SilvaCarbon is an interagency technical cooperation program of the US Government to enhance the capacity of selected tropical countries to measure, monitor, and report on carbon in their forests and other lands. Drawing on expertise and resources from multiple US Government agencies and partners, the program provides targeted technical support to countries in the process of developing and implementing national forest and landscape monitoring systems to support management decisions. SilvaCarbon leverages state-of-the-art science and technology to advance the generation and use of improved information related to forest and terrestrial carbon.
Forest regulates ecosystems, play a key role in carbon in the carbon cycle, support biodiversity, and local livelihoods. Tropical deforestation and forest degradation pose a serious threat to people, economies, and biodiversity worldwide. Tropical forests continue to decline at an alarming rate, undermining economic development and exacerbating social and environmental challenges. To address this concern, decision makers in tropical countries need more and better information about how forests and other landscapes are changing over time. There is a growing need for improved information about changes in forest and terrestrial carbon, in particular, to guide forest and land use management and planning, track and meet national sustainable development goals, and curb forest loss through approaches such as Reducing Emissions from Deforestation and Forest Degradation (REDD+).
Many tropical countries have prioritized the establishment of national forest and landscape monitoring systems. These systems combine remote sensing data with ground-based forest inventory data to generate up-to-date information about forest and landscape dynamics and associated carbon dynamics. This information is essential for sustainably managing natural resources, as well as combating illegal logging, addressing climate change, and fulfilling national and international reporting commitments. National forest and landscape monitoring systems also increase transparency and accountability, helping to level the international playing field for trade and private investment.
Recent years have seen the rapid advancement of forest and landscape monitoring science and technology. This includes impressive improvements in satellite data availability and quality along with improved ground measurements, enhanced modeling capabilities, and increased knowledge through research. In order to take full advantage of these opportunities, countries must first build the technical capacity to identify and adapt monitoring technologies that suit their national circumstances and be able to operationalize those technologies in an integrated national system. Technical cooperation plays an essential role in supporting countries to develop robust, cost-effective forest and landscape monitoring systems that are sustainable over the long term for land management and emission reporting purposes.
With this in mind, US federal agencies have joined together to create the SilvaCarbon program. SilvaCarbon capitalizes on the accumulated expertise of the US scientific and technical community to build capacity for monitoring, measuring, and reporting forest and terrestrial carbon. SilvaCarbon supports national forest and landscape monitoring efforts in partner countries by working directly with in-country technical teams and program leaders, identifying and disseminating good practices and cost-effective technologies, and facilitating technical cooperation at national, regional, and international levels.
SilvaCarbon assists countries to generate and effectively use improved information related to forest and terrestrial carbon to enhance management, monitoring and planning efforts. The SilvaCarbon Results Chain describes the objectives for the program and the pathways for achieving them.
Capacity-building Activities are strategically designed to achieve program Outputs, or short-term objectives. Outputs in turn contribute to program Outcomes, or medium-term objectives. Outcomes set the conditions needed to realize the program’s Impact, or overarching, long-term objective.
Activities in each partner country or region are intended to achieve specific objectives identified with partners, responding to national priorities and needs. Information about specific SilvaCarbon activities is available on the Activities Page.
Better information on forest, landscape, and terrestrial carbon dynamics help countries improve the management of these critical natural resources. Better planning, managing, and monitoring forests and other landscapes can help reduce vulnerability to natural disasters, increase economic activities, and support better governance. Enhanced transparency leads to better accountability to domestic and international stakeholders. These actions often also lead to a better investment and sourcing environment for the private sector.
National forest and land use monitoring systems combine different subsystems and data to generate information that meets a variety of country-specific management, policy, and reporting needs. Most systems include remote sensing, and forest inventory components, and the integration of these data into GHG inventory. SilvaCarbon assists countries to strengthen technical capacities across each of these components, with emphasis on integrating the components and associated workstreams in holistic national systems that support multiple objectives.
Some of the technical issues addressed by SilvaCarbon include:
- Sampling protocols and design
- Satellite data analysis
- Collection and analysis of in situ data
- Integration of remotely sensed and in situ data
- Forest classification and associated carbon estimation
- Carbon emission derived from forest loss, forest disturbance and land use change
- Design of monitoring systems for multiple uses
Collaboration is central to SilvaCarbon’s mission. As an interagency initiative of the US Government, the program mobilizes forest and landscape monitoring expertise and resources from multiple US agencies and domestic and international partners across government, academia, civil society, and industry. Key institutions involved in SilvaCarbon include:
US Government Agencies
Funding for SilvaCarbon has been provided primarily through the USAID Sustainable Landscapes program and the US Department of State’s Bureau of Oceans and International Environmental and Scientific Affairs . Program implementation has been led primarily by USFS, USGS, EPA, and NASA.
