Method for scaling woody surface gas exchange across tree bark

Abstract

A method for scaling carbon related woody surface gas exchange (WSGE) from small-area measurements to a larger scale, comprising the steps of: providing a woody surface area allometry, providing a WAI map to estimate a current and a historical woody methane uptake, providing a WSGE model based on bark surface area across a region of interest; combining the WAI map with WSGE model to estimate total current and historical WSGE, and generating a verified carbon credit based upon the total current and historical WSGE.

Description

METHOD FOR SCALING WOODY SURFACE GAS EXCHANGE ACROSS TREE BARKTECHNICAL FIELD

[0001] The present disclosure relates generally to systems and methods for scaling woody surface gas exchange (WSGE), such as methane, nitrous oxide, or other gas exchange, across tree bark surfaces.BACKGROUND

[0002] The world is currently on a path to reach a peak global warming of 2.8°C if no additional actions are taken, or 2.4 - 2.6°C if conditional or unconditional Nationally Determined Contributions (NDCs) are achieved, respectively. This is far above the 2.0°C limit beyond which extreme impacts on the earth system and human society are expected to be realized. Broad-based economy-wide transformations are necessary to keep the window open to limit climate change to below 2.0°C.

[0003] The planet has lost almost one-third of its forests globally. In addition to the deep impacts on biodiversity, the impacts on climate change are such that it will be about 10% hotter at peak warming and that warming will last much longer than tropical forests had not been cut down. Indeed, land use change globally is responsible for 9-19% of anthropogenic greenhouse gas emissions from 2010-2019, which includes deforestation, forest degradation, and regrowth after agricultural abandonment.

[0004] If realized to its full realistic potential, tropical reforestation can reduce peak global warming by 10%. Currently, however, reforestations rates lag far behind what would be needed to achieve this. A major cause of this lag is the unfavorable economics underlying tropical reforestation in the voluntary carbon market. Large upfront investment requirements, long break-even times, and project risks discourageinvestment in large scale tropical reforestation. However, reforestation may be incentivized by shifting the economic landscape to catalyze tropical reforestation by commercializing a newly discovered climate cooling service provided by forests — methane (CH4) uptake in the woody surfaces (bark) of trees.

[0005] To provide context of the disclosure herein, some knowledge of how the voluntary carbon market works is required. For example, with reference to Figure 1 , in the context of forest carbon, project developers work with local on-the-ground people and organizations to reforest, afforest, or protect threatened forests from deforestation. By following rules set by certification bodies, the carbon taken up from the atmosphere and stored in trees in these projects is recognized with “verified carbon credits.” Those credits can then be purchased by end buyers wanting to “offset” their carbon footprint.

[0006] There are many players in each of the segments of the carbon market. But by generating “extra” carbon credits from existing and future projects, developers can sell those additional credits to generate more profit.

[0007] Currently, forest carbon credits are based solely on the physical process of photosynthesis, whereby CO2 is taken up from the atmosphere in leaves and converted into carbon-containing hydrocarbons (e.g., sugars) and oxygen (O2). Some of these sugars are converted into woody biomass which forms the stem, branches, and roots of the tree. These hardy parts of the tree sequester carbon for the lifetime of the tree, and hence keep carbon out of the atmosphere.

[0008] Accordingly, methods and systems which expand processes by which forests generate carbon credits, for example, by recognizing that not only photosynthesis cools the globe by removing CO2, but also WSGE processes, such as bacteria in the bark of trees that remove methane, thus also cooling the globe are desirable.SUMMARY

[0009] The present disclosure provides systems and methods for scaling a woody surface gas exchange (WSGE), such as methane, nitrous oxide or other gas exchange, from small-area measurements on the woody surface of tree or across a tree’s architecture, to entire trees, or to a larger scale or defined region, comprising the steps of: providing a woody surface area allometry, providing a WAI (woody area index) map to estimate a current and a historical WSGE, providing a WSGE model based on bark surface area across a region of interest; combining the WAI map with the WSGE model to estimate total current and historical WSGE, and generating a verified carbon credit based upon the total current and historical WSGE.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure, and together with the description serve to explain the principles of the disclosure, wherein:

[0011] Figure 1 illustrates the basic structure of a voluntary carbon market in accordance with the present disclosure;

[0012] Figure 2 illustrates a global model of per-surface-area bark-level CH4uptake rates in accordance with the present disclosure;

