Search Results (367)

Accurate assessments of aboveground biomass (AGB) stocks and their changes in extensive Miombo forests are challenging due to the lack of site-specific allometric equations (AEs). Terrestrial Laser Scanning (TLS) is a non-destructive method that enables the calibration of AEs and has recently been validated by the IPCC guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the wet Miombo forest of Katanga, in the Democratic Republic Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented strong performance metrics (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between sites and between plots within sites. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from −35.04 to −19.97% was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga. Full article

The Amazonian forests located within the Guiana Shield store above-average levels of biomass per hectare. However, considerable uncertainty remains regarding carbon stocks in this region, mainly due to limited inventory data and the lack of spatial datasets that account for factors influencing variation among forest types. The present study investigates the spatial distribution of original total forest biomass in the state of Amapá, located in the northeastern Brazilian Amazon. Using data from forest inventory plots, we applied geostatistical interpolation techniques (kriging) combined with environmental variables to generate a high-resolution map of forest biomass distribution. The stocks of biomass were associated with different forest types and land uses. The average biomass was 536.5 ± 64.3 Mg ha⁻¹ across forest types, and non-flooding lowland forest had the highest average (619.1 ± 38.3), followed by the submontane (521.8 ± 49.8) and the floodplain (447.6 ± 45.5) forests. Protected areas represented 84.1% of Amapá’s total biomass stock, while 15.9% was in agriculture and ranching areas, but the average biomass is similar between land-use types. Sustainable-use reserves stock more biomass (40%) than integral-protection reserves (35%) due to the higher average biomass associated with well-structured forests and a greater density of large trees. The map generated in the present study contributes to a better understanding of carbon balance across multiple spatial scales and demonstrates that forests in this region contain the highest carbon stocks per hectare (260.2 ± 31.2 Mg ha⁻¹, assuming that 48.5% of biomass is carbon) in the Amazon. To conserve these stocks, it is necessary to go further than merely maintaining protected areas by strengthening the protection of reserves, restricting logging activities in sustainable-use areas, promoting strong enforcement against illegal deforestation, and supporting the implementation of REDD+ projects. These actions are critical for avoiding substantial carbon stock losses and for reducing greenhouse-gas emissions from this region. Full article

While the methods for monitoring deforestation are relatively well established, there is still no compromise on those for forest degradation. We propose here a systematic review on studies about forest degradation in the Congo Basin. Our analysis focused on seven key anthropogenic causes of forest degradation. Shifting agriculture emerged as the most significant driver, accounting for 61% ± 28.58% (mean ± SD) of canopy opening, 73.16% ± 16.88% aboveground carbon loss, and 30.37% ± 30.67% of tree species diversity loss over a 5–60-year period. Our analysis reveals a significant disconnect. Only 29% of the reviewed studies address this driver, while over 64% focus primarily on the consequences of industrial timber harvesting. Despite its comparatively minor contribution to degradation, with effects range from only 8.98% ± 13.63% of canopy opening, 14.79% ± 22.21 aboveground carbon loss, and 4.27 ± 21.07 tree species diversity loss over 1–20 years. Indeed, most of the methods focus on detecting changes in canopy structure associated with forest logging over a short period (0–5 years). These illustrate the need for a shift in focus in scientific research towards innovative methods, which can be developed over time, to monitor the various impacts of all causes of forest degradation. Full article

