EXPLORING THE AMAZON

Summary

From the 1970s to the present, land use changes in the Amazon have been driven by a combination of government policies, economic incentives, and international commodity demand, especially for cattle, soy, and timber (Kraeski et al., 2023; Pendrill et al., 2020).

Infrastructure projects such as roads and hydroelectric dams, often initiated under military-era development plans, opened up remote forest areas to large-scale deforestation (Fearnside, 2001; Pellegrina & Sotelo, 2021).

Climate change has intensified these pressures by increasing drought frequency, altering rainfall patterns, and raising the risk of wildfires, creating feedback loops that further weaken forest resilience (Malhi et al., 2008; Davidson et al., 2012).

While policies like the Soy Moratorium and the Action Plan for the Prevention and Control of Deforestation (PPCDAm) led to temporary declines in deforestation rates (Climate Policy Initiative, 2012; Kraeski et al., 2023), ongoing threats such as illegal logging, agricultural expansion, and hydropower projects continue to endanger biodiversity and carbon storage (Nobre et al., 2016; Bro, Moran, & Calvi, 2018).

Remote sensing technologies have become critical tools for tracking and understanding land use change across the Amazon (Pettorelli et al., 2014; MapBiomas, 2019).

Land Use Patterns and Trends

From the 1970s to the 1990s, the Amazon Basin underwent significant changes driven by government policies aimed at promoting economic development, particularly through agriculture and infrastructure projects. Social research has revealed that land use changes are often driven by economic policies, international demand for commodities, and infrastructure projects like hydroelectric dams, which disrupt local communities and lead to increased deforestation (Kraeski et al., 2023).

Urbanization, driven by both government policies and economic incentives, has played a significant role in land use changes in the Amazon. As cities expand, often with government support, urban sprawl encroaches on surrounding natural areas. Policies that promote economic growth and attract investment frequently lead to the construction of new urban areas in previously forested regions, resulting in the displacement of local flora and fauna and increased pollution. Government development programs, rather than the natural economic potential of their surroundings or citizens, have been crucial in maintaining the economic sustainability of Amazonian cities and their populations (Richards & VanWey, 2015). According to this perspective, urbanization in the Amazon is seen as the economically irrational and environmentally destructive legacy of the military government’s plans for the region, prioritizing urban growth over environmental preservation and local sustainability (Richards & VanWey, 2015).

One of the key factors contributing to deforestation during this period was the construction of roads, which were often funded and developed by governmental policies such as the March to the west (Marcha para o Oeste policy) which promoted large-scale colonization and infrastructure development in the center-west and northern regions of Brazil, supported by incentives for settlers, including land grants (Pellegrina & Sotelo, 2021). These roads facilitated the movement of goods and people, opening up previously inaccessible areas for agricultural expansion. A prime example of infrastructure created under this policy was the construction of the Trans-Amazonian BR-163 highway in the 1970s, which played a major role in unlocking large portions of the Amazon to deforestation (Fearnside, 2001). By providing easier access to remote areas of the forest, the road accelerated land use changes, especially for cattle ranching and agriculture. As new roads were built, they also paved the way for further infrastructure development, including settlements and services, which attracted more human populations to these regions, further intensifying deforestation. Additionally, the creation of informal logging roads, which extend for tens of thousands of kilometers, contributed to even greater access to timber and increased demand for additional roads. This created a self-perpetuating cycle in which the need for more roads justified the construction of even more, amplifying the rate of deforestation in the Amazon (Fearnside, 2001).

Hydroelectric dam projects have played a significant role in driving deforestation and land use change in the Amazon, largely enabled by government policies that prioritize economic growth and energy production. During the military dictatorship (1964 to 1985), initiatives like the National Integration Plan aimed to industrialize remote areas through infrastructure development, including large hydroelectric projects (Fearnside, 2001). Later programs such as Prodes and the Plano Diretor de Energia (PDE) continued this agenda, promoting hydroelectricity as a renewable and cost-effective energy source. However, the construction of large dams, including the Belo Monte Dam, has resulted in forest flooding, displacement of communities, and ecological disruption (Pellegrina and Sotelo, 2021; Bro, Moran, and Calvi, 2018). These projects also lead to further land conversion by enabling new infrastructure corridors. Beyond Brazil, hydropower development in the Andean Amazon, linked to Brazil’s energy agreements with Peru and Bolivia, poses regional threats. Finer and Jenkins (2012) estimate that over 80 percent of planned dams in this region would result in deforestation, and 60 percent would disrupt river connectivity between protected Andean headwaters and the Amazon Basin, undermining biodiversity and the ecological function of the Amazon floodplain.

