonesia	27.00	460.00	2500.00	Dense deciduous forest	[18]
(9)	y = –244.7x2+614.48x – 154.23	Pakistan	22.00	900.00	600.00	Pine forest	[19]
(10)	y = 52.904x− 10.36	India	26.00	160.00	1170.00	Post-mining reforestation	[20]
(11)	y = 0.0045x + 0.175	India	26.00	160.00	1170.00	Post-mining reforestation	[21]

Table 1. Significant characteristics of the selected formula used in the estimation of reforestation in Thailand.

Eq.	Formula	Region	Average Temperature (°C)	Altitude (MSL)	Average Rainfall Intensity (mm)	Vegetation Type	Source
The Properties of the Study Are in MMM		Thailand	28	340	1100	Deciduous Forest with Agroforestry
(2)	y = 12.019x − 8.6442	Indonesia	27.00	460.00	2500.00	Mixed orchard	[13]
(3)	y = 30,827x − 1587	Cameroon	31.00	667.00	1500.00	Agroforestry	[14]
(4)	y = 0.507 e9.933x	India	26.00	160.00	1170.00	Mangrove and near-shore forest	[15]
(5)	y = 0.2836 e0373x	Thailand	27.50	287.00	1200.00	Mixed orchard	[16]
(6)	y = 537.598x	Thailand	27.50	287.00	1200.00	Dense deciduous forest	[17]
(7)	y = 79.029x − 16.215	Thailand	27.50	287.00	1200.00	Post-mining reforestation	[8]
(8)	y= 204.37x − 102.1	Indonesia	27.00	460.00	2500.00	Dense deciduous forest	[18]
(9)	y = –244.7x2+614.48x – 154.23	Pakistan	22.00	900.00	600.00	Pine forest	[19]
(10)	y = 52.904x− 10.36	India	26.00	160.00	1170.00	Post-mining reforestation	[20]
(11)	y = 0.0045x + 0.175	India	26.00	160.00	1170.00	Post-mining reforestation	[21]

Table 2. Comparison between benefits and limitations of the selected formula in the application with the MMM area.

Eq.	R2	Advantages and Suitability	Disadvantages and Limitation
(2)	0.79	The formula can effectively describe complexity and variation in species	The climatic character of the area contains larger scales of rainfall intensity
(3)	0.66	The formula can moderately describe the carbon stock in the land use type of reforestation and physical properties, which is close to the characteristic of MMM	The average temperature can differentiate the dominant species of vegetation in the area.
(4)	0.79	The formula can moderately describe the carbon stock in the area with similar properties to MMM	The dominant species in the study is not related to the actual vegetation in the MMM
(5)	0.71	The formula can moderately describe the carbon stock in the area with similar properties to MMM	N/A
(6)	0.86	The formula can moderately describe the carbon stock in the land use type of reforestation and physical properties, which is close to the characteristic of MMM	N/A
(7)	0.96	The formula contains the highest RMSE in the determination of ACG in the mine reclamation area of Thailand	The study was performed on the older ages area, ranging from over 17 years of reforestation
(8)	0.73	The formula can moderately describe the carbon stock in the land use type of reforestation in MMM	The climatic character of the area contains larger scales of rainfall intensity
(9)	0.70	The formula can well describe the AGC in the pine forest, which is one of the sub-lands used in the reforestation of MMM	The dominant species in the study cannot reflect the actual vegetation in the MMM
(10)	0.61	The formula can reflect the determination of ACG in the mine reclamation area	N/A
(11)	0.99	The formula contains the highest RMSE in the determination of ACG in the mine reclamation area among the selected equation	N/A

Table 2. Comparison between benefits and limitations of the selected formula in the application with the MMM area.