REDD+ is a global mechanism that reduces greenhouse gas emissions by preventing deforestation and forest degradation, enhancing forest carbon stocks, and promoting sustainable forest management in developing countries. It plays a crucial role for developing countries in achieving climate targets under the Paris Agreement and can be implemented at the project, subnational, and national levels. Subnational REDD+ offers several advantages over project-level, such as reduced risk of overestimating emissions and enhanced management of leakage. However, the comprehensive opportunities and challenges of subnational REDD+ have not been extensively investigated in the literature. This paper aims to undertake a thorough review of subnational REDD+, highlighting its potential and the obstacles it faces. This systematic review synthesizes the existing literature on subnational REDD+ implementation, analyzing 54 peer-reviewed articles published between 2005 and 2024. The review identified three key factors for the effective implementation of subnational REDD+: financial, social, and institutional factors. Within these three factors, both opportunities and challenges were discussed, drawing on case studies and synthesizing practical implications. Our findings demonstrate that successful subnational REDD+ initiatives require integrated approaches that address the causal relationships between financing mechanisms, governance structures, and stakeholder engagement. The discussion further explores these interdependencies, revealing how constraints in one dimension create cascading effects across others. This study provides empirical insights and actionable recommendations for policymakers and project developers engaged in climate change mitigation efforts. Full article

Community Forest Management (CFM) and REDD+ projects have emerged as key strategies for promoting environmental conservation and livelihood improvement. This review explores the effectiveness of incorporating free, prior, and informed consent (FPIC), safeguard principles, grievance redress mechanisms, and benefit-sharing mechanisms into CFM and REDD+ in Cambodia, with a focus on enhancing communities’ livelihoods and climate change adaptation. This paper synthesizes findings from recent literature on CFM and REDD+ in Cambodia and internationally, analyzing key case studies, policy frameworks, and community engagement strategies. Findings suggest that while REDD+ projects offer potential economic and ecological benefits, challenges related to land tenure, equity in benefit-sharing, and community participation remain. This review highlights the need for stronger community engagement, a robust conflict management structure, clear land tenure policies, equitable benefit-sharing mechanisms, and more climate change adaptation activities to ensure the success of CFM and REDD+ projects in Cambodia and the Global South. Full article

This research examines the governance of carbon rights in comparison with 7 other countries, focusing on Vietnam’s carbon markets and Reducing Emissions from Deforestation and Forest Degradation in Developing Countries implementation. Through constitutional theory and comparative analysis, the study explores carbon rights and their governance frameworks. It utilizes surveys, in-depth interviews, and literature reviews to scrutinize governance mechanisms. A comparative analysis of Vietnam with countries such as Australia, New Zealand, Sweden, Brazil, Democratic Republic of Congo, Indonesia, and the Philippines was performed. It highlights differences in legal, institutional, and policy frameworks. Australia and New Zealand, early adopters of carbon rights policies promoting private ownership, have developed strong markets. In contrast, Indonesia and other Global South nations are still evolving their frameworks, with a focus on state-controlled systems that restrict participation and equity. The findings indicate substantial gaps in Vietnam’s carbon rights governance compared to other countries, especially in terms of legal clarity, stakeholder engagement, and policy coherence. Accordingly, this study recommends that Vietnam should adopt a robust legal framework for carbon rights, improve transparency in carbon markets, and integrate Reducing Emissions from Deforestation and Forest Degradation in Developing Countries strategies within broader environmental governance objectives. Vietnam’s carbon rights ought to be designated as national assets to ensure equitable distribution among various forest ownership groups. Benefit-sharing mechanisms could be fashioned following the successful implementation of the Payment for Forest Environmental Services policy. The research concludes that, with these enhancements, Vietnam could emerge as a key player in the global carbon market and effectively leverage Reducing Emissions from Deforestation and Forest Degradation in Developing Countries for sustainable development and climate objectives. Full article

