A Selection of Open Source Research Papers and Studies

Areas of Research
*** Recent Papers ***
  • Authors: Paul J. Chisholm, Andrew N. Gray
  • DOI Link: doi.org/10.1371/journal.pone.0302823
  • Published: May 2024
  • Overview:
    This paper investigates the carbon sequestration potential of forests on the west coast of the USA, focusing on the role of species, productivity, and stockability. It highlights the importance of understanding how different forest management practices and disturbance events impact carbon storage. The study aims to develop robust models that predict live tree carbon density across various vegetation types using data from 7,523 national forest inventory plots in California, Oregon, and Washington. By incorporating site productivity and stockability within the Chapman-Richards equation, the researchers tested the differences between intensively managed private forests and less managed public forests. The models indicate that accounting for stockability and productivity significantly improves predictions of carbon storage potential.
More information: https://doi.org/...
  • Authors: Tomoko Hasegawa et al.
  • DOI Link: doi.org/10.1038/s43247-024-01336-4
  • Published: Apr 2024
  • Overview:
    Investigates how the careful selection of forest types in afforestation can significantly increase carbon sequestration without compromising sustainability. It quantifies the impacts of different forest types combined with food-related measures on carbon sequestration potential and sustainability. The study uses an integrated framework combining economic, land-use allocation, and terrestrial vegetation models to evaluate various climate mitigation scenarios. It provides a detailed examination of the interactions between forest-type selection, afforestation, BECCS, and food policy measures to maximize carbon sequestration while maintaining ecological and food sustainability.
More information: https://doi.org/...
  • Authors: Fangzhou Ma et al.
  • DOI Link: doi.org/10.1016/j.ecolind.2024.111655
  • Published: Feb 2024
  • Overview:
    Examines the carbon sequestration dynamics and drivers in urban forests, focusing on the Shanghai Green Belt over a 20-year period. It addresses the research gap on the carbon benefits of urban forests and identifies suitable tree species for sustainable carbon sequestration. The study compiles comprehensive field inventory data, quantifying and comparing carbon accumulation and growth rates of six monoculture forests and two mixed forest types. The paper also analyzes the effects of stand-related and anthropogenic factors on carbon accumulation and growth rates, highlighting the unique growth patterns of tree biomass carbon (TBC) for each forest type. The Chapman-Richards growth function is used to model carbon dynamics accurately. The findings underscore the early carbon sequestration advantage of Populus L. forests and the long-term potential of broadleaf mixed forests.
More information: https://doi.org/...
  • Authors: Bingqi Zhang et al.
  • DOI Link: doi.org/10.1038/s41598-024-51308-z
  • Published: Jan 2024
  • Overview:
    Explores the potential of forest carbon removal and its implications for sustainable development in Japan. It focuses on afforestation, reforestation, and improved forest management within the context of Japan's J-credit scheme, aiming to quantify CO2 removal potential at various spatial levels and integrate this with sustainable development metrics. The study employs an inclusive wealth framework to assess the monetary value of forest carbon sequestration and its impact on rural communities' sustainability. It combines forest volume growth models, remote sensing data, and the national forest inventory to project CO2 removal until 2042.
More information: https://doi.org/...
  • Authors: Wen He et al.
  • DOI Link: doi.org/10.1016/j.ecolind.2023.111225
  • Published: Dec 2023
  • Overview:
    This paper evaluates the eco-economic benefits of the Grain for Green Program (GFGP) in Sichuan Province, China. Using an improved value equivalent method, it dynamically assesses the ecosystem service value (ESV) from 2001 to 2019. The study investigates the impact of climate and human activities on ESV and constructs an eco-economic benefit model to determine the value efficiency and return rate of afforestation investments across 183 counties. This research aims to provide a comprehensive evaluation of GFGP's effectiveness, highlighting the need for multi-stakeholder participation to enhance eco-economic benefits and inform government strategies.
More information: https://doi.org/...
Afforestation
  • Authors: Colm Duffy et al.