SilvaCarbon also works closely with a variety of other US Government programs and initiatives. This includes the NASA-SERVIR program, which provides state-of-the-art, satellite-based Earth monitoring data, geospatial information, and tools to help improve environmental decision-making among developing nations in Eastern and Southern Africa, the Hindu-Kush-Himalaya region, and the Mekong River Basin in Southeast Asia.
Academia plays a crucial role in developing forest and landscape monitoring tools and approaches and in strengthening the underlying science. SilvaCarbon collaborates with a number of research partners to achieve its capacity-building objectives, including:
- The University of Maryland Global Land Analysis and Discovery (GLAD) group
- The Boston University Boston Education in Earth Observation Data Analysis (BEEODA) program
- The Wageningen University Geo-information Science and Remote Sensing department
- The Oregon State University Environmental Monitoring, Analysis and Process Recognition (eMapR) Lab
Nonprofits and private institutions
Where We Work
SilvaCarbon engages a variety of stakeholders in the countries where it works. This includes technical specialists and program leaders from national ministries and their constituent organizations, such as forestry departments, mapping authorities, and space data agencies, as well as national research institutions and non-governmental organizations engaged in national forest and landscape monitoring and GHG inventory programs. For more information on specific country partners, see activity information here.
SilvaCarbon is global in geographic scope with a focus on tropical forested countries. To date, the program has collaborated with more than 25 countries through a combination of bilateral, regional, and global engagement. Current SilvaCarbon countries and regions are shown below.
SilvaCarbon began working with the Andean Amazon countries of Ecuador, Colombia, and Peru in 2011, and in 2014 expanded to include the Central American and Caribbean countries of Costa Rica, Dominican Republic, El Salvador, Guatemala, Honduras, Nicaragua, and Panama. In 2019, SilvaCarbon began collaborating with Paraguay.
The SilvaCarbon Latin America and Caribbean Regional Program builds capacity for national-level forest carbon Measurement, Reporting, and Verification (MRV) for countries across the REDD+ readiness spectrum, complementing other donor efforts in the region. Countries in the region have demonstrated significant forest monitoring progress in recent years and have advanced significantly in the use of remote sensing products and the implementation of forest inventories. Capacity gaps remain, however, and targeted assistance is needed to support results reporting, mitigation activities, and institutional strengthening to ensure long-term sustainability.
The shared language, depth of experiences, and ongoing communication and technical support across Latin and America and the Caribbean contribute to excellent opportunities for South-South collaboration. SilvaCarbon takes advantage of these opportunities by facilitating cooperation and knowledge exchange at the regional level, empowering technical specialists from different countries to learn from one another to address shared challenges. SilvaCarbon regional support complements the bilateral support provided to individual countries.
The SilvaCarbon Latin America and Caribbean Regional Program currently focuses on three interrelated technical areas: (1) implementing national forest inventories that are consistent and can be integrated with remote sensing products generated to estimate change areas, and consistently mapping land use classes beyond forests with a replicable methodology; (2) tracking and reporting forest degradation; and (3) developing the regional community of forest and terrestrial carbon technical experts.
In 2014 SilvaCarbon began providing bilateral support to Democratic Republic of the Congo (DRC), Republic of Congo (ROC), and Cameroon with the goal of complementing existing support from the Central Africa Regional Program for the Environment (CARPE). A principle CARPE objective is to strengthen capacities to monitor forest cover change, GHG emissions, and biodiversity. In 2019, SilvaCarbon initiated collaboration with Zambia and Ethiopia, in support of the BioCarbon Fund Initiative for Sustainable Forest Landscapes (ISFL) .
The Central African Congo Basin is the second-largest humid tropical forest in the world and is widely recognized as a global priority for forest and carbon conservation and management. A number of Congo Basin countries have committed to reducing forest loss and associated emissions and are working with different international partners to meet those commitments. National stakeholders in the region have sought technical support in developing cost-effective forest and landscape monitoring approaches and systems that are suited to the Congo Basin’s large, dense, and often inaccessible forests.
