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Projections of future climate change impacts suggest an increase of wildfire activity in Mediterranean ecosystems, such as southern California. This region is a wildfire hotspot and fire managers are under increasingly high pressures to minimize socio-economic impacts. In this context, predictions of high-risk fire seasons are essential to achieve adequate preventive planning. Regional-scale weather patterns and climatic teleconnections play a key role in modulating fire-conducive conditions across the globe, yet an analysis of the coupled effects of these systems onto the spread of large wildfires is lacking for the region. We analyzed seven decades (1953–2018) of documentary wildfire records from southern California to assess the linkages between weather patterns and large-scale climate modes using various statistical techniques, including Redundancy Analysis, Superposed Epoch Analysis and Wavelet Coherence. We found that high area burned is significantly associated with the occurrence of adverse weather patterns, such as severe droughts and Santa Ana winds. Further, we document how these fire-promoting events are mediated by climate teleconnections, particularly by the coupled effects of El Niño Southern Oscillation and Atlantic Multidecadal Oscillation. Unlabelled Image • Burned area is associated with adverse weather patterns in southern California. • Climatic teleconnections play a key role in modulating fire-conducive conditions. • Fire-promoting events are mediated by coupled effects of climate teleconnections. [ABSTRACT FROM AUTHOR]

The palm oil industry is one of the major producers of vegetable oil in the tropics. Palm oil is used extensively for the manufacture of a wide variety of products and its production is increasing by around 9% every year, prompted largely by the expanding biofuel markets. The rise in annual demand for biofuels and vegetable oil from importer countries has caused a dramatic increase in the conversion of forests and peatlands into oil palm plantations in Malaysia. This study assessed the area of forests and peatlands converted into oil palm plantations from 1990 to 2018 in the states of Sarawak and Sabah, Malaysia, and estimated the resulting carbon dioxide (CO2) emissions. To do so, we analyzed multitemporal 30-m resolution Landsat-5 and Landsat-8 images using a hybrid method that combined automatic image processing and manual analyses. We found that over the 28-year period, forest cover declined by 12.6% and 16.3%, and the peatland area declined by 20.5% and 19.1% in Sarawak and Sabah, respectively. In 2018, we found that these changes resulted in CO2 emissions of 0.01577 and 0.00086 Gt CO2-C yr−1, as compared to an annual forest CO2 uptake of 0.26464 and 0.15007 Gt CO2-C yr−1, in Sarawak and Sabah, respectively. Our assessment highlights that carbon impacts extend beyond lost standing stocks, and result in substantial direct emissions from the oil palm plantations themselves, with 2018 oil palm plantations in our study area emitting up to 4% of CO2 uptake by remaining forests. Limiting future climate change impacts requires enhanced economic incentives for land uses that neither convert standing forests nor result in substantial CO2 emissions. [ABSTRACT FROM AUTHOR]

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations. [ABSTRACT FROM AUTHOR]

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying "problematic" segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe. [ABSTRACT FROM AUTHOR]

We present a browser-based App for smartphones that is freely available to end-users for collecting geotagged and oriented photos depicting vegetation biomass and fuel characteristics. Our solution builds on advantages of smartphones, allowing their use as easy sensors to collect data by imaging forest ecosystems. The strength and innovation of the proposed solution is based on the following points: (i) using a low memory footprint App, streaming images and data with as little data-volume and memory as needed; (ii) using JavaScript APIs that can be launched from both a browser or as an installed App, as it applies features such as service workers and Progressive Web App; (iii) storing both image and survey data (geolocation and sensor orientation) internally to the device on an indexed database, and synchronizing the data to a cloud-based server when the smartphone is online and when all other safety tests have been successfully passed. The goal is to achieve properly positioned and oriented photos that can be used as training and testing data for future estimation of the surface fuel types based on automatic segmentation and classification via Machine Learning and Deep Learning. [ABSTRACT FROM AUTHOR]

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. [ABSTRACT FROM AUTHOR]