As well as creating infrastructure, over the past five decades agricultural expansion has been a leading cause of deforestation in the Amazon rainforest, with cattle ranching, soy production, and logging identified as the primary drivers (Silveira et al., 2022). This high demand has intensified pressure on Brazil’s forests, particularly in the Amazon and Cerrado regions. The clearing of forested land for pasture has been one of the most significant contributors to the destruction of the Amazon, as vast areas of forest are converted into grazing land for cattle. This is often accomplished through the controversial practice of slash-and-burn, which also leads to increased carbon emissions. Cattle ranching has been the most significant driver of deforestation in the Brazilian Amazon, accounting for approximately 80% of forest loss (World Wildlife Fund, n.d.). Driven by both international and domestic demand for beef and leather, this industry has led to widespread forest clearing, often through slash-and-burn methods that release an estimated 340 million tons of carbon annually, or roughly 3.4% of global emissions. Between 2015 and 2017, major importers of Brazilian beef included China (30.2%), Egypt (12.4%), Russia (10.4%), and the European Union (7.1%), while domestic consumption accounted for about 75% of total production (Pendrill et al., 2020). These practices not only exacerbate climate change but also contribute to biodiversity loss and increased wildfire risk. The environmental impact of cattle ranching in the Amazon is profound. Deforestation for pasture results in the loss of biodiversity, as many species dependent on the Amazon’s ecosystem face extinction risks (Grass Roots Farmers’ Cooperative, n.d.).

In addition to cattle ranching, soy production, driven by increasing global demand, has also significantly contributed to Amazonian deforestation. Soy has become a key agricultural commodity, particularly for animal feed and biofuels, which has expanded agricultural frontiers into the Amazon rainforest. Research shows that large tracts of land in the Amazon were cleared for soy plantations, directly contributing to deforestation in the region. The Monitoring of the Andes Amazon Program (MAAP) report highlights that recent direct soy-related deforestation in the Brazilian Amazon has led to the clearing of at least 42,000 hectares, mostly in Mato Grosso. This deforestation, detected through fire activity, appears to be driven by the expansion of soy plantations, often after primary forest is cleared. The study also notes that this activity is part of a broader pattern of agricultural expansion, with fires being used to prepare land for the soy planting season. The impact of soy demand on the Amazon continues to grow, despite efforts like the Soy Moratorium (MAAP, 2025). The expansion of soy farming in the Amazon is closely linked to global demand for animal feed and biofuel production, particularly from the European Union (EU) and China. China's growing need for soybeans to feed its livestock has been a significant driver of soy industry expansion over the past 15 years, with Brazil exporting approximately 50% of its soy production to China in 2020. This trend is expected to continue as demand for soy is projected to double by 2050, potentially leading to further deforestation in the Amazon (Foley et al., 2023).

Logging, both legal and illegal, has also played a role in deforestation, although its impact has varied over the years. In particular, illegal logging has been identified as a major contributing factor to the destruction of the Amazon’s Forest cover. Satellite data in 2018 revealed that nearly 3,050 square miles of Amazon forest was lost between August 2017 and July 2018, with illegal logging playing a substantial role in this deforestation (Axios, 2018). This deforestation, combined with agricultural expansion, has severely compromised the Amazon's biodiversity and ecosystem services, putting the region at risk of irreversible ecological change (Nobre et al., 2016). Beyond its direct impact on forest cover, logging, particularly illegal and selective logging, also disrupts hydrological cycles by reducing canopy cover and altering infiltration rates.

While much of the attention has focused on visible deforestation and vegetation loss, land use changes also have profound effects belowground, particularly on soil microbial communities that underpin key ecosystem functions. This widespread land-use change has drastically reduced the richness of plant and animal species, while simultaneously contributing to the biotic homogenization of microbial communities in the soil. Rodrigues et al. (2013) demonstrated that while local diversity of soil bacteria may increase following forest-to-pasture conversion, the overall diversity across landscapes significantly decreases. This pattern is driven by the loss of endemic forest bacteria and the dominance of generalist taxa in pasture soils. As a result, microbial communities become more compositionally similar over space, reducing the overall genetic and functional diversity of the ecosystem (Rodrigues et al., 2013).