SilvaCarbon complements other donor efforts in the region by working with DRC, ROC, and Cameroon to address specific forest monitoring needs and gaps. This ranges from building foundational capacities for REDD+, developing tailored forest mapping methodologies, and incorporating different forest types such as carbon-rich wetland forests into existing forest inventory frameworks. In-country SilvaCarbon coordinators help ensure relevancy of programming and support activity implementation.
SilvaCarbon builds forest and landscape monitoring capacity at the global level by supporting the development of key tools, guidance materials, and capacity-building resources; by increasing access to and application of Earth observation data; and by facilitating coordination among USG agencies and international institutions. SilvaCarbon has also supported applied research focused on identifying and implementing methodologies and technologies for measuring and monitoring forest degradation.
SilvaCarbon supports a variety of capacity-building activities that respond to countries’ forest and landscape monitoring needs. Program activities are collaboratively designed to target capacity gaps and complement related assistance provided by other donors and institutions. SilvaCarbon engages country participants through direct technical assistance and hands-on training, tailored workshops on key topics, international study tours, South-South exchanges between countries, development of tools and methodological guidance, and applied research.
Click on the calendar image to view upcoming SilvaCarbon activities
Access reports and materials from selected past SilvaCarbon activities
SilvaCarbon presents a series of e-learnings on open tools for Measurement, Reporting, and Verification (MRV) developed in collaboration with academia partners and in-kind collaboration from private sector.
Google Earth Engine
Google Earth Engine (GEE) developed a robust visualization and analysis tool that help to perform a complex geospatial analysis in both local and global scale. Earth engine is a useful tool for scientists, researchers, and non-traditional users around the globe in monitoring and forecasting forest health. And it became a container or environment for developing and applying different applications and tools that help in monitoring and observing changes and trends in forest in different time series.
To meet REDD+ reporting criteria of unbiasedness and uncertainty quantification, google earth engine platforms are necessary and indispensable for reporting and monitoring forest carbon stock and providing information on land use, deforestation and forest degradation. This e-learning will introduce participants to the concept, platforms and applications of Google Earth Engine (GEE).
Sampling-based Estimation of Area and Map Accuracy
Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forest and enhancement of forest carbon stocks in developing countries (REDD+) is a framework under the United Nations framework Convention on Climate Change (UNFCCC) with the goal of building international cooperation to achieve climate mitigation from forest.
A core element of REDD+ MRV is the monitoring and tracking of forest conservation to other lands due to human activities. Satellite information is the data source with enough periodicity and coverage to provide information on land use, type and intensity of land changes, deforestation, and forest degradation. In order to meet REDD+ reporting criteria of unbiasedness and uncertainty quantification, sampling-based approaches for the estimation of area and map accuracy are necessary.
English Español Français ພາສາລາວ Tiếng Việt
Continuous Degradation Detection (CODED) of Forests in Tropical Countries
This e-learning course will introduce you to the idea of continuous degradation detection, or CODED, and how it can be used to monitor and forecast forest health. By taking this course you will learn about:
- The challenges of forest degradation monitoring
- Spectral un-mixing and the Normalized Difference Fraction Index (NDFI)
- The methodology and purpose of CODED algorithm
- How degradation and natural disturbances are distinguished from deforestation
English Español Français ພາສາລາວ Tiếng Việt
The implementation of LandTrendr Algorithms in Google Earth Engine (LT-GEE) in Tropical Countries
This e-learning course will introduce you to the implementation of Landsat-based detection of Trends in Disturbance and Recovery (LT) Algorithms in Google Earth Engine (LT GEE), and how it can be used to monitor and forecast forest health. By the end of this course, you will have learned about:
- How LandTrendr algorithms distinguish the disturbances and changes in the forest in a different time series
- The methodology and purpose of the LandTrendr algorithms (LT-GEE)
- Explore and identify the requirements to run each LT algorithm in GEE
REDD+ Standards: Considerations, Financing, and Emerging Developments
The purposes of this e-learning module are to provide background and context for REDD+ through the analysis of the key decisions adopted by the United Nations Framework Convention on Climate Change's COP. Review the range of REDD+ standards and financing options that already exist, and themajor reporting differences between these different standards. It also highlights, guess, emerging developments that may impact REDD+ standards, financing and reporting going forward.
And finally, it provides a comprehensive list of resources for participants to consult, particularly countriesin helping them to consider which REDD+ scheme may best suit their own unique national needs.
