Global satellite monitoring of climate-induced vegetation disturbances

Forest disturbance regimes and trends in continental Spain (1985-2023) using dense landsat time series

Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.

Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient

Tropical forests exhibit significant diversity and heterogeneity in species distribution. Some tree species spread abundantly, impacting the functional aspects of communities. Understanding how these facets respond to climate change is crucial. Field data from four protected areas (PAs) were combined with high-resolution Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets to extract large-scale plot data of abundant species and their functional traits. A supervised component generalized linear regression (SCGLR) model was used to correlate climate components with the distribution of abundant species across PAs. The recorded rainfall gradient influenced the proportion of PA-specific species in the observed species assemblages. Community weighted means (CWMs) of biochemical traits showed better correlation values (0.85-0.87) between observed and predicted values compared to biophysical traits (0.52-0.79). The model-based projection revealed distinct distribution responses of each abundant species to the climate gradient. Functional diversity and functional traits maps highlighted the interplay between species heterogeneity and climate. The appearance dynamics of abundant species in dark diversity across PAs demonstrated their assortment strategy in response to the climate gradient. These observations can significantly aid in the ecological management of PAs exposed to climate dynamics.

The Database of European Forest Insect and Disease Disturbances: DEFID2

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca)

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2  = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2  = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

Biophysical and biochemical features' feedback associated with a flood episode in a tropical river basin model

Global climate change scenarios such as frequent and extreme floods disturb the river basins by destructing the vegetation resulting in rehabilitation procedures being more costly. Thus, understanding the recovery and regeneration of vegetation followed by extreme flood events is critical for a successful rehabilitation process. Spatial and temporal variation of biochemical and biophysical features derived from remote sensing technology in vegetation can be incorporated to understand the recovery and regeneration of vegetation. The present study explores the flood impact on vegetation caused by major river basins in Sri Lanka (a model tropical river basin) by comparing pre-flood and post-flood cases. The study utilized enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), the fraction of photosynthetically active radiation (FPAR), and gross primary productivity (GPP) of the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A remarkable decline in EVI, LAI, FPAR, GPP, and vegetation condition index was observed in the post-flood case. Notably, coupled GPP-EVI and GPP-LAI portrayed dependency of features and showed a significant impact triggered by the flood episode by narrowing the feature in post-flood events. EVI depicted the highest regeneration (0.333) while GPP presented the lowest regeneration (0.093) after the flood event. Further, it was revealed that 1.18 years have been on the regeneration. The regeneration of GPP and LAI remained low comparatively justifying the magnitude and impact of the flood event. The study revealed successful implications of vegetation indices on flood basin management of small to large tropical river basins.

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. We found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience-mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience-mortality relationships.

Calibration of Co-Located Identical PAR Sensors Using Wireless Sensor Networks and Characterization of the In Situ fPAR Variability in a Tropical Dry Forest

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs.

Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series

Capturing forest disturbances over time is increasingly important to determine the ecosystem's capacity to recover as well as aiding a timely response of foresters. With changes due to climate change increasing the frequencies, a better understanding of forest disturbances and their role in historical development is needed to, on the one hand, develop forest management approaches promoting ecosystem resilience and, on the other hand, provide quick and spatially explicit information to foresters. A large, publicly available satellite imagery spanning more than two decades for large areas of the Earth's surface at varying spatial and temporal resolutions represents a vast, free data source for this. The challenge is 2-fold: (1) obtaining reliable information on forest condition and development from satellite data requires not only quantification of forest loss but rather a differentiated assessment of the extent and severity of forest degradation; (2) standardized and efficient processing routines both are needed to bridge the gap between remote-sensing signals and conventional forest condition parameters to enable forest managers for the operational use of the data. Here, we investigated abiotic and biotic disturbances based on a set of ground validated occurrences in various forest areas across Germany to build disturbance response chronologies and examine event-specific patterns. The proposed workflow is based on the R-package "npphen" for non-parametric vegetation phenology reconstruction and anomaly detection using MODIS EVI time series data. Results show the potential to detect distinct disturbance responses in forest ecosystems and reveal event-specific characteristics. Difficulties still exist for the determination of, e.g., scattered wind throw, due to its subpixel resolution, especially in highly fragmented landscapes and small forest patches. However, the demonstrated method shows potential for operational use as a semi-automatic system to augment terrestrial monitoring in the forestry sector. Combining the more robust EVI and the assessment of the phenological series at a pixel-by-pixel level allows for a changing species cover without false classification as forest loss.