  • DOI Link: doi.org/10.1016/j.jenvman.2020.110523
  • Published: 2020
  • Overview:
    Examines the potential of afforestation in Ireland as a means to offset greenhouse gas (GHG) emissions from livestock production. Given that agriculture, particularly livestock farming, contributes significantly to Ireland's national GHG emissions, the study aims to evaluate the net GHG benefits of converting land from livestock use (dairy, beef cattle, and sheep) to forestry. The research includes estimates of carbon sequestration in forest biomass and harvested wood products, as well as the emissions avoided from livestock systems on a per hectare basis. Additionally, it compares the social cost of carbon with the average income per hectare from different livestock systems and models a hypothetical national planting scenario to explore the broader implications for carbon abatement and economic impacts. Key Points
    • Purpose: To estimate the net GHG emission benefit of converting land from livestock production to forest in Ireland.
    • Scope: Includes carbon sequestration in forest biomass and harvested wood products, and emissions avoided from dairy, beef cattle, and sheep systems.
    • Methodology: Utilizes forest yield models and EPA national inventory data to estimate carbon sequestration and livestock emissions.
    • Comparison: Assesses the social cost of carbon against the average income per hectare of livestock systems.
    • Hypothetical Scenario: Models a national planting scenario with different rates of afforestation to estimate long-term carbon abatement.
    • Context: Discusses the role of afforestation in meeting Ireland's EU emissions reduction targets and the importance of increasing annual planting rates.
More information: https://doi.org/...
  • Authors: Aamir Saleem et al.
  • DOI Link: x.doi.org/10.15666/aeer/1802_20572072
  • Published: 2020
  • Overview:
    This paper examines the Billion Tree Afforestation Project (BTAP) in Pakistan, part of the Bonn Challenge initiative, focusing on forest restoration and afforestation. The study evaluates the success of plantation efforts by assessing growth performance and survival rates. Using a total of 115 sample plots across 17 randomly selected sites, the research spans an area of 1982 hectares. Sentinel-2 images were utilized to derive vegetation indices, which were then regressed against volume and survival percentage. The study highlights the performance of different species, with Eucalyptus camaldulensis showing the highest growth and Deodar the lowest. Key vegetation indices such as TNDVI and NDVI were analyzed, demonstrating their significance in assessing plantation success and overall vegetation health.
More information: http://dx.doi.org/...
  • Authors: Dejun Li, Shuli Niu, Yiqi Luo
  • DOI Link: doi.org/10.1111/j.1469-8137.2012.04150.x
  • Published: 2012
  • Overview:
    This paper investigates the dynamics of soil carbon (C) and nitrogen (N) stocks following afforestation, addressing gaps in understanding the regulation of soil carbon accumulation by nitrogen dynamics. By synthesizing results from 292 sites through a meta-analysis, the study evaluates changes in soil C and N stocks and their correlation over time. The analysis considers factors such as prior land use, climate zones, and tree species planted, distinguishing between the effects on croplands, pastures, and various climatic regions. It also highlights the different impacts of afforestation with hardwoods and softwoods on soil C and N stocks. The study aims to identify general patterns and controlling factors to inform policy-making on carbon sequestration through afforestation.
More information: https://doi.org/...
Carbon sequestration rates
  • Authors: Gyri Reiersen et al.
  • DOI Link: x.doi.org/10.1609/aaai.v36i11.21471
  • Published: 2022
  • Overview:
    This paper introduces "ReforesTree," a dataset aimed at refining the accuracy of forest carbon stock measurements using advanced machine learning and drone technology. Focused on six agro-forestry carbon offsetting sites in Ecuador, the study evaluates a novel deep learning model that utilizes RGB drone imagery for individual tree detection. This approach promises a significant improvement over traditional methods, which are often labor-intensive, costly, and prone to errors. By leveraging the latest in remote sensing and machine learning, the authors aim to enhance the quality and reliability of data used for forest carbon offsetting certifications.
More information: http://dx.doi.org/...
  • Authors: David Lefebvre et al.