Bayesian Updating of Land-Cover (BULC)
Bayesian Updating of Land-Cover (BULC) is a remote sensing algorithm designed for the ongoing updating of land-cover classifications over time for any set of categories that can be reliably detected in remotely sensed imagery. The Bayesian Updating of the Land-Cover algorithm allows users to update existing classifications or create new classifications based on the analysis of long series of satellite imagery with a few storage usages. Among remote sensing algorithms, the BULC algorithm maintains user-specific land cover categories rather than completely producing new sets of categories. It is designated to update land cover categories, either current or past, to any desired point in time.
This e-Learning course will introduce you to the implementation of the BULC algorithm in Google Earth Engine, or BULC, and how it can be used to monitor and forecast forest health. By the end of this course, you will learn about:
- How BULC algorithms distinguish the disturbances and changes in the forest in a different time series
- The methodology and purpose of the Bayesian Updating of Land-Cover algorithm
- Explore the requirements to run the BULC algorithm
SERVIR and SilvaCarbon Collaboration
SilvaCarbon is a US Government interagency technical cooperation program implemented by USGS and USFS to enhance tropical forested countries' capacity to monitor, measure, and report on carbon in their forests and other lands, leading to better mitigation outcomes.
SERVIR, a joint initiative of NASA, USAID, and leading geospatial organizations in Asia, Africa, and Latin America, works across four thematic service areas. The land cover service area focuses on helping countries use satellite data and geospatial technologies to reduce greenhouse gas emissions through improved land use management.
SERVIR and SilvaCarbon conduct targeted, complementary activities to help countries meet their needs for improved land use information, enhance natural resource management, and foster scientific collaboration and data-driven decision making.
Incorporating the Knowledge of Women
As social scientists, we work to better understand the users, uses, and value of Landsat. For example, how is Landsat used, what is the value of Landsat to its users, and how do citizens, scientists, and decision-makers use Landsat to benefit society? We are currently working to investigate gender-based challenges in forest carbon monitoring. This will support future research-informed capacity building efforts and highlight the strengths that women bring to forest carbon monitoring , despite being less likely to access trainings and other professional and educational resources.
Continuous Change Detection and Classification (CCDC)
The Continuous Change Detection and Classification (CCDC) algorithm uses time series of satellite observations to find areas where the seasonal patterns of surface reflectance no longer match the way they looked in the past. The result can be maps of land cover change, or maps of change in surface conditions. It has been used mostly with Landsat data, as the Landsat archive provides the best record of Earth's surface over multiple decades. CCDC is also being used now with other kinds of satellite data like Sentinel 1 and 2.
Landsat free data policy and long record of Earth observations
With a record of consistent high-quality observations that stretches back to the early 1980s, and that is now continued with the launched
of Landsat 8 and 9, we are able to move away from rudimentary comparisons of individual images to continuous monitoring of complex
With Landsat's free data policy and long record of Earth observations we can now track forest degradation by analysis of time series of Landsat data. By making a prediction of the forest's spectral signature that we compare to observations by Landsat, we can determine if the forest has been/is being disturbed. In a study of forest degradation and deforestation from 1987 to 2019 in the country of Georgia, we could track forest degradation and subsequent growth with an accuracy of 91%. We could identify a pattern forest degradation spreading from valleys to more inaccessible areas over time.
Global Land Analysis and Discovery (GLAD)
Global Land Analysis and Discovery (GLAD) methodology uses Supervised learning approaches to mapping forest cover extent, type, change factors and land use outcomes using Landsat imagery. GLAD aspires to generate new science insights concerning land resources, educate the next generation of remote sensing-based land change scientists, and disseminate land monitoring capabilities to operational settings nationally and internationally.
Continuous Degradation Detection (CODED)
Continuous Degradation Detection (CODED) methodology is used to map forest degradation and deforestation. It is built on Google Earth Engine and uses time series analysis of Landsat or Sentinel-2 data. The approach has three primary components: transformation of reflectance to sub-pixel forest parameters, change detection, and disturbance type attribution. Disturbance maps created using CODED can be used to create activity data for national forest monitoring programs and greenhouse gas inventories. CODED can be executed using an API or graphical user interface.
