Landscape Transformations (1987–2022): Analyzing

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1. Introduction

Mining is an extractive productive activity present in a wide variety of geographical areas around the world. In many cases, it serves as a fundamental basis for the economic livelihood of certain populations [1,2]. The high global demand for mineral resources profoundly impacts ecosystem services [3,4]. This type of activity generates landscape modifications and has consequences on land use organization within the territory. The extraction of gold plays a crucial role in financial markets and its global production footprint, even serving as a store of value in numerous developing economies [5]. Gold is regarded merely as a commodity; for the government, it is a product that, they claim, will boost the economy and bring prosperity to communities. However, the environmental cost generated by this activity is called into question [6].

Mining activities often generate environmental impacts and social conflicts due to land use and other associated resources, such as water, which is affected by significant sources of heavy metals used for mineral extraction, leaving water bodies with contaminant residues [7]. Moreover, when this activity is carried out with minimal environmental controls, extractive practices become unsustainable according to environmental legislation, leading to loss of quality and degradation of ecosystems [8]. Not only is the environment affected by these activities, but workers engaged in illegal gold mining also suffer direct and indirect impacts [9]. Gold extractivism is imposed as a state economic policy framed as a driver of development and prosperity, while extractivism acts as a catalyst for environmental and social conflicts, contributes to climate change, and poses significant threats to both human well-being and ecosystems [6,10].

Mining activities have severely damaged numerous terrestrial habitats and reduced the vegetation’s capacity for carbon retention [11,12]. From the perspective of landscape ecology, mining exploitation is one of the most impactful anthropogenic activities, significantly affecting biodiversity. The severity of these effects is related to the extraction methods and the conditions under which the activity is carried out, which, in turn, depend on the permissibility or control exercised by the authorities and the existing regulatory framework [13,14,15]. Gold extraction techniques have evolved to involve the use of chemicals harmful to the environment. Contaminated water escapes into the environment, polluting rivers, soils, and nearby vegetation, highlighting the need to implement sustainable practices to mitigate ecological damage and protect the affected local ecosystems [16,17].

Colombia stands out for its abundance of various types of mineral resources. In the current century, the government has implemented several policies and regulations to promote the expansion of the mining industry, attracting foreign investors and leading to what became known as the “mining boom” [18]. However, this mining potential contrasts with an increase in environmental degradation, especially concerning water resources. Underground mining activities in areas with high water tables cause a drastic shift in land use, putting significant pressure on both the ecosystem and the environment, resulting in a profound landscape change [19]. This type of change calls for a reconsideration of the landscape from a sustainability perspective, treating it as a dynamic socio-ecological system [20].

Studying landscape transformations from an environmental-geographical perspective is essential for the planning and protection of landscapes. It allows for the establishment of different regulatory parameters to control the development of human productive activities based on the degree of landscape visibility [21]. Colombia is notable for its diversity of landscape mosaics, which are interconnected with a wealth of water resources and abundant mineral wealth. Historically, these resources have played a fundamental role in spatial development and organization. From a territorial planning perspective, it is crucial to consider the environmental dimension [22]. Economic development is the main driver of landscape transformations. Extractivist economies can magnify and accelerate soil-related problems, highlighting the need to find territorial sustainability by balancing economic growth and ecological conservation [23]. Therefore, from a territorial planning perspective, the preservation of the traditional landscape is seen as a sustainability strategy [24,25].

In a regional context, the Colombian Caribbean presents a persistent trend toward landscape fragmentation. This trend may be attributed to government efforts to protect specific ecosystems through the declaration of protected areas [26]. It is essential to approach research from the geographical dimension of the landscape, enabling the measurement of land use changes. This approach serves as a diagnostic tool for formulating more specific land management and governance policies to resolve conflicts between land development and ecological conservation [27].

The deterioration of landscape quality, atmospheric pollution, and the overexploitation of natural resources are just some of the main concerns that are reflected in social and environmental conflicts. To understand and resolve these issues, they must be addressed holistically from the perspective of environmental geography [28,29]. Mining activities cause large-scale disturbances and fragmentation when original habitats are removed, excavated, or buried under the deposition of waste material, exposing deforestation processes that show an increasing trend [30,31]. The gold rush in recent decades has led to an acceleration of deforestation and landscape conversion due to illegal gold mining carried out through extractive practices without environmental regulation, such as the logging and burning of riparian forests, as well as placer mining using sluices, which are almost always managed by criminal organizations [32,33,34]. In this regard, given the polysemic nature of the term “landscape”, it is essential to define its conceptual boundaries to understand the scope of the research. In this case, the landscape was approached methodologically within the physiographic environment, incorporating human interactions to achieve a holistic understanding of the phenomenon. However, there are various ways to approach the landscape, depending on the researcher’s perspective. These approaches may include perception, social participation, ecological functionality, urban aesthetics, and heritage value (both cultural and natural), all of which are framed within the existing regulations of each country [35].

This study analyzes spatial dynamics based on the changes in land cover and classifies the physiographic units of the landscape using geographic information processing. It highlights the spatial changes that occurred during the period 1987–2022 and identifies the landscape units that experienced the most significant transformations due to mining activities. This analysis provides a spatial snapshot of how mining activity has become a driving force behind landscape transformations in the southern part of the municipality of Ayapel in northern Colombia.

The results of this study, in addition to establishing a precedent for the continental Caribbean region of Colombia, provide a basis for territorial and environmental authorities as a tool for land planning and management. This approach not only identifies the areas of greater environmental importance that are affected by mining activities but also assesses the degree of impact on land cover and water resources. As a result, competent authorities can focus their control and monitoring efforts on the most critical areas, optimizing economic and human resources to target areas of interest. This leads to benefits such as land use planning according to potential uses, a reduction in land use and water resource conflicts, mitigation of environmental impacts on vegetative cover, wildlife populations, and water sources, as well as a more comprehensive understanding of the territory. Moreover, it fosters greater social acceptance of mining activities when they are carried out with environmental responsibility.

Research reveals a critical situation from an environmental perspective, as mining becomes an agent of landscape transformation and environmental destruction. This issue is linked academically to the perspectives outlined in the Sustainable Development Goals (SDGs) to address issues related to ecosystem pollution, as highlighted in SDG 15 (Life on Land) [36,37]. It is essential to conduct research that highlights the environmental impacts of extractivism on ecosystems, serving as a tool to promote the sustainable management of forests, the fight against desertification, the halting and reversal of land degradation, and the prevention of biodiversity loss. These efforts call for the implementation of actions to control and reverse ecosystem degradation as a global priority in various geographic contexts [38,39,40].

2. Materials and Methods

2.1. Study Area

The study area is located in the southern region of the municipality of Ayapel, in the Department of Córdoba, Colombia, covering a total area of 684 km². It encompasses all the mining production zones of the municipality, which are distributed along the courses of the following five micro-watersheds: Las Minas, Quebradona, Escobillas, Trejos, and La Ceiba streams. These micro-watersheds are part of the sub-basin of the Ayapel swamp within the Bajo San Jorge region (Figure 1).

The municipality of Ayapel has experienced significant population changes throughout its history. During the 20th century, the settlement pattern was fragmented due to the formation of new municipalities such as La Apartada, Buenavista, and Pueblo Nuevo. Historically, the rural population has declined for multiple reasons, including migration to Venezuela and internal displacement to urban areas in search of better socio-economic conditions [41]. According to estimates from the National Administrative Department of Statistics (DANE), in 2020, Ayapel had a total population of 47,782 inhabitants, of which 28,044 resided in the municipal capital. Projections for the year 2035 estimate a total population of 51,850 inhabitants, with 29,186 residing in the municipal capital [42]. The municipality’s economy is centered on extractive activities such as fishing, gold mining, and the extraction of sand for construction. In addition, agriculture (rice, corn, and cassava) and extensive livestock farming play a significant role in the local economy. Other less relevant activities include commerce, tourism, and certain artisanal trades [41].

It is important to highlight that 13,200 hectares of the study area are part of the RAMSAR site known as the “Convention on Wetlands” of the Ayapel Swamp Complex, covering 19% of the total area. Additionally, the Colombian government has provided legal mechanisms to protect the water body, declaring it a district of integrated management of renewable natural resources (DMI). The DMI covers 636 km² of the study area, representing 93% of the total. Within the DMI, zoning was established under three management categories, each with specific guidelines for compatible and prohibited activities. The categories are as follows: Areas of Protection and Preservation (APT), Areas of Sustainable Production (APS) and Areas of Recovery (AR). The regulations specify that gold mining is prohibited in the APT areas. However, in the APS and AR areas, gold mining is considered compatible as long as it is carried out using technical and sustainable methods [43].

2.2. Methodology

The temporal interval subjected to geospatial analysis spans from 1987 to 2022. The starting year of the study was defined based on two factors: the availability of satellite images and the first evidence of mining activities, which began in the 1980s [44]. This starting point allowed for the establishment of a baseline for the analysis of spatial transformations.

The research was structured into the following methodological phases:

Phase 1: Collection of Documentary and Spatial Information

This phase involved gathering data from published sources provided by various institutions. The main data sources included:

Base cartography from the Agustín Codazzi Geographic Institute (IGAC);
Geological and geomorphological units from the Colombian Geological Service (SGC);
Land cover data from the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM);
Mining title status from the National Mining Agency (ANM).

The satellite images used for the study correspond to the Landsat 5 TM, Landsat 7 ETM, Landsat 8 OLI, and Sentinel 2B sensors, covering the temporal window of 1987, 2002, and 2022, respectively. These images allowed the identification of changes in land cover over time, particularly those associated with mining activities. Images with a spatial resolution of 30 m or higher were considered suitable for the study. The spatial resolution of the Landsat 7 and 8 images was enhanced to 15 m using the pansharpening technique [45]. Additionally, a minimum radiometric resolution of 8 bits was required, along with a spectral resolution covering the visible, near-infrared, and mid-infrared spectra and cloud coverage below 10%. The list of images obtained is presented in Table 1.

