Forest Cover Loss Research Articles

Spatiotemporal Land Use and Land Cover Changes and Their Impact on Landscape Patterns in the Colombian Coffee Cultural Landscape (2014–2034)

The Colombian Coffee Cultural Landscape (CCLC), a UNESCO World Heritage site, faces conservation threats due to changes in land use and land cover (LULC). This study analyzed and predicted the spatiotemporal dynamics of LULC in the CCLC from 2014 to 2034, assessing its effects on the landscape structure. The analyses identified negative impacts and provided insights for developing conservation and land use planning strategies aimed at comprehensive landscape management. A supervised classification methodology using the Random Forest algorithm was implemented by integrating multispectral (Landsat 8) and Synthetic Aperture Radar (SAR) data (Sentinel-1), achieving an overall accuracy of 87.88% and a Kappa coefficient of 84.20%. Future projections were conducted using a hybrid Cellular Automata and Artificial Neural Network model (CA-ANN), reaching an accuracy of 88.12% and a Kappa coefficient of 0.84. The results indicate urban expansion, increasing from 1.46% in 2014 to 15.64% by 2034, accompanied by a forest cover loss of 77.8% and a reduction in coffee-growing areas from 77.91% in 2019 to 68.40% by 2034. Landscape metric analysis revealed increased fragmentation and spatial heterogeneity. The integration of multisensor remote sensing, hybrid predictive models, and landscape metrics within the CCLC provides a quantitative methodological framework to evaluate the transformation of cultural landscapes under anthropogenic pressures.

Integrating seasonal dynamics and human impact on microbial biomass carbon across deep soil profiles in tropical Sal forest of Achanakmar-Amarkantak Biosphere Reserve, India

Forest soil is crucial in climate change mitigation, food security, and biogeochemical nutrient cycling. Mixed Sal forests enhance soil organic matter, improve nutrient availability, and regulate pH dynamics. However, anthropogenic disturbances, including deforestation and land-use changes, significantly alter forest cover, leading to shifts in soil physicochemical and microbial properties. These impacts necessitate rigorous monitoring and comprehensive assessment. Therefore, we investigated the effects of contrasting conditions- closed (no human activities) and open (human interferences) mixed Sal Forest on the vertical and seasonal dynamics of microbial biomass carbon (SMBC). Results revealed that the closed mixed Sal Forest had significantly higher SMBC than the open mixed Sal Forest across the soil profile (D1–D5) with a strong seasonal effect. Closed mixed Sal Forest had 60% higher SMBC in D1 than open mixed Sal Forest while it reduced with depth and 17.1 to 56.7% higher SMBC in the subsurface to bottom-most soil profile (D2–D5). Moreover, SMBC was higher in the monsoon period in both forests. The SMBC reduced by 24.2 to 45.1% in the post-monsoon period while reduction was more intense in the pre-monsoon period (48.1 to 68.2%) compared to the monsoon period under closed mixed Sal Forest. Similarly, the decline was more intense in the open mixed Sal Forest, where SMBC declined 12.1 to 54% in the post-monsoon period and 56.1 to 76.2% in the pre-monsoon period compared to the monsoon period. The study indicates that human interference in mixed Sal forests leads to loss of forest cover, negatively affecting microbiological properties and reducing soil fertility, which weakens the forest’s resilience to climate change. Additionally, SMBC exhibits seasonal variations, reflecting responses to environmental conditions. These results underline the need to reduce human disturbances and enhance forest conservation strategies to ensure soil sustainability and ecosystem stability.

Assessing of driving factors and change detection of mangrove forest in Kubu Raya District, Indonesia