Eq.	R2	Advantages and Suitability	Disadvantages and Limitation
(2)	0.79	The formula can effectively describe complexity and variation in species	The climatic character of the area contains larger scales of rainfall intensity
(3)	0.66	The formula can moderately describe the carbon stock in the land use type of reforestation and physical properties, which is close to the characteristic of MMM	The average temperature can differentiate the dominant species of vegetation in the area.
(4)	0.79	The formula can moderately describe the carbon stock in the area with similar properties to MMM	The dominant species in the study is not related to the actual vegetation in the MMM
(5)	0.71	The formula can moderately describe the carbon stock in the area with similar properties to MMM	N/A
(6)	0.86	The formula can moderately describe the carbon stock in the land use type of reforestation and physical properties, which is close to the characteristic of MMM	N/A
(7)	0.96	The formula contains the highest RMSE in the determination of ACG in the mine reclamation area of Thailand	The study was performed on the older ages area, ranging from over 17 years of reforestation
(8)	0.73	The formula can moderately describe the carbon stock in the land use type of reforestation in MMM	The climatic character of the area contains larger scales of rainfall intensity
(9)	0.70	The formula can well describe the AGC in the pine forest, which is one of the sub-lands used in the reforestation of MMM	The dominant species in the study cannot reflect the actual vegetation in the MMM
(10)	0.61	The formula can reflect the determination of ACG in the mine reclamation area	N/A
(11)	0.99	The formula contains the highest RMSE in the determination of ACG in the mine reclamation area among the selected equation	N/A

2.5. The Examination of the Spatial Relative Standard Deviation (S-RSD)

The precision and repeatability of the results must be taken into consideration due to the utilization of several formulas in predicting carbon sequestration from NDVI, which involves the conversion of secondary data. Precision refers to the level of concordance between duplicated and independent outcomes within predetermined constraints, while repeatability concerns the correctness of independent test findings conducted using the same approach [22]. To evaluate the suitability of these equations for predicting the current state of the reforestation area in Thailand impacted by mining, when direct validation is unfeasible, the spatial standard relative deviation (%S-RSD) is employed [23]. This metric is suitable for evaluating and comparing the precision of different approaches. The formula exhibiting the lowest percentage relative standard deviation (RSD) signifies a greater degree of precision and consistency in estimation. The calculation parameters utilized to determine the percent relative standard deviation (RSD) in this inquiry are presented in Equation (12).

$$\mathrm{\%}\mathrm{R}\mathrm{S}\mathrm{D}=\frac{\mathsf{\sigma }}{\overline{\mathrm{X}}}\times 100$$

(12)

The symbol $\mathsf{\sigma }$ represents the zonal standard deviation of the estimated results obtained from the chosen equation, whereas $\overline{\mathrm{X}}$ denotes the zonal mean value of these findings. The retrieval of these parameters is accomplished through the utilization of spatial statistical analysis methods. In this study, the target parameter, which is aboveground carbon stock (${AGC}_i$), derived from each formula, were compared to their S-RSDs to determine the most suitable equation to estimate carbon sequestration.

2.6. Estimation of Carbon Sequestration

After defining the appropriate estimation formula, the calculations to determine the potential carbon sequestration of the reclamation area in MMM are further conducted according to Equations (13)–(15). This equation is adapted from the certificated rules and guidelines established by the Thailand Voluntary Emission Reduction Program (T-VER). The T-VER prescribed rules and procedures for project development, GHG emission reduction methodology, and certification of emission reduction credit [24,25].

$${BGC}_i={0.26AGC}_i$$

(13)

$${ACS}_t=\sum _{i=0}^n{(AGC}_{i}x Cell Resolution)$$

(14)

$${BCS}_t=\sum _{i=0}^n{(BGC}_{i}x Cell Resolution)$$

(15)

$${CS}_i={ACS}_t+{BCS}_t$$

(16)

where ${AGC}_i$ and ${BGC}_i$ refers to aboveground and belowground carbon stock (ton/ha/cell) at each cell of calculation layers, respectively. ${ACS}_t$ and ${BCS}_t$ are the total carbon stock (t CO₂e), which can be accumulated from aboveground and belowground trees in the reforestation area. The two parameters can be obtained by the raster calculation in the GIS application. The pixel value, equal to the ${AGC}_i$ per area, is calculated in association with the resolution of the images (30 × 30 m) to determine the aboveground carbon stock. The results are further calculated in GIS, using Equation (15), to obtain the ${BCS}_t$. The carbon sequestration of each monitoring year can be estimated based on the summation of aboveground and belowground carbon stock, which is represented as ${CS}_i$ in Equation (16).