Phase 2: Data Processing

This phase was conducted using the ArcGIS Pro 3 user license under the Institutional Educational Agreement Small-Esri-University of Córdoba (Colombia). To quantify the spatial changes in land cover, the CORINE Land Cover methodology was applied, following IDEAM’s publications, including:

“National Legend of Land Cover. CORINE Land Cover Methodology Adapted for Colombia, 1:100,000 scale” (2010);
“Land Cover Pattern Catalog of Colombia” (2012a);
The book “Visual Interpretation of Remote Sensor Images and its Application in Land Cover and Use Surveys” from IGAC (2005).

For this study, land cover was defined at level 2 to ensure homogeneity and optimal graphic representation according to the minimum mappable area. The primary data sources included the 1:100,000 scale Land Cover Map from IDEAM for the periods 2002–2005 and 2018, as these dates correspond to the timeframes that were synchronous with the availability of satellite images [46,47,48,49,50]. Land cover maps were produced for 2002 and 2022, adjusting the extent and shape of the land cover where applicable. For 1987, due to the lack of IDEAM data, land cover was derived using a supervised classification technique. This process was carried out in three key stages:

Segmentation, which involves grouping neighboring pixels with similar characteristics;
Training using representative samples for each type of cover;
Supervised classification based on prior training.

The result was a classified raster image of land cover, which was then converted into a vector format to facilitate topological analysis and adapted to the CORINE Land Cover classification system. To achieve a more precise delineation of land cover, spectral indices such as the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Vegetation Index (NDVI) were analyzed, along with band combinations. This approach enabled the identification of pictorial-morphological characteristics, allowing for a more detailed definition of each type of land cover [51].

Phase 3: Analytical Results

In this phase, an analytical synthesis was performed to develop a cartographic product capable of identifying the landscape units in the study area and highlighting those most affected by mining production. The cartographic representation of the landscapes was based on the methodology proposed by Georges Bertrand, in line with the system of the Soviet school of thought, which was later adopted by Cuban geography. This approach is characterized by classification at different scales and subdivisions into areas that share homogeneous characteristics [52,53,54].

To carry out the GIS processing, guidelines from several published studies were followed [55,56,57,58,59,60]. In summary, a general recognition of the area’s physical geographical characteristics was conducted to group homogeneous elements, which then facilitated the delineation of landscape units. The climatic properties of the area were described, including thermal floors, temperature, and precipitation, to establish climatic or life zones with similar characteristics. Using a digital elevation model (DEM) obtained from the ALOS PALSAR satellite with a spatial resolution of 12.5 m, relief and slope maps were generated. These maps allowed for the classification of areas into slope and elevation ranges according to IGAC’s classification system, which defines the following categories:

0–3%: level;
3–7%: slightly inclined;
7–12%: moderately inclined;
12–25%: strongly inclined;
25–50%: slightly steep or slightly escarped;
50–75%: moderately steep or moderately escarped;
>75%: strongly escarped or strongly steep.

Additionally, the geomorphology of the study area was characterized by delineating geomorphological units. These units were grouped according to the similarities in landforms and morphogenetic environments.

Lithological and geological aspects were also integrated to delineate geological units and soil mapping units. Once the physiographic elements that make up the study area were identified, the classification of landscape units was carried out. These units were organized hierarchically, taking into account homogeneous attributes and specific criteria.

The first level is the most general and is based on the geomorphological component (morphostructural, morphodynamic) and climatological aspects;
The second level is a subdivision that groups relief according to altitude ranges and slope percentages;
The third level further subdivides the units according to geological and lithological criteria.

Once these hierarchical units were established, a thematic overlay analysis was applied to combine all these attributes, resulting in the landscape unit map (Figure 2). The next step was to intersect the landscape unit map with the land cover map, focusing specifically on mining-related land cover. This process facilitated the analysis of conflicts caused by the uncontrolled development of mining activity.

Once the landscape units of the study area were identified, an evaluation of the legality of mining activities was carried out. To achieve this, the mining title data from the National Mining Agency (ANM) were compared with the mining coverages for the year 2022. This analysis allowed for the identification of areas with active mining titles, subcontracting formalization agreements, ongoing mining title requests, as well as areas in a state of illegality. The process involved the overlay of mining coverages with the corresponding polygons, enabling the identification of areas with mining rights and formalization processes. Areas deemed incompatible for mining were also highlighted. These include areas located within protected zones (such as the Ramsar site of the Ayapel Swamp Complex) or areas where no formal mining requests have been made. This analysis provided a comprehensive overview of both legal and illegal mining in the region. It also revealed social conflicts and tensions between the different stakeholders involved in or affected by mining activities.

3. Results

3.1. Spatial Delimitation of Landscape Units

As a starting point, the landscape units were identified. It is important to note that the climatic factor was not included in this classification at the first level, as the area does not present significant differences in temperature or precipitation. Similarly, at the second level, altitude was not considered since the area exhibits minimal altimetric variation, resulting in a uniform thermal floor. The flat zones (0–7%) are the most representative and are primarily located in the eastern and northern parts of the study area. These zones correspond to terraces, stream channels, and floodplains. The slightly inclined zones (7–12%) are located on terraces in the central and eastern parts of the study area. The undulating zones (12–25%) are mainly found in the southeast and are associated with hill and knoll landforms. Zones with slopes ranging from 25% to 50% have minimal coverage and are scattered in the southern portion of the study area (Figure 3).

From a geological perspective, the region is located at the southern end of the Colombian Caribbean, bordering the Bajo Cauca region. It marks the end of the western cordillera foothills, transitioning into the Serranía de Ayapel. The region is composed of sedimentary rocks from the Sincelejo group (Betulia formation) (N2-Sc), as well as alluvial (Q-al) and paludal (Q2-l) deposits (Figure 3). The Sincelejo group, formed during the Neogene period, is composed of interbedded claystones, sandstones (ranging from fine to coarse grain), conglomeratic sandstones, conglomerates, and mudstones of fluvio-lacustrine origin.

The alluvial deposits, which date back to the Holocene epoch, are associated with the San Jorge and Cauca rivers and the area’s main drainage systems. These deposits consist of a mixture of transported and eroded material, including fragments of various lithologies that are poorly sorted and lack stratification. The paludal deposits (fluvio-lacustrine deposits) are found in the northern part of the area, near the floodplains and swampy water bodies. These deposits are characterized by bioturbated silt levels atop moderately yellowish-brown clayey sands [61,62]. From a geomorphological perspective, the area is located in an interfluve between the San Jorge and Cauca rivers, mainly consisting of small hills and floodplains. Two distinct morphogenetic environments are present:

Denudational environment: This environment is composed of mounds, hills, and denuded plains resulting from weathering and intense water erosion. The terrain has a maximum relief of less than 200 m.
Fluvial environment: This environment follows the longitudinal flow of the region’s main watercourses. It is characterized by the presence of alluvial terraces, which are bordered by floodplains and a radially shaped alluvial fan. The fan is associated with the accumulation of torrential and fluvial flows from the Cauca River, which overflows or breaches levees, causing water to be diverted toward the Ayapel Swamp basin (Figure 3).

Regarding soils, the Agustín Codazzi Geographic Institute (IGAC) adopted the USDA land capability classification system to assess land quality. According to the IGAC, the area contains six soil cartographic units (SCU), which are classified from Class 4 to Class 6. Class 6 soils are the most predominant, covering 98% of the study area (Figure 3). Class 6 soils are characterized by severe limitations caused by one or more of the following factors: steep slopes, erosion, rockiness, poor drainage, susceptibility to flooding, frequent waterlogging, high water table, and low rainfall. These soils may also present chemical limitations, such as high aluminum saturation, very strong acidic reactions, and very low fertility [63].

Based on the recognition of the physiographic components of the area, three first-level landscape units were identified: (i) denudational hills and knolls, which groups the geomorphological unit (GU) Dlmd; (ii) fluvio-lacustrine terraces and denudational plains, comprising the geomorphological units Dmo, Dsa, Dpo, Fpa, and Faa; and (iii) alluvial valleys, composed of the units Fta and Fpi. These units are further subdivided into second-level units, which are classified based on slope. The classification includes the following three slope ranges: flat or slightly inclined surfaces (0–7%), moderately inclined surfaces (7–12%), and undulating surfaces (greater than 12%). This classification facilitates a more detailed analysis of the terrain and landscape structure (Table 2, Figure 4).

The soils in the study area are associated with three distinct lithologies: heterogeneous medium-sized sediments associated with the soil cartographic units (SCU) RVH and LVH; finer-textured sediments corresponding to the units RVC and RVE; and mudstones, sandstones, and conglomerates, which are part of the RVG and LVA units.

Based on the presented classification, it is evident that terraces and plains are the predominant landforms, accounting for 75% of the total area, followed by the hilly landscape at 15% and alluvial valleys at 10%. The most representative landscape units are the terraces and plains with slopes of 0–12% on mudstones, sandstones, and conglomerates (units II1c and II2c), covering 63.2% of the total area. This highlights the dominance of the Betulia geological formation (Sincelejo group) on flat denudational surfaces. The least common landscape units are the hilly landscape on heterogeneous sediments (unit I3a) and the alluvial valleys on sloping surfaces over conglomerates, each representing only 0.1% of the total area.