Climate Change Risks to Global Forest Health: Emergence of Unexpected Events of Elevated Tree Mortality Worldwide

Recent observations of elevated tree mortality following climate extremes, like heat and drought, raise concerns about climate change risks to global forest health. We currently lack both sufficient data and understanding to identify whether these observations represent a global trend toward increasing tree mortality. Here, we document events of sudden and unexpected elevated tree mortality following heat and drought events in ecosystems that previously were considered tolerant or not at risk of exposure. These events underscore the fact that climate change may affect forests with unexpected force in the future. We use the events as examples to highlight current difficulties and challenges for realistically predicting such tree mortality events and the uncertainties about future forest condition. Advances in remote sensing technology and greater availably of high-resolution data, from both field assessments and satellites, are needed to improve both understanding and prediction of forest responses to future climate change.

Nature-based framework for sustainable afforestation in global drylands under changing climate

Drylands cover more than 40% of Earth's land surface and occur at the margin of forest distributions due to the limited availability of water for tree growth. Recent elevated temperature and low precipitation have driven greater forest declines and pulses of tree mortality on dryland sites compared to humid sites, particularly in temperate Eurasia and North America. Afforestation of dryland areas has been widely implemented and is expected to increase in many drylands globally to enhance carbon sequestration and benefits to the human environment, but the interplay of sometimes conflicting afforestation outcomes has not been formally evaluated yet. Most previous studies point to conflicts between additional forest area and water consumption, in particular water yield and soil conservation/desalinization in drylands, but were generally confined to local and regional scales. Our global synthesis demonstrates that additional tree cover can amplify water consumption through a nonlinear increase in evapotranspiration-depending on tree species, age, and structure-which will be further intensified by future climate change. In this review we identify substantial knowledge gaps in addressing the dryland afforestation dilemma, where there are trade-offs with planted forests between increased availability of some resources and benefits to human habitats versus the depletion of other resources that are required for sustainable development of drylands. Here we propose a method of addressing comprehensive vegetation carrying capacity, based on regulating the distribution and structure of forest plantations to better deal with these trade-offs in forest multifunctionality. We also recommend new priority research topics for dryland afforestation, including: responses and feedbacks of dryland forests to climate change; shifts in the ratio of ecosystem ET to tree cover; assessing the role of scale of afforestation in influencing the trade-offs of dryland afforestation; and comprehensive modeling of the multifunctionality of dryland forests, including both ecophysiological and socioeconomic aspects, under a changing climate.

Metabolic Profiling and Transcriptome Analysis Reveal the Key Role of Flavonoids in Internode Coloration of Phyllostachys violascens cv. Viridisulcata

Bamboo, being an ornamental plant, has myriad aesthetic and economic significance. Particularly, Phyllostachys violascens cv. Viridisulcata contains an internode color phenotype in variation in green and yellow color between the sulcus and culm, respectively. This color variation is unique, but the underlying regulatory mechanism is still unknown. In this study, we used metabolomic and transcriptomic strategies to reveal the underlying mechanism of variation in internode color. A total of 81 metabolites were identified, and among those, prunin as a flavanone and rhoifolin as a flavone were discovered at a high level in the culm. We also found 424 differentially expressed genes and investigated three genes (PvGL, PvUF7GT, and PvC12RT1) that might be involved in prunin or rhoifolin biosynthesis. Their validation by qRT-PCR confirmed high transcript levels in the culm. The results revealed that PvGL, PvUF7GT, and PvC12RT1 might promote the accumulation of prunin and rhoifolin which were responsible for the variation in internode color of P. violascens. Our study also provides a glimpse into phenotypic coloration and is also a valuable resource for future studies.

Global implications of biodiversity loss on pandemic disease: COVID-19

Unpredictable climate changes and adverse effects on the planetary health due to environmental degradation have led to the rise of concerns regarding human wellness in the recent days. Coronavirus disease 2019 (COVID-19) is said to be originated from wildlife species and represents a significant threat to human health, social, food, and economic security. However, understanding the underlying factors behind the emergence is still rudimentary. This study has made efforts to understand the relationship between the drivers that causes the emergence of infectious diseases (EID). The recent biodiversity crisis that has a severe impact on planetary health is a new contributory factor for the emergence of COVID-19. The biodiversity crisis is an outcome of the land use and land cover (LULC) change, contributing to climate change. In the overall global LULC, 60% are associated with direct human activities and 40% indirect causes such as climate change. Climate change is one of the critical factors that induce landcover change, associated with increasing consumerism, environmental pollution, excessive livestock production, population explosion, and food production. Geospatial techniques provide a viable solution for monitoring the key drivers responsible for EID, such as climate characteristics, LULC, global land cover under food production, and locations affected by infectious diseases in the past. The study’s main objective is to discuss the possibilities of evolving novel solutions to approach the forecasting of emerging infectious disease spread and its mitigation, bridging the sectors and stakeholders, with due relevance to sustainable development goals (SDGs) and other dimensions from global to community levels.