  • DOI Link: doi.org/10.1038/s41598-021-99395-6
  • Published: 2021
  • Overview:
    Examines the carbon capture potential of reforestation projects, using a specific case study in the Peruvian Amazon. It highlights the complexities and challenges of accurately accounting for carbon sequestration in reforestation efforts. The study utilizes a life cycle assessment (LCA) to quantify the carbon footprint of establishing a reforestation plot and combines soil and plant carbon models to predict carbon stock increases post-planting. Additionally, it compares these empirical results with the claims made by reforestation platforms. Key issues addressed include the time required for trees to reach their carbon capture potential, the GHG emissions involved in reforestation, and the impact of natural and anthropogenic disturbances.
More information: https://doi.org/...
  • Authors: Francesco N. Tubiello et al.
  • Link: https://essd.copernicus.org/...
  • Published: 2021
  • Overview:
    Presents updated estimates of national, regional, and global CO2 emissions and removals from forests for the period 1990-2020, based on country reports from the Global Forest Resources Assessment 2020. Utilizing a carbon stock change approach, the Food and Agriculture Organization of the United Nations (FAO) provides insights into net emissions and removals from forests related to net forest conversion and forest land. The study highlights a notable reduction in global emissions from net forest conversion and examines the dynamics of forest land as a carbon sink over three decades. The findings reveal that forests were generally a small net source of CO2 to the atmosphere, with brief periods of net carbon sink activity. The new estimates, particularly for the decade 2011-2020, are significant as they represent the first characterization of forest emissions and removals for this period, showing a near-zero net contribution of forests to atmospheric CO2. The paper also discusses the comparison of FAO estimates with those independently reported by countries to the United Nations Framework on Climate Change, showing strong agreement.
More information: https://essd.copernicus.org/...
  • Authors: Bonnie Waring et al.
  • DOI Link: doi.org/10.3389/ffgc.2020.00058
  • Published: 2020
  • Overview:
    Examines the roles of natural and planted forests in carbon sequestration, evaluating their impact on the global climate system. It highlights the importance of conserving mature natural forests, which store more carbon than plantation forests due to their complex structures and long-term accumulation. The study addresses the ecological, economic, and societal implications of large-scale tree planting efforts, emphasizing the need to avoid policies that harm existing carbon sinks. It also explores how afforestation and reforestation can be optimized by selecting appropriate sites and species, and by managing the harvested wood to maximize carbon capture.
More information: https://doi.org/...
  • Authors: Lucas E. Nave et al.
  • DOI Link: doi.org/10.1073/pnas.1719685115
  • Published: 2018
  • Overview:
    Explores the role of reforestation in sequestering carbon © in US topsoils, which are a significant component of the forest carbon sink. It focuses on the capacity of forest soils to sequester carbon and the impact of reforestation on carbon accumulation rates in topsoils. By combining data from two national-level databases with remote sensing information, the paper aims to quantify carbon stocks in cultivated, reforesting, and natural forest topsoils and assess the rates of carbon accumulation due to reforestation. The study covers the following aspects:
More information: https://doi.org/...
  • Authors: Blanca Bernal et al.
  • Link: https://cbmjournal.biomedcentral.com/...
  • Published: 2018
  • Overview:

    The paper provides an insightful examination of Forest Landscape Restoration (FLR) as a tool for climate change mitigation and ecological restoration. Recognizing a gap in the data on CO2 capture rates across different regions and FLR activities, the study develops new metrics for biomass accumulation and CO2 removal for activities like natural regeneration, planted forests, agroforestry, and mangrove restoration. By grouping these rates by FLR type and regional climate conditions, the research aims to offer a detailed and practical overview that can guide global restoration efforts effectively.

    Key Points:
    • The paper addresses the global need for improved data on CO2 capture in forest landscape restorations.
    • Targets filling data voids in regions with sparse information on biomass growth and CO2 sequestration.
    • Develops new, precise biomass accumulation rates and CO2 removal metrics for various FLR activities.
    • Identifies planted forests and mangrove restorations as particularly effective in early-stage CO2 removal.
    • Data analysis covers diverse geographic regions, enhancing the global applicability of the findings.