Land cover change information is needed to support decision-making in land-based natural resource management, especially in coastal areas and mangrove ecosystems. This study aims to assess the drivers and detect mangrove forest cover change over the last 30 years in Kubu Raya District, Indonesia, using satellite imagery data from the United States Geological Survey (USGS) Earth Explorer. Maximum Likelihood Classification was used to analyze satellite images from four different recording years digitally: 1993 (Landsat 5), 2003 (Landsat 7), 2013 and 2023 (Landsat 8). Getis-Ord Gi* analysis was also used to observe fragmentation distribution patterns to determine areas with hot spots or cold spots with the Reticular Fragmentation Index (RFI) value as a consideration. Binary Logistic Regression (BLR) analysis was used to assess the drivers of social and natural variables, including population density, education, accessibility, soil type, rainfall, temperature, slope, and elevation. The results showed a significant decrease in mangrove forest cover, from 1,011.37 km2 in 1993–964.37 km2 in 2023, with an average loss of mangrove forest cover of 3.25 km2 per year, including mangroves, open areas, ponds, water bodies, agricultural areas, and settlements. The fragmentation pattern that occurs is that in some areas in the northern part, there are insignificant points in 1993 and then turn into hot spots in 2023. Meanwhile, from 1993 to 2023, there were cold spots that shifted and spread in the central part of the study area. In addition, social and natural variables provide values that are directly and inversely proportional to the driving factors. Social factors, especially population density, education, and land access, have a relationship with land change. Regulations made by the government and the presence of an educated community are the main points for mangrove ecosystem conservation; existing land access is not used as exploitation access but only for daily activities. Natural factors, such as alluvial soil types, have a high concentration of nutrients, making them ideal for sustainable agriculture and ponds. Rainfall intensity contributes to higher agricultural production and stable pond water. Conservation efforts must consider these changes and spatial dynamics to effectively protect mangrove ecosystems in the future.

Evaluating the Spatial Relationships Between Tree Cover and Regional Temperature and Precipitation of the Yucatán Peninsula Applying Spatial Autoregressive Models

Deforestation and forest degradation are important drivers of global warming, yet their implications on regional temperature and precipitation patterns are more elusive. In the Yucatán Peninsula, forest cover loss and deterioration has been rapidly advancing over the past decades. We applied local indicators of spatial association (LISA) cluster analysis and spatial autoregressive models (SAR) to evaluate the spatial relationships between tree cover and regional temperature and precipitation. We integrated NASA’s Global Forest Cover Change (GFCC) and WorldClim’s historical monthly weather datasets (2000–2015) to assess the effects of deforested, degraded, and dense forest land cover on temperature and precipitation distributions on the Yucatán Peninsula. LISA cluster analyses show warmer and drier conditions geographically coincide with deforested and degraded tree cover, but outliers allude to the potential influence of forest cover impacts on regional climate. Controlling spatial dependencies and including covariates, SAR models indicate that deforestation is associated with higher annual mean temperatures and minimum temperatures during dry and wet seasons, and decreased precipitation in the dry season. Degraded tree cover was related to higher maximum temperatures but did not relate to precipitation variability. We highlight the complex interactions between forest cover and climate and emphasize the importance of forest conservation for mitigating regional climate change.

Land use changes and the distribution of the red-billed Quelea (Quelea quelea) in Chemba district, Tanzania

The red billed Quelea queleas are known to be used as a source of food in different African countries. The current study aimed at assessing the distribution of these birds and the used trapping methods across the different land use types in Chemba District. The study also, assessed the trends of changes in different land use types and Quelea population in recent 20 and 10 years respectively. Semi-structured questionnaires, focused group discussion and direct observation were used to capture information from the respondents while landsat images were used to process the trends of different land use land cover changes (LULCC). The results show that the occurrence of quelea birds varied significantly across the study villages and land use types (p < 0.05). Quelea birds were predominantly found in farmlands and along the rivers. Further analysis revealed a notable change in land use, characterized by a significant increase in settled lands and a corresponding decrease in forested lands. Forested lands were identified by respondents as potential breeding sites. To address these findings, the research advocates for the restoration of native vegetation and the enhancement of community awareness and participation in restoration initiatives across the study area. This is crucial to offset the loss of forest cover, which serves as prime breeding habitat for the quelea birds. The study also emphasizes the need for a comprehensive assessment to identify suitable native species for effective restoration efforts in the various land use types. The study recommends implementing innovative, environmentally friendly mass capture techniques focused along rivers and farmlands. This approach aims to improve the capture of quelea birds, thereby supporting the overall restoration goals and management of this important avian species. This study offers valuable insights into the evolving land use patterns and their implications for the distribution and habitat of the quelea bird. Limitations include reliance on perception-based data and the need for further ecological research to confirm population trends. It also presents practical recommendations for restoration and management strategies. These findings enhance conservation efforts and inform decision-making to effectively address the challenges posed by the quelea bird population in the region.

