Spatio-Temporal Assessment of the Impact of Artisanal Gold Mining on Land-Cover in Ife-East, Osun State.

By John, OA; Ehisienmhen, NO; Odediran, OO; Kulepa, AA; Akinwande, AR; Odoh, EO; Aribilola T.R: Sedenu, HA; Isa, I; Haruna, RL; Adamu, I; Dirisu, KD (2023)

Greener Journal of Environment Management and Public Safety

ISSN: 2354-2276

Vol. 11(1), pp. 16-27, 2023

https://gjournals.org/GJEMPS

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Spatio-Temporal Assessment of the Impact of Artisanal Gold Mining on Land-Cover in Ife-East, Osun State.

John, Oluwasegun A.¹; Ehisienmhen, Nicholas O.^1*; Ogunleye Funmilayo D.¹; Kulepa, Asimiyu A.¹; Akinwande, Adeola R.¹; Odoh, Evaristus O.¹; Aribilola Toba R.¹Sedenu, Hafeez A.¹; Isa, I.¹; Haruna, Rakiat L.¹; Adamu, Ismaila¹; Dirisu, Kelvin D.¹

*Advanced Space Technology Application Laboratory (ASTAL); National Space Research and Development Agency, Obafemi Awolowo University, Ile-Ife, Nigeria.

ARTICLE INFO	ABSTRACT
*Article No.:* 102723126 *Type: Research* *Full Text:* PDF, PHP, HTML, EPUB, MP3	Ife-East local government area of Osun State in recent time has been experiencing activities of artisanal gold mining which generate adverse impacts on land cover features. In this study, the land use/cover (LULC) dynamics and vegetation index (NDVI) of Kajola Basin, a prominent artisanal mining hotspot within the region was assessed for a period of twenty years with the aid of Landsat 7, 8 and 9 obtained from United States Geologic Survey Departments (USGS) for the year 2002, 2014 and 2022 respectively. The study used ArcGIS 10.4 and ERDAS IMAGINE software to generate the vegetation index (NDVI), supervised classification using the maximum likelihood method to classify the images into five (5) classes: vegetation, cultivation, bare surface/mine tailing, waterbody and built-up and also map the vegetal index and LULC types for the interval. The result from the LULC classification revealed that vegetation and cultivation experienced decline, while built-up, bare surface/mine tailings and waterbody experienced increase between the year 2002 and 2022. The increase experienced by bare-surface/mine tailings: 0.7% to 2.6% and waterbody: 0% to 1.6% was more significant between 2014 and 2022. Vegetal index (NDVI) analysis result also showed fluctuation in the health of vegetation within the interval with values ranging from 0.137 to -0.257 for Landsat7, 0.357 to 0.092 for Landsat8 and 0.334 to 0.045 for Landsat9. Areas with low index values are susceptible to the impact of artisanal gold mining and other anthropogenic activities, while high-index areas shows good vegetal health connoting little or no impact. This study implies that artisanal gold mining is a threat to vegetal covers and green environment within the basin hence endangers biodiversity and capable of causing ecological dislocation. The outcome of this research is crucial as it provides cost effective tools for environmental monitoring and remediation project for affected areas in the basin.
*Accepted:* 29/10/2023 *Published:* 07/11/2023
Corresponding Author Ehisienmhen, Nicholas O.* *E-mail:* unclenick2020@ yahoo.com, dejijohns@ gmail.com,
*Keywords:* Artisanal Gold Mining, Land use land cover, Supervised Classification.

INTRODUCTION

Nigeria is rich in diverse solid mineral deposits, including precious metals, stones, and industrial minerals like coal, tin, gold, marble, and limestone. Gold, one of these minerals, is found in various forms in northwest and southwest regions of Nigeria. Notable gold-rich areas include Maru, Anka, Malele, Tsohon Birin Gwari-Kwaga, Gurma, Bin Yauri, Luku, Okolom-Dogondaji, Ife-Ijesha, Itagunmodi, Igun, and Iperindo, all associated with schist belts.