    • The methodology supports decision-makers by providing actionable data for planning and evaluating FLR initiatives.
More information: https://cbmjournal.biomedcentral.com/...
  • Authors: Heather Keith et al.
  • DOI Link: doi.org/10.1073/pnas.0901970106
  • Published: 2009
  • Overview:
    This paper analyzes global biomass data from 136 primary forest sites to identify factors contributing to high biomass carbon densities. The study reveals that the world's highest known total biomass carbon density is found in Australian temperate moist Eucalyptus regnans forests, with an average of 1,867 tonnes of carbon per hectare. The research highlights that temperate moist forests generally have higher biomass carbon densities than tropical and boreal forests. The paper proposes a framework for identifying forests critical for carbon storage, focusing on factors like cool temperatures, moderate precipitation, and minimal human disturbance. These findings are pertinent to forest conservation, management, and restoration discussions under the United Nations Framework Convention on Climate Change. The study emphasizes the importance of conserving high biomass forests to avoid significant carbon emissions and the potential of managing forests to enhance their carbon sequestration capabilities.
More information: https://doi.org/...
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Economic feasibility
  • Authors: Grafton, R., et al.
  • DOI Link: doi.org/10.1787/e4d45973+-en
  • Published: 2021
  • Overview:

    Evaluates the cost-efficiency of forest carbon sequestration globally, providing a ranking of 166 countries. It assesses various cost factors, such as land and labor costs, business environment, forest productivity, and wildfire risk, offering a more nuanced understanding compared to the traditional view that tropical regions are the most cost-effective. The study also explores the impact of project type and the valuation of co-benefits on cost-efficiency, and includes a sensitivity analysis to confirm the robustness of the findings.

    Key Points:

    • Global Ranking: Proposes a cost-efficiency ranking of countries for forest carbon sequestration, challenging the tropical region bias.
    • Cost Factors Considered: Analyzes land and labor costs, business environment, forest productivity, and wildfire risks.
    • Project Types: Assesses how afforestation, reforestation, and forest conservation impact cost-efficiency.
    • Co-benefits Valuation: Examines how the economic valuation of wood harvest and environmental/social benefits affects costs.
    • Sensitivity Analysis: Tests the robustness of cost-efficiency rankings against uncertainties in cost and quantity factors.
    • Literature Review: Provides an overview of existing studies on forest carbon sequestration costs and offset schemes.
More information: https://doi.org/...
Ecosystem impacts
  • Authors: Ziyue Yu et al.
  • DOI Link: doi.org/10.3390/rs14235940
  • Published: 2022
  • Overview:
    This paper examines the impacts of the Grain for Green Program (GFGP) on ecosystem services in the Loess Plateau (LP), China. The study investigates how the GFGP, a large-scale ecological restoration project, affects soil erosion, vegetation cover, and other ecosystem services. It explores the synergies and trade-offs between different ecological services, particularly the balance between soil conservation and other benefits. The research compares changes in vegetation cover and ecosystem services before and after GFGP implementation, highlighting significant improvements in carbon sequestration, soil conservation, and habitat quality. Additionally, the study analyzes the regional trade-offs and synergies influenced by topographic and climatic variables, aiming to provide insights for optimizing ecological restoration efforts.
More information: https://doi.org/...
  • Authors: Emily Sigman and Marlène Elias
  • DOI Link: doi.org/10.3368/er.39.1-2.27
  • Published: 2021
  • Overview:
    This paper examines three distinct approaches to ecological restoration within the context of the Bonn Challenge and the UN Decade on Ecosystem Restoration, focusing on their implications for social inclusion. The study identifies and analyzes "return" restoration, which aims to restore ecosystems to their previous state; "reorganization" restoration, which creates functional ecosystems away from the original site of degradation; and "resilience" restoration, which emphasizes adaptive management and community involvement. The paper critiques the social inclusion aspects of each approach, highlighting the challenges and potential for community participation and empowerment. It emphasizes the need for restoration initiatives to move beyond productivity-based inclusion and address historical inequities and legacies of exploitation. The study suggests that "resilience" restoration offers the most promise for meaningful social inclusion when communities are actively engaged as agents of change.