Bayesian Updating of Land Cover (BULC)
Bayesian Updating of Land Cover (BULC) methodology uses a variety of satellite information to track fires. We applied the methodology in two steps. First, we use machine learning to accurately detect very old fires from the early years of Landsat 1-4. Second, we reveal early result of the prototype BULC-Z algorithm, which can integrate data from multiple sensors to produce a sub weekly, ongoing map of fire occurrence in the present data. These two algorithms, though distinct from each other, hold promise both for understanding fire spread at fine time scales and fire history at decadal scales.
Multi-Mission Algorithm and Analysis Platform (MAAP)
Multi-Mission Algorithm and Analysis Platform (MAAP) methodology uses Lidar (GEDI and ICESat02) to estimate forest aboveground biomass, and Landsat to create wall to wall high resolution biomass maps. Products are created on an open science tool called the NASA ESA Multi-mission Algorithm and Analysis Platform (MAAP).
SilvaCarbon publishes the 16th GFOI Regional Workshop white paper
The 16th GFOI Regional Workshop: How is REDD+ data use for management, policy, and other reporting mechanisms in Latin America introduces a White Paper for this GFOI workshop offered in Fort Collins, Colorado in January, 2020. For more details regarding this workshop visit the site here.
Second SilvaCarbon e-learning on Continuous Degradation Detection (CODED)
The second in a series of SilvaCarbon e-learning modules is on Continuous Degradation Detection (CODED) and it is now available at https://bit.ly/CODEDe-learning. The course introduces the idea of continuous degradation detection, or CODED, and how it can be used to monitor and forecast forest health. By taking this course participants will learn about: 1. The challenges of forest degradation monitoring. 2. The methodology and purpose of CODED algorithm. 3. Spectral un-mixing and the Normalized Difference Fraction Index (NDFI) and 4. How degradation and natural disturbances are distinguished from deforestation. Future e-learning courses will address topics such as LandTrendr, Continuous Change Detection and Classification (CCDC), and Google Earth Engine basics (GEE101).
SilvaCarbon releases its first e-learning course on Sampling-Based Estimation of Area and Map Accuracy in collaboration with Boston University
The SilvaCarbon program is pleased to announce the release of a new series of e-learning courses focused on forest monitoring and Measuring, Reporting, and Verification (MRV) for REDD+. The course series was developed in collaboration with Boston University to provide free, targeted virtual training resources for forest monitoring and MRV technical teams around the world. The first e-learning course covers Sampling-Based Estimation of Area and Map Accuracy and is available at the Department of Interior University. Future e-learning courses will address topics such as Continuous Degradation Detection (CODED), LandTrendr, Continuous Change Detection and Classification (CCDC), Google Earth Engine 101 (GEE101), and Breaks For Additive Season and Trend (BFAST). To enroll, click this link and follow the prompt to create a login account. If you have any questions about the course content, please email Sylvia Wilson (email@example.com).
MINAM Develops Early Alert System for Deforestation
Peru has successfully developed an early alert system for detecting deforestation, building on technical support provided by the SilvaCarbon program since 2012. The early alert system uses freely available Landsat satellite data to detect and transparently share up-to-date information about forest cover loss occurring in the humid tropical forests of the Peruvian Amazon. These efforts are a result of collaboration between Peru’s Ministry of the Environment (MINAM), SilvaCarbon, and SilvaCarbon partners at the University of Maryland together with Global Forest Watch. Today MINAM is able to independently generate and use the early alert data without reliance on external financial or technical resources, reflecting a significant step toward sustainability in the country’s efforts to improve forest management and curb forest loss and the associated carbon emissions. The early warning alerts are publically available through Peru’s Geobosques platform at http://geobosques.minam.gob.pe, and more information about the early alert system is available at https://iopscience.iop.org/article/10.1088/2515-7620/ab4ec3
GFOI Launches Inventory of Activities
The Global Forest Observations Initiative (GFOI) partners launched the first comprehensive portal to track international capacity development support for forest monitoring. The GFOI Inventory of Activities is a one-stop shop with easy-to-access information on some 400 forest monitoring activities in 70 developing countries across Africa, Asia, and the Pacific, Latin America and the Caribbean.
Synthetic Aperture Radar (SAR)
SERVIR announces release of the Synthetic Aperture Radar (SAR) Handbook to empower the monitoring and protection of forests worldwide.