2.7. Evaluation of Reclamation Efficiency

An assessment of the efficacy of the reclamation development at the Mae Moh mine can be accomplished through a comparison between the annual carbon storage quantity and a reference value established as the mean carbon sequestration that took place throughout the research period. The average carbon storage in the region from 2013 to 2023 was computed and averaged in this study in order to illustrate regional trends. Further operations may be considered and analyzed considering these data.

Since the effectiveness of reclamation has not been accounted for in the form of numerical investigation, the average carbon sequestration of the monitoring area from 2013 to 2023 was set up as the target baseline for forest productivity of the area. The estimated carbon sequestrations of each monitoring year are then compared with the baseline to calculate the effectiveness of the reclamation according to Equation (17). Where $E_r$ is the percentage of effectiveness of reclamation in the MMM, while ${CS}_B$ is the baseline value of carbon sequestration in the monitoring area.

$$E_r=({CS}_i-{CS}_B)/100$$

(17)

3. Results

3.1. Satellite Image Analysis Result

By combining and applying NDVI analysis to the Landsat 8 OLI satellite images acquired from August 2013 to 2023, the vegetation area over the MMM can be illustrated. The results of the NDVI analysis conducted using a GIS application are depicted in Figure 3. Based on the findings, the annual NDVI of the MMM reclamation area fluctuates between −0.30 and 0.56. The results indicate that the vegetation area has experienced growth since 2013, as supported by the increase in the highest NDVI values for each monitoring year.

3.2. Formula Determination

The procedure for ascertaining which formula would function as the principal method of computation commenced. As indicated by the results, the calculated value exhibited a fluctuating range. This is the result of the complexities and distinctions between the locally prevalent species in each formula. By applying the formula, which is derived from an area exhibiting characteristics similar to the vegetation of MMM, to the calculation, it is possible to obtain more precise values for the sequestration. On the other hand, the formula, which fails to characterize the sequestration in the MMM adequately, yields a value less than zero and is thereby omitted from evaluation.

Spatial statistical analysis is performed on each resultant entity to assess the accuracy and reliability of the information it contributes to the estimation procedure. The results obtained from the comparison of the selected equations are presented in

Table 3. Comparison between the estimated ${\mathrm{A}\mathrm{G}\mathrm{C}}_{\mathrm{i}}$ from each selected formula.

Eq.	Min	Max	RSD
(2)	−16.81	−6.86	$\mathrm{Exclude} \mathrm{due} \mathrm{to} \mathrm{MAX} {AGC}_i\le 0$
(3)	−16,906.21	2999.91	−45.94%
(4)	0.00	2.03	92.57%
(5)	0.24	0.30	4.24%
(6)	−267.15	79.99	−57.96%
(7)	−55.48	−4.46	$\mathrm{Exclude} \mathrm{due} \mathrm{to} \mathrm{MAX} {AGC}_i\le 0$
(8)	−203.66	−71.69
(9)	−399.16	−57.38
(10)	−36.65	−2.48
(11)	0.17	0.18	0.29%

. Within the realm of spatial analysis, a spatial relative standard deviation (S-RSD) value of zero or an extremely small value signifies that the data are highly precise and consistent [26,27]. Furthermore, this indicates that the data lack variation, thereby failing to sufficiently reflect the diversity of species prevalent in the reforestation region. Due to the suitability of Equation (4) (as demonstrated by the results), it is utilized to derive the estimation formula for this investigation.

3.3. Estimation Result of Carbon Sequestration in the Mae Moh Mine

In

Table 4. Estimation results of carbon sequestration in the reclamation area of the Mae Moh mine from 2013 to 2023.