More information: https://doi.org/...
  • Authors: Vicky M. Temperton et al.
  • DOI Link: doi.org/10.1111/rec.12989
  • Published: 2019
  • Overview:
    This paper examines the ecological and policy framework of the Bonn Challenge, a U.N. initiative aimed at restoring degraded landscapes globally to enhance biodiversity and combat climate change. It emphasizes the need to broaden the focus beyond forests to include various ecosystem types for more inclusive and effective restoration. The paper explores the scientific, ecological, and practical dimensions of implementing large-scale landscape restoration, addressing the biases and assumptions that currently prioritize forest ecosystems. It discusses the integration of biodiversity safeguards and the necessity for robust scientific knowledge to inform restoration practices that balance multiple ecosystem services.
More information: https://doi.org/...
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Forest conservation
  • Authors: A. Mujetahid et al.
  • DOI Link: doi.org/10.3390/f14030652
  • Published: Mar 2023
  • Overview:
    This paper investigates the use of remote sensing technology, particularly Google Earth Engine (GEE), to monitor illegal logging activities in Sulawesi Selatan Tropical Forest, Indonesia. The study addresses the high rate of forest destruction exacerbated by weak law enforcement and insufficient forest monitoring. By leveraging Sentinel 1 and 2 data, which can penetrate cloud cover using radar sensors, the research aims to provide a comprehensive analysis of forest conditions and identify illegal logging events. The methodology involves the use of a random forest classification algorithm on the GEE platform to process data on forest conditions in 2021, covering a significant portion of Sulawesi Selatan Province. The paper also examines the functional areas of the forest, distinguishing between non-forest estates and forest areas, and identifies numerous spots of forest change events throughout the year. This research highlights the critical need for improved monitoring techniques to combat illegal logging and preserve forest ecosystems.
More information: https://doi.org/...
  • Authors: Thiago Almeida Teixeira et al.
  • DOI Link: doi.org/10.3390/ecsa-10-16188
  • Published: 2023
  • Overview:
    Presents the development of a real-time monitoring system to combat illegal deforestation in the Amazon Rainforest using artificial intelligence (AI) algorithms. The system detects deforestation attempts through audio signals from tractors and chainsaws, and communicates data via long-range (LoRa) communication to a base station up to 1 km away. Additionally, a user interface provides alerts, including attack identification, occurrence times, device locations, and battery status. The device operates with ultra-low power consumption, employing various power management modes, making it sustainable for prolonged use in dense forest areas.
More information: https://doi.org/...
  • Authors: by Fabien H. Wagner et al.
  • DOI Link: doi.org/10.3390/rs15020521
  • Published: 2023
  • Overview:
    This paper investigates the use of a U-net deep learning model to map monthly tropical tree cover and deforestation in the Brazilian state of Mato Grosso from 2015 to 2021. Utilizing 5-meter spatial resolution Planet NICFI satellite images, the study achieves high accuracy in tree cover detection, validated by LiDAR data. The research focuses on building biannual deforestation maps from these monthly tree cover maps and compares the results with existing deforestation maps from Brazil and the Global Forest Change (GFC) dataset. The study demonstrates the potential of high-resolution imagery combined with deep learning techniques to improve deforestation mapping in tropical regions, highlighting the increasing deforestation trend in Mato Grosso.
    This paper investigates the use of a U-net deep learning model to map monthly tropical tree cover and deforestation in the Brazilian state of Mato Grosso from 2015 to 2021. Utilizing 5-meter spatial resolution Planet NICFI satellite images, the study achieves high accuracy in tree cover detection, validated by LiDAR data. The research focuses on building biannual deforestation maps from these monthly tree cover maps and compares the results with existing deforestation maps from Brazil and the Global Forest Change (GFC) dataset. The study demonstrates the potential of high-resolution imagery combined with deep learning techniques to improve deforestation mapping in tropical regions, highlighting the increasing deforestation trend in Mato Grosso.
More information: https://doi.org/...