3.2. Identification of Mining Coverages

The land cover for the years 1987, 2002, and 2022 was geographically delineated (Figure 5). The largest land cover category in 1987 was heterogeneous agricultural areas (Unit 2.4), covering 276 km². This category was characterized by a mosaic pattern of crops, pastures, and natural spaces coexisting, reflecting a process of land colonization for the expansion of the agricultural frontier during that time. Subsequently, pastures (Unit 2.3) became more predominant within the study area, particularly associated with livestock activities, reaching 379 km² by 2002. Other significant land covers included forests (Unit 3.1) and areas with herbaceous and shrub vegetation (Unit 3.2). These covers had experienced less human intervention during the initial period, totaling 220 km². They were primarily located in the central and western parts of the study area, possibly because these regions were uncolonized and more difficult to access.

By 2022, clear evidence of intensive gold resource exploitation was observed, with continuous open spaces along the courses of several streams, especially Trejos, La Ceiba, Mala Noche, Escobillas, Quebradona, Las Claras, El Contento, Las Minas, Monteadero, and Las Mellizas. In many cases, the streams were impacted almost from their source to their confluence.

The scenario obtained for this multitemporal window shows a more artificial landscape, characterized by the expansion of the agricultural frontier at the expense of natural covers, which have been relegated to isolated areas such as riparian buffers, commonly known as gallery or riparian forests, decreasing from 91 to 28 km² over the past 35 years. The increase in mining fronts has led to the destruction of other land covers and the deactivation of other land uses. Analyzing previous uses reveals that the most transformed cover into mining was clean pastures, with 25.5 km² of this cover degraded into mining areas, followed by heterogeneous agricultural areas, 14.7 km² of which were converted. Additionally, natural areas, including forests and herbaceous and shrub vegetation, were affected, with 8.5 km² deforested for mining activities.

Over the three time periods analyzed, there was a significant increase in the area associated with mining activities during the 35-year study period (Figure 6). In 1987, mining activity, although incipient, was already present. It was mainly concentrated in the Quebradona and Monteadero creek basins, located in the western part of the study area, covering an area of only 6.1 km².

During the period 1987–2002, mining-related coverages experienced moderate growth, increasing from 6.1 km² to 7.9 km². This growth was mainly concentrated in the Quebradona creek basin, where mining patches expanded longitudinally along the watercourse. There was also an increase in the number of mining production units along the Escobillas creek and more evidence of alluvial mining activities along the Trejos and Las Minas creeks. By 2022, mining activity had become an evident transformation within the study area. Unlike the previous periods, when mining-related polygons were dispersed, the mining coverages in 2022 reached 55 km², reflecting a progressive increase since 2002. Mining activities now account for 8% of the total study area. There was clear evidence of intensive exploitation of gold resources, characterized by large, open mining areas extending along the courses of several creeks, especially along Trejos, Ceiba, Mala Noche, Escobillas, Quebradona, and Las Minas. Many of these creeks were intervened from their headwaters to their mouths, highlighting the extent and intensity of mining activity in the region.

3.3. Spatial Alterations in Landscape Units Affected by Mining

The landscape units have undergone transformations related to production techniques and their evolution over time. In 1987, the most affected unit was Unit I3c (inclined hills and knolls with slopes greater than 12% on mudstone, sandstone, and conglomerate soils), accounting for 44.5% of the total mining intervention area (270 ha) (Figure 7). This occurred because the largest mining operations were initially concentrated at the confluence of the Quebradona and El Contento creeks, where denudational hills are present. These slopes were rich in metal deposits, prompting miners to extract materials manually and with the use of motor pumps.

By 2002, the situation remained similar, with the same landscape units continuing to experience the highest levels of intervention. However, there was an expansion of mining fronts, especially on terraces and plains in the lower reaches of the Quebradona creek. In 2022, significant changes in production methods were observed. Mining activities primarily affected Unit III1a (alluvial valleys on flat surfaces with slopes of 0–7%, on heterogeneous, medium, and fine sediments), which experienced accelerated growth, reaching 2342 ha and representing 43% of the total mining coverage (Figure 7). This trend reflects an increased interest in exploiting alluvial valleys and terraces, which were affected by the use of dredges and dragon boats for resource extraction.

Another unit with significant mining activity was Unit II1c (fluvio-lacustrine terraces with slightly inclined slopes of 0–7% on soils of mudstones, sandstones, and conglomerates). This unit experienced steady progressive intervention, beginning with an affected area of 81 ha in 1987 and reaching 1224 ha in 2022 (22% of the total area). This mining activity was mainly focused on the lower sections of the Quebradona creek and areas adjacent to the alluvial valleys of the Escobillas, Trejos, and Mala Noche creeks. The third most affected unit is Unit I3c, which had been the most significant unit in previous timeframes. However, by 2022, its participation in mining activity had decreased. Since these areas are elevated hills and knolls with steep slopes, they are not suitable for operating dredges and dragon boats, resulting in less growth in mining operations compared to other units.

These three landscape units—III1a, II1c, and I3c—represent 82% of the total area occupied by mining. Other units, such as II2c, III2a, and III3a, are located in the southern and eastern parts of the study area, but they show significantly lower levels of mining activity.

The described situation highlights the presence of areas that are subject to mining intervention. The analysis and quantification of the percentage of affected areas allow for the identification of the most vulnerable zones. Based on the collected data, it was determined that the most vulnerable landscape units are units III1a, III2a, and III3a, all of which have surface intervention percentages exceeding 30%. Additionally, units I1a and III2c exhibit more than 20% of their surfaces being affected (Figure 8). This indicates that the most vulnerable and susceptible landscape units for mining intervention are alluvial valleys. These areas possess physiographic characteristics that provide the appropriate conditions for the exploitation of mineral resources. In contrast, units II1b, II2b, and I3a showed no evidence of mining intervention, indicating that fluvio-lacustrine plains with very fine sediments do not hold a significant interest in mining activities. This information serves as a valuable tool for territorial planning and land use management. It facilitates the classification of areas based on the degree of intervention and the identification of units that are most susceptible to exploitation. This classification can be integrated into official territorial planning documents to promote better governance and more sustainable land use practices.

In this context of exploitation and weak control, the absence of a state presence allowed illegal actors to take over mining activities in the region. In the absence of state authority, these areas were occupied by illegal armed groups that exercised territorial control, such as the United Self-Defense Forces of Colombia (AUC) before their demobilization and the Gaitanista Self-Defense Forces of Colombia (AGC) in more recent years. These groups imposed levies on mining production. During fieldwork, interviews were conducted with individuals directly involved in mining activities, providing key information on the dynamics and consequences of mining in the region. For instance, Oscar, a former miner who worked for many years in mining units in the area, stated that these groups demanded a 10% commission from miners. Furthermore, landowners were forced to lease their properties for mining activities under the threat of violent retaliation. This information was corroborated by another source, who revealed that illegal groups imposed fees ranging from one to five million Colombian pesos (COP) per backhoe excavator. These groups exercised strict control over sales and smelters to monitor production [44].

From a legal standpoint, most gold extraction in the area occurs outside the framework of the law. Studies on this issue have established that 91% of mining activity is considered illegal [64]. The situation is further aggravated by the fact that a large percentage of mining-related land cover is located in areas that are not formally recognized by the National Mining Agency (ANM). This includes areas with no association with mining titles, subcontract formalizations, or pending applications.

Another significant portion of the mining area overlaps with protected areas (Figure 9). These areas are considered exclusion zones because of their environmental importance, and mining activities in these locations directly contravene environmental conservation efforts. The situation changed with the implementation of Decree 358 of 2018, through which the national government designated the Ayapel Swamp Complex as a Ramsar site. This designation provided legal tools for land use planning and introduced restrictions on incompatible activities within the site’s boundaries. Consequently, mining is considered an incompatible activity and is therefore prohibited within the limits of the Ramsar site. This legal measure aims to preserve the ecological integrity of the Ayapel Swamp Complex and reduce the environmental impacts caused by illegal mining.

Although there are legal tools in place for the protection and technical use of mineral resources, the institutional control and management of mining activities have proven to be insufficient. Despite the efforts of the CVS (the regional environmental authority), along with the police and national army, which have carried out sanctioning processes, confiscations, destruction of machinery, and arrests, the reality is that these actions have not been enough to curb illegal mining. Evidence of this insufficiency is reflected in the fact that, during this same period, there was a significant acceleration in mining expansion, rendering any planning tools or regulations essentially ineffective or a “dead letter”. The territorial control of mining activities is predominantly in the hands of criminal gangs (Bacrim), which serve the interests of drug trafficking networks. It is evident that, over the past few decades, the landscape has undergone significant spatial transformations as a result of the exploitation of geo-mineral resources, particularly gold. These transformations underscore the need for stronger institutional presence, effective territorial planning, and enforcement of environmental regulations to ensure the protection of ecosystems and the sustainable use of natural resources.

4. Discussion

The results of this study show a clear pattern of landscape transformation in the Ayapel region due to the expansion of mining, especially in the areas of alluvial valleys. These findings are consistent with previous studies in other regions affected by mining activities, such as in the Magadan region, Russia, where alluvial mining has caused significant degradation of the landscape and the loss of vegetation cover in the last two decades [65]. Similar to Ayapel, Magadan experienced an accelerated expansion of gold mining, with the affected area increasing from 41,206 ha in 2000 to 72,602 ha in 2022, reflecting a similar increase in the scale of mining intervention reported in this study. This pattern of landscape transformation is also observed in the Peruvian Amazon, where gold mining has caused soil degradation and a loss of essential nutrients, limiting natural regeneration [66]. Similarly, in West Kalimantan, spatial models have demonstrated a correlation between the age of mining activities and the severity of degradation, highlighting the importance of multitemporal monitoring [67].