[0013] Figure 3 illustrates climate model data, 3D tree models, and forest census data to scale up CH4 bark flux measurements used to estimate CH4 uptake in trees, forests, project areas, regions, and the globe in accordance with the present disclosure; and

[0014] Figure 4A-4C is a flow diagram (over three pages) of a method in accordance with the present disclosureDETAILED DESCRIPTION

[0015] Persons skilled in the art will readily appreciate that various aspects of the present disclosure can be realized by any number of methods and systems configured to perform the intended functions. Stated differently, other methods and systems can be incorporated herein to perform the intended functions. It should also be noted that the accompanying drawing figures referred to herein are not all drawn to scale but may be exaggerated to illustrate various aspects of the present disclosure, and in that regard, the drawing figures should not be construed as limiting. Finally, although the present disclosure can be described in connection with various principles and beliefs, the present disclosure should not be bound by theory.

[0016] The above being noted, methods in accordance with the present disclosure expand the processes by which forests and reforestation can be used to generate carbon credits, recognizing that not only photosynthesis cools the globe by removing carbon dioxide (CO2), but also that bacteria in the bark of trees removes methane (CPU), thus also cooling the planet. After CO2, CPU is the most important anthropogenically enhanced greenhouse gas in the atmosphere, contributing an additional 26% of human produced greenhouse warming since 1750.

[0017] A major challenge in estimating CPU fluxes across the bark of trees is scaling up the measurement of CPU using a small, measured bark surface area (determined by now known or as yet unknown instruments, typically handheld) to the bark surface area of the entire tree. Because biomass has been the focus of many studies due to a focus on carbon and other stocks of material in trees, much work has been done on building “biomass allometries,” or relationships between an easily measured metric such as stem diameter and tree biomass of trees across the globe.Much less work has been conducted on tree surface area allometries.

[0018] As discussed in more detail below, a variety of devices, models and algorithms, now known or as yet unknown, may be employed in accordance with various aspects of the present disclosure. Various examples include, among others:• Terrestrial Laser Scanning (TLS): Ground-based remote sensing method that uses millions of laser beams to create detailed 3D representations of structures and objects. Traditionally used in engineering, this technique has been applied to measure ecosystem structure more recently since the 2000s.. The technology has seen advancements, with modern devices capable of capturing high- resolution point clouds, even in challenging conditions, more and more quickly.• Quantitative Structure Models (QSMs): Algorithms that transform TLS data into structured 3D tree models. These are used to convert 3D point clouds into structures more easily analyzed by software.• Satellite Imagery: Modern satellites, equipped with advanced sensors, can capture high-resolution images, allowing for detailed analyses of forested areas.• Forest Census Databases: These have provided datasets on the number and size (diameter at breast height or “DBH”) of trees, and sometimes their species, height, and other characteristics, in particular locations across diverse regions, which is important for scaling up woody surface area and WSGE across stands, regions, and the planet.

[0019] In accordance with various aspects of the present disclosure, using tree models built from terrestrial laser scanning (TLS) data is an option to bridge this gap. In this regard, TLS devices emit laser pulses and, in instruments employing“time of flight” methods, measure the time it takes for the laser pulse to return. By knowing the speed of light and the azimuth and elevation of the emitted laser pulse, the instrument then calculates where in space relative to the instrument the reflecting material is. When done millions of times (accomplished in a couple minutes in advanced instruments), a 3D model of the area scanned results. Specifically, a large cloud of millions of points or a “pointcloud” is created.

[0020] In accordance with the present disclosure, to scan forests with TLS instruments, if entire tree models are desired, scans are conducted in a number of positions in the area of interest. For example, a grid may be established to minimize areas “shaded” by other objects, also referred to as “occlusion.” Once scanned, the separate scans across the area must be assembled or “co-registered” by specialized software with the goal of minimizing errors induced during the co-registration process. Once co-registered into large pointclouds, individual trees are extracted from the larger large pointcloud. In accordance with various aspects of the present disclosure, now known or as yet unknown automated processes may be used in the extraction process, though manual “cleaning” of tree pointclouds may also be used.

[0021] In accordance with various aspects of the present disclosure, if woody vs. leafy structure is also of interest, then points that are reflected from woody surfaces (“wood points”) are distinguished from those reflected from leafy surfaces (“leaf points”). Presently known and currently unknown algorithms may be used to separate leaf from wood points in TLS data from forests.

[0022] Once individual trees have been extracted from the pointclouds and wood points have been separated, in accordance with the present disclosure, solid models are derived from the pointcloud-based models in order to calculate properties such as volume and surface area. While complex triangulated surfacesmay be fit to the pointclouds, in other embodiments, cylindrical models are used. Once models are in a solid format, various important metrics can be calculated.