  • Authors: David Dominguez et el.
  • DOI Link: doi.org/10.3390/su14020691
  • Published: 2022
  • Overview:
    Analyzes Amazon rain-forest deforestation using a hybrid machine learning model. Utilizing data from 760 Brazilian Amazon municipalities, the study combines dense neural networks for static variables with a recurrent Long Short Term Memory (LSTM) neural network for temporal data. The model employs data from 1999-2019 to predict deforestation trends from 2020-2030. Through multiple iterations and hyper-parameter optimizations, the study aims to accurately forecast deforestation rates, highlighting the impact of human activities on the Amazon.
More information: https://doi.org/...
  • Authors: James G. C. Ball et al.
  • DOI Link: doi.org/10.1111/2041-210X.13953
  • Published: 2022
  • Overview:
    This paper explores the use of deep convolutional neural networks (CNNs) to forecast spatial patterns of deforestation in the Amazon. It addresses the complexity of predicting deforestation due to the interplay of natural and human systems. The study utilizes deep learning techniques to process Earth observation data, aiming to provide accurate and timely forecasts that can enhance planning and enforcement efforts. The research leverages a variety of data layers, including multispectral satellite imagery and digital surface models, to train several CNN model architectures. These models produce spatial maps indicating the risk of deforestation at a pixel level, focusing on two tropical forest regions in the Southern Peruvian Amazon. The study emphasizes the importance of innovative machine learning approaches to harness the increasing volume of remote sensing data for effective forest conservation.
More information: https://doi.org/...
  • Authors: M. Cecilia Oliveira, Leandro Siqueira
  • DOI Link: doi.org/10.1016/j.esg.2022.100135
  • Published: 2022
  • Overview:
    Analyzes the use of digital satellite data on deforestation in the Amazon, within the context of poststructuralist studies of scientific knowledge practices and Science and Technology Studies (STS). It focuses on changes under President Jair Bolsonaro's government, which has sought to discredit and dismantle established knowledge infrastructures and transparency regimes that made deforestation visible and manageable. The study examines the establishment of a competing alethurgy (attempts by competing groups establish and promote their versions of truth about deforestation in the Amazon), a set of procedures claiming to manifest the 'truth' about the Amazon. This new alethurgy relies on hypertransparency, which promotes environmental counter-activism and legitimizes extractivist practices. The paper provides a genealogical analysis of digital satellite data usage, highlighting two phases: transparency-driven environmental activism and hypertransparency-driven counter-activism.
More information: https://doi.org/...
  • Authors: Fabian Surya Pramudya et al.
  • DOI Link: doi.org/10.1016/j.procs.2022.12.145
  • Published: 2022
  • Overview:
    Examines the deforestation trends in Papua Province, Indonesia, utilizing geospatial artificial intelligence and remote sensing data over a period of twenty years. The study employs Global Forest Change Datasets to analyze, calculate, and map forest loss from 2001 to 2021, highlighting the uneven distribution of deforestation between western and eastern Indonesia. By using Google Earth Engine for cloud data processing, the research provides insights into the total forest loss at a district level within Papua. The methodology includes three key indicators: the tree cover baseline in 2000, annual forest gain from 2000 to 2012, and annual forest loss from 2001 to 2021. The study underscores the importance of consistent and efficient monitoring of forest loss to understand its role in the global carbon cycle and biodiversity conservation, as well as to develop potential strategies for reducing deforestation. Despite the relatively low threat of deforestation in Papua compared to other Indonesian provinces, the paper warns of significant future risks due to anthropogenic activities.
More information: https://doi.org/...
  • Authors: Talita O. Assis et al.
  • DOI Link: doi.org/10.1126/sciadv.abj3309
  • Published: 2022
  • Overview:
    This paper examines the issue of forest degradation in the Brazilian Amazon, which has increasingly impacted larger areas than deforestation. It aims to identify and analyze the socioeconomic and environmental factors that influence forest degradation, project future scenarios, and assess the impact on regional carbon balances. By combining the land change model LuccME, the carbon emission bookkeeping model INPE-EM, and deforestation scenarios, the paper seeks to provide a comprehensive understanding of forest degradation dynamics and their implications for CO2 emissions from 2020 to 2050. This approach offers insights into the interactions between forest degradation and deforestation, projecting future scenarios based on current trends and policies.
