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The 2014 Northwest Territories fires are one of the largest wildfires in history. However, it is difficult to explain what caused such devastating wildfires simply with meteorological conditions and hydrological drought. There is a lack of large-scale Near-Real-Time (NRT) observations that characterize fuel conditions. To fill this research gap, we provide the new earth observations that the meso-scale vegetation heat represented by L-band microwave-retrieved fuel (or canopy) temperature serves as a predictor of fire spread and lightning. We studied two million-ha-scale extreme fire events in the Northwest Territories in 2014 and British Columbia in 2018 to demonstrate that preheated endothermic vegetation condition (canopy temperature>295 K) ahead of flaming is a prerequisite for mega-fires. Canopy temperature is thus proposed as an indicator to modulate convective heating ahead of combustion, and fire spread, which strongly correlated (R2 of 0.8 ∼ 0.9) with pre-fire canopy temperature increments. It is possible to predict large-wildfires with this threshold of canopy temperature. We suggested a mechanism for vegetation under heat stress to trigger ignition and spread large fires. Our findings provide additional evidence that continued warming of the Earth's surface will lead to more severe firestorms and carbon emissions.

Sandy beaches are subject to changes due to multiple factors, that are both natural (e.g. storms) and anthropogenic. Great efforts are being made to monitor these ecosystems and understand their dynamics in order to assure their conservation. The identification of anthropogenic changes and its differentiation from natural ones is an important task for coastal monitoring. In this study, we present a methodology for the detection of anthropogenic changes in a coastal ecosystem by automatically detecting active bulldozers in continuous beach video data. PCA is used to highlight changes in consecutive images due to moving objects. Next, the YOLO object detection algorithm is used to identify the bulldozers in the change images. YOLO was specifically trained for the task, obtaining a precision of 0.94 and a recall of 0.81. An automatic tool was developed, and the process was carried out on two months of video data, consisting of approximately 19 000 images. The resulting information was compared with changes derived from 3D data obtained from a permanent laser scanner. The correlation among the results of the two methodologies was computed. For a validation area and daily time frame a correlation of 0.88 was obtained between the number of detected bulldozers and the area affected by changes in height larger than 0.3 m.

Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when only SAR data was used the R2 was 0.76 (rRMSE = 25.6 %). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE = 23.2 %). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.

The United Nations Sustainable Development Goals (SDGs) include social and ecological goals for humanity. Navigating towards reaching the goals requires the systematic inclusion of perspectives from a diversity of voices. Yet, the development of global sustainability pathways often lacks perspectives from the Global South. To help fill this gap, this paper introduces a participatory approach for visioning and exploring sustainable futures - the Three Horizons for the Sustainable Development Goals (3H4SDG). 3H4SDG facilitates explorations of (a) systemic pathways to reach the SDGs in an integrated way, and (b) highlights convergences and divergences between the pathways. We illustrate the application of 3H4SDG in a facilitated dialogue bringing together participants from four sub-regions of Africa: West Africa, Central Africa, East Africa, and Southern Africa. The dialogue focused on food and agricultural systems transformations. The case study results incorporate a set of convergences and divergences in relation to the future of urbanization, population growth, consumption, and the role of agriculture in the African economy. These were subsequently compared with the perspectives in global sustainability pathways, including the shared socioeconomic pathways (SSPs). The study illustrates that participatory approaches that are systemic and highlight divergent perspectives represent a promising way to link local aspirations with global goals.