Mining, the extraction of minerals from the Earth's crust, offers socio-economic benefits and infrastructure development. However, it also produces ecological and environmental effects, often negatively impacting the environment. In Osun State, Nigeria, gold mining is conducted at both industrial-scale and artisanal mines. Artisanal miners employ surface mining techniques, often without technology to mitigate environmental harm. Illegally operating miners, unknown to the government, make regulation and environmental management challenging.

Artisanal gold mining leads to environmental degradation, including deforestation, loss of aquatic life, water and air pollution, and social disruption. Health issues arise due to the release of toxic materials like lead, cyanide, and mercury, endangering miners, their families, and communities. Such mining also alters land cover, crucial for natural resource management and spatial planning.

Remote sensing, Geographic Information System (GIS), and Earth Observation Satellites (EOs) provide effective tools to assess land cover changes caused by mining and develop sustainable policies. Satellite imagery offers a global, high-resolution view of the Earth's surface, aiding in monitoring and measuring environmental changes over time.

Addressing artisanal mining is essential for achieving Sustainable Development Goals (SDGs) set by the United Nations. These goals aim to combat poverty, improve lifestyles, combat climate change, and reduce environmental degradation. Earth observation satellites play a crucial role in monitoring compliance with SDGs, particularly for SDG 15 (Life on Land), SDG 3 (Good Health and Wellbeing), SDG 6 (Clean Water and Sanitation), and SDG 2 (Zero Hunger).

This study focuses on the impact of artisanal gold mining in Ife-East Local Government Area, South-western Nigeria, between 2002 and 2022, providing a framework for monitoring and assessing its effects on land cover.

The global rise in gold prices and the desire for improved livelihoods are fueling a growing demand for gold mineral resources worldwide. However, artisanal gold mining is having a significant negative impact on the environment. These miners prioritize mining primary and alluvial gold deposits without regard for the ecological consequences. As a result, heavy metals and toxic by-products associated with quartz are being released into the environment and water sources like wells and rivers, leading to landscape degradation and harm to local ecosystems and biodiversity.

Artisanal gold mining is considered an anthropogenic activity responsible for various environmental challenges, including deforestation and the disruption of ecosystem services. Although the harmful effects of such mining on health and the environment are well-known, most research has been concentrated in specific regions, such as Ijesha-Itagumodi-Igun and Iperindo. This current study aims to investigate and analyze the impacts of artisanal gold mining in the Ife-East Local Government Area of South-western Nigeria, with a focus on assessing changes in land cover from 2002 to 2022. It seeks to establish a framework for monitoring and assessing these environmental impacts in the area.

Research Questions

1. What is the present state of Land cover in the study area?

2. What is the extent of change that has occurred overtime attributable to artisanal gold mining within the study interval?

Aim

The aim of the study is to assess the impact of artisanal gold mining activities on land-cover features in Ife-East Local Government Area.

Objectives

The specific objectives are to:

identify locations of artisanal gold mining within the study area;

ii. examine the land use/land cover dynamics between the year 2002 and 2022.

Study Area

Location, Extent, and Population

The study covers Kajola basin which occupies an area of about 89.98 km². Kajola basin forms part of the drainage system of Ife stream network in Ife-East Local Government Area of Osun State, Nigeria. Ife-East is one of the 30 Local Government Areas in Osun State, Southwest geopolitical zone of Nigeria with the LGA’s headquarters situated in the town of Oke Ogbo. The LGA is geographically located between latitude 7° 15′ 54″ and 7 ° 35 ′ 08 ″ North of the Equator and longitude 4 ° 32 ′ 45 ″ and 4 ° 40 ′ 06 ″ East of the Greenwich Meridian with an area of 172 km² and a population of 188,087 (NPC, 2006).