The standardization of microbial communities has serious ecological implications. Soil microbes are vital for nutrient cycling, carbon sequestration, and maintaining soil health. The reduction in microbial diversity and trait richness can impair these ecosystem services, lowering the Amazon’s capacity to recover from disturbances and increasing its vulnerability to irreversible ecological shifts. The conversion of the Amazon’s complex and spatially heterogeneous microbial landscape into more uniform pasture communities thus signals not only a loss of biodiversity but also a fundamental erosion of the ecosystem services that sustain life in this globally important biome (Rodrigues et al., 2013).

Efforts to curb deforestation in the Amazon have yielded mixed results. For instance, by 2012, deforestation rates dropped by nearly 80%, largely due to government regulations and policies aimed at reducing forest clearance, as well as the implementation of monitoring systems. The Brazilian government's Action Plan for the Prevention and Control of Deforestation in the Legal Amazon (PPCDAm), introduced in 2004, coordinated multi-level efforts and implemented remote-sensing technologies to track and control illegal logging (Climate Policy Initiative, 2012). This action plan has been credited with preventing the loss of 23,938 square miles between 2005 and 2009. However, despite these successes, the underlying drivers, cattle ranching, soy production, and logging, continue to present significant challenges in addressing deforestation in the Amazon. These trends highlight the urgent need for sustainable land-use practices and policies that address both the direct and indirect drivers of deforestation.

Land Use Trends

The Changing Climate and its Effects on the Amazon

The Amazon rainforest, one of the world’s most critical ecosystems, is increasingly threatened by climate change, which poses significant risks to its stability and biodiversity. According to Malhi et al. (2008), the region has experienced a warming trend of approximately 0.25°C per decade, and climate models project a rise in temperatures of up to 3.3°C or more by the end of the 21st century. These shifts are expected to intensify dry seasons, particularly in the southern and eastern Amazon, where rainfall may decline substantially. Such changes could lead to increased drought stress, reduced forest resilience, and elevated risks of large-scale forest dieback. The authors highlight that Amazonian ecosystems rely heavily on consistent rainfall and deep-rooted trees that access soil moisture during dry periods, but prolonged droughts may overwhelm these adaptive mechanisms. Moreover, the feedback loop between deforestation and climate change could exacerbate the drying trend, as forest loss diminishes transpiration and rainfall recycling. If current deforestation and warming trends continue unchecked, much of the Amazon could shift to a permanently drier regime, threatening its role as a global carbon sink and its extraordinary biodiversity (Malhi et al., 2008).

Rainfall patterns in the Amazon are closely tied to complex atmospheric processes, making the region particularly sensitive to climate change. Malhi et al. (2008) explains that much of the Amazon's rainfall is generated locally through evapotranspiration, where water taken up by deep-rooted trees is released into the atmosphere and recycled as precipitation. However, large-scale deforestation disrupts this cycle, reducing atmospheric moisture and weakening rainfall feedback. In addition, global climate drivers, such as the El Niño–Southern Oscillation (ENSO) and warming of the tropical Atlantic, alter atmospheric circulation and shift the Intertropical Convergence Zone (ITCZ), leading to suppressed rainfall in northern and eastern Amazonia. These changes are especially impactful during the dry season, which is already a critical stress period for the forest. The resulting decline in rainfall not only increases drought risk but also reduces the forest's ability to sustain its own climate, potentially pushing parts of the Amazon into a permanently drier state. This shift threatens the region’s ecological stability, as less rainfall limits plant growth, increases fire risk, and disrupts the delicate balance of the rainforest-atmosphere system (Malhi et al., 2008).

Rising temperatures, prolonged dry seasons, and more frequent extreme droughts, conditions intensified by global climate change, reduce forest resilience and increase the likelihood of fire ignition and spread. These climatic stressors are compounded by land-use activities such as deforestation and logging, which open the canopy and dry out the forest floor (Davidson et al., 2012). Once burned, forests become increasingly vulnerable to subsequent fires, which reduce canopy cover, biomass, and biodiversity. Repeated disturbances such as fire lead to slower forest regrowth and impaired nitrogen cycling, especially on highly weathered tropical soils that are already nutrient-poor and prone to erosion and leaching (Davidson et al., 2012). The cumulative effects of fire and climate change thus threaten to shift Amazonian ecosystems toward a more degraded, savannah-like state, undermining their ability to sequester carbon and support biodiversity.