Trends and population size of White-necked Rockfowl Picathartes gymnocephalus within the Nyamibe Bepo Forest Reserve in Ghana

The White-necked Rockfowl Picathartes gymnocephalus (family Picathartidae) is endemic to the Upper Guinea forest and has a global population of <10 000 mature individuals. This species is listed as Vulnerable on the IUCN Red List owing to its declining population, mainly as a result of habitat fragmentation, degradation and predation within its range states. Previous studies showed that many nesting colonies of the species in Ghana have disappeared following extensive loss of forest cover. However, recent field visits identified nesting colonies in several forest reserves, including the Nyamibe Bepo Forest Reserve where a significant proportion of the species’ population in the country is reported to occur. This study investigated the population size of the White-necked Rockfowl in Nyamibe Bepo to compare the extent of the current nesting colonies to those documented in 2011. A survey of nesting sites in the Bronko area of the reserve returned a total of 54 nests distributed across 10 rock-faces, and 88 individuals. Compared with the 53 nests reported in 2011, this indicates a stable population over the past decade in this section of the reserve. In contrast, colonies within the Ashilvikrom and Amanokrom sections have declined significantly. Thus, this indicates an overall population decline of the species within the Nyamibe Bepo Forest Reserve. By hosting 3.3% of the global minimum population and 41 reproductive units of the Vulnerable White-necked Rockfowl, the Nyamibe Bepo Forest Reserve could qualify as an Important Bird Area (IBA) under criterion A1a and a Key Biodiversity Area (KBA) under criterion A1b. We advocate that further steps be taken to engage conservation organisations and the government for more-formal protection of the site for the conservation of this Vulnerable species.

Evaluating impacts of opencast mining of Gondwana Coalfield (India) on environment and socioeconomic using multitemporal satellite data

The spatio-temporal mapping of coal mines is crucial for efficient management and sustainable utilization of coal resources. This study presents a spatio-temporal mapping of coal mines in Gondwana Coalfield, integrating geospatial technologies with Landsat satellite data (2000, 2016, and 2022) and field surveys. A change detection method based on image classification was applied to multi-temporal satellite datasets, enabling the identification of mining areas from 2000 to 2022. The analysis is focused on the expansion or contraction of existing mining pits and changes in land use and land cover patterns associated with mining activities. The key findings indicate an increase in mining area from 1.39 km2 in 2000 to 8.70 km2 in 2022 (525.9%) with an annual growth rate of 22.86%. The expansion of mining areas was mostly attributed to changes in forest cover and agriculture. A decline in forest cover from 290.67 km2 to 265.95 km2 occurred over the same period, leading to a loss of forest cover by 8.5%. The agriculture area is replaced by mining activity, especially in Zone 1 (i.e. Urimari coal mines) and Zone 3 (i.e. Thriveni Sanik mining project), declining 46–86% of agricultural areas and 47–90% of forest areas. Furthermore, the study assessed the development of mining operations and potential environmental impacts, revealing a continuous increase in collapsed homes and displacement of people from their native places due to mining activities. The study concludes that spatio-temporal maps of coal mines provide essential information for mine planning, resource estimation, and monitoring. They are also crucial for regulatory agencies in conducting environmental impact assessments to ensure sustainable development.

Forest Change Detection Using Google Earth Engine: A Temporal Analysis of Shirani District

Forest fires are a common and devastating natural disaster that causes widespread damage to forest vegetation and poses significant threats to ecosystems. Detecting and monitoring forest fires are crucial for mitigating their impact on the environment and human communities. This research paper focuses on remotely monitoring the change detection in the Sherani Balochistan Pine Nut Forest, which experienced extensive fires, resulting in substantial damage to the Pine Nut crop. Being the world’s largest Pine Nut crop, this event has significant implications for global nut crop production. The proposed solution utilizes remote sensing techniques to detect major changes in the Pine Nut Forest, with images depicting the Sherani forest fire collected from Landsat 9 satellite imagery. It involves actual fire detection, monitoring of damaged areas, and risk hazard analysis. The research employs temporal analysis, which examines the burned area at different time series to observe changes in the geographic area and potential loss of forest cover. Satellite imagery is obtained through the GEE for geospatial analysis, using Landsat data with a spatial resolution of 30 meters for improved comparison and collation of semi-centennial forest data. The approach involves the calculation of indices for the Pine Nut Forest using the NBR and dNBR. These indices help identify the extent of affected land and the severity of the burn. By utilizing this novel approach, the forest department can effectively detect changes in land and climate, enabling better decision-making based on the collected data. Overall, this research contributes to improved forest fire management and conservation efforts.