Wildfire events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfire risk to communities focused on modeling wildfire behavior in the wildland to aid in developing fuel reduction and fire suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfire behavior that fully integrates the wildland's characteristics and the built environment's features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfire behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufficient attention. Predicting damage to the built environment is essential in understanding and developing fire mitigation strategies to make communities more resilient to wildfire events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfire-affected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from 58 - 64 % for the historical wildfires tested. From the results, we observe that the modified random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modified degree formulation provides better predictions for nodes with mid-range vulnerability values. [ABSTRACT FROM AUTHOR]

Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA's mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia's tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests. [ABSTRACT FROM AUTHOR]

Tropical rainforests of Latin America (LATAM) are one of the world's largest carbon sinks, with substantial future carbon sequestration potential and contributing a major proportion of the global supply of forest carbon credits. LATAM is poised to contribute predominantly towards high-quality forest carbon offset projects designed to reduce emissions from deforestation and forest degradation, halt biodiversity loss, and provide equitable conservation benefits to people. Thus, carbon markets, including compliance carbon markets and voluntary carbon markets continue to expand in LATAM. However, the extent of the growth and status of forest carbon markets, pricing initiatives, stakeholders, amongst others, are yet to be explored and extensively reviewed for the entire LATAM region. Against this backdrop, we reviewed a total of 299 articles, including peer-reviewed and non-scientific gray literature sources, from January 2010 to March 2023. Herein, based on the extensive literature review, we present the results and provide perspectives classified into five categories: (i) the status and recent trends of forest carbon markets (ii) the interested parties and their role in the forest carbon markets, (iii) the measurement, reporting and verification (MRV) approaches and role of remote sensing, (iv) the challenges, and (v) the benefits, opportunities, future directions and recommendations to enhance forest carbon markets in LATAM. Despite the substantial challenges, better governance structures for forest carbon markets can increase the number, quality and integrity of projects and support the carbon sequestration capacity of the rainforests of LATAM. Due to the complex and extensive nature of forest carbon projects in LATAM, emerging technologies like remote sensing can enable scale and reduce technical barriers to MRV, if properly benchmarked. The future directions and recommendations provided are intended to improve upon the existing infrastructure and governance mechanisms, and encourage further participation from the public and private sectors in forest carbon markets in LATAM. • Latin America (LATAM) is a major contributor to the global supply of forest carbon credits. • Forest carbon market projects continue to expand to support the rainforest carbon sequestration. • Substantial challenges undermine the quality and integrity of forest carbon market projects. • Recommendations are provided to improve existing governance structures of forest carbon markets. • Remote sensing can reduce technical barriers to measurement, reporting and verification (MRV). [ABSTRACT FROM AUTHOR]

Background: LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. Results: The results show that LiDAR pulse density of 5 pulses m provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. Conclusion: LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory. [ABSTRACT FROM AUTHOR]

Quantifying fuel load over large areas is essential to support integrated fire management initiatives in fire-prone regions to preserve carbon stock, biodiversity and ecosystem functioning. It also allows a better understanding of global climate regulation as a potential carbon sink or source. Large area assessments usually require data from spaceborne remote sensors, but most of them cannot measure the vertical variability of vegetation structure, which is required for accurately measuring fuel loads and defining management interventions. The recently launched NASA's Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor holds potential to meet this demand. However, its capability for estimating fuel load has yet not been evaluated. In this study, we developed a novel framework and tested machine learning models for predicting multi-layer fuel load in the Brazilian tropical savanna (i.e., Cerrado biome) using GEDI data. First, lidar data were collected using an unnamed aerial vehicle (UAV). The flights were conducted over selected sample plots in distinct Cerrado vegetation formations (i.e., grassland, savanna, forest) where field measurements were conducted to determine the load of surface, herbaceous, shrubs and small trees, woody fuels and the total fuel load. Subsequently, GEDI-like full-waveforms were simulated from the high-density UAV-lidar 3-D point clouds from which vegetation structure metrics were calculated and correlated to field-derived fuel load components using Random Forest models. From these models, we generate fuel load maps for the entire Cerrado using all on-orbit available GEDI data. Overall, the models had better performance for woody fuels and total fuel loads (R2 = 0.88 and 0.71, respectively). For components at the lower stratum, models had moderate to low performance (R2 between 0.15 and 0.46) but still showed reliable results. The presented framework can be extended to other fire-prone regions where accurate measurements of fuel components are needed. We hope this study will contribute to the expansion of spaceborne lidar applications for integrated fire management activities and supporting carbon monitoring initiatives in tropical savannas worldwide. • GEDI allows large scale multi-layer characterization of fuels in tropical savannas. • A novel framework for fuel load modeling from simulated GEDI data is developed. • Random Forest models are derived for estimating multi-layer fuel loads. • Models performed better for woody and total than surface fuel loads. • A step change toward large scale fuel load mapping from space in tropical savanna. [ABSTRACT FROM AUTHOR]