The guidelines in this brand book describe the visual and verbal elements that represent SilvaCarbon’s identity. This includes our name, logo and other elements such as color, font and graphics. Owning a consistent and controlled message of who we are is essential to presenting a strong, uniﬁed image to our partners. These guidelines reﬂect SilvaCarbon’s commitment to quality, consistency and style. The SilvaCarbon brand, including the logo, name, colors and identifying elements, are valuable program assets. Each of us is responsible for protecting the program’s interests by preventing unauthorized or incorrect use of SilvaCarbon’s name and brands.
SilvaCarbon Logo: JPEG versions for web and print
SilvaCarbon Logo: PNG versions for web and print
SilvaCarbon has supported applied research grants addressing practical carbon measurement challenges identified by country partners. These grants, initiated in 2013 and 2014, examined the use of emerging approaches to monitoring forest degradation, the interoperability of different remote sensing systems and sensors, and carbon estimation methodologies. Findings from the research support SilvaCarbon capacity-building efforts and help to strengthen the scientific basis for forest and landscape monitoring efforts worldwide.
This dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data. Annual maps of land cover were created for each Landsat scene and then post-processed and mosaicked. Land cover types include unclassified, forest, natural grasslands, urban, pastures, secondary forest, water, or highly reflective surfaces. The training data are not included with this dataset.
This dataset contains 16 data files in GeoTIFF (.tif) format, one for each year 2001-2016.
- Arévalo, P. 2020, CMS: Landsat-derived Annual Land Cover Maps for the Colombian Amazon, 2001-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1783
Improving forest emission estimates requires better biomass measurements before and after the change events at local levels, and the effective use and integration with remote sensing data to monitor impacts over larger areas. Novel technologies such as terrestrial laser scanning that provide detailed 3D measurements of tree, canopy structure and allometry rapidly and non-destructively, and the use of high-resolution remote sensing time series (i.e. from RapidEye) offer avenues to increase REDD+ measurement accuracy and precision and support improved monitoring capacities in developing countries. The research team aimed to use both in combination to systematically explore this potential by improving the underlying science, conduct a research synthesis across multiple tropical tests site, and make a direct contribution to monitoring and training in REDD+ countries.
- Gonzalez de Tanago, J. et al. 2018, Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9: 223-234. https://doi.org/10.1111/2041-210X.12904
- Lau et al. 2019, Tree Biomass Equations from Terrestrial LiDAR: A case study in Guyana. Forests 10(6), 527. https://doi.org/10.3390/f10060527
- Rosca et al. 2018, Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy in tropical forests. Interface Focus 8: 20170038. https://doi.org/10.1098/rsfs.2017.0038
- Wilkes, P. et al. 2017, Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sensing of Environment 196:140-153. https://doi.org/10.1016/j.rse.2017.04.030
Degraded forests are poorly studied. Despite the rapidly accumulating number of lidar studies, degraded forests are rarely used for calibration. With the exception of some long-term studies of logging, permanent tropical forest research plots have generally avoided degraded forests. Lidar studies of degraded forest structure, particularly for forests that have suffered understory fires, are rare although there are excellent counter-examples of specific studies in logged forests.
This study aimed to resolve whether field calibration in degraded forest is necessary for accurate lidar biomass estimation in degraded forests. The research team tested whether lidar biomass calibrations developed from old-growth and secondary forests or “universal” approaches are sufficient for biomass estimation in degraded forests.
- Dos Santos, N. et al. 2020, Fire Effects on Understory Forest Regeneration in Southern Amazonia. Frontiers in Forests and Global Change 3. https://doi.org/10.3389/ffgc.2020.00010
- Sato L. et al. 2016, Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia. Remote Sensing 8(10), 839. https://doi.org/10.3390/rs8100839
In this proposal, the research team proposed an alternative method for monitoring land change that makes use of all available observations ever acquired by the Landsat satellite for a pixel. Studying a time series of observations rather than comparing individual images or maps makes it possible to continuously monitor the land cover at pixel-level in time. While never implemented in Colombia, the proposed methodology has proven capable of mapping stable and changing land cover with high levels of accuracy and certainty. This research evaluates the full utility of US satellite data for the development of MRV systems in deforestation hotspots. It provides a methodology compliant with IPCC Approach 3 for securing activity data for Colombia, and when combined with emission factors provide estimates of carbon emissions and removals as a result of land transitions.
