Abstract. High severity wildfires, driven by a complex interplay between climate change, vegetation growth, and human activity, present a critical threat to human safety and to the ecosystems of California. Forecasting and managing these fire systems is hindered in part by a lack of comprehensive, long-term, and regularly updated data regarding the vegetation fuel loads and forest structural conditions that drive fire behavior. To address this data need, we developed and launched the California Forest Observatory in the fall of 2020, which is a system designed to map forest structure and fuels at fine spatial and temporal scales. An extensive airborne LiDAR training dataset was integrated with multi-sensor satellite data from Sentinel-1, Sentinel-2, and PlanetScope constellations. LiDAR-derived canopy fuel metrics — including canopy height, cover, base height, and ladder fuel density — were modeled using convolutional neural networks, which leveraged spatial context and sensor fusion to generate predictive models at 10-meter and 3-meter resolutions. Canopy bulk density and surface fuel data were estimated from process-based models. The canopy fuel models demonstrated strong performance: 10-meter model predictions ranged from r2 = 0.51 to r2 = 0.79, 3-meter performance metrics ranged from r2 = 0.72 to r2 = 0.79, with low net bias across all variables, outperforming comparable, recently-released data products. The output data have successfully supported wildfire hazard mapping, utility risk mitigation, and carbon neutrality planning across the state, and are available via PANGAEA (https://doi.org/10.1594/PANGAEA.989871). By providing a data-driven evidence base for monitoring vegetation change, the California Forest Observatory offers a blueprint for the next generation of satellite-based conservation technologies aiming to inform sustainable land management strategies.

Carbon markets finance natural climate solutions to accelerate carbon sequestration and reduce carbon emissions. Monitoring, reporting, and verification (MRV) systems are now emerging to provide transparency to voluntary and jurisdictional carbon markets. In forests, MRV systems must estimate patterns of forest structure, tree growth, tree loss, and the corresponding carbon removals or emissions over time. Estimating forest biomass at scale is now possible with spaceborne LiDAR data, which can be fused with other satellite data to map wall-to-wall change over long periods of time. This manuscript describes a novel digital MRV system — Forest Carbon Diligence — which fuses multimodal satellite measurements. This system provides annual maps of canopy cover, canopy height, aboveground carbon density, change detection, and their uncertainties. Validation and intercomparison with open data were used to evaluate performance, providing a replicable approach for describing global forest carbon products. Globally, forest structure regression metrics were high (canopy height r2=0.83, canopy cover r2=0.79), as were carbon metrics (biomass r2=0.64 at 30m, r2=0.82 at 1km). The intercomparison analysis found strong agreement with 8 independent datasets in tropical, temperate, and boreal systems. Rigorous digital MRV systems can support policy and market needs by providing scientific, well-calibrated, and consistent estimates of aboveground forest carbon stocks, increasing trust in carbon markets.

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals-reflectance across wavelengths-from liana-infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering 775 ha of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short-wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy-reflectance signal are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Natural climate solutions (NCS) are recognized as an important tool for governments to reduce greenhouse gas emissions and remove atmospheric carbon dioxide. Using California as a globally relevant reference, we evaluate the magnitude of biological climate mitigation potential from NCS starting in 2020 under four climate change scenarios. By mid-century NCS implementation leads to a large increase in net carbon stored, flipping the state from a net source to a net sink in two scenarios. Forest and conservation land management strategies make up 85% of all NCS emissions reductions by 2050, with agricultural strategies accounting for the remaining 15%. The most severe climate change impacts on ecosystem carbon materialize in the latter half of the century with three scenarios resulting in California ecosystems becoming a net source of carbon emissions under a baseline trajectory. However, NCS provide a strong attenuating effect, reducing land carbon emissions 41-54% by 2100 with total costs of deployment of 752-777 million USD annually through 2050. Rapid implementation of a portfolio of NCS interventions provides long-term investment in protecting ecosystem carbon in the face of climate change driven disturbances. This open-source, spatially-explicit framework can help evaluate risks to NCS carbon storage stability, implementation costs, and overall mitigation potential for NCS at jurisdictional scales.

Background Quantifying the carbon balance of forested ecosystems has been the subject of intense study involving the development of numerous methodological approaches. Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a "gain-loss" approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). Results We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year(-1). Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source. Conclusions The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