Figure : Methodology Workflow

Table 1: Data Sources

1Features	Landsat Imagery	Landsat7	Landsat8	Landsat9
Type	Landsat7(2002)	Landsat7(ETM+)	Landsat7(OLI/TIRS)	Landsat7(OLI/TIRS)
WRS-P/R	190/55	190/55	190/55	190/55
Acquisition Date		03/01/2002	12/01/2012	18/12/2022
Attribute	Orth-rectified	Orth-rectified	Orth-rectified	Orth-rectified
Format(Extension)		TIFF	TIFF	TIFF
Orbit	Sun Synchronous	Sun Synchronous	Sun Synchronous	Sun Synchronous
Altitude(Km)	705	705	705	705
Period(min)	99	99	99	99
Inclination (°)	98.2 - 99	98.2 - 99	98.2 - 99	98.2 - 99
Temporal Resolution (days)	16	16	16	16
Swath (km)	183	185	185	185
Bands	7	11	11	11
Colour Composition (IR)		4 3 2	5 4 3	5 4 3
Spatial Resolution (m)	30	30	30	30

Source: U.S. Geological Survey

The following softwares were used for data acquisition and analysis: ArcGIS, ERDAS Imagine, Google Earth, UMD and Ms Excel. Google Earth to access and carryout ground truthing, UMD was used to download high resolution images of the study area. Supervised classification and Accuracy Assessment was done using the ERDAS Imagine software, ArcGIS software was used for NDVI and Map Composition. All statistical analysis showing tables and charts were done using Ms Excel.

Image Data Acquisition and Processing

The raster images of Landsat 7 (EMT⁺) 2002, Landsat 8 and 9 (OLI/TIRS) for the year 2012 and 2022 with path and row 190/55 were downloaded from USGS. All the downloaded images had less than 10% cloud coverage. They were pre-processed on ERDAS IMAGINE software to enhance them for better interpretation using pixel value information.

i. Layerstacking and Colour Composition

Layerstacking of the satellite imagery bands was carried out using the ERDAS Imagine software, Landsat 7 imagery bands were layerstacked in order of band 2, 3 and 4 and then composed with the Infrared false colour composition were band 4 (NIR) placed into the R-channel, band 3 (RED) placed into the G-channel and band 2 (GREEN) placed into the B-channel while Landsat 8 and 9 were layerstacked in order of band 3, 4 and 5. The IR False Colour Composition was done placing band 5 (NIR) placed into the R-channel, band 4 (RED) placed into the G-channel and band 3 (GREEN) placed into the B-channel. Vegetation was then displayed in red colour, built-up in cyan etc.

ii. Image Extraction (Subset AOI)

Subset of the study area was created for the images to delineate the study area by clipping the Area of interest (AOI) from the full scene using the shapefile of the study area.

iii. Supervised Classification

This study utilised supervised classification method, this was conducted with the maximum likelihood classification (MLC), as adapted from Bayes theorem (Xie et al., 2016). Supervised classification and maximum likelihood algorithm scheme base on visual interpretation of the satellite data imagery, it focused on the probability that a pixel with a particular feature vector belongs to a specific land-cover class based on the training of a visual classifier for five different categories of land-use and land-cover dynamic. The process uses a sorting feature to assign each pixel with the highest probability of the category. Class mean vector and covariance matrix are the main inputs to the function and were derived from the training data of a specific class (Perumal and Bhaskaran 2010). The land-cover and land-use classes used for the supervised maximum likelihood classification include bare surface and mine tailing, built-up area, vegetation, waterbody and cultivation.

iv. Accuracy Assessment

Accuracy evaluation of the final classified maps was conducted for each data (2002, 2012 and 2022) using the ERDAS Imagine software. This was done to verify its reliability as noted in Owojori and Xie (2005). This study employed stratified random method, ground truth and visual interpretation to represent all the LULCC classes in the study area. After the comparison of the reference data and the classified images, the statistical results were represented in error matrices that determine the user’s and producer’s accuracy (Bakr et al., 2010). A Kappa coefficient test was executed as suggested by Congalton, R. G. (2001).

NDVI

Landsat imageries of year 2002, 2012 and 2022 were used to carry out Normalized Differential Vegetation Index Analysis (NDVI) of the study area. This analysis was done by calculating visible and near-infrared light reflected by vegetation using the raster calculator on ArcGIS software. Healthy vegetation absorbs most of the incoming visible light and reflects a small portion (about 25%) of the near-infrared (NIR) light, but a low portion in the red band (RED). Stressed or sparse vegetation reflects more visible light and less NIR light.