- Arévalo, P. et al. 2020, Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sensing of Environment 238: 111051 https://doi.org/10.1016/j.rse.2019.01.013
- Bullock, E. et al. 2020, Satellite-based estimates reveal widespread forest degradation in the Amazon. Global Change Biology 26: 2956-2969. https://doi.org/10.1111/gcb.15029
- Olofsson, P. et al. 2020, Mitigating the effects of omission errors on area and area change estimates. Remote Sensing of Environment 236: 111492 https://doi.org/10.1016/j.rse.2019.111492
Human-caused disturbance to tropical forests, such as through intentional use and resource extraction or through unintentional wildfires, cause substantial losses of carbon stocks. But does tropical forest degradation lead to permanent carbon losses? This is a critical question to address in the context of policy discussions to implement REDD+ (Reduced Emissions from Deforestation and Forest Degradation Plus enhancement of forest carbon stocks through conservation and sustainable forest management). We proposed to review the current scientific knowledge about the temporal and spatial dynamics of degradation--‐induced carbon emissions to build a coherent picture of the pattern of emissions from different types of degradation across tropical forest regions. Using best available information, we will: i) develop emissions factors (per area) for different types and scenarios of degradation; ii) describe the temporal pattern of degradation emissions and recovery trajectory post--‐disturbance; and iii) assess the evidence that demonstrates how tropical forest degradation leads to a lower carbon state, either through arrested succession, a switch to an alternate vegetation state, or facilitation of future deforestation. The overarching goal of this research is to synthesize existing knowledge on the range of initial gross and longer-term net carbon emissions from different types of degradation activities across tropical regions.
- Andrade, R.B., Balch, J.K., Parsons, A.L., Armenteras, D., Roman-Cuesta R.M., Bulkan J. 2017, Scenarios in tropical forest degradation: Carbon stock trajectories for REDD+. Carbon Balance and Management 12 (6). https://doi.org/10.1186/s13021-017-0074-0
Deforestation and forest degradation account for nearly 20% of global greenhouse gas emissions, more than any sector other than the energy sector (UN REDD, 2009). REDD (Reducing Emissions from Deforestation and Forest Degradation in Developing Countries) is a mechanism designed under the United National Framework Convention on Climate Change to financially support developing countries that are willing and able to reduce emissions from deforestation and invest in low carbon paths to sustainable development. The term deforestation refers to the permanent removal of forests and withdrawal of land from forest use, whereas the term forest degradation refers to detrimental changes that limit a forest’s production capacity. A relevant question pertains to the persistence component of degradation, i.e., is a forest degraded if it recovers from detrimental change that only temporarily limits its productivity? The overall objective of the proposal is to elaborate the definition of degradation by clarifying the persistence component.
- McRoberts, R. et al. 2015, Use of global map products to support gain-loss method of estimating carbon emissions. Canadian Journal of Forest Research 46: 924-932. https://doi.org/10.1139/cjfr-2016-0064
- Moser, P. et al. 2016, Methods for variable selection in LiDAR-assisted inventories. Forestry 90: 112-124. https://doi.org/10.1093/forestry/cpw041
- Methods and Guidance from the Global Forest Observations Initiative, Edition 2.0.
The purpose of this proposed work is to explore the use of Landsat time series data for mapping and monitoring forest degradation in Vietnam. The degradation caused by tree harvest and slash and burn agriculture is of serious concern in Vietnam (Manley et al., 2013). The proposed work fits under the SilvaCarbon “third stream of work on degradation,” whereby alternative approaches are being solicited for detecting, measuring and monitoring tropical forest degradation. In general, the spatial, spectral and radiometric qualities of Landsat data are particularly well suited for providing landscape characterization, and monitoring degradation in tropical environments (Hansen et al., 2008; Lambin, 1999).
- Vogelmann, J. et al. 2017, Assessment of Forest Degradation in Vietnam Using Landsat Time Series Data. Forests 8(7), 238. https://doi.org/10.3390/f8070238
Previous studies conducted at Antimary State Forest (western Brazilian Amazon) have indicated that low-intensity selective logging activities can be detected using three-dimensional canopy structure information derived from measurements from airborne laser scanning data (d’Oliveira et al., 2012). Using very-high-density lidar data (> 24 pulses/sq.m.), a relative density model (RDM) can be developed that represents the density of lidar returns (and vegetation) within a layer in the forest canopy between 1 and 5 meters height above ground. Variability in the density within this layer was found to be highly sensitive to forest impacts associated with selective logging, such as the development of skid trails and logging roads. A more recent study utilized multi-temporal airborne lidar data sets to quantify the reduction in biomass/aboveground carbon due to selective logging activities (Andersen et al., in review). This study shows that even relatively low levels of biomass change (10-20 Mg/ha) could be detected and quantified using changes observed in airborne lidar structural metrics. While the results of these studies are highly encouraging and indicate the potential utility of airborne lidar as a tool in detecting and characterizing forest degradation in tropical areas, there are several remaining critical research questions that will determine practical value of lidar for this application. For example, it is unclear how much the lidar density can be reduced and still maintain an adequate level of accuracy. The lidar data used in these studies was very high density (24 pulses/sq.m. and 10 pulses/sq.m. for 2010 and 2011 data respectively). While these densities were appropriate for research studies, they are not economically feasible for large-area acquisitions, where we would expect densities closer to 1-4 pulses/sq.m.