Terrestrial ecosystems are an important sink for atmospheric carbon dioxide (CO2), sequestering ~30% of annual anthropogenic emissions and slowing the rise of atmospheric CO2. However, the future direction and magnitude of the land sink is highly uncertain. We examined how historical and projected changes in climate, land use, and ecosystem disturbances affect the carbon balance of terrestrial ecosystems in California over the period 2001–2100. We modeled 32 unique scenarios, spanning 4 land use and 2 radiative forcing scenarios as simulated by four global climate models. Between 2001 and 2015, carbon storage in California's terrestrial ecosystems declined by −188.4 Tg C, with a mean annual flux ranging from a source of −89.8 Tg C/year to a sink of 60.1 Tg C/year. The large variability in the magnitude of the state's carbon source/sink was primarily attributable to interannual variability in weather and climate, which affected the rate of carbon uptake in vegetation and the rate of ecosystem respiration. Under nearly all future scenarios, carbon storage in terrestrial ecosystems was projected to decline, with an average loss of −9.4% (−432.3 Tg C) by the year 2100 from current stocks. However, uncertainty in the magnitude of carbon loss was high, with individual scenario projections ranging from −916.2 to 121.2 Tg C and was largely driven by differences in future climate conditions projected by climate models. Moving from a high to a low radiative forcing scenario reduced net ecosystem carbon loss by 21% and when combined with reductions in land‐use change (i.e., moving from a high to a low land‐use scenario), net carbon losses were reduced by 55% on average. However, reconciling large uncertainties associated with the effect of increasing atmospheric CO2 is needed to better constrain models used to establish baseline conditions from which ecosystem‐based climate mitigation strategies can be evaluated.

Integration of multiple technologies greatly increases the spatial and temporal scales over which ecological patterns and processes can be studied, and threats to protected ecosystems can be identified and mitigated. A range of technology options relevant to ecologists and conservation practitioners are described, including ways they can be linked to increase the dimensionality of data collection efforts. Remote sensing, ground-based, and data fusion technologies are broadly discussed in the context of ecological research and conservation efforts. Examples of technology integration across all of these domains are provided for large-scale protected area management and investigation of ecological dynamics. Most technologies are low-cost or opensource, and when deployed can reach economies of scale that reduce per-area costs dramatically. The large-scale, long-term data collection efforts presented here can generate new spatio-temporal understanding of threats faced by natural ecosystems and endangered species, leading to more effective conservation strategies. 2 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Modeling efforts focused on future greenhouse gas (GHG) emissions from energy and other sectors in California have shown varying capacities to meet the emissions reduction targets established by the state. These efforts have not included potential reductions from changes in ecosystem management, restoration, and conservation. We examine the scale of contributions from selected activities in natural and agricultural lands and assess the degree to which these actions could help the state achieve its 2030 and 2050 climate mitigation goals under alternative implementation scenarios. By 2030, an Ambitious implementation scenario could contribute as much as 147 MMTCO2e or 17.4% of the cumulative reductions needed to meet the state's 2030 goal, greater than the individual projected contributions of four other economic sectors, including those from the industrial and agricultural sectors. On an annual basis, the Ambitious scenario could result in reductions as high as 17.9 MMTCO2e⋅y-1 or 13.4% of the state's 2030 reduction goal. Most reductions come from changes in forest management (61% of 2050 projected cumulative reductions under the Ambitious scenario), followed by reforestation (14%), avoided conversion (11%), compost amendments to grasslands (9%), and wetland and grassland restoration (5%). Implementation of a range of land-based emissions reduction activities can materially contribute to one of the most ambitious mitigation targets globally. This study provides a flexible, dynamic framework for estimating the reductions achievable through land conservation, ecological restoration, and changes in management regimes.

Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO2 emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon stocks was used as a model of the country’s carbon geography.

Tropical forests play an important role in the global carbon cycle, but knowledge of interannual variation in the total tropical carbon flux and constituent carbon pools is highly uncertain. One such pool, branchfall, is an ecologically important dynamic with links to nutrient cycling, forest productivity, and drought. Identifying and quantifying branchfall over large areas would reveal the role of branchfall in carbon and nutrient cycling. Using data from repeat airborne light detection and ranging campaigns across a wide array of lowland Amazonian forest landscapes totaling nearly 100 000 ha, we find that upper canopy gaps—driven by branchfall—are pervasive features of every landscape studied, and are seven times more frequent than full tree mortality. Moreover, branchfall comprises a major carbon source on a landscape basis, exceeding that of tree mortality by 21%. On a per hectare basis, branchfall and tree mortality result in 0.65 and 0.72 Mg C ha−1 yr−1 gross source of carbon to the atmosphere, respectively. Reducing uncertainties in annual gross rates of tropical forest carbon flux, for example by incorporating large-scale branchfall dynamics, is crucial for effective policies that foster conservation and restoration of tropical forests. Additionally, large-scale branchfall mapping offers ecologists a new dimension of disturbance monitoring and potential new insights into ecosystem structure and function.

Integration of multiple technologies greatly increases the spatial and temporal scales over which ecological patterns and processes can be studied, and threats to protected ecosystems can be identified and mitigated. A range of technology options relevant to ecologists and conservation practitioners are described, including ways they can be linked to increase the dimensionality of data collection efforts. Remote sensing, ground-based, and data fusion technologies are broadly discussed in the context of ecological research and conservation efforts. Examples of technology integration across all of these domains are provided for large-scale protected area management and investigation of ecological dynamics. Most technologies are low-cost or open-source, and when deployed can reach economies of scale that reduce per-area costs dramatically. The large-scale, long-term data collection efforts presented here can generate new spatio-temporal understanding of threats faced by natural ecosystems and endangered species, leading to more effective conservation strategies.

Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase.