Rarámuri Criollo (RC) cattle have been raised by the isolated Tarahumara communities of Chihuahua, Mexico, for nearly 500 years, mostly under natural selection and minimal management. RC cattle were introduced to the United States Department of Agriculture-Agricultural Research Service Jornada Experimental Range (RCJER) in 2005 to begin evaluations of beef production performance and their adaptation to the harsh ecological and climatic conditions of the Northern Chihuahuan Desert. While this research unveiled crucial information on their phenotypic plasticity and adaptation, the genetic diversity and structure of the RCJER population remains poorly understood. This study analyzed the genetic diversity, population structure, ancestral composition, and selection signatures of the RCJER herd using a ~64 K SNP array. The RCJER herd exhibits moderate genetic diversity and low population stratification with no evident clustering, suggesting a shared genetic background among different subfamilies. Admixture analysis revealed the RCJER herd represents a distinctive genetic pool within the Criollo cattle breeds, with significant Iberian ancestry. Selection signatures identified candidate genes and quantitative trait loci (QTL) for traits associated with milk composition, growth, meat and carcass, reproduction, metabolic homeostasis, health, and coat color. The RCJER population represents a distinctive genetic resource adapted to harsh environmental conditions while maintaining productive and reproductive attributes. These findings are crucial to ensuring the long-term genetic conservation of the RCJER and their strategic expansion into locally adapted beef production systems in the USA. Full article

This study developed an integrated approach for estimating tropical forest aboveground biomass (AGB) by combining UAV–LiDAR structural metrics and Sentinel-2B spectral data, optimized through successive projections algorithm (SPA) feature selection and random forest (RF) regression. Field surveys across three tropical forest sites in Hainan Province (49 plots) provided ground-truth AGB measurements, while UAV–LiDAR (1 m resolution) and Sentinel-2B (10 m) data were processed to extract 98 and 69 features, respectively. The results showed that LiDAR-derived elevation metrics (e.g., percentiles and kurtosis) correlated strongly with the AGB measurements (r = 0.652–0.751), outperforming Sentinel-2B vegetation indices (max r = 0.520). SPA–RF models with selected features significantly improved accuracy compared to full-feature RF, achieving R² = 0.670 (LiDAR), 0.522 (Sentinel-2B), and 0.749 (coupled data), with the fusion model reducing errors by 46–54% in high-biomass areas. Despite Sentinel-2B’s spectral saturation limitations, its integration with LiDAR enhanced spatial heterogeneity representation, particularly in complex canopies. The 200-iteration randomized validation ensured a robust performance, with mean absolute relative errors of ≤0.071 for fused data. This study demonstrates that strategic multi-sensor fusion, coupled with SPA-optimized feature selection, significantly improves tropical AGB estimation accuracy, offering a scalable framework for carbon stock assessments in support of Reducing Emissions from Deforestation and Forest Degradation (REDD+) and climate mitigation initiatives. Full article

Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In this study, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate forest ecosystems when provided with a sufficiently large training dataset. Using wavelet-transformed airborne hyperspectral imagery, we trained a shallow neural network (SNN) to model AGB. An existing global AGB map developed as part of the European Space Agency’s DUE GlobBiomass project served as the training data for all study sites. At the temperate site, we also trained the model on airborne-LiDAR-derived AGB. In comparison, for all study sites, we also trained a separate deep convolutional neural network (3D-CNN) with the hyperspectral imagery. Our results show that extracting both spatial and spectral features with the 3D-CNN produced the lowest RMSE across all study sites. For example, at the tropical forest site the Tortuguero conservation area, with the 3D-CNN, an RMSE of 21.12 Mg/ha (R² of 0.94) was reached in comparison to the SNN model, which had an RMSE of 43.47 Mg/ha (R² 0.72), accounting for a ~50% reduction in prediction uncertainty. The 3D-CNN models developed for the other tropical and temperate sites produced similar results, with a range in RMSE of 13.5 Mg/ha–31.18 Mg/ha. In the future, as sufficiently large field-based datasets become available (e.g., the national forest inventory), a 3D-CNN approach could help to reduce the uncertainty between hyperspectral reflectance and forest biomass estimates across tropical and temperate bioclimatic domains. Full article