[0023] In accordance with various aspects of the present disclosure, CH4 fluxes across tree bark surfaces using established methods at successively higher points along tree stems were examined in seven forests ranging from tropics to boreal regions. In contrast to studies in inundated wetland areas where trees are known to emit CH4 at high rates, it was discovered that bacteria residing in tree bark, in noninundated sites, consume atmospheric CH4. The consumption rate is well correlated with mean annual temperature (MAT; Figure 3).

[0024] In accordance with various aspects of the present disclosure, a novel method to upscale small bark measurements to estimate methane uptake of entire trees, forest stands, regions, and the globe is provided. For example, in accordance with one embodiment, methane uptake is first measured by placing a small PVC tube on a known surface area of a tree trunk and connecting that tube to an instrument that measures methane flux. These measurements are then combined with 3D tree models built with laser scanning techniques, global forest census data, satellite remote sensing products, and climate models. Using this scaling technique, it was found that methane uptake in regrowing tropical forests increases climate cooling effects by 29%. This “extra” carbon uptake is neither quantified nor commercialized, and results in forest-based ecosystem services being undersold in the carbon marketplace.

[0025] Scaling methods in accordance with various aspects of the present disclosure can be applied to entire trees, forests, project areas, regions, and the globe. This method can be applied to forest carbon project areas to estimate the amount of CH4 being taken up by the trees in that area. By using a CH4 to CO2equivalent conversion, we are able to estimate the equivalent amount of CO2 that equates to the methane uptake in terms of its warming effect on the planet (CO2 warming equivalent or “C02-we”). This scaling method allows an assignment of a CO2 value for the methane uptake of a project area, and thus allows the issuance of carbon credits for this methane uptake.

[0026] As illustrated in Figure 3, climate model data, 3D tree models, and forest census data to scale up CH4 bark flux measurements can be used to estimate CH4 uptake in trees, forests, project areas, regions, and the globe.

[0027] In accordance with various aspects of the present disclosure and as shown in Figure 2, to do so, first a global model of per-surface-area bark-level CH4 uptake rates using climate models and the predictive equation is generated. Next, a terrestrial laser scanning (TLS) technique is used to build structural models of forests that consist of millions of points in space (the pointclouds).

[0028] Tree models are built by extracting individual trees from the entire pointcloud, removing leaf points and fitting cylindrical models to the remaining wood points. The woody surface area of the model is then calculated and an allometry between tree stem diameter (DBH) and woody surface area of the tree is built. Then forest census data and measured DBH and the surface area allometry are used to build a global model of woody surface area (Wood Area Index or WAI) using models and algorithms as well as machine learning methods and ecological datasets.

[0029] Finally, the resulting WAI map is multiplies by the modeled barklevel CH4 uptake at each location (calculated above) to get total per hectare CH4 uptake. Conversion from CH4 to C02-we may be calculated using then current best practices. Currently, it is estimated that CH4 warms earth 128 times more than CO2 for the first 20 years after emission begins.

[0030] In accordance with various aspects of the present disclosure, there are various considerations related to the method, as follows.

[0031] Uncertainty propagation. To convey statistical reliability, measurement error is propagated through every stage of the workflow. In accordance with various aspects of the present disclosure, a Monte Carlo simulation is employed using QSM fit variation, allometric parameters, TLS co-registration error, and chamber flux variance are characterized by probability distributions (e.g., normal or log normal). A random draw from each distribution is used to generate a complete set of inputs, the scaling calculation is executed, and the resulting WSGE estimate is stored. Repeating this process at least one thousand times yields an empirical distribution from which a 95 percent confidence interval (Cl) is reported.

[0032] Alternative propagation techniques may be substituted where appropriate, including:• Analytical error propagation using first order Taylor expansion when variances are small and input distributions are well approximated by Gaussians.• Parametric bootstrap in which field measurement datasets are resampled with replacement and the full scaling model is refitted for each resample.• Bayesian hierarchical modelling that directly returns posterior distributions for woody surface area and gas flux parameters; the posterior predictive distribution provides the Cl.• Any method that produces a statistically valid confidence interval may be used without departing from the scope of the present disclosure.

