Variation In Cover Research Articles

Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks

Intrusion detection systems (IDSs) are crucial for identifying cyberattacks on in-vehicle networks. To enhance IDS robustness and preserve user data privacy, researchers are increasingly adopting federated learning (FL). However, traditional FL-based IDSs depend on a single central aggregator, creating performance bottlenecks and introducing a single point of failure, thereby compromising robustness and scalability. To address these limitations, this paper proposes a Hierarchical Federated Learning (H-FL) framework to deploy and evaluate the performance of the IDS. The H-FL framework incorporates multiple edge aggregators alongside the central aggregator, mitigating single-point failure risks, improving scalability, and efficiently distributing computational load. We evaluate the proposed IDS using the H-FL framework on two car hacking datasets under realistic non-independent and identically distributed (non-IID) data scenarios. Experimental results demonstrate that deploying the IDS within an H-FL framework can enhance the F1-score by up to 10.63%, addressing the limitations of edge-FL in dataset diversity and attack coverage. Notably, H-FL improved the F1-score in 16 out of 24 evaluated scenarios. By enabling the IDS to learn from diverse data, driving conditions, and evolving threats, this approach substantially strengthens cybersecurity in modern vehicular systems.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Responses of vegetation dynamics to complex environmental changes in the Runoff Producing area of the World’s Sixth Longest River: Evolution, Identification, and prediction

Vegetation plays a crucial role as a resilient safeguard for both natural ecosystems and human livelihoods in the source area of great rivers. Analyzing the variability of vegetation cover (VC) within the context of various complex drivers holds significant practical importance. In this study, an Evolution-Identification-Prediction (EIP) framework was developed to achieve the integrated assessment of vegetation dynamics and to identify its response to environmental changes. It is designed not only to display spatio-temporal features of vegetation change but also to emphasize determining the linkages between climate change (i.e., meteorological elements, climatic extremes, atmospheric circulation), human activities, anthropogenic factors (i.e., population, gross domestic product, land-use type), and vegetation dynamics. Furthermore, future patterns of vegetation were estimated by extracting information on historical changes in VC. In the above process, various methods and novel ideas with strong applicability have been proposed or introduced to achieve the stated objective. Then, the driving mechanisms of environmental changes on vegetation dynamics in the Upper Yellow River basin (UYRB) of China were examined across multiple dimensions, scales, and aspects. Results show that: a) complex correlation between NDVI and climate change appears, which is reflected in the differences of regions, factors, and correlation intensity; b) R10, PRCP, SDII and CWD are the main influences among extreme climate indices with explanatory power above 0.3; c) climate change and human activity jointly dominate vegetation dynamics in the UYRB, with the impact of human activity obviously enhanced; d) human activity has an overall positive effect on VC, and higher NDVI is generally found in forested, low population density and gross domestic product regions; e) the VC of the UYRB is projected to further improve under the combined influence of environmental factors, with the rate of high VC increase from 21.87% (2019) to 30.44% (2030). The findings can support the implementation of vegetation conservation and restoration initiatives to address the risks posed by environmental change.

Suspended sediment connectivity analysis: Snowmelt-driven dynamics in an alpine basin

Understanding snowmelt and its related suspended sediment transport in mountain streams is a crucial issue in the world of hydraulic and sediment research. Field-based approaches are still poorly used to investigate the response of mountain basins to different stages of snowmelt. To explain the suspended sediment dynamics of the Rio Cordon basin (Italy, Dolomites), the snowmelt in the year 2021 was analysed. First, a descriptive analysis of temporal trends of meteorological, hydrological, and sedimentological variables was carried out. Second, suspended sediment budgets of the main channel were quantified, considering the contribution of the tributaries at week- and event- scale, and supported by hysteresis analyses. Third, the role of sediment sources and snow cover was examined and integrated to comprehend variations in sediment supply from tributaries. Data were collected using a (i) multiparametric sonde installed at the outlet, (ii) water and sediment samples at significant points along the channel network, (iii) satellite images and sediment sources inventory. The whole snowmelt period 2021 featured 210.8 mm of precipitation and a mean temperature of 4.6°C. The total load of suspended sediment was 100 t. The main period of ablation was May, which showed contrasting suspended sediment dynamics in the four weeks. Sediment availability exceeded transport capacity in the initial two weeks, whereas transport capacity exceeded sediment availability in the subsequent two weeks. The main snowmelt event analysis mirrored the same trend: limited transport during the rising limb and the opposite during the falling limb. Such results were confirmed by the hysteresis analysis, proving to be an effective tool for detecting changes in suspended sediment connectivity. The variations of snow cover and the extent of sediment sources across the sub-basins influence the sediment supply. This study enhances our comprehension of snowmelt-driven hydrological and sedimentological dynamics, providing insights into the resilience of mountains to changing climate conditions.