The findings are also consistent with those observed in the research conducted in Buriticá (Antioquia, Colombia), where gold mining has caused significant impacts on the natural environment and the health of the population [68]. The contamination of water bodies is one of the main effects observed in both Ayapel and Buriticá. Similarly, studies in the Atrato River, Colombia, highlight the release of heavy metals into sediments and their accumulation in fish species, affecting both biodiversity and food security [69]. On the other hand, in the context of the Umbará neighborhood in Curitiba, Brazil, it has been shown that environmental transformation due to mining does not necessarily result in effective ecological recovery but rather a reorganization of the landscape with alternative uses that often fail to restore its original functions [70]. In addition to the environmental impact, severe social consequences were identified in Buriticá, such as cultural clashes and increased crime, aspects that are also present in the context of Ayapel due to the presence of illegal groups that have taken control of illegal mining activities.

On the other hand, in the spatial context of the Cruz Verde-Sumapaz Páramo Complex, the effects of mining include not only landscape transformation but also deep socio-environmental conflicts due to the lack of clarity in the legal provisions for mining in páramo areas. Although the context of Ayapel differs in terms of the nature of the ecosystem, this study shares similarities in the uncontrolled expansion of mining in sensitive areas and the lack of adequate government management [71]. In both cases, the absence of effective regulations allows for exploitation without adequate control of the long-term impacts. Recent studies in West Kalimantan, Indonesia, also highlight how unregulated mining creates similar patterns of environmental degradation, particularly in water bodies and soils [67]. Additionally, research conducted in Tandilia, Argentina, shows that mining in fragmented ecosystems increases the colonization of invasive species, exacerbating biodiversity loss and disrupting the original ecological structure [72].

Regarding changes in land cover, the results of this research are consistent with what has been documented in another study conducted in the region of Nechí, Antioquia, where gold mining caused a significant loss of vegetation cover, with a 21.9% reduction in forested areas between 1986 and 2010 [73]. This transformation of the landscape was also observed in Ayapel, with a similar trend in the loss of forests due to the expansion of mining. However, important differences were observed in Nechí, where water bodies increased by 66.3% due to the nature of alluvial mining, while in Ayapel, the impacts on water bodies have been more negative, with high levels of contamination and sedimentation, as noted in this study. Studies in Zimbabwe indicate that the impacts on biodiversity are also evident in the structure of woody vegetation, with significant differences in species density and mortality between mining and non-mining areas [74].

In contrast, a study on mining in eastern Cameroon shows a trend toward the expansion of artisanal and semi-industrial mining activities from the year 2000, with more recent environmental impacts compared to Ayapel, where mining has developed over a longer period of time [75]. In the same vein, in Kalimantan, Indonesia, high NDVI values were identified in less affected areas, highlighting the importance of preserving ecological corridors in active mining regions [67]. Despite the temporal difference, the effects on land cover are similar, with a significant reduction in vegetation areas and an increase in bare land. On the other hand, in the case of the Wassa West district, Ghana, gold mining between 1986 and 2002 caused significant deforestation, with a loss of 3168 ha of forests in the three main mining concessions. However, unlike Nechí and Ayapel, the conversion of agricultural land to mining sites was more prominent, highlighting the differences in local economies and land use patterns in the various mining-affected regions. The greater conversion of agricultural land may suggest that livelihood strategies in Ghana depend more on agriculture, which influences the magnitude of the impact [76].

The growth of mining in Ayapel is driven by multiple factors, most notably the growing demand for gold in international markets and the weak regulation of illegal extractive activities. This phenomenon has also been observed in other parts of the world, where the lack of government control and illegal mining activities have exacerbated environmental degradation, as documented by Shikhov et al. (2023) for the Magadan region, Russia. In the context of Ayapel, artisanal mining has specific impacts on water and soil pollution, similar to those observed in West Kalimantan [72], where alarming levels of mercury have been reported in surface and groundwater. In Curitiba, the lack of implementation of restoration plans demonstrates that legislative measures do not always translate into effective recovery, leaving significant ecological scars [70]. In the context of Ayapel, the expansion of mining is closely linked to mechanization, which has enabled large-scale extraction in previously unexplored areas, similar to the use of heavy machinery in the periglacial areas of Russia. In Latin American and African cases, the factors driving change are similar: the growing demand for minerals and the lack of effective regulations are parallel to those documented in eastern Cameroon. Studies highlight how mining in that region has experienced accelerated growth due to exploration by national and international companies since 2004 [75]. In Ayapel, this process has been less formalized, which has intensified illegal mining, aggravating the environmental impacts.

In the Colombian context, the growth in international demand for minerals and the lack of effective control over illegal and formal mining are the most determining factors [77]. While in Ayapel, the expansion of mining seems to be mainly linked to gold extraction and, in Buriticá and Sumapaz, the intervention of multinational companies has played an important role in the acceleration of impacts, particularly through the creation of socio-environmental conflicts. Studies conducted in the Peruvian Amazon have demonstrated how the lack of effective regulations and the involvement of multinational companies exacerbate socio-environmental conflicts, a pattern that can also be observed in the contexts of Sumapaz and Buriticá [66]. The lack of regulatory updates has allowed for the exploitation of protected areas, something that could be relevant for the context of Ayapel if the regulations are not adapted to protect these fragile ecosystems [71].

The consequences of territorial transformations would require a multi-scalar analysis, as demonstrated by the alteration experienced by fishing activities as a result of the discharge of contaminated water that drains through the hydrographic network until it reaches the Ayapel swamp [78,79,80]. This results in negative impacts from the use of mercury (Hg), which causes extensive environmental degradation during and after mining activities [12,81]. In a similar context, in the Tandilia district of Argentina, severe impacts on aquatic systems have been documented due to the sedimentation and toxic waste generated by mining, affecting the water quality and aquatic biodiversity [72]. As a result, there is a negative impact on the hydraulic characteristics of the swamp and the fish populations, in addition to the contamination of fish, which are an important part of the daily diet of local populations [82]. In terms of biodiversity, faunal and floral communities were seriously affected by the destruction of natural and forested areas to convert them into mining sites [83]. These impacts on biodiversity are particularly relevant given the ecological characteristics of the swamp complex in the municipality of Ayapel.

Additionally, mining wastewater contains high concentrations of solids (sediments) and mercury (a toxic heavy metal) that degrade the water quality in the swamp. This water body is used as a source of supply for the municipal urban center, which generates conflicts over the use of the resource for human consumption [84,85,86]. Studies conducted on the Madeira River (Brazil-Bolivia) show elevated mercury levels in the waters, which not only impact aquatic biodiversity but also threaten the food security of communities relying on these water bodies, a phenomenon also observed in Ayapel [69,87].

The ability to adequately monitor the impacts of mining in Ayapel, similar to what has been reported in other studies, is limited by the lack of adequate infrastructure to measure water quality and other environmental indicators, making it difficult to accurately assess the impacts [68,73]. To improve the precision of monitoring, future studies could incorporate more advanced technologies, such as the use of high-resolution satellite images and sensors to evaluate water quality and vegetation in real time, as has been implemented in other international mining studies [73,88]. In this context, research in West Kalimantan has demonstrated the effectiveness of combining remote sensing with field techniques to identify specific sources of pollution and assess changes in the water quality [67].

The methodology based on the use of satellite images, such as those from Landsat, is useful for identifying general patterns of change in land cover in extensive mining areas. However, the spatial resolution of these images presents significant limitations as it does not capture small-scale details, such as alterations in vegetation structure or changes in the soil that can affect biodiversity and ecological stability in a differentiated way [60,62]. This is particularly relevant in highly heterogeneous landscapes, such as the alluvial valleys studied, where the loss of specific species and the fragmentation of habitats may not be detected using only vegetation indices like NDVI. The integration of higher-resolution sensors and multi-source techniques, including UAVs and hyperspectral sensors, could provide a deeper analysis of the changes in the ecological and social structures of the affected area, improving the precision and detail of the findings [68,75]. A study in Zimbabwe highlights how hyperspectral imagery allows for more precise identification of alterations in soil and vegetation structures, providing a complementary tool for studies in Ayapel [74].

Additionally, although the current approach focuses on identifying changes in land cover, recent studies emphasize the importance of incorporating socio-economic and health variables in the evaluation of mining impacts. For example, the study in Buriticá, Colombia, documents how mining not only alters the environment but also generates social conflicts and affects the health of communities, thus underscoring the need to evaluate these factors together with ecological changes [68,71]. Similarly, research in the Peruvian Amazon highlights the interconnection between environmental degradation and socio-economic consequences, such as the depletion of essential natural resources for local communities [66,89]. The development of models that include these factors would allow for the anticipation and mitigation of impacts on local communities, creating a more comprehensive tool for planning and environmental management.

5. Conclusions

The landscape in Ayapel has been closely tied to water sources from its earliest settlements to the present, fostering an amphibious culture shaped by the interaction between humans and the aquatic environment. The landscape has been transformed by the exploitation of natural resources by various stakeholders. However, in the early decades of the 21st century, the transformation of land cover accelerated significantly, increasing from 6.1 km² in the late 1980s to 54 km² by 2022—an increase of more than six times the initially mined area. This has caused significant functional, social, and environmental imbalances, with notable impacts on natural areas such as riparian forests and zones of herbaceous and shrub vegetation.

A total of 16 landscape units were identified, of which 13 units experienced some degree of intervention due to mining activity. Special attention was given to the landscape units associated with alluvial valleys on heterogeneous sediments, as they have experienced over 30% of their total area being occupied. This demonstrates that these areas are preferred sites for mining exploitation due to their high potential for mineral deposits. This impact has worsened over the past decade due to the introduction of new production techniques with machinery (dredges, dragon boats, backhoes, and bulldozers), which offer higher production and economic yields but simultaneously leave behind significant environmental liabilities due to the lack of adequate controls.