[0033] Using the methods described and contemplated herein, it is estimated that woody surface areas in new (regrowing) forests take up 0.131 and0.586 Mg CO2-we-C ha’1y1in temperate and tropical regions, respectively, corresponding to a 7% and 29 % additional CO2 uptake benefit of reforestation projects in these respective biomes. The exact amount of additional uptake will depend on the specific location of the project (specifically, the MAT and other factors there) and the growth rate of woody surface areas.

[0034] In accordance with various aspects of the present disclosure, a planning tool uses the process disclosed herein along with satellite-derived data on forest cover (10 - 250m resolution24’28) to estimate current woody methane uptake in a polygon input by a user. A prototype certification process uses higher resolution satellite imagery (30cm - 3m29) to build a more precise estimate for WAI in the project area, hence, better methane uptake estimates.

[0035] In accordance with various aspects of the present disclosure, technical de-risking focuses on gaining data to improve aspects of the processes outlined in the present disclosure to achieve more accurate estimates of CH4 uptake. Of importance are modeling CH4 uptake through tree branching networks, modeling WAI trajectories as reforestation plots grow, and modeling the variation of CH4 uptake across environmental gradient.

[0036] Additionally, further improvements to accuracy may be achieved by hardware-based development, for example: systems to measure WSGE gas concentrations in vertical gradients through forest canopies, Unmanned Aerial Vehicle (UAV) platforms to characterize WSGE gas fluxes, mobile WSGE gas flux platforms, internet-of-things WSGE gas sensors, and UAVs capable of autonomous understory navigation to quantify woody surface area efficiently at scale.

[0037] In accordance with various aspects of the present disclosure, estimating the WSGE from small-area measurements to entire trees, areas, regions,and the globe involves the following process:1 . Develop a woody surface area allometry

[0038] In accordance with various aspects of the present disclosure, a woody surface area allometry may be generated by:• scanning numerous forests around the globe, across environmental and ecosystem gradients, with a TLS instrument using methods shown to produce high quality scans for the purpose of tree model construction;• extracting individual trees from the scan and separating leaf points from wood points and then fitting cylindrical models to the wood points to create individual tree models;• computing a surface area and a stem diameter at about 1.3m DBH of the individual tree models; and• applying a statistical model to relate tree the surface areas and the stem diameters to woody surface area.

[0039] Additionally, in accordance with various aspects of the present disclosure, methods are used to determine the regions and tree types across which to split the allometric equations, for example, to determine different allometries for conifers vs broadleaf trees, and tropical versus temperate trees. Additionally, for small saplings that do not have a sizable stem at 1 ,3m, the diameter of the stem at the soil surface can be used.2. Develop a map of Woody Area Index (woody surface area (m2) per ground area (m2)) to estimate current, historical, and projected woody methane uptake

[0040] In accordance with various aspects of the present disclosure, aWAI map may be generated by:collecting tree census data, including DBH for each tree, from the region of interest, or across the globe if global estimates are desired; applying the woody surface area allometry to the tree census data; and• dividing the resulting total woody surface area by the land area of that census of the region to determine a woody surface area per ground area.

[0041] In accordance with various aspects of the present disclosure, the WAI map may be built with simple spatial extrapolation, machine learning with covariates, or other extrapolation models using ancillary datasets, including forest cover remote sensing products, to extrapolate WAI across the region of interest. This results in a map of WAI for the time period of the remote sensing and other ancillary products. By using historical remote sensing and ancillary data, WAI can be estimated for many years into the past, depending on the availability of those datasets during those years. By using forest growth models and ancillary data, WAI can be projected into the future, given assumptions about land use and management.3. Develop a model of WSGE per bark surface area across the region of interest

[0042] In accordance with various aspects of the present disclosure, a WSGE model is generated by determining a sampling of trees across the region of interest, including potential influencing factors such as mean annual temperature (MAT), precipitation, humidity, and elevation, a number of plots, location of plots, and number of trees within the plots. In accordance with various aspects, the WSGE model may be generated by measuring the WSGE of a known bark surface area on the sampling of trees at various elevations along the stem of each tree. In accordancewith various aspects, the sampling of trees is based on at least one of a mean annual temperature, a precipitation, a humidity, an elevation, a tree species, a number of plots, a location of plots, and a number of trees within a plot.

[0043] In accordance with various aspects of the present disclosure, an enhanced method for generating a WSGE model is done by measuring WSGE throughout an architecture of each tree in the sampling of trees, and controlling for a stem diameter, a height above the ground, and a hydraulic path length at a point of measurement across each entire tree.