Asymmetric sensitivity of boreal forest resilience to forest gain and loss.

Forest gains and losses may have unequal effects on forest resilience, particularly given their distinct temporal dynamics. Here, we quantify the sensitivities of boreal forest resilience to forest cover gain and loss using a resilience indicator derived from the temporal autocorrelation (TAC) of the kernel normalized difference vegetation index from 2000 to 2020. Our findings unveil pronounced asymmetric sensitivities, with stronger sensitivity to forest loss (-4.26 ± 0.14 × 10-3; TAC increase per 1% forest cover loss) than to forest gain (-1.65 ± 0.12 × 10-3; TAC decrease per 1% forest cover gain). Locally, ~73% of the boreal forest exhibits negative sensitivity, indicating enhanced resilience with forest cover gain and vice versa, especially in intact forests compared to managed ones. This sensitivity is affected by various trajectories in forest cover change, stemming primarily from temporal asynchrony in the recovery rates of various ecosystem functions. The observed asymmetry underscores the importance of prioritizing forest conservation over reactive management strategies following losses, ultimately contributing to more sustainable forest management practices.

Forest land-use change affects soil organic carbon in tropical dry forests of the Peruvian Amazon

Aim of study: The loss of forest cover is a global problem that alters ecosystems, contributing to carbon emissions. This study measured the soil organic carbon (SOC) at different soil depths in tropical dry forests of the Huallaga Central in the Peruvian Amazon. Area of study: San Martín Region, Peruvian Amazon. Material and methods: A total of 24 plots of 100 m2 were selected in primary (~200 years), intervened (~50 years since intervention), and deforested forests (10 years ago), with 120 soil samples collected across five depths. Soil texture (hydrometer), bulk density (cylinder method), SOC content, SOC density, and erodibility (K parameter) were calculated. Main results: SOC content in the 0-20 cm soil horizon was 79.5±21.3 t ha-1 for the primary forest, 58.5±11.8 t ha-1 for the intervened forest, and 41.8±10 t ha-1 for the deforested forest. A soil erodibility K of 0.065 was observed for primary forests and 0.076 and 0.093 for intervened and deforested forests. In average, the SOC density obtained in this study was 7.6±5.1 t ha-1 in the primary forest, 6.2±3.6 t ha-1 in the intervened forest, and 4.7±2.7 in the deforested forest. Research highlights: Primary forests had the highest SOC content and SOC density, followed by intervened and deforested forests, while the opposite pattern was found for soil erodibility. These patterns were especially marked in the first 40 cm of soil depth.

Forest cover change mapping based on Deep Neuron Network, GIS, and High-resolution Imagery

With the rapid advancement of technology, monitoring forest cover changes has become increasingly quantifiable through various techniques and methods. In this study, we developed a procedure that utilizes the Deep Neuron Network (DNN) model and the Geographic Information Systems (GIS) based on high-resolution imagery captured at different time points to create forest cover change maps in Nui Luot, Chuong My, Hanoi. Two RGB (Red-Green-Blue) spectral images were captured by Unmanned Aerial Vehicle (UAV) at two different time points (pre-scene and post-scene) and used to extract information for the DNN model to produce land cover maps for these two time points. The land cover classification was divided into four classes: (1) Trees, (2) Vacant, (3) Built area and others, and (4) Water surface. Combined with GIS analysis, the forest cover change maps were developed to quantify detailed increases or losses in forest cover based on the "Trees" class. The model's accuracy was evaluated using parameters such as the area Under the ROC Curve (AUC), Accuracy (ACC), Precision, Recall, F1-Score, Kappa, and Root Mean Square Error (RMSE). The analysis results indicate that from January 31, 2023, to October 20, 2023, the forest cover in the study area decreased by 0.53%. The accuracy metrics for the pre-change scene were: average AUC = 0.922, ACC = 76.86%, average Precision = 0.743, average Recall = 0.73, average F1-Score = 0.723, Kappa = 0.692, and RMSE = 0.297. For the post-change scene, the accuracy metrics were: average AUC = 0.954, ACC = 81.89%, average Precision = 0.823, average Recall = 0.815, average F1-Score = 0.818, Kappa = 0.758, and RMSE = 0.262. A deforestation scenario was constructed to evaluate the effectiveness of the DNN models in assessing and monitoring forest dynamics.