Calculation of NDVI for a given pixel always results in a number that ranges from minus one (-1) to plus one (+1): Bare soils give a value close to zero and very dense green vegetation have values close to +1 (0.8-0.9). The NDVI technique was adopted to assess the possible effect of artisanal gold mining on vegetation within the drainage basin. NDVI images were generated using the algorithm developed by Rouse et al., (1974) as follows:

NDVI = (NIR – R) / (NIR + R)

Where NIR and R are the radiance or reflectance in the near-infrared and red spectral channel respectively. For Landsat 7, band 4 AND 3 represent the NIR and R channel while for Landsat 8 and 9, band 5 and 4 represent NIR and R channel.

RESULTS

This study evaluates the land use land cover dynamics in Kajola Basin using spatiotemporal analysis.

Land use land cover for 2002

The study area (Kajola Basin) has a total land area of 8998 hectares which is equivalent to about 90 km². In the year 2002, Vegetation covers 7117.7 hectares, representing 71.2 km² which is approximately 79% of the total land area. Cultivation was 1720.4 hectares covering 17.2 km² representing 19%. Built-up occupied 150.6 hectares equivalent to 1.5 km²and 1.7%, Bare-surface/Mine tailings occupied 8.9 hectares equivalent to 0.1 km²and 0.1%. While Waterbody occupied 0.4 hectares of the total land area.

NDVI for 2002

The Normalized Differential Vegetation Index (NDVI) values range from 0.136691 to -0.256757. The high value (0.136691) represents pixels covered by a substantial proportion of healthy vegetation while the low value (-0.256757) represents pixels covered by non- vegetated surfaces including water, anthropogenic features such as mining activities and built-up, bare soil, and unhealthy or stressed vegetation.

This information is shown in Table 2, Figure 3, and Figure 4 representing pie and bar charts, Figure 6 showing the NDVI map.

Table 2: 2002 Land Use/ Land Cover

Class	Hectares	km²	%
Bare-surface/Mine Tailings	60.8	0.6	0.7
Built-up	516.9	5.2	5.7
Cultivation	1334.6	13.3	14.8
Vegetation	7085.2	70.9	78.7
Waterbody	0.6	0.0	0.0
Total	8998	90.0	100

Figure 3: 2002 LULC area pie chart

Figure 4: 2002 LULC area bar chart

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Figure 5: 2002 Classification study area map

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Figure 6: 2002 NDVI of study area

Land use land cover for 2014

In the year 2014, Vegetation covers 7085.2 hectares, representing 70.9 km² which is approximately 79% of the total land area. Cultivation was 1334.6 hectares covering 13.3 km² representing 15% approximately. Built-up occupied 516.9 hectares equivalent to 5.2 km²and 6% approximately; Bare-surface/Mine tailings occupied 60.8 hectares equivalent to 0.16 km²and 1%. While Waterbody occupied 0.6 hectares of the total land area.

NDVI for 2014

The Normalized Differential Vegetation Index (NDVI) values range from 0.357159 to 0.0917302. The high value (0.357159) represents pixels covered by a substantial proportion of healthy vegetation while the low value (0.0917302) represents pixels covered by non- vegetated surfaces including water, anthropogenic features such as mining activities and built-up, bare soil, and unhealthy or stressed vegetation.

This information is shown in Table 3, Figure 7 and Figure 8 representing pie and bar charts and Figure 10 showing the NDVI values.