Spatio-temporal variations of vegetation cover and its influencing factors in highland lake basin

Spatio-temporal variations of vegetation cover and its influencing factors in highland lake basin

Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean

The Dark Target (DT) algorithm has been applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi-NPP (SNPP) and NOAA-20 satellites to extend the aerosol data record from the Moderate Resolution Imaging Spectrometer (MODIS). The newly released VIIRS DT Version 2.0 (V2.0) dataset includes NOAA-20 aerosol products for the first time. This study provides the first evaluation of NOAA-20 VIIRS DT aerosol products, including aerosol optical depth (AOD) and Ångstrom exponent (AE), for the period 2018–2022 using Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) measurements. In addition, the operational DT aerosol products from SNPP VIIRS (V2.0) and MODIS (Collection 6.1) are also used for comparison purposes. Overall, NOAA-20 AODs over land and ocean exhibit good validation metrics globally, showing comparability to MODIS products and superiority over overestimated SNPP products. Nevertheless, it is worth noting that in South Africa specifically, NOAA-20 land AODs tends to display more pronounced negative biases, despite all products being underestimated. Furthermore, all ocean AE products demonstrate high correlation coefficients (>0.83) with the ground-based data, meeting the fraction of expected accuracy (>80%). Encouragingly, NOAA-20 AE product has significantly improved the persistent issue of overestimation at low-value in MODIS product, making it a more preferable choice for usage. Error analysis reveals that the performance of all three land products decreases in sparsely vegetated areas and when the solar zenith angle is small. However, NOAA-20 AOD exhibits relatively stable performance and is less affected by variations in aerosol loading, observation geometry, and surface vegetation cover. In addition, the performance of AOD and AE over ocean is significantly influenced by scattering angle and wind speed. This research is anticipated to serve as a reference for the utilization of operational VIIRS DT aerosol products and possible algorithm optimization.

Vegetation Density Mapping of Urban Solid Waste Landfill Coverage Using Vegetation Indexes Obtained with UAV

Objective: The objective of this study is to map the vegetation cover of a municipal solid waste landfill using high-resolution vegetation index images obtained with UAV, as a tool for geotechnical monitoring. Theoretical Framework: The theoretical framework describes the state of the art of vegetation indexes, starting from the first proposal of NVDI by Rouse et al. (1973), through to the NDWI by McFeeters (1996). Additionally, it presents the method of representing radiometric variables, considering that these indexes operate based on the ratio between spectral bands, which are displayed within intervals ranging from -1 to +1. Method: The study used a UAV equipped with MAPIR multispectral camera to capture images of the landfill. NDVI and NDWI indexes were applied to assess vegetation health and coverage. Field control points determined with GNSS-RTK, along with protogrametry software, ensured mapping accuracy, creating high-resolution orhomosaics for monitoring and determining vegetation indexes. Results and Discussion: The results indicate variation in vegetation cover, revealing areas of exposed soil ang vegetation at different stages of vigor, from senescence to full growth. The NDVI and NDWI indexes highlighted regions with water stress and soil exposure, demonstrating the effectiveness of the method in environmental and geotechnical monitoring. Conclusions: It is concluded that the use of UAVs and vegetation indexes are effective for monitoring vegetation health and landfill stability, enanbling the identification of erosion areas and the need for vegetation cover restoration.

δ13C and δ18O-based interpretation of Miocene carbonates of the Miranda-Trebiño basin, NE Iberia: preliminary insights

The stable isotopes (C and O), bulk mineralogy and sedimentary facies types of the Miocene lacustrine palustrine carbonates of the Cucho Section (Miranda-Trebiño basin, N Iberia) are studied. The results indicate deposition in a freshwater carbonate lake recording significant biogenic CO2 input, changing Precipitation/Evaporation rates and variations in vegetation cover and pedogenesis on palustrine areas. Short-lived stages of higher salinity and evaporative conditions are recorded by the relative abundance of fine-grained dolomite. The highest δ13C values suggest 12C sequestration in anoxic bottom waters due to water column stratification. These new data support the general deepening trend of the lake system established from previous sedimentological analyses.

Spatial patterns of evaporation in a small catchment

Abstract In this study a network of three eddy covariance stations rotated between five measurement sites is used to measure evaporation (E) within the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria for 8 years. Discharge measurements at the tributaries and outlet of the main catchment allow for E to be estimated for 6 subcatchments using the water balance method. Year to year variability in monthly E measured by the eddy covariance stations is found to be driven primarily by net radiation and temperature and annual E by net radiation. Year to year variability in the water balance-based E estimate was driven by precipitation. The two methods are found to be consistent, when storage and leakage are accounted for. Daily and seasonal patterns can be seen resulting from the agricultural land use cycle, due to the variations in land cover during the growing season.