How Does Landscape Structure Affect Dung Beetle Assemblages in Amazon Cities?

The growth of cities is one of the main direct and indirect factors responsible for the loss of native vegetation cover. Urbanization directly affects the biological communities inhabiting forest remnants inserted in cities, compromising the maintenance of urban and natural ecosystems. By understanding the effects of landscape transformation due to urbanization, we can have insights regarding the distribution of land uses that allow a proper maintenance of the urban ecosystems. This work assessed the effects of landscape structure variables (forest cover, agricultural area, edge density, and number of forest patches) on dung beetle assemblages and functional groups (i.e., diet and resource removal strategy) sampled in 38 sites located along an urban-rural gradient of six cities belonging to the metropolitan area of Manaus in Central Amazonia. Losses of forest cover were the most determining factor, negatively affecting species richness, abundance, and body size. The increases in agriculture cover negatively affected dung beetle abundance, while edge density positively affected their abundance. The number of forest patches positively affected dung beetle abundances-except for dweller species-and negatively affected the body size of diet-generalist species. These results demonstrate that changes in ecological diversity caused by urbanization are driven mostly by forest cover loss, although forest configuration is important for dung beetle abundance. This study contributes to the understanding of how changes in the amount and distribution of forest cover in tropical cities affect the taxonomic diversity of dung beetle assemblages.

Analyzing Drivers of Tropical Moist Forest Dynamics in the Kahuzi-Biega National Park Landscape, Eastern Democratic Republic of Congo from 1990 to 2022

The protected areas (PA) of the Democratic Republic of the Congo serve as vital carbon reservoirs and are crucial for biodiversity conservation and climate regulation. Despite their significance, these areas face escalating rates of deforestation and degradation, often poorly understood at the local level. This study focuses on the dynamics of tropical moist forest (TMF) and the relative importance of the driving factors in the landscape of Kahuzi-Biega National Park (KBNP), one of the country’s prominent PAs. Analyzing annual TMF dynamics from 1990 to 2022 using data classified by Vancutsem and his collaborators in 2021 from Landsat imagery alongside spatial datasets of deforestation and degradation drivers, we employed a comprehensive analytical approach. This included meshing, multi-scale analysis, principal component analysis, zoning, multiple linear regression, and relative importance analysis through bootstrapping. The findings indicate that the grid size considered does not significantly influence TMF dynamics in the KBNP landscape (p-value = 0.67, α = 0.05). The edge and outer zones experienced substantial dynamics, with approximately 30% forest cover loss in both areas, contrasting with the relatively stable TMF cover (~100%) in the inner zone. Fire emerged as the most influential driver, explaining TMF dynamics with a relative importance of approximately 55%, 30%, and 23% in the inner, edge, and outer zones, respectively. This study underscores KBNP’s efficacy in curbing TMF loss but highlights the need for enhanced protection around its periphery. Management efforts should prioritize sustainable land use practices, livelihood improvement, and the establishment of an officially recognized buffer zone.

A 30-Year Analysis of Forest Cover and Land Surface Temperature in Attapeu Province, Lao PDR