The transformation of the landscape resulting from mining activities in the municipality of Ayapel entails various issues, such as geomorphological alterations, contamination of water sources like the Trejos, La Ceiba, Mala Noche, Escobillas, Quebradona, Las Claras, El Contento, Las Minas, Monteadero, and Las Mellizas creeks, conflicts over land and water resource use, impacts on natural covers, and changes in the socio-economic conditions of the inhabitants. One of the first steps to address this situation is to transition these mining fronts into formal operations, where applicable, in a sustainable manner and to close those that cannot be legalized in accordance with environmental restrictions and current regulations.

This study provides a comprehensive perspective on the landscape transformations in the Ayapel region caused by mining activity, highlighting the complexity of ecological and socio-economic effects. The methodology, based on the use of satellite images, proved to be a useful tool for identifying areas affected in terms of vegetation cover and water bodies. However, it presents limitations in terms of spatial resolution and the ability to capture small-scale changes, such as habitat fragmentation and the loss of specific biodiversity. As shown in previous studies on mining contexts in Russia and Cameroon, the integration of high-resolution technologies, such as drones and hyperspectral sensors, could improve the precision and scope of the analysis, allowing for the identification and management of critical areas with greater effectiveness.

The results underscore the importance of cartographic overlays of physical geographic characteristics and multitemporal analysis to identify epicenters of mining intervention, which contributes to a deeper understanding of how extractive activity transforms the landscape and affects local ecosystems. The findings of this study provide valuable information for local actors and policymakers to guide territorial planning decisions. The identification of critical areas impacted by gold mining and the data on vulnerable landscape units can help inform restoration and conservation strategies, promote more sustainable mining practices, and minimize environmental degradation. This highlights the importance of establishing continuous monitoring mechanisms to evaluate the effectiveness of implemented policies and to adapt environmental management strategies to the changes observed in the landscape.

For future research, a multidimensional methodology is recommended that also encompasses socio-economic and health aspects, as this would enrich the evaluation of environmental impacts by incorporating social and health dynamics, such as those resulting from population migration and the intensive use of natural resources, as documented in regions like Buriticá. Additionally, the development of geo-statistical models and the application of high-resolution remote sensing techniques would make it possible to predict the long-term effects of mining and facilitate the creation of territorial planning and mitigation policies adapted to the reality of the territory. These approaches would enhance monitoring and control capabilities, promoting more responsible and sustainable environmental management.

Author Contributions

Conceptualization, J.D.P.-A., O.P.-A., J.J.-C. and A.C.-C.; methodology, J.D.P.-A., O.P.-A., J.J.-C. and A.C.-C.; software, J.D.P.-A., O.P.-A. and J.J.-C.; validation, A.C.-C.; formal analysis, J.D.P.-A., O.P.-A. and J.J.-C.; investigation, J.D.P.-A., O.P.-A. and J.J.-C.; resources, J.D.P.-A., O.P.-A. and J.J.-C.; data curation, A.C.-C.; writing—original draft preparation, J.D.P.-A., O.P.-A. and J.J.-C.; writing—review and editing, A.C.-C.; visualization, J.D.P.-A., O.P.-A. and J.J.-C.; supervision, A.C.-C.; funding acquisition, J.D.P.-A., O.P.-A. and J.J.-C. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. (A) Spatial delimitation of the study area; (B) municipality of Ayapel, northern Colombia.

Figure 2. Methodological framework for the delineation of landscape units.

Figure 3. Spatial overlay of layers to delineate landscape units.

Figure 4. Landscape units of southern Ayapel.

Figure 5. Land cover for the years 1987, 2002, and 2022 in the southern area of the municipality of Ayapel.

Figure 6. Mining covers the years 1987, 2002, and 2022 in the southern area of the municipality of Ayapel.

Figure 7. Landscape units affected by mining activities in 1987, 2002, and 2022.

Figure 8. Percentage of area affected by mining intervention for each landscape unit.

Figure 9. Mining coverages in 2022 and their legal status with the National Mining Authority (ANM).

Table 1

List of Satellite Images Obtained for the Study.

Sensor	Capture Date	Path/Row	Spatial Resolution
Landsat 5 TM	30 January 1987	009/054	30 m
Landsat 7ETM	1 December 2002	009/054	15 m
Landsat 8 OLI	9 July 2022	009/054	15 m
Sentinel 2B	22 February 2022	No Aplica	10 m

Table 2

Detailed legend of the landscape units for the study area.

First Level Units	Second Level Units	Third Level Units	Code
I. Denudational hills and hills	Flat or slightly inclined surface with slopes of 0 to 7%	a. On heterogeneous, medium, and fine sediments	I1a
	Flat or slightly inclined surface with slopes of 0 to 7%	c. On mudstones, sandstones, and conglomerates	I1c
	2. Moderately inclined with slopes of 7–12%	c. On mudstones, sandstones, and conglomerates	I2c
	3. Inclined with slopes greater than 12%	a. On heterogeneous, medium, and fine sediments	I3a
	3. Inclined with slopes greater than 12%	c. On mudstones, sandstones, and conglomerates	I3c
II. Fluvio-lacustrine terraces and denudational plains	Flat or slightly inclined surface with slopes of 0 to 7%	a. On heterogeneous, medium, and fine sediments	II1a
		b. Fine sediments	II1b
		c. On mudstones, sandstones, and conglomerates	II1c
	2. Moderately inclined with slopes of 7–12%	b. Fine sediments	II2b
	2. Moderately inclined with slopes of 7–12%	c. On mudstones, sandstones, and conglomerates	II2c
	3. Inclined with slopes greater than 12%	c. On mudstones, sandstones, and conglomerates	II3c
III. Alluvial Valleys	Flat or slightly inclined surface with slopes of 0 to 7%	a. On heterogeneous, medium, and fine sediments	III1a
	2. Moderately inclined with slopes of 7–12%	a. On heterogeneous, medium, and fine sediments	III2a
	2. Moderately inclined with slopes of 7–12%	c. On mudstones, sandstones, and conglomerates	III2c
	3. Wavy with slopes greater than 12%	a. On heterogeneous, medium, and fine sediments	III3a
	3. Wavy with slopes greater than 12%	c. On mudstones, sandstones, and conglomerates	III3c

References

1. Monsalve Friedman, L.M. Conflicto sociopolítico e impacto territorial del extractivismo minero. Bitácora Urbano Territ.; 2022; 32, pp. 59-72. [DOI: https://dx.doi.org/10.15446/bitacora.v32n1.98485]

2. Damonte, G.H.; Godfrid, J.; Minchán, A.P.L.; Quispe, E.A. The Surge for Local Innovative Institutions for Transformation: Community-Based Monitoring in the Andes. Glob. Sustain.; 2024; 7, e34. [DOI: https://dx.doi.org/10.1017/sus.2024.26]

3. Kumi, S.; Addo-Fordjour, P.; Fei-Baffoe, B. Mining-Induced Changes in Ecosystem Services Value and Implications of Their Economic and Relational Cost in a Mining Landscape, Ghana. Heliyon; 2023; 9, e21156. [DOI: https://dx.doi.org/10.1016/j.heliyon.2023.e21156]

4. Mathioudakis, S.; Xiroudakis, G.; Petrakis, E.; Manoutsoglou, E. Alluvial Gold Mining Technologies from Ancient Times to the Present. Mining; 2023; 3, pp. 618-644. [DOI: https://dx.doi.org/10.3390/mining3040034]

5. Trench, A.; Baur, D.; Ulrich, S.; Sykes, J.P. Gold Production and the Global Energy Transition—A Perspective. Sustainability; 2024; 16, 5951. [DOI: https://dx.doi.org/10.3390/su16145951]

6. Machado-Mosquera, M. Re-existencias de comunidades negras del Norte del Cauca-Colombia por la permanencia en el territorio, y haciéndole frente al extractivismo minero. Gestión Ambiente; 2021; 24, (Suppl. 1), pp. 225-247. [DOI: https://dx.doi.org/10.15446/ga.v24nsupl1.93299]

7. Fotie, B.M.; Boukari, H.; Daou, I.E.; Momegni, C.K.; Eta, M.A.E.; Tchuikoua, L.B.; Ngueyep, L.L.M. Physicochemical Characterisation of Water and Sediment of the Semimechanized Artisanal Gold Mining Environment of the Béké Locality. Appl. Environ. Soil. Sci.; 2024; 2024, pp. 1-12. [DOI: https://dx.doi.org/10.1155/2024/6888252]

8. Cheepurupalli, N.R.; Abraha, H.; Belay, M.; Anuradha, B. Impacts of Artisanal Gold Mining on Miners and the Environment in the Asgede Tsimbila Woreda of Northern Tigray, Ethiopia. NeuroQuantology; 2022; 20, pp. 981-992. [DOI: https://dx.doi.org/10.48047/NQ.2022.20.1.NQ22381]

9. Fashya, I.; Suseno, B.D. Illegal gold mining grandil: Empirical trail in south citorek lebak regency, indonesia. Int. J. Inf. Bus. Manag.; 2023; 15, pp. 7-21. Available online: https://ijibm.elitehall.com/index4.htm (accessed on 4 January 2024).