The Changing Climate

Policy Responses and Conservation Efforts

Integrated models that combine environmental, social, and economic data have become instrumental in predicting future land use changes in the Amazon. These models help policymakers anticipate deforestation trends and design strategies for sustainable land management. Studies in the Amazon have linked deforestation rates to economic cycles, demonstrating how soybean and cattle ranching expansions correlate with global commodity prices (Kraeski et al., 2023). The Soy Moratorium, an agreement established in 2006 to reduce deforestation for soybean production, has been studied using integrated models to assess its impact on forest loss (Kraeski et al., 2023). Additionally, hydrological and land-use models have been used to evaluate how deforestation affects water resources, with findings indicating that continued land conversion could lead to significant hydrological disruptions in the Amazon (Kraeski et al., 2023). By integrating various data sources, researchers can better understand the complex interactions driving land use changes and develop strategies for conservation and sustainable development.

Sustainable development programs in the Amazon emphasize eco-friendly agricultural practices that improve soil fertility, reduce deforestation, and enhance local livelihoods. One such approach is no-till alley cropping, which integrates leguminous trees with crops to enhance nutrient cycling and reduce soil degradation. This method has been particularly effective in maintaining long-term soil productivity without relying on chemical fertilizers (de Moura et al., 2022). The incorporation of nitrogen-fixing leguminous trees, such as Inga edulis, enriches the soil and reduces the need for synthetic inputs. Additionally, this system minimizes soil erosion, enhances carbon sequestration, and promotes biodiversity by mimicking natural forest ecosystems (de Moura et al., 2022). Studies have shown that alley cropping systems improve soil organic matter content and nutrient availability, making them a viable alternative to traditional slash-and-burn agriculture.

Despite these benefits, challenges persist in implementing sustainable land use programs at scale. Farmers often face economic barriers in establishing agroecological systems, as well as a lack of technical support and infrastructure. Market accessibility for sustainably grown products remains limited, and without adequate incentives, small-scale producers may struggle to transition away from conventional practices that favor short-term gains over long-term ecological resilience (de Moura et al., 2022).

Brazil’s energy policy, including the National Energy Plan (PNE), continues to prioritize large-scale hydroelectric development as a key component of its energy matrix. While regulatory tools like Environmental Impact Assessments (EIA) and licensing processes are intended to mitigate damage, they have been widely criticized for failing to adequately protect local ecosystems and communities (Fearnside, 2001). Large dams such as Belo Monte and Tucuruí have caused widespread displacement, forest flooding, and aquatic habitat degradation. As a result, Brazil faces mounting domestic and international pressure to reconcile its energy demands with conservation priorities and human rights (Bro et al., 2018).

In contrast, community-based conservation models have shown promising results in advancing both environmental and social goals. The Mosaico do Baixo Rio Negro (MBRN), located in the central Amazon near Manaus, is a leading example of integrated and participatory conservation planning. Recognized by Brazil’s National System of Conservation Units (SNUC), the MBRN brings together over ten protected areas under a shared governance framework. The mosaic spans more than 7 million hectares and includes both strict protection and sustainable use units. Its council is composed of local community members, NGOs, and government representatives who collaboratively define land-use priorities, monitor restoration, and advocate for basic services such as sanitation, education, and healthcare (Conselho do Mosaico do Baixo Rio Negro, 2025).

According to Panorama Solutions (n.d.), the success of the MBRN lies in three interrelated strategies: (1) multi-level governance that integrates local and regional decision-making; (2) partnerships that empower communities through co-management and knowledge exchange; and (3) the inclusion of both scientific and traditional ecological knowledge in restoration and planning. This structure allows for the dynamic allocation of resources and responsibilities, fostering a sense of ownership among local communities. It also ensures that policies are culturally appropriate and ecologically grounded.