Since 1990, approximately 420 million hectares of forest have been lost worldwide due to land conversion for various uses, including agriculture, infrastructure development, urbanization, and other human activities. This study aims to investigate the critical drivers contributing to deforestation and forest degradation (DFD) in Ondo State, Nigeria, thereby identifying areas where REDD+ (Reducing Emissions from Deforestation and Forest Degradation) interventions could be most effective in reducing greenhouse gas emissions, particularly carbon dioxide (CO₂), which is released through forest loss and degradation. A questionnaire survey was used to obtain data from construction professionals such as architects, engineers, builders, quantity surveyors, and project managers. Collected data were analyzed using frequencies and percentages to report the background information of professionals, Mean Item Scores (MIS) to rank critical drivers of DFD, and Fuzzy Synthetic Evaluation (FSE) to identify the most critical drivers. FSE analysis revealed that DFD is primarily motivated by agricultural expansion (including cattle ranching and shifting cultivation) and infrastructure extension (particularly transportation networks and market and service infrastructure) among the proximate drivers. The analysis also identified demographic, economic, and policy and institutional factors as the most significant underlying drivers. The emphasis on agricultural expansion and infrastructure extension suggests that targeted interventions in these areas could significantly mitigate DFD in the study site under consideration. This may involve implementing stricter regulations and incentives to promote sustainable land use practices among farmers and landowners. Additionally, integrating environmental impact assessments into infrastructure projects can help minimize forest loss associated with road construction and urban expansion. This study introduces an innovative approach by applying the Geist and Lambin conceptual framework of ‘proximate causes and underlying driving forces’. It is among the pioneering studies conducted in the study area to comprehensively analyze the drivers contributing to DFD using these frameworks. Although conducted in Ondo State, Nigeria, the findings can be extrapolated to similar regions facing similar challenges of DFD worldwide. Full article

Simple theoretical von Thünenian models of deforestation and agricultural expansion have been extensively studied in the literature but have not yet been adapted to reflect contemporary conservation paradigms, such as the emergence of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) initiatives, related payments for forest conservation, and payments for ecosystem services (PES) more broadly. We revisit Angelsen’s 1999 seminal adaptation of the 1826 von Thünenian model of deforestation and agricultural expansion and propose a “toy model” to incorporate the potential revenues from conservation payments and build on the concept of additionality in the payments for environmental services literature. As theorized, our extended model illustrates how such payments are more effective when they approach the profit margins of geographically peripherical crops that replace the forest. Moreover, it illustrates how conservation payments influence the agricultural frontier while quantifying the avoided deforestation area. Full article

REDD+ is a UN-backed framework aimed at reducing carbon emissions in developing countries through sustainable forest management and the protection and enhancement of forest carbon stocks. These are key goals for the international community to achieve climate change mitigation through forestry. REDD+ programs deliver carbon, environmentally based, and social benefits through incentives provided to local societies. This study focuses on a quantitative assessment of the REDD+ framework from the perspective of localized socio-economic shifts. The drivers–pressures–state–impact and partial least squares–structural equation models were employed to evaluate impacts of socio-economic change on multiple REDD+ benefits and their influential factors in the tropical rainforests of Xishuangbanna, China. The results revealed that land-use changes form essential and complex links between socio-economic and eco-environmental changes. Socio-economic shifts in the recent twenty years in Xishuangbanna impacted carbon emissions mainly through land-use change (impact coefficient = 0.909), which was nearly three times the impact of land-use change on environmental degradation (0.322) and more than twice its impact on social benefits (0.363). Such unbalanced impacts suggest a need to optimize local policies through contextualized measures in a way that effectively addresses livelihood improvements, enhancing carbon storage and environmental services to achieve REDD+ targets in the tropical rainforests of China. Full article