More information: https://doi.org/...
  • Authors: Simon L. Bager, Eric F. Lambin
  • DOI Link: doi.org/10.1016/j.jclepro.2022.134056
  • Published: 2022
  • Overview:
    This paper examines how companies are implementing their zero-deforestation commitments (ZDCs) within forest-risk commodity supply chains. By leveraging theories of corporate social responsibility strategy and policy implementation, the study explores the processes and conditions that affect ZDC implementation. The research is based on 35 semi-structured interviews with company representatives and sector actors, along with an analysis of publicly available ZDC data and company reports. The aim is to uncover the opportunities and challenges faced by companies in executing their ZDCs, providing insights from the companies' perspectives on this environmental governance regime. Key Points
More information: https://doi.org/...
  • Authors: Jonas Botelho et al.
  • DOI Link: doi.org/10.3390/rs14153625
  • Published: 2022
  • Overview:
    This paper outlines the automation of detecting unofficial roads in the Brazilian Amazon using artificial intelligence (AI). Unofficial roads, constructed by loggers, goldminers, and unauthorized settlements, lead to significant deforestation and fire hotspots. The research utilizes the Amazon Road Dataset (ARD), derived from previous studies using Landsat imagery, to train a modified U-Net algorithm for detecting rural roads from Sentinel-2 imagery on the Azure Planetary Computer platform. A post-AI detection protocol was implemented to connect and vectorize the detected roads, creating a new ARD. The paper discusses the methodology for estimating recall and precision accuracy, and the implications of the detected road network for forest conservation and regional planning.
More information: https://doi.org/...
  • Authors: Claudia Arantes Silva et al.
  • DOI Link: doi.org/10.1080/22797254.2021.2025154
  • Published: 2021
  • Overview:
    This paper presents a study on identifying clear-cut deforested areas in the Brazilian Amazon using a Neural Network (NN) algorithm based on Sentinel-1 satellite images. The study addresses the limitations of optical-based deforestation alert systems during the rainy season. By computing statistical parameters from C-band, VV- and VH-polarized Sentinel-1 images, the researchers developed a Multi-Layer Perceptron (MLP) network to detect near-real time forest disturbances larger than 2 hectares. The paper evaluates various input sets for the MLP and compares the performance of the NN algorithm using datasets from different years and reference data.
More information: https://doi.org/...
Forest management practices
  • Authors: Asif Raihan
  • DOI Link: x.doi.org/10.24294/nrcr.v6i2.3825
  • Published: 2023
  • Overview:
    Presents a comprehensive review of how artificial intelligence (AI) and machine learning (ML) algorithms are applied in forest management and biodiversity conservation globally. The integration of these technologies addresses the significant threat posed by developmental projects, agriculture, and urban expansion to biodiversity. The paper aims to explore the utilization of AI in efficiently monitoring, managing, and preserving forest resources and biodiversity. It also examines the challenges faced in implementing AI technology within these fields. By enhancing the availability of extensive data related to forests and biodiversity and leveraging cloud computing and digital and satellite technology, the adoption of AI can be facilitated. The study encourages forest officials, scientists, researchers, and conservationists to explore AI's potential for forest management and biodiversity conservation.
More information: http://dx.doi.org/...
  • Authors: Kadukothanahally Nagaraju Shivaprakash et al.
  • DOI Link: doi.org/10.3390/su14127154
  • Published: 2022
  • Overview:
    This paper explores the potential applications of artificial intelligence (AI) and machine learning (ML) in biodiversity conservation and forest management in India. The study examines the current state of AI adoption in these sectors globally and within India, identifying challenges and opportunities for improving AI integration. By leveraging advancements in digital and satellite technology, the research aims to enhance monitoring, management, and conservation efforts for India's rich biodiversity and forest resources.
More information: https://doi.org/...