Building on this, the mosaic’s 2025 draft management plan identifies restoration as a core strategic pillar, with specific objectives to recover degraded lands, improve biodiversity corridors, and support natural regeneration through both passive and active approaches (Conselho do Mosaico do Baixo Rio Negro, 2025). Remote sensing and community-based monitoring are emphasized as tools to evaluate restoration outcomes, aligning with the methodologies used in this study. Additionally, the plan outlines socio-political efforts such as improving access to clean energy, promoting bioeconomy initiatives like sustainable forest products, and integrating traditional medicine and telehealth systems into rural areas, all of which contribute to long-term landscape resilience.

This plan further illustrates how restoration policy can be operationalized through participatory governance and ecological zoning. As outlined in its draft 2025 integrated management plan, the mosaic prioritizes both active and passive ecological restoration, particularly in degraded areas near traditional communities where land abandonment or decreased extraction pressure has created opportunities for natural forest regeneration (Conselho do Mosaico do Baixo Rio Negro, 2025). This emphasis on passive restoration aligns directly with the findings of this study, which identified spontaneous vegetation recovery in areas such as abandoned pastures using NDVI and SWIR remote sensing tools. The plan further outlines the use of specific ecological indicators, such as canopy closure, vegetation cover change, and epiphyte diversity, as central to monitoring restoration success. These parameters are mirrored in this project’s methodology, reinforcing the relevance and applicability of satellite-derived indices and field-based biodiversity assessments as part of the mosaic’s larger restoration monitoring system.

Moreover, the mosaic’s monitoring framework, detailed in both the 2025 draft plan and the 2017 Monitoring Plan, calls for the integration of remote sensing technologies, geospatial analysis, and ground-based data collection to evaluate vegetation dynamics across multiple conservation units (WCS Brasil, 2017). This includes the use of open-access Landsat imagery and community-generated observations to validate changes in forest structure, water availability, and biodiversity. These practices not only improve the scientific accuracy of land use change assessments but also empower local communities to contribute to adaptive management. In particular, local associations are supported in developing technical capacity and accessing funding for restoration and infrastructure projects that align with environmental goals - such as the re-establishment of forest connectivity, the protection of headwaters, and the sustainable use of non-timber forest products. Restoration in the MBRN is embedded in a broader socio-ecological vision, where improved forest health is directly tied to human well-being and autonomy.

The mosaic’s policy also emphasizes the importance of cross-boundary coordination between conservation units such as RESEX Rio Unini, Parque Nacional de Anavilhanas, and multiple RDS (sustainable development reserves), allowing for restoration and land use planning to be implemented at the landscape scale (ICMBio, 2011). Zoning regulations and use agreements are co-developed with community input, ensuring that restoration strategies respect both ecological potential and traditional land use rights. Through its governance council and technical chambers, the mosaic facilitates regular planning workshops, monitoring initiatives, and advocacy for improved public services, which further contribute to long-term landscape resilience and community empowerment.

By centering restoration within a framework of territorial governance, the MBRN demonstrates how conservation can transcend ecological objectives and become a vehicle for equitable development. The mosaic model not only safeguards biodiversity but also strengthens social cohesion and adaptive capacity in the face of changing environmental and economic pressures. These insights reinforce the importance of locally grounded, participatory conservation strategies in addressing the complex challenges of land use change in the Amazon.

Policy Response and Conservation

Approaches to Studying Land Use Change

Vegetation indices, particularly the Normalized Difference Vegetation Index (NDVI), play a critical role in assessing forest health, biomass, and deforestation trends in tropical regions such as the Amazon. NDVI is derived from the red and near-infrared (NIR) bands of remote sensing data, where higher values indicate dense and healthy vegetation, while lower values suggest degradation or deforestation (Pettorelli et al., 2014). In addition to NDVI, other spectral indices contribute to vegetation monitoring. Tasseled Cap Wetness (TCW) is effective in distinguishing between pasture, cropland, and regenerating forests by capturing moisture variations, which are particularly relevant in tropical ecosystems (Müller et al., 2016). Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) further refine vegetation assessments by minimizing atmospheric and soil background effects, improving accuracy in densely forested environments (Huete et al., 2002). These remote sensing tools are not only critical for tracking general deforestation trends but also directly support this study’s objectives, namely, improving land cover classification accuracy and linking satellite-derived vegetation metrics to field-based indicators of forest integrity, such as epiphyte diversity.