Afforestation/reforestation (A/R) programs spearheaded by Civil Society Organizations (CSOs) play a significant role in reaching global climate policy targets and helping low-income nations meet the United Nations (UN) Sustainable Development Goals (SDGs). However, these organizations face unprecedented challenges due to the COVID-19 pandemic. Consequently, these challenges affect their ability to address issues associated with deforestation and forest degradation in a timely manner. We discuss the influence COVID-19 can have on previous, present and future A/R initiatives, in particular, the ones led by International Non-governmental Organizations (INGOs). We provide thirty-three recommendations for exploring underlying deforestation patterns and optimizing forest policy reforms to support forest cover expansion during the pandemic. The recommendations are classified into four groups - i) curbing deforestation and improving A/R, ii) protecting the environment and mitigating climate change, iii) enhancing socio-economic conditions, and iv) amending policy and law enforcement practices. [Display omitted] • Civil Society Organizations promote afforestation/reforestation (A/R) endeavors. • A/R programs help meet the United Nations Sustainable Development Goals. • COVID-19 can alter the trends of deforestation and forest degradation. • Increased budget cuts cause new challenges for A/R. • Remotely sensed data can aid in optimizing resource allocation. [ABSTRACT FROM AUTHOR]

• We explored subregional fire-weather associations within the Iberian Peninsula. • We investigated four pyroregions and three temporal scales of data aggregation. • Analyses revealed four different large-fire weather typologies. • ' Heat driven' fires govern the broad pattern of large fire incidence. • Larger fires start under ' seasonal droughts' in the Northwest region. The catastrophic events occurred in the Mediterranean basin in the last two decades have made large fires an increasingly prominent feature in the characterization of fire regimes. Large fires have turned forest and fire management focus to landscape fuels and extreme meteorological conditions at different spatial and temporal scales, since understanding fire-weather relations is essential to protect lives and assets from severe fires. For instance, synoptic conditions leading to anomalous and sudden warm episodes quickly increase fine fuel dryness, antecedent and persistent drought events decrease the moisture of coarse fuels, whereas strong winds increase fire spread by transferring heat to new burnable fuels more rapidly, or allowing spotting. 'Aggregate' indexes based on local meteorological variables like the Canadian Fire Weather Index (FWI) have proved useful in estimating and summarizing fire danger into straightforward numerical representations, with the caveat that a same result may be produced by different combinations of weather variables. Analyzing fire-weather and danger components separately may help to understand the relative importance of each factor, or reveal specific interactions between them that trigger specific fire typologies (i.e. wind-driven). In this work we analyzed the influence of two FWI's components and their input weather variables in large fire incidence across the entire Iberian Peninsula. We explored several spatial (four regions) and temporal (three levels of aggregation, i.e., daily, weekly and monthly) aggregations to account for potential dissimilarities on fire-weather associations in space and time. Statistical analyses involved a multi-group PCA analysis to identify large fire-weather typologies (LFWT), later submitted to optimized hierarchical clustering to reveal underlying associations among LFWT. Results revealed four distinctive LFWT, labelled: 'heat-driven', 'heat wave', 'seasonal drought' and 'wind-driven', and six cluster associations with noticeable spatial differences. The bulk of fires started in the vicinity of the 'Sierra de Estrela' (Portugal), under average conditions of the four typologies, but leaning towards 'seasonal drought' conditions. Fires in the Mediterranean side, the largest within the IP, were associated to hot and dry spells ('heat wave') without remarkable drought events. Most relevant combinations of LWTs included temperature, wind speed and DC at daily and monthly scale, making them the main fire-weather factors driving large fires in the Iberian Peninsula. [ABSTRACT FROM AUTHOR]