[0044] In accordance with various aspects of the present disclosure, the WAI map is combined with the WSGE model to estimate total current, historical, and projected WSGE and for estimating WSGE of plots that have existing census data. WAI can be estimated by applying the allometric equations to the census data of the plot of interest or WSGE can be estimated by combining the WAI estimate with the ancillary data required by the model for that region in the WSGE model.

[0045] In accordance with various aspects of the present disclosure, further details on exemplary technology and processes which may be used herein follow.Terrestrial Laser Scanning (TLS)

[0046] In accordance with various aspects of the present disclosure, the methodology employs Terrestrial Laser Scanning (TLS) as a core technology for data acquisition in forest plots. TLS is a ground-based laser scanning method typically used for precision surveying applications, capable of generating detailed 3D pointcloud representations of scanned areas. For example, one potential laser scanner is the RIEGL VZ-600i laser scanner. This scanner’s field of view ranges from a fixed sector to a complete hemisphere, with the angular resolution configurable in azimuth andzenith to a minimum sampling step. Retro-reflective targets may be used for coarse co-registration across scan locations, aiding in achieving accurate alignment of scans for subsequent analysis.Sampling Strategies and Scanning Techniques

[0047] In accordance with various aspects of the present disclosure, the methodology is informed by experiences from 27 TLS campaigns conducted over the past five years. These campaigns focused on deriving Geometrical Modelling metrics, such as tree volume, from forest plots. In this regard, for single trees, various scanning approaches were employed, including a radial pattern around the tree stem and a regular grid. A minimum of three scan locations is typically required, but for large trees in dense canopies, six or more scan locations may be necessary.

[0048] When scanning large areas, a systematic grid is established on existing sampling plots. This approach helps ensure uniform point density and adequate sampling of the canopy, particularly when understory vegetation is dense. In dense tropical forests, a 10m grid has been found to result in sufficiently complete pointclouds.Data Processing and Analysis

[0049] In accordance with various aspects of the present disclosure, scans are initially co-registered using RIEGL’s RiSCAN PRO® software package. A multi-station adjustment (MSA) approach is then applied to refine the co-registration by modifying the orientation and position of each dataset. The pointclouds are filtered to remove noisy data, often characterized by a significant difference in pulse shape compared to the outgoing pulse. This filtration is essential for accurate analysis, as differences in pulse shape can arise from partial backscatter from leaves or tree edges. Further processing and analysis, including tree extraction, are conductedusing software designed for the task such as TLS2trees or the treeseg software package.Optimizing Sampling Patterns for Geometrical Modelling Metrics

[0050] In accordance with various aspects of the present disclosure, the sampling pattern creates a pointcloud enabling precise co-registration of adjacent scans, uniform point distribution, and maximal canopy coverage. This methodology has been successfully implemented in various campaigns, achieving co-registration with errors less than 1 cm.Logistical Considerations

[0051] In accordance with various aspects of the present disclosure, logistical aspects of TLS campaigns include planning for scanner specifications, instrument settings, and weather conditions. Time efficiency is a key factor, with each position reguiring between 2 minutes to over an hour to complete depending on conditions, instrument used, and the complexity of the target area, and a hectare containing around 121 positions if a 10 meter grid is used.Quantitative Structure Models (QSMs)

[0052] In accordance with various aspects of the present disclosure, a variety of Quantitative Structure Models, which produce solid models of trees from pointclouds, are contemplated herein as follows.Algorithm Function

[0053] In accordance with various aspects of the present disclosure and as used herein, QSMs are advanced algorithms designed to transform raw 3D pointcloud data obtained from Terrestrial Laser Scanning (TLS) into structured, highly detailed, solid 3D models of trees. These algorithms systematically process the pointcloud data, differentiating between various structural components of trees, suchas trunks, major branches, minor branches, and twigs. The models generated are a result of intricate data segmentation and dimensional analysis, allowing for precise reconstruction of tree architecture. In accordance with various aspects of the present disclosure, the methods described herein may use publicly available QSM algorithms, which have been validated and widely used in forest ecology studies. These algorithms leverage advanced computational techniques to accurately represent the complex geometry of tree structures.Parameter Estimations

[0054] In accordance with various aspects of the present disclosure, to ensure the QSMs accurately represent real trees, finely tuned algorithm parameters are used. The tuning process involves calibrating the QSMs to accommodate variations in tree forms, species-specific traits, and forest stand characteristics. The calibration is based on extensive field data and empirical observations, ensuring that the generated 3D models closely mirror the physical structure of the trees.