Changing forest areas can have a complex range of impacts on ground temperatures, from increasing temperatures due to loss of shade to creating drier and hotter local climate environments. This study aims to identify spatiotemporal changes in forest cover and retrieve land surface temperature (LST) using thermal infrared sensor (TIRS) data in Attapeu province, Lao PDR from 1994 to 2024. Geographic information system (GIS) techniques were employed to extract spatiotemporal changes in land use and land cover (LULC) and Normalized Difference Vegetation Index (NDVI). The analysis of LULC change revealed a notable decrease of 18.43% in dense forest areas, accompanied by an increase of 7.87% in sparse forest and 11.35% in cropland, indicating a transition from dense forest to sparse forest and cropland for the study area. The analysis of LST utilizing TIRS revealed a consistent negative correlation with NDVI. The coefficient of determination (R2) indicated values of 0.5896 in 1994, 0.5691 in 2009, and 0.4344 in 2024. By correlating remotely-sensed thermal data with in situ observations, this research delineated the prolonged alterations in LST due to fluctuations in forest cover. The urgency of enacting policies is underscored to mitigate the ongoing loss of forest cover. These findings emphasize the need for immediate policy actions, such as enhanced forest conservation and reforestation programs, to mitigate rising temperatures and ensure ecological sustainability in Attapeu province. The insights garnered from this investigation hold significant implications for the conservation endeavors aimed at preserving the forests of Attapeu province.

Modelling the present and future distribution of Ambystoma altamirani in the Transmexican Volcanic Belt, Mexico

Ambystoma altamirani is a critically endangered, microendemic amphibian species inhabiting the high-altitude rivers and streams of the Trans-Mexican Volcanic Belt (TMVB), a region experiencing severe ecological disturbances. This study aims to assess the current and future distribution of A. altamirani under different climate and land-use change scenarios using ecological niche modelling (ENM). We also evaluate the connectivity of suitable habitats and the overlap with existing natural protected areas (NPAs). Using occurrence records and environmental variables, we modelled the species’ potential distribution under two climate models (CN85 and MP85) for 2050. The results indicate a significant reduction in suitable habitat, particularly in areas such as the Sierra de las Cruces and the Chichinautzin Biological Corridor, with habitat losses projected to reach up to 13.95% by 2050 under the CN85 scenario. Forest cover loss between 2001 and 2023 further exacerbates this threat, especially in municipalities like Tlalpan and Ocuilan. Our analysis highlights the urgent need for targeted conservation efforts, including the preservation of mixed Abies-Pinus forests and the restoration of degraded ecosystems. The findings underscore the critical importance of integrated conservation strategies that address habitat degradation, climate resilience and ecological connectivity to ensure the long-term survival of A. altamirani.

Monitoring Postfire Biodiversity Dynamics in Mediterranean Pine Forests Using Acoustic Indices

In recent decades, climate change has significantly influenced the frequency and intensity of wildfires across Mediterranean pine forests. The loss of forest cover can bring long-term ecological changes that impact the overall biodiversity and alter species composition. Understanding the long-term impact of wildfires requires effective and cost-efficient methods for monitoring the postfire ecosystem dynamics. Passive acoustic monitoring (PAM) has been increasingly used to monitor the biodiversity of vocal species at large spatial and temporal scales. Using acoustic indices, where the biodiversity of an area is inferred from the overall structure of the soundscape, rather than the more labor-intensive identification of individual species, has yielded mixed results, emphasizing the importance of testing their efficacy at the regional level. In this study, we examined whether widely used acoustic indicators were effective at capturing changes in the avifauna diversity in Pinus halepensis forest stands with different fire burning histories (burnt in 2001, 2009, and 2018 and unburnt for &gt;20 years) on the Sithonia Peninsula, Greece. We recorded the soundscape of each stand using two–three sensors across 11 days of each season from March 2022 to January 2023. We calculated for each site and season the following five acoustic indices: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Normalized Difference Soundscape Index (NDSI), and Bioacoustic Index (BI). Each acoustic index was then assessed in terms of its efficacy at predicting the local avifauna diversity, as estimated via two proxies—the species richness (SR) and the Shannon Diversity Index (SDI) of vocal bird calls. Both the SR and SDI were calculated by having an expert review the species identification of calls detected within the same acoustic dataset by the BirdNET convolutional neural network algorithm. A total of 53 bird species were identified. Our analysis shows that the BI and NDSI have the highest potential for monitoring the postfire biodiversity dynamics in Mediterranean pine forests. We propose the development of regional-scale acoustic observatories at pine and other fire-prone Mediterranean habitats, which will further improve our understanding of how to make the best use of acoustic indices as a tool for rapid biodiversity assessments.