There is a growing field that is devoted to developing inexpensive microscopes and measurement devices by leveraging low-cost commercial parts that can be controlled using smartphones or embedded devices, such as Arduino and Raspbery Pi. Examples include the use of Blu-ray optical heads like the PHR-803T to perform cytometry, spinning disc microscopy, and lensless holographic microscopy. The modular or disposable nature of these devices means that they can also be used in contaminating and degrading environments, including radioactive environments, where replacement of device elements can be expensive. This paper presents the development and operation of a confocal microscope that uses the PHR-803T optical device in a Blu-ray reader for both imaging and detection of temperature variations with between 1.5 and 15 µm resolution. The benefits of using a PHR-803T confocal system include its relatively inexpensive design and the accessibility of the components that are used in its construction. The design of this scanning confocal thermal microscope (SCoT) was optimized based on cost, modularity, portability, spatial resolution, and ease of manufacturability using common tools (e.g., drill press, 3D printer). This paper demonstrated the ability to resolve microscale features such as synthetic spider silk and measure thermal waves in stainless steel using a system requiring <USD 1000 in material costs. Full article

The REDD+ framework (Reducing Emissions from Deforestation and Forest Degradation, along with sustainable forest management and the conservation and enhancement of forest carbon stocks in developing countries) incentivizes developing countries to reduce greenhouse gas emissions and enhance carbon storage by mitigating deforestation and forest degradation. To receive results-based payments, participating countries must meet United Nations Framework Convention on Climate Change (UNFCCC) requirements for Measurement, Reporting, and Verification (MRV) capacities. This study categorizes developing countries into three phases based on MRV implementation levels: phase 1 (readiness), phase 2 (demonstration), and phase 3 (implementation). Unlike the higher implementation levels observed in phase 2 and phase 3 countries, phase 1 countries have received limited attention due to their early stages of REDD+ implementation. However, assessing the potential of these countries for future REDD+ engagement and Internationally Transferred Mitigation Outcome (ITMO) collaboration is crucial for achieving REDD+ goals. Thus, this study quantitatively assessed MRV capacity among phase 1 countries using an MRV capacity assessment tool, with the goal of identifying high-potential candidates for REDD+ advancement. We applied an MRV capacity assessment tool to 48 phase 1 countries out of the 71 developing countries registered on the REDD+ web platform as of September 2024. The results reveal that (1) the countries with the highest MRV scores were Ghana, India, Guatemala, Liberia, and Mongolia, with Ghana demonstrating strong potential for progression to the implementation phase due to its robust performance in both Measurement and Reporting components. In contrast, Chad scored the lowest, followed by Uruguay, Namibia, Mali, Cuba, and Benin. (2) Overall, phase 1 countries scored lower in the Reporting (R) component, which emphasizes administrative capacity, compared to the Measurement (M) component, which is technically oriented, highlighting the need for improved administrative capacity, particularly in developing and submitting the National Strategy/Action Plan and Safeguard Information System report to meet Cancun Agreement standards. While this study evaluates REDD+ implementation potential in phase 1 countries based on MRV capacity, future research should explore the effectiveness of strengthening MRV capacity through Official Development Assistance (ODA), assessing potential emissions reduction and ITMO potential. Full article

Quantifying forest carbon storage to better manage climate change and its effects requires accurate estimation of forest structural parameters such as canopy height. Variables from remote sensing data and machine learning models are tools that are being increasingly used for this purpose. This study modeled the canopy height of forest–savanna mosaics in the Sudano–Guinean zone of Togo. Relative heights were extracted from GEDI and ICESat-2 products, which were combined with optical, radar, and topographic variables for canopy height modeling. We tested four methods: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). The RF algorithm obtained the best predictions using 98% relative height (RH98). The best-performing result was obtained from variables extracted from GEDI data (r = 0.84; RMSE = 4.15 m; MAE = 2.36 m) and compared to ICESat-2 (r = 0.65; RMSE = 5.10 m; MAE = 3.80 m). Models that were developed during this study can be applied over large areas in forest–savanna mosaics, enhancing forest dynamics monitoring in line with REDD+ objectives. This study provides valuable insights for future spaceborne LiDAR and other remote sensing data applications in similar complex ecosystems and offers local decision-makers a robust tool for forest management. Full article