[0055] In this regard, parameters such as point density, scan resolution, and noise filtering are adjusted to optimize the model fidelity. This fine-tuning is critical for accurate surface area calculations and biomass estimations.

[0056] Some QSM algorithms contain some random (stochastic) parameters. Once optimal parameters are determined, a number of models for each tree can be produced that serves to help understand the uncertainty of the QSM reconstruction.Applications within the Invention

[0057] In accordance with various aspects of the present disclosure, the QSMs are primarily for deriving precise cylindrical (sometimes triangular mesh) models of tree architecture. These models are crucial for calculating the woody surfacearea, which is a key factor in estimating WSGE by trees. In this regard, the surface area calculated from these models is combined with WSGE flux measurements to scale up and estimate the overall WSGE of forests. This scaling is essential for accurately assessing the climate cooling effect of forests and for quantifying the additional carbon credits attributable to WSGE. In addition, the QSMs aid in creating a comprehensive spatial database of forest structures, which can be used for further ecological analyses and for improving the accuracy of forest carbon stock assessments.Integration with Satellite Imagery

[0058] In accordance with various aspects of the present disclosure, a variety of methods for integration with satellite imagery may be used and are contemplated herein as follows.Satellite Data Acquisition

[0059] In accordance with various aspects of the present disclosure, using high-resolution satellites, vast stretches of forested areas are imaged. These satellites employ multi-spectral or hyperspectral sensors, capturing data not just in visible ranges but also in infrared or near-infrared wavelengths, offering a broader perspective of the forest canopy.Scaling Process

[0060] In accordance with various aspects of the present disclosure, the detailed 3D models from QSMs serve as a reference template. When these are superimposed onto the satellite imagery, a scaling mechanism is established. This mechanism extrapolates WSGE data from a single tree or a cluster of trees to an entire forest or a specific type of wooded ecosystem.Temporal Dynamics

[0061] In accordance with various aspects of the present disclosure, by integrating time-series satellite data, the system can track seasonal or annual changes in canopy cover, tree growth, or even forest degradation. This dynamic data integration ensures the WSGE estimations remain current and adapt to environmental or anthropogenic changes.Pine Forest Application

[0062] In accordance with various aspects of the present disclosure, in one example, a vast pine forest is contemplated. Once a detailed 3D model for a specific set of pine trees (using TLS and QSMs) is created, the model, when juxtaposed with the satellite imagery of the region, aids in extrapolating the CPU uptake data for the entire forested area. This synthesis makes regional or even broader estimations feasible and precise. Through this progressive detailing, in accordance with various aspects, the method successfully marries microscopic tree-level details with macroscopic forest-level insights. The outcome is a robust, dynamic, and scalable system for WSGE estimation across expansive woody terrains.WSGE measurement

[0063] In accordance with various aspects of the present disclosure, measurement of WSGE flux at the bark surface is conducted using established or as yet unknown methods. For example, the procedure may involve connecting a gas analyzer (e.g., LGR Methane Analyzer or LiCOR Li-7810) to a collar of known dimensions that attaches to the bark surface of a tree. Thus, the surface area of bark subtended by the measurement is known, as is the volume of the collar and instrument measurement chamber. The change in methane concentration over time is recorded, and interpreted as a flux across the bark surface.USES