- Solichin, M. et al. 2017, Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation 56: 24-35. https://doi.org/10.1016/j.jag.2016.11.002
In Central African countries, where deforestation has historically been low, but where logging occurs in over 70% of the forests in some countries, forest degradation may be the most important source of carbon emissions. The uncertainty in quantifying the area affected and the carbon loss through degradation, particularly from selective logging, is large because conventional methods of remote sensing and surveying are not sensitive enough to precisely measure degradation. However, advances in high resolution remote sensing techniques using Light Detection and Ranging (LiDAR) provide an opportunity to improve NFMS by accurately monitoring areas affected by degradation, quantifying the emission factors from different types of logging and estimating carbon sequestration after degradation.
- Poulsen, J. et al. 2020, Old growth Afrotropical forests critical for maintaining forest carbon. Global Ecology and Biogeography 29: 1785-1798. http://dx.doi.org/10.1111/geb.13150
There are two general approaches to carbon estimation that show promise for MRV-model-based and model-assisted estimation. Model-based estimation in the context of forest attribute mapping relies on a set of modeled, pixel-based estimates, generally in the form of a map derived from remotely sensed data. Precision estimates can come from analyzing the set of pixel values, their uncertainties, and their spatial covariance. Model-assisted estimation is based on a probabilistic design in which ground plots along with auxiliary data from maps derived from remote sensing are used to generate estimates of forest parameters and their variance.
The Republic of Congo is one of a subset of countries where the suspected dominant factor in greenhouse gas emissions from land use change is forest degradation rather than deforestation. For a region such as central Africa, where few trees are harvested per hectare, direct methods of mapping partial canopy cover are not feasible. Indirect methods have been implemented to delineate degraded natural forests. Such approaches use indications of human activity to assign degradation to adjacent natural forests. Quantifying biomass dynamics within degraded forests is a challenge.
This study combines remotely sensed-derived degradation time-series maps with field data collection to assess biomass change within the logged forests of the Republic of Congo. By combining time-series of indirectly mapped degradation, the research team has in effect swap space for time, targeting forests of varying intervals since disturbance. Additionally, directly observable forest cover loss due to infrastructure development in support of logging will be employed to estimate aboveground biomass loss.
This research integrates large area forest monitoring data from earth observations and in situ inventory data. Forest degradation maps helps target the allocation of biomass plots in assessing carbon stock dynamics within logged areas. The research will advance national-scale RoC monitoring by developing a new method for integrating remotely sensed-derived forest cover loss and degradation maps with inventory data collection. The proposed activity quantifies carbon loss and gain through the life cycle of RoC logging concessions by sampling various aged concessions from 1990 to present. In doing so, a targeted method for quantifying carbon stock changes due to logging activities is realized.
While change detection from optical time series has progressed well in recent years, the use of radar data for forest cover change detection, which due to its ability to penetrate clouds could be a valuable additional source of information on forest cover change in areas where cloud cover tends to be persistent (i.e., the tropics), is largely underutilized. On April 3rd 2014, the European Space Agency (ESA) successfully launched the first in a new series of earth observation satellites, SENTINEL-1, which will acquire, for the first time, dense time series of C-band (~5 cm wavelength) radar data at medium (~25 m) spatial resolution consistently every three days and at a global scale. According to the European Delegated Act on Copernicus data, ESA will provide free, full and open access to Sentinel-1 data.
SENTINEL-1 hence opens up new possibilities for mining time series of spaceborne optical and radar data for improved operational forest monitoring, in particular in tropical countries. This study supports the development of national MRV systems by investigating, in collaboration with governmental agencies in Mexico, Colombia, and Peru, the potential of SENTINEL-1 data for mapping forest cover change.
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