Beach Surface Moisture (BSM) is a key attribute in the coastal investigations of land-atmospheric water and energy fluxes, groundwater resource budgets and coastal beach/dune development. In this study, an attempt has been made for the first time to estimate BSM from terrestrial LiDAR intensity data based on the Support Vector Regression (SVR). A long-range static terrestrial LiDAR (Riegl VZ-2000) was adopted to collect point cloud data of high spatiotemporal resolution on the Ostend-Mariakerke beach, Belgium. Based on the field moisture samples, SVR models were developed to retrieve BSM, using the backscattered intensity, scanning ranges and incidence angles as input features. The impacts of the training samples’ size and density on the predictive accuracy and generalization capability of the SVR models were fully investigated based on simulated BSM-intensity samples. Additionally, we compared the performance of the SVR models for BSM estimation with the traditional Stepwise Regression (SR) method and the Artificial Neural Network (ANN). Results show that SVR could accurately retrieve the BSM from the backscattered intensity with high reproducibility (average test RMSE of 0.71% ± 0.02% and R2 of 0.98% ± 0.002%). The Radial Basis Function (RBF) was the most suitable kernel for SVR model development in this study. The impacts of scanning geometry on the intensity could also be accurately corrected in the process of estimating BSM by the SVR models. However, compared to the SR method, the predictive accuracy and generalization performance of SVR models were significantly dependent on the training samples’ coverage, size and distribution, suggesting the need for the training samples of uniform distribution and representativeness. The minimum size of training samples required for SVR model development was 54. Under this condition, SVR performed similarly to ANN with a test RMSE of 1.06%, but SVR still performed acceptably (with an RMSE of 1.83%) even using extremely few training samples (only 16 field samples of uniform distribution), far better than the ANN (with an RMSE of 4.02%).

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we present a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wildland fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence.

Accurate mapping of burned area is of key importance for fire emissions modeling and post-fire rehabilitation planning. In this research, Sentinel-2 data were used to analyze the difference in spectral signature between burned area and false positives from agricultural harvests. 26 fires that were mapped in the field using Global Navigation Satellite System during 2017 and 2018 were analyzed over California and Utah, USA. Individual Sentinel-2 bands and a wide range of commonly used spectral indices for burned area were tested using a spectral separability index. The separability index assessed discrimination between the classes burned area and 1) unburned area and 2) area in agricultural land that were flagged as false positive from agricultural harvest. Separability values higher than one indicate good separation and the higher the values, the better the separation. For each class, we first determined the multitemporal difference, i.e. the absolute value of the pre-minus-post-change value. Second, we compared with other classes by using the spectral separability index. We found that for the burned-to-unburned comparison the near and shortwave infrared spectral regions and spectral indices that make use of these spectral regions, were the best discriminators (separability (M) values of approximately 2), corroborating findings from earlier works. For the burned-to-agricultural false positive comparison Sentinel-2 bands 4 and 5, corresponding with the Red and Red-edge spectral regions, were the best discriminator (M−values greater than 2). Consequently, spectral indices containing the Red band show a similar strong separability of agricultural false positives. The results from our research reveal an additional layer of information that could be exploited to minimize false positives in agricultural lands in space-borne burned area products.

Non-technical summary Our time seems to be trapped in a paradox. On the one hand, the capacity to master information has tremendously increased, but on the other hand the capacity to use the knowledge humanity produces seems at stake. There is a gap between our capacity to know and our capacity to act. We attempt to better understand that situation by considering the evolution of knowledge processing along human history, in particular the relation between the development of information technologies and the complexity of societies, the balance between the known and the unknown, and the current emergence of autonomous machines allowing intelligent processes. Technical summary Information-processing capacities developed historically in conjunction with the complexity of human societies. Positive feedback loops contributed to the co-evolution of knowledge, social organization, environmental transformation, and information technologies. Very powerful loops now drive the rapid emergence of global digital platforms, disrupting legacy organizations and economic equilibria. The simultaneous emergence of the awareness of the sustainability conundrum and the digital revolution is striking. Both are extremely disruptive and contribute to a surge in complexity, but how do they relate to each other? Paradoxically, as the capacity to master information increases, the capacity to use the knowledge humanity produces seems to lag. The objective of this paper is to analyze the current divergence between knowledge and action, from the angle of the co-evolution of information processing and societal transformation. We show how the interplay between perception and action, between the known and the unknown, between information processing and ontological uncertainty, has evolved toward a sense of control, a hubris, which abolishes the unknown and hinders action. A possible outcome of this interplay might lead to a society controlled to stay in its safe operating space, involving a strong delegation of information processing to autonomous machines, as well as extensive forms of biopolitics. Social media summary The sustainability conundrum and the digital revolution are entangled phenomena leading to complexity and disruption.