Year	AGC (ktCO2e)	BGC (ktCO2e)	CS (ktCO2e)	Standard Deviation (SD)
2013	1.34	0.35	1.69	0.05
2014	41.89	10.89	52.78	1.43
2015	63.64	16.55	80.19	2.47
2016	43.49	11.31	54.79	1.23
2017	55.11	14.33	69.44	1.91
2018	148.76	38.68	187.44	5.56
2019	43.32	11.26	54.58	1.64
2020	85.26	22.17	107.43	3.66
2021	195.02	50.71	245.73	11.27
2022	262.92	68.36	331.28	11.89
2023	163.90	42.61	206.51	7.65

and Figure 4, the estimation of carbon sequestration is shown. This estimation is based on the data from the previous session. The findings indicate that there is a growing pattern of carbon sequestration in the reclamation area of MMM. The highest level of carbon sequestration is projected to be detected in 2022, with a value of 331.28 ± 11.89 ktCO₂e recorded. Even though the trend of carbon sequestration in this monitoring region is regarded as positive, there are some variations in the estimation. This is evidenced by the significant decrease in sequestration, which experienced a significant decrease from 187.44 ± 5.56 ktCO₂e in 2018 to 54.58 ± 1.64 in 2019. Furthermore, it is worth noting that the lowest carbon sequestration was recorded in the year 2013, with a value of 1.69 ± 0.05 ktCO₂e. This was also the year that the building and plantation of the reclamation area were in the process of being developed.

3.4. Determination of Reclamation Effectiveness

Figure 5 demonstrates the effectiveness of MMM’s reclamation during the installation of reclamation from 2013 to 2023. It can be seen that in the early stages (2013–2015), the effectiveness was lower than the target at 126.53 ktCO₂e because the monitoring area was under the expansion and development of the plantation state, especially in 2013 when the area was still uncovered. Since 2018, the effectiveness of the area has continuously increased and reached the maximum value of 161.81% of forest productivity in 2022.

4. Discussion and Suggestion

The Mae Moh mine reclamation area has expanded continuously due to the copious productivity of the forest. The observed surge in carbon sequestration during the region’s most rapid development phase may be indicative of the cultivation technique employed to nurture the vegetation over the previous decade. The region experienced its peak carbon sequestration in 2022, a significant increase from its previous level. Despite the fact that the practices have contributed to an upward trend in the region’s effectiveness, the ongoing development of sequestration faces a number of obstacles. Furthermore, these were the consequences of constraints imposed by operational policies and natural occurrences.

In order to sustain the area’s productivity, ongoing surveillance and nursing efforts should be directed toward operational factors. During the period spanning from 2019 to 2020, when the mine experienced a significant dissemination of the COVID-19 pandemic, all operational activities ceased. As a consequence, there was an absence of vegetation area monitoring and development. As a result, the efficacy of the area’s reclamation is diminishing. The decline in productivity from 2018 to 2019 can be attributed to the drought in the northern region of Thailand, particularly during 2019. The total rainfall throughout the year was 24% below the average, leading to a decrease in sequestration in the reforestation area [28]. This was due to the limited growth and expansion of the area caused by the drought. As a result, the reduced sequestration in the reforestation area had a direct impact on the overall productivity of the region. Additionally, the drought affected the availability of water for irrigation, further hampering agricultural activities and contributing to the decline in productivity.

Although it is possible to regulate operation conditions, natural elements, including climatic conditions, can have a significant impact on the productivity of the forest in the reclamation zone. The comparison between the annual cumulative rainfall recorded in the monitoring area of MMM and the carbon sequestration estimated using this methodology is illustrated in Figure 6. It is evident that the sequestration tendency corresponds to the precipitation records. Based on the comparison, it is apparent that a decrease in accumulated precipitation results in an equivalent reduction in the amount of carbon dioxide that is stored in the reclamation area. As a result, any decrease in the quantity of accumulation leads to a corresponding decrease in the effectiveness of remedial actions carried out in the surrounding area. Based on the results obtained, it is evident that this methodology can be incorporated with other pertinent records pertaining to the reclamation site and serves as a viable instrument in developing suitable protocols, strategies, and countermeasures to improve the quality of the reclamation.