Table 3: 2014 Land Use/ Land Cover

Class	Hectares	km²	%
Bare-surface/Mine Tailings	60.8	0.6	0.7
Built-up	516.9	5.2	5.7
Cultivation	1334.6	13.3	14.8
Vegetation	7085.2	70.9	78.7
Waterbody	0.6	0.0	0.0
Total	8998	90.0	100

Figure 7: 2014 LULC area pie chart

Figure 8: 2014 LULC area bar chart

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Figure 9: 2014 Classification study area map

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Figure 10: 2014 NDVI of study area

Land use land cover for 2022

Vegetation in the year 2022 covers 6009.6 hectares, representing 60.1 km² which is approximately 67% of the total land area. Cultivation was 1269.9 hectares covering 12.7 km² representing 14.1%. Built-up occupied 1336.1 hectares equivalent to 13.4 km²and 15%, Bare-surface/Mine tailings occupied 237.2 hectares equivalent to 2.4 km²and 3%. While Waterbody occupied 145.3 hectares of the total land area signifying 1.5km² and 1.6%.

NDVI for 2022

The Normalized Differential Vegetation Index (NDVI) values range from 0.333822 to 0.0446844. The high value (0.333822) represents pixels covered by a substantial proportion of healthy vegetation while the low value (0.0446844) represents pixels covered by non- vegetated surfaces including water, anthropogenic features such as mining activities and built-up, bare soil, and unhealthy or stressed vegetation.

This information is shown in Table 4, Figure 11, Figure 12 representing pie and bar charts and Figure 14: showing the NDVI values.

Table 4: 2022 Land Use/ Land Cover

Class	Hectares	km²	%
Bare-surface/Mine Tailings	237.2	2.4	2.6
Built-up	1336.1	13.4	14.8
Cultivation	1269.9	12.7	14.1
Vegetation	6009.6	60.1	66.8
Waterbody	145.3	1.5	1.6
Total	8998	90.0	100

Figure 10: 2022 LULC area pie chart

Figure 12: 2022 LULC area bar chart

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Figure 13: 2022 Classification study area map 4

Table 6: indicate changes in land cover features over time, vegetation and cultivated areas experienced reduction while other land cover features experienced significant increase.

Table 6: Land cover changes

Class	Area			Change in Area		Rate of Change
	2002	2014	2022	2002-2014	2014-2022	2002-2014	2014-2022
	km²	km²	km²	km²	km²	(km²/yr)	(km²/yr)
Bare-surface/Mine Tailings	0.1	0.6	2.4	0.5	1.8	0.0432	0.2205
Built-up	1.5	5.2	13.4	3.7	8.2	0.305283	1.024038
Cultivation	17.2	13.3	12.7	-3.9	-0.6	-0.32156	-0.08082
Vegetation	71.2	70.9	60.1	-0.3	-10.8	-0.02715	-1.3445
Waterbody	0.0	0.0	1.5	0.0	1.4	0.000225	0.180788
Total	90.0	90.0	90.0

Figure 15: below present a graphical visualization of the changes experienced overtime.

Figure 15: LULCC area bar chart between 2002 and 2022

Accuracy results for the Landsat 7, 8 and 9 images derived from the MLC method, with producers’ accuracies (PA) and users’ accuracies (UA), overall accuracy and kappa coefficient. Ground truth points were used as verification for each LULCC class.

$Description: C:\Users\JOHN OLUWASEGUN A\Desktop\John\mine.jpg$

Figure 16: 2022 Classified Image Map Showing Mine Areas

DISCUSSION

Accuracy Assessment

In this study, the accuracy assessment classification was done with reference to the raw satellite images. Reference data and the classified images data were compared and the statistical results were represented in error matrices which determine the users’ and producers’ accuracy, overall accuracy and Kappa coefficient (Congalton and Green 1999; Bakr et al., 2010).

The accuracy assessments that were conducted are summarised in Table 5. The classification results show that the MLC approach produced a considerably higher overall accuracy 92%–96% and a kappa coefficient of 0.88–0.93 for the entire data within interval of study. Of all the five classes considered in this study, vegetation, cultivation and built-up classes were the most accurately classified classes with accuracies of about 93–98%, while bare surface and mine tailings class had the lowest producers and user’s accuracies in 2002 with about 88 and 90% respectively. The reason for the low accuracies obtained in this class for this period might be due to the confusion between bare surface and built-up area class due to open surfaces which may cause confusion between both surfaces. These misclassifications could also be due to the medium spatial resolution of the Landsat images used in this study and the “mixed pixel” effect (Pei et al., 2017).