Non-technical summary All of humanity is facing the increasingly urgent challenge of finding pathways to the emergence of new, more sustainable patterns of living that promotes the co-evolution of natural and cultural systems. We address this challenge by proposing changes in scientific and scholarly research communities and transformations in roles, resources, actors, and institutions of scholarship (i.e., natural and social sciences, humanities, and arts), which can contribute substantially and effectively to co-designing solutions for sustainable, just, and equitable human societies. Technical summary The critical challenge facing humanity is the increasingly urgent need to find and implement pathways that lead humankind into a new stage of dynamic equilibrium that promotes the co-evolution of natural and cultural systems. We address this challenge for scientific and scholarly research communities and the transformations in roles, resources, actors, and institutions of scholarship (encompassing natural and social sciences, humanities, and arts), which can contribute substantially and effectively to co-designing solutions for coping with unsustainable practices and systemic risks. Our perspective builds upon a series of four workshops to identify and address global sustainability challenges at a regional scale. It is anchored in the view that nature and society are inextricably interwoven, that planetary boundaries are fundamentally societal, rather than solely environmental issues, that viable solutions to the global challenges mentioned above can be developed and most effectively implemented at a regional to local scale in conjunction with substantive changes in the education systems at all levels, and that these considerations require a complex adaptive systems approach to seeking and implementing solutions. We call for rethinking, finding creative approaches, and acting to make scholarship more capable of effectively creating just and equitable sustainable futures in diverse cultures and contexts. Social media summary Transforming scholarship and education to enable co-design of societal transformations to sustainable futures.

Increases in fire frequency, extent, and severity are expected to strongly impact the structure and function of boreal forest ecosystems. An important function of the boreal forest is its ability to sequester and store carbon (C). Increasing disturbance from wildfires, emitting large amounts of C to the atmosphere, may create a positive feedback to climate warming. Variation in ecosystem structure and function throughout the boreal forest is important for predicting the effects of climate warming and changing fire regimes on C dynamics. In this study, we compiled data on soil characteristics, stand structure, pre-fire C pools, C loss from fire, and the potential drivers of these C metrics from 527 sites distributed across six ecoregions of North America’s western boreal forests. We assessed structural and functional differences between these fire-prone ecoregions using data from 417 recently burned sites (2004–2015) and estimated ecoregion-specific relationships between soil characteristics and depth from 167 of these sites plus an additional 110 sites (27 burned, 83 unburned). We found that northern boreal ecoregions were generally older, stored and emitted proportionally more belowground than aboveground C, and exhibited lower rates of C accumulation over time than southern ecoregions. We present ecoregion-specific estimates of depth-wise soil characteristics that are important for predicting C combustion from fire. As climate continues to warm and disturbance from wildfires increases, the C dynamics of these fire-prone ecoregions are likely to change with significant implications for the global C cycle and its feedbacks to climate change.

The various crises that have emerged since 2000 are driven by an increasing maladaptation of our societies’ information processing capabilities to the dynamics in which our societies find themselves. These capabilities have been built up path dependently over centuries, and to understand them we need to look closely at their history. Changes in technology, demography and resource use and environmental change are all part of a co-evolution in which societies’ information processing capacities play a central role. The information and communications technology revolution has accelerated developments in all of these domains and has weakened some fundamental institutions. This paper discusses how these processes might affect the long-term future of our societies.