10. Teixeira, B.M.; Nicoson, C.J. Transforming Environmental Peacebuilding: Addressing Extractivism in Building Climate Resilient Peace. Ecol. Soc.; 2024; 29, 2. [DOI: https://dx.doi.org/10.5751/ES-14860-290302]

11. Wang, X.; Han, J.; Lin, J. Response of Land Use and Net Primary Productivity to Coal Mining: A Case Study of Huainan City and Its Mining Areas. Land; 2022; 11, 973. [DOI: https://dx.doi.org/10.3390/land11070973]

12. Capparelli, M.V.; Cabrera, M.; Rico, A.; Lucas-Solis, O.; Alvear-S, D.; Vasco, S.; Galarza, E.; Shiguango, L.; Pinos-Velez, V.; Pérez-González, A. et al. An Integrative Approach to Assess the Environmental Impacts of Gold Mining Contamination in the Amazon. Toxics; 2021; 9, 149. [DOI: https://dx.doi.org/10.3390/toxics9070149] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34206785]

13. Jin, W.; Dong, Z.; Bian, Z.; Zhang, X.; Wei, Z. Spatiotemporal Variations in the Impacts of Small-to Medium-Scale Mines Agglomeration Scale on Landscape Pattern and Ecological Risk in the Watershed in a Semi-Arid Ecologically Fragile Area. Ecol. Indic.; 2024; 166, 112319. [DOI: https://dx.doi.org/10.1016/j.ecolind.2024.112319]

14. Daipan, B.P.O.; Tinio, C.E.; Pampolina, N.M. Biodiversity Conservation in Mining Landscapes: A Systematic Review of Assessment Approaches in the Philippines. Philipp. J. Sci.; 2023; 152, pp. 1455-1474. [DOI: https://dx.doi.org/10.56899/152.04.15]

15. Salgado-Almeida, B.; Briones-Escalante, A.; Falquez-Torres, D.; Filián-Haz, K.; Guzmán-Martínez, F.; Escobar-Segovia, K.; Peña-Carpio, E.; Jiménez-Oyola, S. Assessment of Environmental Pollution and Risks Associated with Tailing Dams in a Historical Gold Mining Area of Ecuador. Resources; 2024; 13, 105. [DOI: https://dx.doi.org/10.3390/resources13080105]

16. Mensah, A.K.; Tuokuu, F.X.D. Polluting Our Rivers in Search of Gold: How Sustainable Are Reforms to Stop Informal Miners from Returning to Mining Sites in Ghana?. Front. Environ. Sci.; 2023; 11, 1154091. [DOI: https://dx.doi.org/10.3389/fenvs.2023.1154091]

17. Ngom, N.M.; Baratoux, D.; Bolay, M.; Dessertine, A.; Abass Saley, A.; Baratoux, L.; Mbaye, M.; Faye, G.; Yao, A.K.; Kouamé, K.J. Artisanal Exploitation of Mineral Resources: Remote Sensing Observations of Environmental Consequences, Social and Ethical Aspects. Surv. Geophys.; 2023; 44, pp. 225-247. [DOI: https://dx.doi.org/10.1007/s10712-022-09740-1]

18. Departamento Nacional de Planeación (DNP). Plan Nacional de Desarrollo 2014–2018. Todos Por Un Nuevo País: Paz, Equidad y Educación; Departamento Nacional de Planeación (DNP): Bogotá, Colombia, 2015; Available online: https://colaboracion.dnp.gov.co/cdt/pnd/pnd%202014-2018%20tomo%201%20internet.pdf (accessed on 1 June 2024).

19. Hutchins, M.G.; Abesser, C.; Prudhomme, C.; Elliott, J.A.; Bloomfield, J.P.; Mansour, M.M.; Hitt, O.E. Combined Impacts of Future Land-Use and Climate Stressors on Water Resources and Quality in Groundwater and Surface Waterbodies of the Upper Thames River Basin, UK. Sci. Total Environ.; 2018; 631–632, pp. 962-986. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.03.052] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29728007]

20. Cockburn, J.; Rosenberg, E.; Copteros, A.; Cornelius, S.F.; Libala, N.; Metcalfe, L.; van der Waal, B. A Relational Approach to Landscape Stewardship: Towards a New Perspective for Multi-Actor Collaboration. Land; 2020; 9, 224. [DOI: https://dx.doi.org/10.3390/land9070224]

21. La Rosa, D.; Izakovičová, Z. Visibility Analysis to Enhance Landscape Protection: A Proposal of Planning Norms and Regulations for Slovakia. Land; 2022; 11, 977. [DOI: https://dx.doi.org/10.3390/land11070977]

22. González González, M.J. Analysis, Systemization of the Impacts of Planning on the Territory: Applied to the Ordesa National Park. Land; 2020; 9, 527. [DOI: https://dx.doi.org/10.3390/land9120527]

23. Guo, Z.; Yan, Z.; He, R.; Yang, H.; Ci, H.; Wang, R. Impacts of Land Use Conversion on Soil Erosion in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains. Land; 2024; 13, 550. [DOI: https://dx.doi.org/10.3390/land13040550]

24. Gullino, P.; Mellano, M.G.; Beccaro, G.L.; Devecchi, M.; Larcher, F. Strategies for the Management of Traditional Chestnut Landscapes in Pesio Valley, Italy: A Participatory Approach. Land; 2020; 9, 536. [DOI: https://dx.doi.org/10.3390/land9120536]

25. Mohamed, M.A.; Anders, J.; Schneider, C. Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS. Land; 2020; 9, 226. [DOI: https://dx.doi.org/10.3390/land9070226]

26. Argel-Fernandez, A.J.; Puerta Avilés, O. Composición y Configuración Del Paisaje En Municipios Con Bosques Remanentes En La Franja Noroeste de Córdoba y Sucre, Colombia (1985–2020). Estud. Socioterritoriales Rev. Geogr.; 2023; 33, pp. 1-23. [DOI: https://dx.doi.org/10.37838/unicen/est.33-151]

27. Wu, C. Study on the Spatial Differences in Land-Use Change and Driving Factors in Tibet. Land; 2022; 11, 1584. [DOI: https://dx.doi.org/10.3390/land11091584]

28. Reboratti, C. Geografía y Ambiente. Geografía y Ambiente en América Latina; Bocco, G.; Urquijo, P.S.; Vieyra, A. CIGA-UNAM, SEMARNAT, INEEC: Mexico City, Mexico, 2011; pp. 21-44. [DOI: https://dx.doi.org/10.22201/ciga.9786070224966p.2011]

29. Laker, M.C. Environmental Impacts of Gold Mining—With Special Reference to South Africa. Mining; 2023; 3, pp. 205-220. [DOI: https://dx.doi.org/10.3390/mining3020012]

30. Garate-Quispe, J.; Ramírez, M.V.; Becerra-Lira, E.; Baez-Quispe, S.; Abril-Surichaqui, M.; Rodriguez-Achata, L.; Muñoz-Ushñahua, A.; Nascimento Herbay, P.; Fernandez-Mamani, Y.; Alarcon-Aguirre, G. et al. Influence of Distance from Forest Edges on Spontaneous Vegetation Succession Following Small-Scale Gold Mining in the Southeast Peruvian Amazon. Diversity; 2023; 15, 793. [DOI: https://dx.doi.org/10.3390/d15060793]

31. Siqueira-Gay, J.; Sánchez, L.E. The Outbreak of Illegal Gold Mining in the Brazilian Amazon Boosts Deforestation. Reg. Environ. Change; 2021; 21, 28. [DOI: https://dx.doi.org/10.1007/s10113-021-01761-7]

32. Engstrand, R.C.; Espejo, J.C.; Silman, M.R.; Asner, G.P. Repeated Mining Accounts for the Majority of Artisanal and Small-Scale Gold Mining Activity in Southeastern Peru. Environ. Res. Lett.; 2024; 19, 064036. [DOI: https://dx.doi.org/10.1088/1748-9326/ad44b0]

33. Bruno, D.E.; De Simone, F. ASGM Mercury Discharges in Tropical Basins: Assessment of the Criticality of Their Geographical Distribution. Sustainability; 2024; 16, 2991. [DOI: https://dx.doi.org/10.3390/su16072991]

34. Xu, Q.; Xia, G.; Yan, W.; Aili, A.; Yuan, K. Responses of Vegetation and Soil to Artificial Restoration Measures in Abandoned Gold Mining Areas in Altai Mountain, Northwest China. Diversity; 2022; 14, 427. [DOI: https://dx.doi.org/10.3390/d14060427]

35. Pérez, J.D.; Puerta, O. El Paisaje: Una Aproximación Conceptual Desde La Geografía Ambiental. Rev. Geogr. Venez.; 2024; 65, pp. 463-476. Available online: https://dialnet.unirioja.es/servlet/articulo?codigo=9759685 (accessed on 3 December 2023).

36. Nižić, M.K.; Grdić, Z.; Endres, R. Will the Goals of Sustainable Development be Achieved in the European Union?. Tourism and Hospitality Industry 2024: Trends and Challenges: Congress Proceedings, Proceedings of the Tourism and Hospitality Industry 2024, Opatija, Croatia, 6–7 June 2024; Faculty of Tourism and Hospitality Management, University of Rueka: Opatija, Croatia, 2024; pp. 75-84. [DOI: https://dx.doi.org/10.20867/thi.27.31]

37. Alsayegh, M.F.; Ditta, A.; Mahmood, Z.; Kouser, R. The Role of Sustainability Reporting and Governance in Achieving Sustainable Development Goals: An International Investigation. Sustainability; 2023; 15, 3531. [DOI: https://dx.doi.org/10.3390/su15043531]

38. López-Carrión, A.E.; Martí-Sánchez, M. Analysis of the Coverage and Discourse of the Sustainable Development Goals and the 2030 Agenda in the Spanish Digital Press (2015–2022). Rev. Lat. Comun. Soc.; 2024; 82, pp. 1-21. [DOI: https://dx.doi.org/10.4185/rlcs-2024-2057]

39. Vásquez, M.A.P. Retos de la política ambiental colombiana frente a los desafíos de la ocde y los ods. Análisis Político; 2020; 33, pp. 101-120. [DOI: https://dx.doi.org/10.15446/anpol.v33n99.90970]

40. Giraldo, N.O.; Cachopo, J.P.C. Coherencia en y entre las políticas públicas para el desarrollo sostenible y la innovación: Análisis del caso de Colombia a partir de documentos Conpes. Estud. Derecho; 2024; 81, pp. 7-33. [DOI: https://dx.doi.org/10.17533/udea.esde.v81n177a1]

41. Aguilera, M.M. Ciénaga De Ayapel: Riqueza En Biodiversidad y Recursos Hìdricos. Documentos de Trabajos Sobre Economía Regional; Banco de la Republica: Cartagena de Indias, Colombia, 2009; 70. [DOI: https://dx.doi.org/10.32468/dtseru.112]

42. DANE. DANE-Proyecciones de Población. Proyecciones y Retroproyecciones de Población Municipal para el Periodo 1985–2019 y 2020–2035 con Base en el CNPV 2018. Available online: https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion (accessed on 3 December 2023).