  • Authors: Todd A Ontl et al.
  • DOI Link: doi.org/10.1093/jofore/fvz062
  • Published: 2020
  • Overview:
    This paper examines the intersection of forest management and climate adaptation, focusing on strategies to maintain or enhance forest carbon stocks under changing climatic conditions. The research introduces the Forest Carbon Management Menu, a resource designed to translate broad carbon management strategies into actionable tactics for land managers. These tactics are aimed at reducing the risks posed by climate change while supporting other forest management objectives. The paper emphasizes the importance of integrating climate vulnerability assessments and considering the long-term implications of management actions. It provides examples from real-world projects that combine climate change information with forest management plans to highlight the potential synergies between carbon sequestration and other ecological goals. The discussion extends to the challenges posed by climate-induced disturbances and the need for adaptive management practices to sustain forest productivity and carbon storage.
More information: https://doi.org/...
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Natural forest regeneration
  • Authors: Jean-François Bastin et al.
  • DOI Link: doi.org/10.1126/science.aax0848
  • Published: 2019
  • Overview:
    This paper explores the significant role of tree restoration in mitigating climate change by mapping the potential for global tree coverage. It examines the current and future potential for canopy cover, considering the impact of existing land use and climate change. The study identifies areas where tree restoration could be most effective and quantifies the carbon storage capacity of additional tree cover. The research underscores both the opportunities and challenges in using global tree restoration as a carbon drawdown strategy, particularly in light of anticipated changes due to ongoing climate change.
More information: https://doi.org/...
Reforestation
  • Authors: Shanshan Song et al.
  • Link: https://www.nature.com/...
  • Published: 2023
  • Overview:
    This paper examines the blue carbon benefits of mangrove reforestation compared to afforestation on a global scale. Using data from over 370 restoration sites worldwide, the study demonstrates that mangrove reforestation, which involves reestablishing mangroves where they previously existed, has a greater carbon storage potential per hectare than afforestation, which establishes mangroves in areas that were not previously mangroves. The higher carbon accumulation in reforested areas is attributed to more favorable intertidal positioning, greater nitrogen availability, and lower salinity. The paper suggests that reforesting all physically feasible deforested mangrove regions globally could significantly enhance carbon uptake over a 40-year period, outperforming afforestation by 60%. The study emphasizes the importance of prioritizing reforestation in mangrove restoration projects to maximize blue carbon benefits and avoid conflicts arising from habitat conversion.
More information: https://www.nature.com/...
  • Authors: Alice Di Sacco et al.
  • DOI Link: dx.doi.org/10.1111/gcb.15498
  • Published: 2020
  • Overview:
    This paper emphasizes the importance of well-planned and executed tree-planting initiatives to optimize carbon sequestration, biodiversity recovery, and livelihood benefits. It discusses the environmental risks associated with poorly planned reforestation efforts, which could lead to increased CO2 emissions and negative impacts on biodiversity, landscapes, and livelihoods. The paper outlines ten golden rules based on recent ecological research to guide effective forest ecosystem restoration. These rules focus on protecting existing forests, involving all stakeholders, maximizing biodiversity, selecting appropriate areas and species, using natural regeneration, ensuring genetic resilience, planning infrastructure, adopting adaptive management, and ensuring economic sustainability. The paper aims to provide long-term strategies for tackling climate and biodiversity crises while supporting local communities and their knowledge.
More information: https://dx.doi.org/...
  • Authors: Deborah Goffner et al.
  • Link: https://link.springer.com/...
  • Published: 2019
  • Overview:
    This paper examines the Great Green Wall for the Sahara and the Sahel Initiative (GGW), a pan-African reforestation program aimed at addressing social and ecological challenges in the Sahel. The paper explores the potential of the GGW to enhance resilience in Sahelian landscapes and livelihoods by integrating interdisciplinary knowledge and resilience thinking. It provides a historical overview of large-scale reforestation in the region and the evolution of the GGW concept. The proposed transdisciplinary research framework focuses on analyzing social-ecological systems and their interactions, with the goal of supplying diverse and durable ecosystem services.
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