Monitoring forest cover and land use change in the Congo Basin under IPCC climate change scenarios

The Congo Basin tropical forests are home to many endemic and endangered species, and a global hotspot for forest fragmentation and loss. Yet, little has been done to document the region’s rapid deforestation, assess its effects and consequences, or project future forest cover loss to aid in effective planning. Here we applied the Random Forest (RF) supervised classification algorithm in Google Earth Engine (GEE) to map and quantify decadal changes in forest cover and land use (LCLU) in the Congo Basin between 1990 and 2020. We cross-validated our LCLU maps with existing global land cover products, and projected our validated results to 2050 under three climate change scenarios, using the Multiperceptron Artificial Neural Network and Markov chain algorithms of the Idrissi Land Change modeller from TerrSet. We found that, over 5.2% (215,938 km2), 1.2% (50,046 km2), and a 2.1% (86,658 km2) of dense forest cover were lost in the Congo Basin between 1990–2000, 2000–2010, and 2010–2020, totaling approximately 8.5% (352,642 km2) loss estimated between 1990–2020. For the period 2020–2050, we estimated a projected 3.7–4.0% (174,860–204,161 km2) loss in dense forest cover under all three climate change scenarios (i.e., 174,860 km2 loss projected for SSP1-2.6, 199,608 km2 for SSP2-4.5, and 204,161 km2 for SSP5-8.5), suggesting that approximately 12.3–12.6% (527,502 km2–556,803 km2) of dense forest cover could be lost over a 60-year period (1990–2050). Our study represents a novel application of spatial modeling tools and Machine Learning algorithms for assessing long-term deforestation and forest degradation within the Congo Basin, under human population growth and IPCC climate change scenarios. We provide spatial and quantitative results required for supporting long-term deforestation and forest degradation monitoring within Congo Basin countries, especially under the United Nations Framework Convention on Climate Change (UNFCCC) REDD+ (Reduce Emissions from Deforestation and Forest Degradation) program.

Analysis of the Impact of Agriculture and Logging on Forest Habitat Structure in the Ankasa and Bia Conservation Area of Ghana.

Ghana's Ankasa (ACA) and Bia Conservation Area (BCA) are experiencing forest loss due to agricultural conversions. However, there is limited comprehensive analysis of these conversions and their impact on the forest habitat structure in these areas. This study aims to analyse anthropogenic-induced forest habitat loss and fragmentation in the ACA and BCA. Landsat images for the epochs 1980, 2000 and 2020 were pre-processed, and subsets were created using a 5 km buffer of the two conservation areas. The images were classified into forest, agriculture and built-up. The classified images were analysed for landscape pattern changes using patch density (PD), edge density (ED), largest patch index (LPI), landscape shape index (LSI) and aggregation index (AI). The Wilcoxon signed-rank test was used to analyse changes in landscape structure. The results indicate that forest cover in the ACA decreased by 16.4% from 100,941.6 ha in 1980 to 84,410.6 ha in 2020, and in the BCA, it decreased by 14.4% from 70,211.8 to 60,117.36 ha. There was no encroachment from agricultural activities within the conservation areas, but agricultural activities, mainly cocoa expansion, increased within the 5 km buffer, leading to the decline in forest cover. The landscape analysis shows that the forest patches have become fragmented, disjointed and isolated, especially within the 5 km buffer. This is indicated by increased PD, decreased AI, decreased LPI and increased ED. The immediate loss of forest habitat cover in the off-reserve landscape and the significant levels of forest fragmentation, resulting in the loss of forest connectivity, have significant implications for wildlife conservation. Ecological restoration and conservation efforts are needed to reduce this potential impact. Ecologists have recommended transitioning from monoculture cocoa to cocoa agroforestry to improve forest habitat connectivity within adjoining cocoa farms in the landscapes of these conservation areas.

The importance of legal reserves in the conservation of the Dark-winged Trumpeter, Psophia obscura (Aves − Psophiidae), given climate change in the eastern Amazon

The importance of legal reserves in the conservation of the Dark-winged Trumpeter, Psophia obscura (Aves − Psophiidae), given climate change in the eastern Amazon

Effect of land use and land cover changes on land surface warming in an intensive agricultural region

Effect of land use and land cover changes on land surface warming in an intensive agricultural region