[0064] In accordance with various aspects of the present disclosure, a variety of applications and uses of the methods are contemplated herein. For example:• Voluntary carbon market: generate additional, verifiable removal credits for existing and new forest-carbon projects by quantifying bark-mediated CH4(and, where applicable, N2O) uptake, increasing revenue per hectare without altering planting design.• Compliance carbon market: provide activity-data and emission-factor inputs that enable regulated schemes (e.g., Article 6.4 mechanism, Ell LULUCF Regulation, California ARB, New Zealand ETS) to issue non-CO2removal units or adjust baseline accounting for managed forests.• Nationally Determined Contributions (NDC) and Greenhouse-Gas Inventories: supply spatially explicit CH4 / N2O sink estimates at municipal, state and national scales for incorporation into IPCC AFOLU tables and Paris-Agreement pledges, allowing Parties to claim additional mitigation beyond CO2sequestration.• Greenhouse gas inventories: municipal, district, state, and national governments will be interested in including woody methane uptake in their greenhouse gas inventories. These are often reported to the United Nations Framework Convention on Climate Change or other bodies. This scaling method can be used to provide the estimates needed for such reports.Land-Restoration and Brown-Field Mitigation: plant high-uptake tree species on or near landfills, abandoned mines or well-pads to biologicallyoxidize fugitive CH4 / N2O emissions and to generate offsets linked to site remediation.• Silvopastoral and Agro-Forestry Systems: deploy trees as rows or otherwise in pastures, rice paddies and other methane-intense landscapes to draw down enteric, soil, and atmospheric CH4 / N2O, while delivering shade, fodder and soil-health co-benefits..• Reforestation and Afforestation consulting: use species-level bark-uptake coefficients, woody surface area allometries, forest growth models, and WAI trajectory modeling to design planting mixes and spacings that maximize combined CO2, CH4and N2O benefits for NGOs, carbon developers, buyers, and commercial timber estates.• Urban Planning and Green Infrastructure: guide city foresters and landscape architects to select street-tree and park species that optimize methane removal while reducing urban-heat-island effects; uptake estimates can be reflected in C40 / CDP reporting and support green-bond issuances.• Construction and Real-Estate Development: help developers specify landscape plans that deliver measurable non-CO2removal, supporting green-building certifications such as LEED, BREEAM and Green Star.• Energy-Sector Partnerships: collaborate with pipeline operators, LNG terminals and compressor stations to establish vegetative buffers that offset residual CH4leaks and bolster corporate ESG performance.• Forestry-Management Optimization: allow public and private forest managers to adjust silvicultural regimes (e.g., species mix, thinning, rotation length) to enhance woody-surface CH4 / N2O uptake and unlockstacked credit revenue.• Environmental Consultancy and Impact Assessment: integrate bark-uptake modules into ElAs, ESG audits and natural-capital valuations, providing clients with defendable non-CO2mitigation metrics.• Climate Change Mitigation Products: develop and market a line of products or services specifically for landowners looking to improve the methane uptake of their forests, including testing kits, analysis software, or consultancy services.• Climate-Risk Analytics and Finance: feed uptake layers into TCFD-aligned risk models and natural-capital accounting tools used by insurers, investors and sovereign-risk analysts.• Biodiversity and Ecosystem-Service Stacking: combine bark-mediated CH4 / N2O removal credits with biodiversity or water-quality credits on the same hectare without double-counting, improving conservation finance viability.• Technology and Product Development: offer sensor kits, API endpoints and SaaS dashboards that enable landowners to monitor bark-flux performance and automatically generate crediting reports.• Corporate Social Responsibility (CSR) Initiatives: provide an audited channel for corporates to fund planting or maintenance of high-uptake forests to satisfy science-based net-zero commitments.• Education, Citizen Science and Research: publish open-data layers of uptake coefficients and uncertainties to support Earth-system modelling, university curricula and community tree-planting campaigns.Ecosystem Service Insurance: Insurance companies rely on estimatesof risk to gauge the cost of insurance. One risk in ecosystem service projects is the risk of default. By adding, for example, CH4 uptake to the value of forest carbon projects, risk estimates can change due to reduced default risk or otherwise. CH4 uptake data and model outputs can be provided to insurance companies to better understand risk.

[0065] In accordance with various aspects of the present disclosure, future developments or applications include quantifying CH4 uptake across the architecture of a tree (uptake will change with height, branch diameter, etc.) and using metabolic scaling components to make surface area estimates and integrated into CH4 uptake models.

[0066] In accordance with various aspects of the present disclosure, alternative methodologies contemplated herein include using field measured WAI or using gross WAI estimates based on forest age or canopy cover. Alternative, direct measurement of WSGE via flux towers or gradient methods, with other sources such as soil being measured concurrently so as to account for their contribution, and / or remote sensing may be feasible.

[0067] As required, detailed aspects of the present disclosed subject matter are disclosed herein. However, it is to be understood that the disclosed aspects are merely exemplary of the disclosed subject matter, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosed subject matter in virtually any appropriately detailed structure.

[0068] Likewise, numerous characteristics and advantages have been set forth in the preceding description, including various alternatives together withdetails of the structure and function of the devices and / or methods. The disclosure is intended as illustrative only and as such is not intended to be exhaustive. It will be evident to those skilled in the art that various modifications may be made, especially in matters of composition, ingredients, structure, materials, elements, components, shape, size and arrangement of parts including combinations within the principles of the invention, to the full extent indicated by the broad, general meaning of the terms in which the appended claims are expressed. To the extent that these various modifications do not depart from the spirit and scope of the appended claims, they are intended to be encompassed therein.