43. Corporación Autónoma Regional de los Valles del Sinú y del San Jorge (CVS). Acuerdo de Consejo Directivo N° 133. Montería 2009. Available online: https://cvs.gov.co/acuerdos/ (accessed on 15 December 2023).

44. Paz, A. Oro y Bacrim, El Verdadero Precio. Semana; 2014; Available online: https://www.semana.com/medio-ambiente/multimedia/oro-bacrim-verdadero-precio/32229/ (accessed on 16 December 2023).

45. Zhang, H.K.; Roy, D.P. Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening. Remote Sens.; 2016; 8, 180. [DOI: https://dx.doi.org/10.3390/rs8030180]

46. IDEAM. Leyenda Nacional de Coberturas de La Tierra. Metodología CORINE Land Cover Adaptada Para Colombia, Escala 1:100,000; IDEAM: Bogotá, Colombia, 2010.

47. IDEAM. Catálogo de Patrones de Coberturas de La Tierra Colombia; IDEAM: Bogotá, Colombia, 2012.

48. IGAC. Interpretación Visual de Imágenes de Sensores Remotos y Su Aplicación En Levantamientos de Cobertura y Uso de La Tierra; 1st ed. IGAC: Bogotá, Colombia, 2005.

49. IDEAM. Mapa de Coberturas de La Tierra. Metodología Corine Land Cover Adaptada Para Colombia. Período 2000–2002. Escala 1:100,000; IDEAM: Bogotá, Colombia, 2012.

50. IDEAM. Mapa de Coberturas de La Tierra. Metodología Corine Land Cover Adaptada Para Colombia. Año 2018. Escala 1:100,000; IDEAM: Bogotá, Colombia, 2021.

51. Pérez, J.D.; Puerta, O. Transformaciones Espaciales de Las Coberturas Por La Minería Aurífera En El Municipio de Ayapel (Córdoba): Un Análisis Geográfico-Ambiental. Geografía Para la Vida-Tomo 1; IGAC: Bogota, Colombia, 2024; Volume 1, pp. 32-41.

52. Bertrand, G. Paysage et Géographie Physique Globale. Esquisse Méthodologique. Rev. Geogr. Pyren. Sud. Ouest; 1968; 39, pp. 249-272. Available online: https://www.persee.fr/doc/rgpso_0035-3221_1968_num_39_3_4553 (accessed on 10 December 2024). [DOI: https://dx.doi.org/10.3406/rgpso.1968.4553]

53. Giné, D.S.; Romero, A.G.; Sánchez, L.A.G.; Chávez, E.S. Un Nuevo Método de Cartografía Del Paisaje Para Altas Montañas Tropicales. Cuad. Geográficos; 2019; 58, pp. 83-100. [DOI: https://dx.doi.org/10.30827/CUADGEO.V58I1.6517]

54. Mateo, J.M. Geografía de Los Paisajes. Primera Parte. Paisajes Naturales; Editorial Universitaria: La Habana, Cuba, 2002; Available online: https://es.scribd.com/document/382376188/Geografia-de-Los-Paisajes-Prim-Jose-Manuel-Mateo-Rodriguez (accessed on 1 December 2024).

55. Jiménez Olivencia, Y.; Porcel Rodríguez, L.; Caballero Calvo, A. Medio Siglo En La Evolución de Los Paisajes Naturales y Agrarios de Sierra Nevada (España). Boletín Asoc. Geógrafos Españoles; 2015; 68, pp. 205-232. [DOI: https://dx.doi.org/10.21138/bage.1859]

56. Caballero Calvo, A.; Jiménez Olivencia, Y. Evaluación de recursos locales. Los paisajes del árbol fuera del bosque en el municipio de Válor. Desarrollo Local en Tiempos de Crisis: “El Retorno a los Recursos Endógenos”; Rodríguez Martínez, F. Universidad de Granada: Granada, Spain, 2013; pp. 297-316. Available online: https://digibug.ugr.es/handle/10481/86791 (accessed on 1 December 2024).

57. Salinas, E.; García, A.E.; Miravet Sánchez, B.L.; Remond Noa, R.; Cruañas López, E. Delimitación, Clasificación y Cartografía de Los Paisajes de La Cuenca Ariguanabo, Cuba, Mediante El Uso de Los SIG. Rev. Geográfica; 2019; 154, pp. 9-30. [DOI: https://dx.doi.org/10.35424/regeo.v0i154.326]

58. Salinas, E.; Mateo, J.M.; Costa de Souza, L.; Moreira, A. Cartografía de Los Paisajes: Teoría y Aplicación. Physis Terrae-Rev. Ibero-Afro-Am. Geogr. Física Ambiente; 2019; 1, pp. 7-29. [DOI: https://dx.doi.org/10.21814/physisterrae.402]

59. Salinas, E.; Ramón, A.M.; Amaro, L.R. La Cartografia de Los Paisajes y Los Sistemas de Información Geográfica: Aspectos Conceptuales y Metodológicos. Cartografía Biogeográfica e da Paisagem. Volume II; Salinas Chávez, E.; Seolin Dias, L. ANAP: São Paulo, Brazil, 2019; Volume II, pp. 37-54.

60. Salinas, E.; Ribeiro, Á.F. La Cartografía de Los Paisajes Con El Empleo de Los Sistemas de Información Geográfica: Caso de Estudio Parque Nacional Sierra de Bodoquena y Su Entorno. Geogr. Sist. Inf. Geográfica (GeoSIG); 2017; 9, pp. 186-205.

61. Servicio Geológico Colombiano. Memoria Explicativa-Mapa Geológico de La Plancha 83 Nechí; Servicio Geológico Colombiano: Medellin, Colombia, 2012; Available online: https://catalogo.sgc.gov.co/cgi-bin/koha/opacdetail.pl?biblionumber=49247&shelfbrowse_itemnumber=48594 (accessed on 16 January 2024).

62. Servicio Geológico Colombiano. Memoria Explicativa-Mapa Geológico de La Plancha 73 Ayapel; Servicio Geológico Colombiano: Medellín, Colombia, 2015; Available online: https://catalogo.sgc.gov.co/cgi-bin/koha/opac-detail.pl?biblionumber=73464 (accessed on 16 January 2024).

63. IGAC. Estudio General de Suelos y Zonificación de Tierras Del Departamento de Córdoba; IGAC: Bogotá, Colombia, 2009; Available online: https://es.scribd.com/document/640120345/Untitled (accessed on 25 January 2024).

64. UNODC. Colombia. Explotación de Oro de Aluvión. Evidencias a Partir de Percepción Remota 2021; UNODC: Vienna, Austria, 2022; Available online: https://www.unodc.org/documents/colombia/2022/Junio/Informe_Colombia_Explotacion_de_Oro_de_Aluvion_Evidencias_a_Partir_de_Percepcion_Remota_2021_SP_.pdf (accessed on 5 January 2025).

65. Shikhov, A.; Ilyushina, P.; Makarieva, O.; Zemlianskova, A.; Mozgina, M. Satellite-Based Mapping of Gold-Mining-Related Land-Cover Changes in the Magadan Region, Northeast Russia. Remote Sens.; 2023; 15, 3564. [DOI: https://dx.doi.org/10.3390/rs15143564]

66. Alarcón-Aguirre, G.; Sajami Quispe, E.; Vásquez Zavaleta, T.; Ponce Tejada, L.V.; Ramos Enciso, D.; Rodríguez Achata, L.; Garate-Quispe, J. Vegetation Dynamics in Lands Degraded by Gold Mining in the Southeastern Peruvian Amazon. Trees For. People; 2023; 11, 100369. [DOI: https://dx.doi.org/10.1016/j.tfp.2023.100369]

67. Romiyanto, R.; Barus, B.; Sudadi, U. Model Spasial Kerusakan Lahan Dan Pencemaran Air Akibat Kegiatan Pertambangan Emas Tanpa Izin Di Daerah Aliran Sungai Raya, Kalimantan Barat. J. Ilmu Tanah Dan Lingkung.; 2015; 17, pp. 47-53. Available online: https://journal.ipb.ac.id/index.php/jtanah/article/view/14836/0 (accessed on 4 January 2025). [DOI: https://dx.doi.org/10.29244/jitl.17.2.47-53]

68. Dominguez Rave, S.L.; Romero Bejarano, L.R.; Torra Ruiz, L.C.; López Arango, Y.L. Valoración Participativa de Impactos Socioambientales y Sanitarios En Minería de Oro: Buriticá (Antioquia), Colombia. Rev. Fac. Nac. De Salud Pública; 2020; 38, pp. 1-29. [DOI: https://dx.doi.org/10.17533/udea.rfnsp.e338882]

69. Palacios-Torres, Y.; de la Rosa, J.D.; Olivero-Verbel, J. Trace Elements in Sediments and Fish from Atrato River: An Ecosystem with Legal Rights Impacted by Gold Mining at the Colombian Pacific. Environ. Pollut.; 2020; 256, 113290. [DOI: https://dx.doi.org/10.1016/j.envpol.2019.113290]