Seasonal evolution modes of tropical sea surface temperature anomalies and their links to antarctic sea ice anomalies

Abstract Previous studies have explored the teleconnections between variability of Antarctic sea ice cover and tropical sea surface temperature (SST) across the Pacific, Atlantic, and Indian Ocean basins, typically focusing on each basin individually. However, there has been limited investigation into the impact of tropical SSTs—particularly from a seasonal evolution perspective—on Antarctic sea ice cover. In this study, we employ the self-organizing map method to identify and analyze the primary modes of seasonal SST evolution in the tropical oceans from 1854 to 2022. We also project changes in the frequency of these modes through the 21st century. Moreover, we examine the seasonal variability of Antarctic sea ice concentration in relation to these tropical SST modes over the past four decades. Our results reveal that tropical SST anomalies display both uniform and shifted seasonal evolution patterns. Notably, the frequency of switched modes—namely, transitions from La Niña to El Niño (node 8) and from El Niño to La Niña (node 3)—is expected to increase in future climate. Interestingly, nearly mirrored SST seasonal evolution patterns do not lead to entirely opposite atmospheric circulation anomalies in the southern mid-high latitudes, nor do they result in completely inverse Antarctic sea ice cover anomalies.

Household, sociodemographic, building and land cover factors affecting residential summer electricity consumption: A systematic statistical study in Phoenix, AZ

Understanding determinants of residential electricity consumption is crucial for urban sustainability efforts for planners and policy makers to develop targeted strategies to lower energy use, reduce greenhouse gas emissions, and to increase community resilience. This study presents a systematic approach to build an interpretable multivariate linear model, addressing challenges like outlier detection, multicollinearity, non-normality, and heteroscedasticity. Using 2019 summer residential electricity data for 426 census tracts in Phoenix and 30 variables, the approach involves (1) addressing multicollinearity and regression outliers through Variance Inflation Factor and studentized residual analysis, (2) comparing an automatic variable selection method with Ridge, Lasso, and Elastic Net regression, (3) evaluating the final model, and (4) interpreting variable effects. Critical findings reveal multicollinearity in land cover and racial variables, while 21 census tracts on the urban periphery exhibit outliers with unique features. Variable selection demonstrates the significance of household and building information in influencing residential electricity consumption. Household variables alone account for 84 % of electricity usage variation. Incorporating building information and land cover variables reduces errors by 35 % and 26 % respectively, emphasizing the significance of including household characteristics as predictors or control variables when modeling electricity consumption. A final model with 93 % explanatory power enables precise predictions.

Dynamic fine-tuning of anti-predator behaviour in snowshoe hares illustrates the context dependence of risk effects.

Research Highlight: Shiratsuru, S., & Pauli, J. N. (2024). Food-safety trade-offs drive dynamic behavioural antipredator responses among snowshoe hares. Journal of Animal Ecology, DOI: 10.1111/1365-2656.14183. Predation-risk effects are known to be context dependent, with impacts of perceived predation threat on individual antipredator responses, prey population demography, species interactions and community organization hinging on traits of the prey, the predator(s) and setting of the interaction. Yet, few empirical studies to date have simultaneously explored how these three drivers shape contingency in antipredator behaviour, the key first step in the process by which predation-risk effects play out, especially in free-living vertebrates. In a new study, Shiratsuru & Pauli (2024) address this knowledge deficit by showing that snowshoe hares (Lepus americanus) trade foraging for anti-predator vigilance dynamically as a function of winter food availability (a proxy for individual energetic state), the timing and intensity of predator activity, and environmental properties associated with elevated vulnerability to predator-induced mortality, notably including coat colour mismatch caused by variation in snow cover. These results offer new insight into the complexity of predation-risk effects and should serve as a guide for research aiming to better understand the expression of these effects under varying circumstances.

MAPPING AND GEOSPATIAL DISTRIBUTION OF AÇAÍ TREES (EUTERPE OLERACEA MART) IN THE TOCANTINA-PA AMAZON

The article aims to map and analyze açaí management and planting systems in floodplain and terra firma areas in the Tocantina Amazon, specifically in the municipality of Mocajuba, Pará. To achieve this work, remote sensing and geoprocessing techniques were used, where we sought characterize the spatial and temporal distribution of açaí groves and evaluate changes in the landscape over the years. The application of the spectral indices NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index) and NDBI (Normalized Difference Construction Index) proved to be fundamental to identify areas with greater plant biomass, humidity levels and urban expansion, respectively. The results point to the importance of sustainable management practices to guarantee productivity and environmental preservation. It is concluded that the analysis of spectral indices proved to be effective in detecting variations in vegetation cover and water availability, providing crucial information for the planning and management of plantations in the region.