In the event that the method can be included in efficient practices for the purpose of monitoring the reclamation area, there are still some areas in which the method can be improved to produce more accurate estimating standards. There is a great deal of uncertainty that is provided by the characteristics of satellite images, which are the fundamental input for computations carried out by remote sensing. There are several factors that have the ability to cause interference in the data. These aspects include characteristics such as atmospheric conditions and spatial resolution. The adverse effects of cloud cover and humidity in the atmosphere make it difficult for optical satellites to obtain significant information about the surface of the Earth [29]. This is the case on numerous levels. Although this study ensures that the cloud coverage in each and every image is less than 20% [29,30,31], the effects of clouds in terms of generating uncertainty continue to be present.

By lowering uncertainty in the reforestation of complex landscapes with greater differentiation among various types of vegetation and land cover, a higher quality of imaginaries can considerably increase the accuracy of NDVI analysis, which leads to mitigating the adverse consequences that are being experienced [32]. These improvements are attainable through the utilization of innovative portable sensors, such as multi-spectral unmanned aerial vehicles (Multispectral-UAV), which have the potential to provide a multitude of advantages in the process of eliminating these unfavorable outcomes. Because the instrument has a greater spatial resolution of up to 5 × 5 m, it is possible to collect targeted imagery in the same way that an operating satellite would, regardless of whether or not there is cloud cover. These qualities, which are essential for accurate carbon sequestration in the mine-reforestation zone, make it possible to install the system quickly, is cost-effective, and can record data that are both precise and detailed [33].

Furthermore, the suitable formula for determining the specific carbon sequestration of the region is another crucial component in the subsequent development of this strategy. As a result of the fact that the ecology of each reclamation area differed due to factors such as climate, species, and elevation, the empirical formula, which is directly derived from the area, has the potential to provide a more accurate number associated with carbon sequestration. It is also possible that this will be beneficial for calculating the carbon offset and determining the economic value of the area that is being reclaimed.

Through the analysis of data acquired using this technology, researchers can gather useful insights regarding the influence of accumulated precipitation on carbon dioxide storage and the overall efficacy of reclamation measures. Subsequently, these data can be utilized to influence decision-making procedures and direct future endeavors to improve the quality of reclamation sites. Furthermore, integrating this approach with other pertinent data can offer a thorough comprehension of the site’s circumstances and assist in the formulation of more effective procedures, tactics, and measures for continuous enhancement. Furthermore, incorporating the specific carbon sequestration formula into the strategy can help assess the effectiveness of reclamation efforts and monitor progress over time. This information can be valuable for policymakers and stakeholders to make informed decisions regarding the allocation of resources and potential economic benefits that can be derived from the reclaimed area.

5. Conclusions

A quantitative assessment of the reclamation area in the Mae Moh coal mine is successfully conducted in this study through the utilization of remote sensing technology. A comparison was made among ten equations that have the capability to calculate aboveground carbon storage. Spatial statistical analysis was employed to determine which equation was most suitable for the study area. The chosen formula computes %S-RSD at a rate of 98.25% and is additionally utilized to determine the carbon sequestration of the research site in conjunction with LANDSAT 8 OLI satellite images. The studied area has been experiencing a consistent increase in carbon sequestration since 2015, when the region was already densely vegetated and undergoing reforestation. In 2022, the reclamation area will achieve its maximum carbon sequestration of 331.28 ± 11.89 ktCO₂e, representing an efficacy increase of 161.81% compared to the baseline of 126.53 ktCO₂e. The proposed methodology can be an effective tool in quantifying the reclamation operation, which is mostly discussed in terms of qualitative description.In order to gain more accuracy in using this procedure, the study on specific formulas derived from sufficient sampling plots of each reclamation area should be conducted.