The acceptable accuracy when mapping the LULCC using Landsat data was higher than 85% with no classes less than 70% in this analysis. This is confirmed in the study conducted by Tilahun and Teferie (2015) who provided land use and land cover accuracy of 82.00% and Kappa (K) statistics of 77.02% which is acceptable in both accuracy total and Kappa statistics. Also, the study conducted by Butt et al., (2015) achieved overall classification accuracies of 95.32% and 95.13% and overall kappa statistics of 0.9237 and 0.9070 respectively for the classification of 1992 and 2012 images.

Vegetation

Vegetation cover in the study area experienced a slight decrease from 7117.7 hectares, representing 71.2 km² which is approximately 79% of the total land area in 2002 to 7085.2 hectares, equivalent to 78.7% in 2014. While in 2022, the vegetal cover reduced drastically to about 6009.6 hectares, representing 60.1 km² which is approximately 67%, representing about 11% reduction in 8 years. This development connotes that there may be other external factors responsible for this variability; for instance, deforestation as a result of artisanal gold mining activities and increased in built-up as a result of population explosion and development might have played a vital role in this degradation of the vegetation (Glantz 2019).

Cultivation

The results as seen on Table 6 and Fig. 16: cultivated areas experienced gradual decrease for the entire study interval. In 2002, cultivation was 1720.4 hectares, equivalent to 17.2 km² and about 19% of the total land area, which reduced to 1334.6 hectares covering 13.3 km² representing 15% approximately in 2014 and further reduction was experienced in 2022 to 1269.9 hectares covering 12.7 km² representing 14.1%. The land use land cover dynamics experienced may be attributed to increase in built-up, cultivated land adjacent to settlements are easily converted to built-up. This is seen in Fig. 13, showing the trend of urban sprawl in the study area. Much of the changes experienced between 2014 and 2022 can also be attributed to recent artisanal gold mining activities as the people may consider this to be more rewarding than the traditional agricultural based occupation (Madasa et al., 2020; Orimoloye and Ololade 2020).

Built-up

Built-up area experienced an increase from 150.6 hectares equivalent to 1.5 km²and 1.7% in 2002 to 516.9 hectares which is equal to 5.2 km²and 6% approximately in 2014. The area further experienced another tremendous growth of built-up in 2022 to about 1336.1 hectares equivalent to 13.4 km²and 15%. This change can be traced to growth in population; the study area is located in Ife East which forms part of the Ile-Ife metropolis. Availability of social amenities and infrastructures serves as attraction for commercial activities and job opportunities, coupled with the prospect for gold mining opportunities. This may be attributed to the rapid growth of built-up in the study area particularly in the last decade.

Bare Surface and Mine Tailings

Bare-surface and Mine tailings in this study includes areas made bare as result of construction, mining activities and tailings for exploited gold ore processing. In 2002, area occupied by this features was 8.9 hectares equivalent to 0.1 km²and 0.1%. Which experienced an increased to 60.8 hectares equivalent to 0.16 km²and 1% in 2014 and then to 237.2 hectares equivalent to 2.4 km²and 3% in 2022. The increment of this feature may be attributed to substantial loss of vegetal cover and cultivated areas resulting from mining activities to pave way for mining operations such as open pits development and mine tailings (Schueler et al., 2011).

Water body

The water body coverage for the area was about 0.4 hectares in 2002 which then increased to 0.6 hectares in 2014. The area experienced a vast increase of water body in 2022, the coverage increased to 145.3 hectares of the total land area signifying 1.5km² and 1.6% of the total land area. This tremendous increase of surface water body between 2014 and 2022 may be linked to recent activities of artisanal gold mining in the area. Creation of open cut pit for gold exploration and damming of drainage line for washing of extracted gold ore contributed to this change of land cover feature.

However, as reported by Li et al., (2016) other anthropogenic activities might have also contributed to the land-use/land-cover dynamics in the area, with mining activities accounting for a significant change in natural land cover and surface water.