Systematic Upstream Large-Scale Control of Subtropical Low-Cloud Properties

Abstract It has recently been shown that the slow adjustment of the atmospheric boundary layer (ABL) to perturbations of the large-scale atmospheric conditions translate into an upstream control of the low-cloud cover (LCC) variability at climatological timescales in the subtropics. In this study we expand upon this recent study to investigate upstream control of the climatology of low-cloud radiative effect (CRE), as well as cloud properties relevant to their radiative effect: cloud liquid water path (LWP), and cloud optical depth (COD).We use machine-learning statistical models with feature selection capabilities (random-forests) to determine the influence of the local and upstream large-scale conditions in monthly data. These conditions are determined using back-trajectories in monthly-mean wind fields. Total CRE, dominated by the shortwave contribution, exhibits a dependence on upstream Estimated Inversion Strength (EIS), but not on upstream Sea-Surface Temperature (SST) as LCC does. Upstream control of COD was present but was not as consistent as LCC or CRE across different regions and cloud regimes, while LWP does not exhibit strong upstream control at all. This implies that the control of other boundary layer properties could explain the differences in response between LCC and CRE to SST and EIS. Looking at five subtropical eastern basins, it appears that key lead times are region-specific. The inclusion of upstream control provides significant improvements to the predictive skill of statistical models over local linear regression, with improvements in variance explained well over 20% in many cases.

People or predators? Comparing habitat‐dependent effects of hunting and large carnivores on the abundance of North America's top mesocarnivore

Variation in animal abundance is shaped by scale‐dependent habitat, competition, and anthropogenic influences. Coyotes Canis latrans have dramatically increased in abundance while expanding their range over the past 100 years. Management goals typically seek to lower coyote populations to reduce their threats to humans, pets, livestock and sensitive prey. Despite their outsized ecological and social roles in the Americas, the factors affecting coyote abundance across their range remain unclear. We fit Royle–Nichols abundance models at two spatial scales in a Bayesian hierarchical framework to three years of data from 4587 camera trap sites arranged in 254 arrays across the contiguous USA to assess how habitat, large carnivores, anthropogenic development and hunting regulations affect coyote abundance. Coyote abundance was highest in southwestern USA and lowest in the northeast. Abundance responded to some factors as expected, including positive (soft mast, agriculture, grass/shrub habitat, urban–natural edge) and negative (latitude and forest cover) relationships. Colonization date had a negative relationship, suggesting coyote populations have not reached carrying capacity in recently colonized regions. Several relationships were scale‐dependent, including urban development, which was negative at local (100‐m) scales but positive at larger (5‐km) scales. Large carnivore effects were habitat‐dependent, with sometimes opposing relationships manifesting across variation in forest cover and urban development. Coyote abundance was higher where human hunting was permitted, and this relationship was strongest at local scales. These results, including a national map of coyote abundance, update ecological understanding of coyotes and can inform coyote management at local and landscape scales. These findings expand results from local studies suggesting that directly hunting coyotes does not decrease their abundance and may actually increase it. Ongoing large carnivore recoveries globally will likely affect subordinate carnivore abundance, but not in universally negative ways, and our work demonstrates how such effects can be habitat and scale dependent.

Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction

Understanding spatiotemporal variations in forest cover is crucial for effective forest resource management. However, existing models often lack accuracy in simultaneously capturing temporal continuity and spatial correlation. To address this challenge, we developed ResConvLSTM-Att, a novel hybrid model integrating residual neural networks, Convolutional Long Short-Term Memory (ConvLSTM) networks, and attention mechanisms. We evaluated ResConvLSTM-Att against four deep learning models: LSTM, combined convolutional neural network and LSTM (CNN-LSTM), ConvLSTM, and ResConvLSTM for spatiotemporal prediction of forest cover in Tasmania, Australia. ResConvLSTM-Att achieved outstanding prediction performance, with an average root mean square error (RMSE) of 6.9% coverage and an impressive average coefficient of determination of 0.965. Compared with LSTM, CNN-LSTM, ConvLSTM, and ResConvLSTM, ResConvLSTM-Att achieved RMSE reductions of 31.2%, 43.0%, 10.1%, and 6.5%, respectively. Additionally, we quantified the impacts of explanatory variables on forest cover dynamics. Our work demonstrated the effectiveness of ResConvLSTM-Att in spatiotemporal data modelling and prediction.