Reducing the climate change-induced risk of uncontrollable fires in landscapes under nature management, with severe impacts on landscape and society, is particularly urgent in densely-populated and fragmented areas. Reducing fire risk in such areas requires active involvement of a wide diversity of stakeholders. This research letter investigates stakeholders’ needs with regard to fire risk reduction in the Veluwe area in the Netherlands. This densely populated landscape is a popular tourist attraction, and it is one of the most fire-prone landscapes of the Netherlands, with abundant fuels and human ignition sources. We draw upon seven in-depth qualitative interviews with key stakeholders in the Veluwe area, which we situate in a wider review of existing literature. Our analysis demonstrates that the rising incidence of uncontrollable fires poses four types of new challenges to these stakeholders in the Veluwe area. First, stakeholders express the need to reshape existing policy tools and develop novel ones that create synergies between existing policy-priorities (e.g. biodiversity conservation) and fire risk reduction. Second, stakeholders argue for a critical rethinking of the value of landscapes in society, and the diverse roles that fire may play in landscape management research and practice. Third, developing such policy tools requires new modalities and platforms for multi-stakeholder and multi-level collaboration, which are currently lacking because the current and expected future risk of uncontrollable fire is unprecedented. And fourth, the development of effective policy tools requires new knowledge that is interdisciplinary, sensitive towards the local social and ecological characteristics of the area, and which approaches current fire risk challenges and their possible solutions dynamically. While our stakeholder analysis is specific to the Veluwe area in the Netherlands, our findings are also likely to be relevant to other fire-prone nature areas in fragmented landscapes, particularly in Northwestern Europe.

Forest characteristics, structure, and dynamics within the North American boreal region are heavily influenced by wildfire intensity, severity, and frequency. Increasing temperatures are likely to result in drier conditions and longer fire seasons, potentially leading to more intense and frequent fires. However, an increase in deciduous forest cover is also predicted across the region, potentially decreasing flammability. In this study, we use an individual tree-based forest model to test bottom-up (i.e. fuels) vs top-down (i.e. climate) controls on fire activity and project future forest and wildfire dynamics. The University of Virginia Forest Model Enhanced is an individual tree-based forest model that has been successfully updated and validated within the North American boreal zone. We updated the model to better characterize fire ignition and behavior in relation to litter and fire weather conditions, allowing for further interactions between vegetation, soils, fire, and climate. Model output following updates showed good agreement with combustion observations at individual sites within boreal Alaska and western Canada. We then applied the updated model at sites within interior Alaska and the Northwest Territories to simulate wildfire and forest response to climate change under moderate (RCP 4.5) and extreme (RCP 8.5) scenarios. Results suggest that changing climate will act to decrease biomass and increase deciduous fraction in many regions of boreal North America. These changes are accompanied by decreases in fire probability and average fire intensity, despite fuel drying, indicating a negative feedback of fuel loading on wildfire. These simulations demonstrate the importance of dynamic fuels and dynamic vegetation in predicting future forest and wildfire conditions. The vegetation and wildfire changes predicted here have implications for large-scale changes in vegetation composition, biomass, and wildfire severity across boreal North America, potentially resulting in further feedbacks to regional and even global climate and carbon cycling.

Overwintering fires are a historically rare phenomenon but may become more prevalent in the warming boreal region. Overwintering fires have been studied to a limited extent in boreal North America; however, their role and contribution to fire regimes in Siberia are still largely unknown. Here, for the first time, we quantified the proportion of overwintering fires and their burned areas in Yakutia, eastern Siberia, using fire, lightning, and infrastructure data. Our results demonstrate that overwintering fires contributed to 3.2 ± 0.6% of the total burned area during 2012–2020 over Yakutia, compared to 31.4 ± 6.8% from lightning ignitions and 51.0 ± 6.9% from anthropogenic ignitions (14.4% of the burned area had unknown cause), but they accounted for 7.5 ± 0.7% of the burned area in the extreme fire season of 2020. In addition, overwintering fires have different spatiotemporal characteristics than lightning and anthropogenic fires, suggesting that overwintering fires need to be incorporated into fire models as a separate fire category when modelling future boreal fire regimes.