NDVI

Areas of high vegetal cover within the study area is highly reflected, while areas around mining portions and poor vegetation or other non-vegetal activities shows low reflections with values tending towards the negative. Figure 6 Figure 10 and Figure 14 showing low and high values of -0.2568 to 0.1367, 0.0917 to 0.3572 and 0.0447 to 0.334 for the year 2002, 2014 and 2022 respectively. The result of the analysis indicates that the study area experienced poor biomass formation in the year 2002. This variation may be attributed to activities like bush burning and other anthropogenic activities causing deforestation as the images used for this analysis were captured in the dry season. However, the vegetative biomass experienced an increase in the year 2014; this increase can probably be attributed to improved farming knowledge in the basin and also sensitisation on the effect of deforestation to the environment. The vegetal biomass experienced a slight decrease in 2022 compared to 2014; this may be due to the activities of artisanal gold mining which recently gain prominence in the basin compared to previous years as shown on the classification image (Figure 14). The Normalized Differential Vegetation Index (NDVI) of the basin reveals clearly that vegetation is mostly impacted around settlements and along stream channels.

CONCLUSION AND RECOMMENDATION

Conclusion

Artisanal gold mining is an example of anthropogenic activity that can change an entire landscape due to the impact on land cover, leaving implications on the immediate and adjacent environment. Remote sensing and GIS techniques were used to analyse and map the land cover dynamic in the study area within 20 years period showing changes overtime. Prominent changes experienced were significant increase in water body, bare surface and mine tailings between the year 2014 and 2022 showing recent and intensive activities of artisanal gold mining in the last decade of the study area. Within this period, water body grew from 0% to 1.6% while bare surface and mine tailing grew from 0.7% to 2.6% with vegetation remaining the dominant feature despite the decline throughout the entire study interval. Various patterns of change experienced shows that changes vary in the study area. For instance, built up, water body, bare surface and mine tailings all experienced increase while cultivation and vegetation declined in coverage. The increase experienced by these features is a direct driver of deforestation in the basin.

However, results from LULC classification and the spectral signatures of artisanal gold mining sites showed that mining activities is the dominant driver of deforestation in the basin between the years 2014 and 2022, which is responsible for the significant increase of water body from open pit and damming of stream and also bare surface and mine tailings from washing and pre-processing of extracted gold ore. This claim is supported with the decline in biomass as shown by the NDVI values between these periods showing recent intensive artisanal gold mining activity. The study concluded that artisanal gold mining could trigger extensive terrain deformation with consequent loss of biodiversity, ecological modification, drainage obstruction and health implications on miners and settlers within the basin. Consequently, continuous monitoring of the transformation of the different land cover feature classes is important for implementing environmental management programmes that will advance sustainability in mining operations. The findings will also help in taking proactive measures in appraising and enforcing mining and development laws to manage and limit the recent and fast depletion of the natural land cover within the study area.

Recommendation

The study recommends that government agencies, mining commission and other relevant Environmental Protection Agency should reinforce the need to regulate land concession and monitor artisanal mining activities within the Basin. Reclamation and restoration projects should be intensified in order to manage degraded environments around the mining sites.

Community engagement is recommended – Education, Awareness program and Sensitization of local community within the basin on the impact and consequences of unregulated artisanal mining activities on the environment. Local monitoring team can be set up to monitor and report irregular activities of licensed miners for immediate and appropriate sanctions.

Encourage groups of artisans to pull funds together for modern tools and mining equipment and also to enable them leverage on improved and recent mining technologies with less harmful impact on the environment.

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Table 5: Accuracy Assessment Report (%)
	2002		2014		2022
Class	Producers Accuracy	Users Accuracy	Producers Accuracy	Users Accuracy	Producers Accuracy	Users Accuracy
Bare-surface/Mine Tailings	88	90	91	96	90	98
Built-up	91	95	90	92	90	97
Cultivation	91	93	92	93	91	91
Vegetation	97	98	92	91	96	98
Waterbody	90	89	90	93	92	87
Overall Accuracy	92.97%		96.09%		92.97%
Kappa Coefficient	0.8831		0.9339		0.8918