Spectral analysis techniques utilizing satellite imagery from Landsat and MODIS sensors are essential for mapping land cover changes and detecting forest degradation. MODIS, with its high temporal resolution, enables the classification of land cover by capturing seasonal vegetation dynamics, facilitating the identification of agricultural cycles and forest regeneration in the Brazilian Amazon (Almeida et al., 2016). The integration of spectral mixture analysis (SMA) with Landsat data enhances the classification and monitoring of land cover changes over time, improving the identification of vegetation transitions and land use shifts across Brazilian biomes (Souza et al., 2020). The combination of spectral analysis with vegetation indices strengthens land use research by enabling multi-temporal assessments, which are crucial for understanding the drivers of deforestation and land degradation in regions such as Manaus.

The Landsat program, jointly operated by NASA and the United States Geological Survey (USGS), has continuously monitored Earth since 1972, providing digital, multispectral, and medium spatial resolution imagery. Its extensive archive has significantly contributed to advancements in remote sensing and land use monitoring, offering systematic and reliable data for tracking environmental changes over time (Wulder et al., 2022). The introduction of a free and open data policy in 2008 has further increased the accessibility and utility of Landsat imagery, facilitating large-scale and long-term analyses of land cover dynamics and human-environment interactions (Wulder et al., 2022). A notable application of Landsat data is in forest and agricultural assessments, where it has been instrumental in mapping global forest cover change, yielding valuable insights into deforestation and reforestation trends (Wulder et al., 2022).

Over the past five decades, remote sensing technology and GIS methods have significantly advanced in monitoring land use changes in the Amazon. The launch of modern high-resolution satellites such as Landsat 8 and 9 has improved the accuracy and frequency of environmental assessments (Kraeski et al., 2023). Image classification techniques, including supervised classification and machine learning algorithms, have further enhanced the ability to map and track land use transitions. For instance, a study in the Teles Pires River Basin analyzed Landsat imagery from 1986 to 2020, applying supervised classification to detect a 643% increase in agricultural areas, illustrating the significant transformation of the region (Kraeski et al., 2023). Additionally, initiatives such as MapBiomas provide annual land cover maps that enhance land use monitoring for conservation planning.

MapBiomas is a collaborative initiative that integrates civil society organizations, universities, technology companies, and governmental agencies to generate large-scale land-use and land-cover change (LUC/LCC) maps at a high spatial resolution of 30 meters (MapBiomas, 2019). The primary objective of MapBiomas is to estimate land-use-related greenhouse gas (GHG) emissions by mapping historical and contemporary land-use changes. A key feature of MapBiomas is its reliance on cloud computing and machine-learning algorithms for processing vast amounts of satellite imagery. By leveraging Google Earth Engine, the initiative automates land cover classification, enabling rapid and cost-effective large-scale mapping (MapBiomas, 2019). Unlike traditional land-use mapping efforts that often depend on manual interpretation and are constrained by resource availability, MapBiomas produces consistent annual datasets dating back to 1985, allowing for comprehensive analyses of land-use trends, including deforestation, agricultural expansion, and urbanization (MapBiomas, 2019).

Beyond data provision, MapBiomas actively supports environmental monitoring and policy enforcement. One of its most significant contributions is MapBiomas Alerts, a system designed to enhance deforestation monitoring through near-real-time, high-resolution satellite imagery (MapBiomas, 2019). By refining existing deforestation alerts with machine-learning algorithms, the Alerts system allows authorities to remotely verify environmental violations, reducing the need for costly and logistically complex field inspections. This advancement in environmental governance facilitates legal enforcement against illegal deforestation and improves land-use regulation at national and regional levels (MapBiomas, 2019). However, early iterations of MapBiomas exhibited classification limitations, particularly in distinguishing between natural and anthropogenic landscapes. Common sources of error included confusion between croplands and pasturelands, as well as between planted and native forests (MapBiomas, 2019).

Field studies and social surveys remain essential for understanding the socio-economic and cultural dimensions of land use change in the Amazon. Ground-truthing efforts, which validate remote sensing data with on-the-ground observations, are critical for ensuring data accuracy. For example, a study in the Teles Pires River Basin included field verification of 1,477 locations to confirm land classifications, underscoring the importance of field surveys in improving remote sensing data reliability (Kraeski et al., 2023). The integration of remote sensing, GIS, and field validation provides a comprehensive approach to monitoring land use dynamics, supporting conservation efforts and sustainable land management in the Amazon.

Approaches to Land Use

Conclusion

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