Recent studies indicate that lianas are increasing in size and abundance relative to trees in neotropical forests. As a result, forest dynamics and carbon balance may be altered through liana-induced suppression of tree growth and increases in tree mortality. Increasing atmospheric CO2 is hypothesized to be responsible for the increase in neotropical lianas, yet no study has directly compared the relative response of tropical lianas and trees to elevated CO2. We explicitly tested whether tropical lianas had a larger response to elevated CO2 than co-occurring tropical trees and whether seasonal drought alters the response of either growth form. In two experiments conducted in central Panama, one spanning both wet and dry seasons and one restricted to the dry season, we grew liana (n=12) and tree (n=10) species in open-top growth chambers maintained at ambient or twice-ambient CO2 levels. Seedlings of eight individuals (four lianas, four trees) were grown in the ground in each chamber for at least 3months during each season. We found that both liana and tree seedlings had a significant and positive response to elevated CO2 (in biomass, leaf area, leaf mass per area, and photosynthesis), but that the relative response to elevated CO2 for all variables was not significantly greater for lianas than trees regardless of the season. The lack of differences in the relative response between growth forms does not support the hypothesis that elevated CO2 is responsible for increasing liana size and abundance across the neotropics.

Tropical forests convert more atmospheric carbon into biomass each year than any terrestrial ecosystem on Earth, underscoring the importance of accurate tropical forest structure and biomass maps for the understanding and management of the global carbon cycle. Ecologists have long used field inventory plots as the main tool for understanding forest structure and biomass at landscape-to-regional scales, under the implicit assumption that these plots accurately represent their surrounding landscape. However, no study has used continuous, high-spatial-resolution data to test whether field plots meet this assumption in tropical forests. Using airborne LiDAR (light detection and ranging) acquired over three regions in Peru, we assessed how representative a typical set of field plots are relative to their surrounding host landscapes. We uncovered substantial mean biases (9-98%) in forest canopy structure (height, gaps, and layers) and aboveground biomass in both lowland Amazonian and montane Andean landscapes. Moreover, simulations reveal that an impractical number of 1-ha field plots (from 10 to more than 100 per landscape) are needed to develop accurate estimates of aboveground biomass at landscape scales. These biases should temper the use of plots for extrapolations of forest dynamics to larger scales, and they demonstrate the need for a fundamental shift to high-resolution active remote sensing techniques as a primary sampling tool in tropical forest biomass studies. The potential decrease in the bias and uncertainty of remotely sensed estimates of forest structure and biomass is a vital step toward successful tropical forest conservation and climate-change mitigation policy.

Lack of knowledge about the distributions of plant and animal species can severely hamper management efforts, For invasive plants, distribution and abundance data can inform early detection and rapid response (EDRR) programs aimed at treating initial infestations. These data can be used to create invasion risk models at landscape and regional scales. Further, regional maps of invasive plant abundance are useful for communicating the scope of the invasive species problem to the public and policymakers. Here, we present a set of regional distribution maps for 10 problematic invasive plants in the western United States, created from the expert knowledge of weed managers in over 300 counties. Invasive plant experts identified infestations on paper, and the results were digitized into a regional GIS. Over 40% of requests were returned, resulting in maps with good spatial coverage and distribution data suitable for assessing invasive plant abundance across the western United States. Cheatgrass (Bromus tectorum) and Canada thistle (Cirsium arvense) were the most abundant and widespread of the surveyed species; however, the high concentrations and broad spatial extents of other invasive plants, such as hounds tongue (Cynoglossum officinale), white top (Lepidium draba), and Dalmatian toadflax (Linaria dalmatica), highlight the ongoing problems invasive species pose for western ecosystems, rangelands, and croplands. These results reinforce the critical role that regional mapping efforts can play in assessing and communicating invasion risk. This study suggests that knowledge about plant invasions exists locally and that experts are willing to participate in regional efforts to compile that information.

Regional distribution and abundance data for invasive plant species are urgently needed for management planning, modeling of invasion risks and impacts, and communicating the scope of the problem. Yet, regional distribution data are rare in the United States. Here, we present a web-based mapping tool designed for efficient collection of expert opinion of invasive species abundance. We use this approach to generate distribution maps of three prominent invasive plants in the southeastern United States: (1) Chinese/European privet (Ligustrum sinese/vulgare), (2) kudzu (Pueraria montana), and (3) cogongrass (Imperata cylindrica). We validated the maps for internal consistency, based on multiple submissions for the same location, and with two other independent data sources: the U.S. Forest Service's Forest Inventory and Analysis plots (FIA) (for privet) and point-location data gathered from various sources (for all three species). Percent cover data were collected for each species across 30% of the Southeast U.S. The web-based mapping system yielded high participation rates (187 users). Internal consistency was 69% for Chinese/European privet, 72% for kudzu, and 88% for cogongrass. For Chinese/European privet, percent cover accuracy was 64% relative to the U.S. Forest Service FIA data. A web-based mapping system is an effective means of collecting regional distributional data from a broad but loose network of experts. Regional abundance maps complement point presence data typically used in invasive plant management. Regional distribution maps are useful for cross-jurisdictional management of invasive species, biogeographical research, and attracting support for containment and restoration programs.