The Core of the Matter-Importance of Identification Method and Biological Replication for Benthic Marine Monitoring.

Benthic macrofauna are important and widely used biological indicators of marine ecosystems as they have limited mobility and therefore integrate the effects of local environmental stressors over time. Recently, environmental DNA (eDNA) analysis has provided a potentially more resource-efficient approach for benthic biomonitoring than traditional morphology-based methods. Several studies have compared eDNA with morphology-based monitoring, but few have compared the two approaches using the exact same sediment cores. In addition, the meiofauna and pelagic organisms obtained as 'bycatch' using eDNA have largely been disregarded from comparisons. Here, we address these shortcomings through comparative invertebrate analyses of six sediment sample replicates from each of four stations in Denmark, using eDNA metabarcoding and morphological identification. Our results revealed large variation between the six replicates for both methods and little overlap in taxon compositions between methods. While the morphological dataset was dominated by molluscs and annelids, the eDNA dataset was dominated by arthropods and annelids. Using community composition data, we found that sampling stations could be distinguished both with eDNA and morphology. Finally, we inferred expected total richness from extrapolated accumulation curves of detected taxa from each method. This indicated that eDNA metabarcoding requires less replication than morphology for maximum coverage of diversity to be reached. However, both methods required high levels of replication, and our results on taxonomic composition add to the evidence that morphological and eDNA-based methods should preferably be used as complimentary tools for marine bioassessment.

ECOLOGICAL NICHES AND IMPLICATIONS FOR REFORESTATION OF PTEROCARPUS ERINACEUS, KHAYA SENEGALENSIS AND ISOBERLINIA DOKA IN THE CLASSIFIED FORESTS OF NORTHERN COTE DIVOIRE

Deforestation in Cote dIvoire, particularly in the Pale and Pouniakele classified forests, threatens biodiversity and essential ecosystem services. This study explores the potential of Pterocarpus erinaceus, Khaya senegalensis, and Isoberliniadoka to restore degraded ecosystems through reforestation projects, using maximum entropy modeling. The objective is to analyze the ecological niches of these species in order to optimize reforestation strategies. The results show that Pterocarpus erinaceus has a high tolerance to climate variations, with an average AUC of 0.878, and a concentrated distribution in the central and northern savannahs. The main factors influencing its presence are the standard deviation of temperatures (47%) and humidity (15.4%). Khaya senegalensis, on the other hand, is well adapted to open environments with low vegetation cover and high climate variability. Its mean AUC of 0.893 indicates a robust prediction, with areas of presence in the central-eastern part of the country. Major contributions include temperature standard deviation (21.4%) and NDVI (18.5%). Finally, Isoberliniadoka shows greater sensitivity to stable climatic conditions, with a mean AUC of 0.848 and a presence limited to northern regions, where forest cover (32.1%) and mean temperature (39.7%) are the dominant variables. Ecological differences are observed between species, mainly influenced by climate variability and vegetation density. This implies the importance of adapting reforestation programs to the environmental specificities of each species. This study offers practical recommendations to improve the sustainability of reforestation projects in Cote dIvoire, taking into account local ecological challenges.

Spatiotemporal Variability in Snow and Land Cover in Sefid-Rud Basin, Iran

Snow cover has a key role in balancing the Earth’s surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen’s slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen’s slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of −0.51 and −0.68 (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change.

EVALUATION OF DEFORESTATION IN THE MUNICIPALITY OF BRASIL NOVO IN THE STATE OF PARÁ - BRAZIL, USING MACHINE LEARNING TECHNIQUES

The agricultural frontier of the Brazilian Amazon, is undergoing significant changes in its land cover, primarily driven by the expansion of cattle ranching and cocoa cultivation. Through the analysis of Landsat 8 satellite images and the WorldCover 10m 2020 product, combined with climatic and socioeconomic data, this study investigated land use dynamics between 2015 and 2023, on the municipality of Brasil Novo. The results revealed a substantial reduction in forest cover, with the expansion of pasturelands as the main driver of deforestation. The temporal analysis of the Normalized Difference Vegetation Index (NDVI) indicated interannual variations in vegetation cover, associated with extreme weather events and anthropogenic pressure. Although the municipality still maintains 43.8% of forest cover, with the Arara Indigenous Land being a notable example, the pressure on natural resources demands the implementation of public policies and sustainable management practices to mitigate environmental impacts and ensure the conservation of Amazonian biodiversity. Keywords: Amazon, NDVI, remote sensing, climate change, livestock, land use.