Accurate measuring, mapping, and monitoring of mangrove forests support the sustainable management of mangrove blue carbon in the Asia-Pacific. Remote sensing coupled with modeling can efficiently and accurately estimate mangrove blue carbon stocks at larger spatiotemporal extents. This study aimed to identify trends in remote sensing/modeling employed in estimating mangrove blue carbon, attributes/variations in mangrove carbon sequestration estimated using remote sensing, and to compile research gaps and opportunities, followed by providing recommendations for future research. Using a systematic literature review approach, we reviewed 105 remote sensing-based peer-reviewed articles (1990 - June 2023). Despite their high mangrove extent, there was a paucity of studies from Myanmar, Bangladesh, and Papua New Guinea. The most frequently used sensor was Sentinel-2 MSI, accounting for 14.5 % of overall usage, followed by Landsat 8 OLI (11.5 %), ALOS-2 PALSAR-2 (7.3 %), ALOS PALSAR (7.2 %), Landsat 7 ETM+ (6.1 %), Sentinel-1 (6.7 %), Landsat 5 TM (5.5 %), SRTM DEM (5.5 %), and UAV-LiDAR (4.8 %). Although parametric methods like linear regression remain the most widely used, machine learning regression models such as Random Forest (RF) and eXtreme Gradient Boost (XGB) have become popular in recent years and have shown good accuracy. Among a variety of attributes estimated, below-ground mangrove blue carbon and the valuation of carbon stock were less studied. The variation in carbon sequestration potential as a result of location, species, and forest type was widely studied. To improve the accuracy of blue carbon measurements, standardized/coordinated and innovative methodologies accompanied by credible information and actionable data should be carried out. Technical monitoring (every 2-5 years) enhanced by remote sensing can provide accurate and precise data for sustainable mangrove management while opening ventures for voluntary carbon markets to benefit the environment and local livelihood in developing countries in the Asia-Pacific region., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Freshwater ecosystems host disproportionately high biodiversity and provide unique ecosystem services, yet they are being degraded at an alarming rate. Fires, which are becoming increasingly frequent and intense due to global change, can affect these ecosystems in many ways, but this relationship is not fully understood. We conducted a systematic review to characterize the literature on the effects of fires on stream ecosystems and found that (1) abiotic indicators were more commonly investigated than biotic ones, (2) most previous research was conducted in North America and in the temperate evergreen forest biome, (3) following a control-impact (CI) or before-after (BA) design, (4) predominantly assessing wildfires as opposed to prescribed fires, (5) in small headwater streams, and (6) with a focus on structural and not functional biological indicators. After quantitatively analyzing previous research, we detected great variability in responses, with increases, decreases, and no changes being reported for most indicators (e.g., macroinvertebrate richness, fish density, algal biomass, and leaf decomposition). We shed light on these seemingly contradicting results by showing that the presence of extreme hydrological post-fire events, the time lag between fire and sampling, and whether the riparian forest burned or not influenced the outcome of previous research. Results suggest that although wildfires and the following hydrological events can have dramatic impacts in the short term, most biological endpoints recover within 5-10 years, and that detrimental effects are minimal in the case of prescribed fires. We also detected that no effects were more often reported by BACI studies than by CI or BA studies, raising the question of whether this research field may be biased by the inherent limitations of CI and BA designs. Finally, we make recommendations to help advance this field of research and guide future integrated fire management that includes the protection of freshwater ecosystems., (Global Change Biology© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.)