Lightning-induced fire is the primary disturbance agent in boreal forests. Recent large fire years have been linked to anomalously high numbers of lightning-caused fire starts, yet the mechanisms regulating the probability of lightning ignition remain uncertain and limit our ability to project future changes. Here, we investigated the influence of lightning properties, landscape characteristics, and fire weather on lightning ignition efficiency—the likelihood that a lightning strike starts a fire—in Alaska, United States of America, and Northwest Territories, Canada, between 2001 and 2018. We found that short-term fuel drying associated with fire weather was the main driver of lightning ignition efficiency. Lightning was also more likely to ignite a wildfire in denser, evergreen forest areas. Under a high greenhouse gas emissions scenario, we predicted that changes in vegetation and fire weather increase lightning ignition efficiency by 14 ± 9% in Alaska and 31 ± 28% in the Northwest Territories per 1 °C warming by end-of-century. The increases in lightning ignition efficiency, together with a projected doubling of lightning strikes, result in a 39%–65% increase in lightning-caused fire occurrence per 1 °C warming. This implies that years with many fires will occur more frequently in the future, thereby accelerating carbon losses from boreal forest ecosystems.

Climate science provides strong evidence of the necessity of limiting global warming to 1.5 °C, in line with the Paris Climate Agreement. The IPCC 1.5 °C special report (SR1.5) presents 414 emissions scenarios modelled for the report, of which around 50 are classified as ‘1.5 °C scenarios’, with no or low temperature overshoot. These emission scenarios differ in their reliance on individual mitigation levers, including reduction of global energy demand, decarbonisation of energy production, development of land-management systems, and the pace and scale of deploying carbon dioxide removal (CDR) technologies. The reliance of 1.5 °C scenarios on these levers needs to be critically assessed in light of the potentials of the relevant technologies and roll-out plans. We use a set of five parameters to bundle and characterise the mitigation levers employed in the SR1.5 1.5 °C scenarios. For each of these levers, we draw on the literature to define ‘medium’ and ‘high’ upper bounds that delineate between their ‘reasonable’, ‘challenging’ and ‘speculative’ use by mid century. We do not find any 1.5 °C scenarios that stay within all medium upper bounds on the five mitigation levers. Scenarios most frequently ‘over use’ CDR with geological storage as a mitigation lever, whilst reductions of energy demand and carbon intensity of energy production are ‘over used’ less frequently. If we allow mitigation levers to be employed up to our high upper bounds, we are left with 22 of the SR1.5 1.5 °C scenarios with no or low overshoot. The scenarios that fulfil these criteria are characterised by greater coverage of the available mitigation levers than those scenarios that exceed at least one of the high upper bounds. When excluding the two scenarios that exceed the SR1.5 carbon budget for limiting global warming to 1.5 °C, this subset of 1.5 °C scenarios shows a range of 15–22 Gt CO _2 (16–22 Gt CO _2 interquartile range) for emissions in 2030. For the year of reaching net zero CO _2 emissions the range is 2039–2061 (2049–2057 interquartile range).

Effectively addressing climate change requires significant changes in individual and collective human behavior and decision‐making. Yet, in light of the increasing politicization of (climate) science, and the attempts of vested‐interest groups to undermine the scientific consensus on climate change through organized “disinformation campaigns,” identifying ways to effectively engage with the public about the issue across the political spectrum has proven difficult. A growing body of research suggests that one promising way to counteract the politicization of science is to convey the high level of normative agreement (“consensus”) among experts about the reality of human‐caused climate change. Yet, much prior research examining public opinion dynamics in the context of climate change has done so under conditions with limited external validity. Moreover, no research to date has examined how to protect the public from the spread of influential misinformation about climate change. The current research bridges this divide by exploring how people evaluate and process consensus cues in a polarized information environment. Furthermore, evidence is provided that it is possible to pre‐emptively protect (“inoculate”) public attitudes about climate change against real‐world misinformation.

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California’s wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990–2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.

Protected areas (PAs) are the leading forest conservation policy, so accurate evaluation of future PA impact is critical in conservation planning. Yet by necessity impact evaluations use past data. Here we argue that forward-looking plans should blend such evaluations with anticipation of shifts in threats. Applying improved methods to evaluate past impact, we provide rigorous support for that conceptual approach by showing that PAs’ impacts on deforestation shifted with land use. We study the Republic of Panama, where species-dense tropical forest faces real pressure. Facing variation in deforestation pressure, the PAs’ impacts varied across space and time . Thus, if shifts in pressure levels and patterns could be anticipated, that could raise impact.