70. Moletta, I.M.; Nucci, J.C. Área degradada pela extração de areia: Um estudo da derivação da paisagem no bairro do umbará, curitiba-pr. Revista RAEGA; 2006; 12, Available online: https://revistas.ufpr.br/raega/article/view/4300 (accessed on 6 December 2024). [DOI: https://dx.doi.org/10.5380/raega.v12i0.4300]

71. Salas Barreto, A.D.; Estupiñan Cruz, A.L. Explotación Minera y Conflictividad Socioambiental En El Páramo Cruz Verde-Sumapaz, Colombia, 2011–2021. Cuad. De Geogr. Rev. Colomb. De Geogr.; 2024; 33, pp. 467-484. [DOI: https://dx.doi.org/10.15446/rcdg.v33n2.111054]

72. Carabia-Sanz, I.; Simoy, M.V.; Cortelezzi, A.; Trofino-Falasco, C.; Berkunsky, I. Spatial Distribution and Landscape Impact Analysis of Quarrying in the Highly Fragmented Ecosystem of Tandilia System (Province of Buenos Aires, Argentina). Environ. Earth Sci.; 2024; 83, 319. [DOI: https://dx.doi.org/10.1007/s12665-024-11657-4]

73. Gómez-Rodríguez, M.E.; Molina-Pérez, F.J.; Agudelo-Echavarría, D.M.; Cañón-Barriga, J.E.; de Jesús Vélez-Macías, F. Changes in soil cover in Nechí, Antioquia: An approach to the environmental impact of mining, 1986–2010. Fac. De Ing.; 2017; 26, pp. 149-163. [DOI: https://dx.doi.org/10.19053/01211129.v26.n45.2017.6423]

74. Dzoro, M.; Mashapa, C.; Gandiwa, E. Impact of Artisanal Gold Mining on Woody Vegetation in Chewore South Safari Area, Northern Zimbabwe. Trees For. People; 2023; 11, 100375. [DOI: https://dx.doi.org/10.1016/j.tfp.2023.100375]

75. Kamga, M.A.; Nguemhe Fils, S.C.; Ayodele, M.O.; Olatubara, C.O.; Nzali, S.; Adenikinju, A.; Khalifa, M. Evaluation of Land Use/Land Cover Changes Due to Gold Mining Activities from 1987 to 2017 Using Landsat Imagery, East Cameroon. GeoJournal; 2020; 85, pp. 1097-1114. [DOI: https://dx.doi.org/10.1007/s10708-019-10002-8]

76. Schueler, V.; Kuemmerle, T.; Schröder, H. Impacts of Surface Gold Mining on Land Use Systems in Western Ghana. Ambio; 2011; 40, pp. 528-539. [DOI: https://dx.doi.org/10.1007/s13280-011-0141-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21848141]

77. Oficina de las Naciones Unidas Contra la Droga y el Delito(UNODC). La Respuesta a La Minería Ilegal y El Tráfico de Metales y Minerales. Guía Sobre Buenas Prácticas Legislativas. Nac. Unidas Viena; 2023; 142, Available online: https://sherloc.unodc.org/cld/uploads/pdf/Illegal_Mining_and_Trafficking_in_Metals_S.pdf (accessed on 5 January 2025).

78. Vargas Licona, S.P.; Marrugo Negrete, J.L. Mercury, Methylmercury and Other Heavy Metals in Fish in Colombia: Risk from Ingestion. Acta Biolo Colomb.; 2019; 24, pp. 232-242. [DOI: https://dx.doi.org/10.15446/abc.v24n2.74128]

79. Marrugo, J.; Lans, E.; Benítez, L. Finding of Mercury in Fish From the Ayapel Marsh. Rev. MVZ Cordoba; 2007; 12, pp. 878-886. Available online: https://www.redalyc.org/pdf/693/69312103.pdf (accessed on 10 December 2024).

80. Gracia, H.L.; Marrugo, N.J.L.; Alvis, R.E.M. Contaminación Por Mercurio En Humanos y Peces En El Municipio de Ayapel, Córdoba, Colombia, 2009. Rev. Fac. Nac. De Salud Pública; 2010; 28, pp. 118-124. [DOI: https://dx.doi.org/10.17533/udea.rfnsp.1753]

81. Okviyani, N.; Trimahyuni, E.; Ma’rif, A.A.F. The Analysis of Mercury Concentrations of Water in Gold Mining Environment at Poboya, East Palu Subdistrict, Palu, Central Sulawesi Province. IOP Conf. Ser. Earth Environ. Sci.; 2023; 1209, 12023. [DOI: https://dx.doi.org/10.1088/1755-1315/1209/1/012023]

82. Estupiñán-Aponte, C.A. Ciénaga de Ayapel, Afluente Hídrico En Aprietos. Perspect. En Intel.; 2016; 8, pp. 51-62. Available online: https://revistascedoc.com/index.php/pei/article/view/90 (accessed on 10 December 2024).

83. Pedraza, M.L.; Espinosa-Ramírez, A.J. The Legacy of Arsenic and Mercury in the Ramsar Ayapel Wetland Complex (Córdoba, Colombia): An Approach to the Magdalena-Cauca Macro-Basin. Acta Biolo Colomb.; 2022; 27, pp. 164-176. [DOI: https://dx.doi.org/10.15446/ABC.V27N2.89084]

84. Vicente, A.; Martin, N.; Slee, D.; Birss, M.; Lefebvre, S.; Bauer, B. Minería En Colombia “A Qué Precio”. PBI Colomb.; 2011; 18, 48.Available online: https://www.peacebrigades.org/fileadmin/user_files/projects/colombia/files/colomPBIa/111122_boletin_final_web.pdf (accessed on 5 January 2025).

85. Restrepo, L.C.; Muñoz, H.; Zúñiga, L.M.; Castillo, B. Minería Ilegal y Sus Implicaciones En El Conflicto Armado En El Departamento de Córdoba y Bajo Cauca Antioqueño. Rev. Nuevo Derecho; 2017; 13, pp. 1-28. [DOI: https://dx.doi.org/10.25057/2500672X.1018]

86. Zabala, A.M. Diagnóstico Ambiental De La Ciénaga De Ayapel a Través De La Variación Temporal De Los Aspectos Morfo Funcionales Del Fitoplancton Y Un Indicador De Calidad Ecológica, Universidad de Antioquia Corporación. 2017; Available online: https://bibliotecadigital.udea.edu.co/handle/10495/9074 (accessed on 5 December 2024).

87. Quintarelli, J.M.; Garnier, J.; Rudrigues de Souza, J.P.; de Sousa Tonhá, M.; Barral, U.M.; Roig, H.L.; Martinez, J.-M.; Santini, W.; Puita, O.; Seyler, P. et al. Mercury in River Samples with Artisanal and Small-Scale Gold Mining Influence: The Case of the Upper Madeira River Basin (Brazil/Bolivia). J. S. Am. Earth Sci.; 2024; 145, 105070. [DOI: https://dx.doi.org/10.1016/j.jsames.2024.105070]

88. Hong, F.; He, G.; Wang, G.; Zhang, Z.; Peng, Y. Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data. Remote Sens.; 2023; 15, 3439. [DOI: https://dx.doi.org/10.3390/rs15133439]

89. Schubert, H.; Rauchecker, M.; Caballero Calvo, A.; Schütt, B. Land use changes and their perception in the Hinterland of Barranquilla, Colombian Caribbean. Sustainability; 2019; 11, 6729. [DOI: https://dx.doi.org/10.3390/su11236729]

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Abstract

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Gold mining is an activity that has developed in Colombia due to the great availability of mineral resources geographically distributed throughout the territory. The extraction techniques used are linked to the domain of illegality and to armed actors who have generated notable landscape impacts. This study, focused on the Municipality of Ayapel, Colombia, identifies the landscape units and analyzes the changes in land use and cover resulting from gold mining between the years 1987, 2002, and 2022, applying the CORINE Land Cover methodology, an adapted legend for Colombia, using Landsat satellite images. For this, the recognition of the physical geographical characteristics of the area was carried out in order to group homogeneous landscape units through a cartographic overlay of various layers of information, considering variables such as topography, geomorphology, and lithology. This research identifies a total of 16 landscape units, 8 of which were intervened in 1987, mainly associated with denudational hills. However, in 2022, 13 landscape units were intervened, with a considerable increase in the affected area. Particularly noteworthy is the occupation of landscape units associated with alluvial valleys, with an average of more than 30% of their total area. This demonstrates that they are the most attractive and vulnerable areas for mining exploitation, as they are the zones with the greatest potential for hosting mineral deposits. This impact has worsened over the last decade due to the introduction of other extraction techniques with machinery (dredges, dragon boats, backhoes, and bulldozers) that generate higher productive and economic yields but, at the same time, cause deep environmental liabilities due to the lack of administrative controls. The changes in extraction techniques, the increase in the international price of the commodity, and the absence of government attention have been the breeding ground that has driven gold mining activity.

Details

Title

Landscape Transformations (1987–2022): Analyzing Spatial Changes Driven by Mining Activities in Ayapel, Colombia

Author

Pérez-Aristizábal, Juan David¹

; Puerta-Avilés, Oscar¹

; Jiménez-Caldera, Juan²

; Caballero-Calvo, Andrés³

¹ Departamento de Geografía, Facultad de Ciencias Básicas, Universidad de Córdoba, Montería 230002, Colombia; [email protected] (J.D.P.-A.); [email protected] (O.P.-A.)
² Departamento de Ingeniería Ambiental, Facultad de Ingenierías, Universidad de Córdoba, Montería 230002, Colombia; [email protected]
³ Department of Regional Geographic Analysis and Physical Geography, Faculty of Philosophy and Letters, Cartuja Campus, University of Granada, 18071 Granada, Spain

First page

157

Publication year

2025

Publication date

2025

Publisher

MDPI AG

e-ISSN

2073445X

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.3390/land14010157

ProQuest document ID

3159534503