Claims

CLAIMSI claim:

1. A method for scaling a woody surface gas exchange from small-area measurements to entire to a larger scale, comprising the steps of: providing a woody surface area allometry; providing a WAI map to estimate a current and a historical woody surface gas exchange; providing a woody surface gas exchange model based on bark surface area across a region of interest; and combining the WAI map with the woody surface gas exchange model to estimate total current and historical woody surface gas exchange.

2. The method of claim 1 , wherein the woody surface gas exchange is at least one of CH4uptake and N2O uptake.

3. The method of claim 1 , wherein the larger scale comprises a defined region.

4. The method of claim 1 , wherein the woody surface area allometry is generated by: scanning at least one forest with a terrestrial laser scanning device to create a forest scan; extracting individual trees from the scan and separating leaf points from wood points and fitting cylindrical models to the wood points to create individual tree models; computing a surface area and a stem diameter at 1.3m DBH of theindividual tree models; and applying a statistical model to relate tree the surface areas and the stem diameters to woody surface area.

5. The method of claim 4, further comprising the step of using a diameter of a stem at the soil surface when a small sapling does not have a predetermined stem size at 1 ,3m DBH.

6. The method of claim 3, wherein the WAI map is generated by : collecting tree census data from the defined region; applying the woody surface area allometry to the tree census data; and dividing the tree census data by a land area of the defined region to determine a woody surface area per ground area; and using remote sensing data to determine forest cover across space to project the WAI map across areas where that remote sensing data is available.

7. The method of claim 6, wherein the remote sensing date is from a satellite.

8. The method of claim 1 , further comprising the step of propagating a measurement and modelling uncertainty using a calculation and reporting a confidence interval associated with an estimated woody surface gas exchange value.

9. The method of claim 1 , wherein the woody surface gas exchange model is a CH4uptake model.

10. The method of claim 9, wherein the CH4 uptake model is generated by determining a sampling of trees in the region and measuring a CH4 uptake of a known bark surface area on the sampling of trees at various elevations along a stem of each tree.11 . The method of claim 10, wherein the sampling of trees us based on at least one of a mean annual temperature, a precipitation, a humidity, an elevation, a number of plots, a location of plots, and a number of trees within a plot.

12. The method of claim 10, wherein the CH4 uptake model is generated by measuring a CH4 uptake throughout an architecture of each tree in the sampling of trees, and controlling for a stem diameter, a height above the ground, and a hydraulic path length at a point of measurement across each entire tree.

13. The method of claim 1 , further comprising generating a verified carbon credit based upon the total current and historical woody surface gas exchange.

14. The method of claim 1 , further comprising a planning tool showing historical, current, and projected woody surface gas exchange across a map.

15. The method of claim 3, further comprising estimating a woody surface gas exchange when the defined region is completely forested.

16. A method for scaling methane uptake from small-area measurements to entire to a defined region, comprising the steps of: providing a woody surface area allometry, the woody surface area allometrygenerated by: scanning at least one forest with a terrestrial laser scanning device to create a forest scan; extracting individual trees from the scan and separating leaf points from wood points and fitting cylindrical models to the wood points to create individual tree models; computing a surface area and a stem diameter at 1.3m DBH of the individual tree models; and applying a statistical model to relate tree the surface areas and the stem diameters to woody surface area; providing a WAI map to estimate a current and a historical woody methane uptake, wherein the WAI map is generated by : collecting tree census data from the region; applying the woody surface area allometry to the tree census data; and dividing the tree census data by a land area of the region to determine a woody surface area per ground area; and using remote sensing data to quantify forest cover percentage across a region to determine CH4 uptake across the region; providing a CH4 uptake model based on bark surface area across a region of interest; combining the WAI map with the CH4 uptake model to estimate total current and historical CH4 uptake; and generating a verified carbon credit based upon the total current and historical CH4 uptake.

17. The method of claim 16, further comprising the step of using a diameter of a stem at the soil surface when a small sapling does not have a predetermined stem size at 1.3m DBH.

18. The method of claim 16, wherein the CH4 uptake model is generated by determining a sampling of trees in the region and measuring a CH4 uptake of a known bark surface area on the sampling of trees at various elevations along the stem of each tree.

19. The method of claim 18, wherein the CH4 uptake model is generated by measuring a CH4 uptake throughout an architecture of each tree in the sampling of trees, and controlling for a stem diameter, a height above the ground, and a hydraulic path length at a point of measurement across each entire tree.

20. The method of claim 18, wherein the sampling of trees us based on at least one of a mean annual temperature, a precipitation, a humidity, an elevation, a number of plots, a location of plots, and a number of trees within a plot.

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