Urban forests provide direct and indirect benefits to human well-being that are increasingly captured in residential property values. Remote Sensing (RS) can be used to measure a wide range of forest and vegetation parameters that allows for a more detailed and better understanding of their specific influences on housing prices. Herein, through a systematic literature review approach, we reviewed 89 papers (from 2010 to 2022) from 21 different countries that used RS data to quantify vegetation indices, forest and tree parameters of urban forests and estimated their influence on residential property values. The main aim of this study was to understand and provide insights into how urban forests influence residential property values based on RS studies. Although more studies were conducted in developed (n = 55, 61.7%) than developing countries (n = 34, 38.3%), the results indicated for the most part that increasing tree canopy cover on property and neighborhood level, forest size, type, greenness, and proximity to urban forests increased housing prices. RS studies benefited from spatially explicit repetitive data that offer superior efficiency to quantify vegetation, forest, and tree parameters of urban forests over large areas and longer periods compared to studies that used field inventory data. Through this work, we identify and underscore that urban forest benefits outweigh management costs and have a mostly positive influence on housing prices. Thus, we encourage further discussions about prioritizing reforestation and conservation of urban forests during the urban planning of cities and suburbs, which could support UN Sustainable Development Goals (SDGs) and urban policy reforms., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

Climate teleconnections (CT) remotely influence weather conditions in many regions on Earth, entailing changes in primary drivers of fire activity such as vegetation biomass accumulation and moisture. We reveal significant relationships between the main global CTs and burned area that vary across and within continents and biomes according to both synchronous and lagged signals, and marked regional patterns. Overall, CTs modulate 52.9% of global burned area, the Tropical North Atlantic mode being the most relevant CT. Here, we summarized the CT-fire relationships into a set of six global CT domains that are discussed by continent, considering the underlying mechanisms relating weather patterns and vegetation types with burned area across the different world's biomes. Our findings highlight the regional CT-fire relationships worldwide, aiming to further support fire management and policy-making., (© 2023. The Author(s).)

The assessment of burn severity is highly important in order to describe and measure the effects of fire on vegetation, wildlife habitat and soils. The estimation of burn severity based on remote sensing is a powerful tool that, to be useful, needs to be related and validated with field data. The present paper explores the relationships between field accessible variables and Relative Differenced Normalized Burn Ratio (RdNBR) index by using linear mixed-effects models and boosted regression trees, based on data from 28 large fires and 668 field measurements across three countries in southern Europe. The RdNBR clearly reflected the mean height of charred stem and loss of ligneous, living shrub and tree cover during the fire. The paper confirms that remote sensing indices provide an acceptable assessment of fire induced impact on forest vegetation but also highlights there are important between-fire variations due to specific contexts that modify these relationships. These variations can be effectively assessed and should be taken into account in future predictive efforts., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Large wildfires can cover millions of hectares of forest every year worldwide, causing losses in ecosystems and assets. Fire simulation and modeling provides an analytical scheme to characterize and predict fire behavior and spread in several and complex environments. Spatial dynamics of large wildfires can be analyzed using satellite active fire data, a cost-effective way to acquire information systematically worldwide. The simulated growth of three large wildland fires from the USA, Chile and Spain with different fire spread pattern, duration and size has been compared to satellite active fire data. Additionally, a new approach to reinitialize fire simulations in near real-time and predict a more accurate fire spread is shown in this work. Discrepancies between the simulated fire growth and satellite active data were measured spatially and temporally in the three fires, increasing along the fire duration. The reinitialization approach meaningfully improved the accuracy of fire simulations in all case studies. Satellite active fire data showed a high potential to be used in real fire incidents, improving fire monitoring and simulation and, therefore, supporting the decision-making process of the fire analyst. The reinitialization approach could be applied by using the current satellite active fire data such as MODIS or VIIRS as well as Unmanned Aerial Vehicles or GPS locations from suppression resources., (Copyright © 2018 Elsevier Ltd. All rights reserved.)