Assessing Drought Vegetation Dynamics in Semiarid Grass- and Shrubland Using MESMA

Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.

Multi-Model Approaches to the Spatialization of Tree Vitality Surveys: Constructing a National Tree Vitality Map

It is essential to maintain the health of forests so that they are protected against a diverse range of stressors and show improved resilience. An area-based forest health map is required for efficient forest management on a national scale however, most national forest inventories are based on in-situ observations. This study examined methodologies to establish an area-based map on tree vitality grade using field survey data, particularly that containing information on several trees at one point. The forest health monitoring dataset of the Republic of Korea was used in combination with 37 satellite-based environmental predictors. Four methods were considered: Multinomial logistic regression (MLR), random forest classification (RF), indicator kriging (IK), and multi-model ensemble (MME) approaches using species distribution models. The MLR and RF produced biased results, whereby almost all regions were classified as first grade; the spatialization results of these methods were considered inappropriate for forest management. The maps produced using the IK and MME methods improved the distinctions between the distributions of five grades compared to the previous two methodologies however, the MME method produced better results, reliably reflecting topographical and climatic characteristics. Comparisons with the vegetation condition index and bioclimate vulnerability index also emphasized the usefulness of the MME. This study is particularly relevant to the national forest managers who struggle to find the most effective forest monitoring and management strategies. Suggestions to improve spatialization of field survey data are further discussed.

Evolutionary relationships between drought-related traits and climate shape large hydraulic safety margins in western North American oaks

Quantitative knowledge of xylem physical tolerance limits to dehydration is essential to understanding plant drought tolerance but is lacking in many long-vessel angiosperms. We examine the hypothesis that a fundamental association between sustained xylem water transport and downstream tissue function should select for xylem that avoids embolism in long-vessel trees by quantifying xylem capacity to withstand air entry of western North American oaks (Quercus spp.). Optical visualization showed that 50% of embolism occurs at water potentials below -2.7 MPa in all 19 species, and -6.6 MPa in the most resistant species. By mapping the evolution of xylem vulnerability to embolism onto a fossil-dated phylogeny of the western North American oaks, we found large differences between clades (sections) while closely related species within each clade vary little in their capacity to withstand air entry. Phylogenetic conservatism in xylem physical tolerance, together with a significant correlation between species distributions along rainfall gradients and their dehydration tolerance, suggests that closely related species occupy similar climatic niches and that species' geographic ranges may have shifted along aridity gradients in accordance with their physical tolerance. Such trends, coupled with evolutionary associations between capacity to withstand xylem embolism and other hydraulic-related traits, yield wide margins of safety against embolism in oaks from diverse habitats. Evolved responses of the vascular system to aridity support the embolism avoidance hypothesis and reveal the importance of quantifying plant capacity to withstand xylem embolism for understanding function and biogeography of some of the Northern Hemisphere's most ecologically and economically important plants.

Emergent vulnerability to climate-driven disturbances in European forests

Forest disturbance regimes are expected to intensify as Earth’s climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979–2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes.

Natural disturbances imperil healthy and productive forests, but quantifying their effects at large scales is challenging. Here the authors apply machine learning to disturbance records and satellite data to quantify and map European forest vulnerability to fires, windthrows, and insect outbreaks through 1979-2018.

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky–Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. In addition to spatially detailed maps, zonal statistics on different spatial scales provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances to facilitate sustainable forest management.

Large-scale early-wilting response of Central European forests to the 2018 extreme drought

The combination of drought and heat affects forest ecosystems by deteriorating the health of trees, which can lead to large-scale die-offs with consequences on biodiversity, the carbon cycle, and wood production. It is thus crucial to understand how drought events affect tree health and which factors determine forest susceptibility and resilience. We analyze the response of Central European forests to the 2018 summer drought with 10 × 10 m satellite observations. By associating time-series statistics of the Normalized Difference Vegetation Index (NDVI) with visually classified observations of early wilting, we show that the drought led to early leaf-shedding across 21,500 ± 2,800 km2 , in particular in central and eastern Germany and in the Czech Republic. High temperatures and low precipitation, especially in August, mostly explained these large-scale patterns, with small- to medium-sized trees, steep slopes, and shallow soils being important regional risk factors. Early wilting revealed a lasting impact on forest productivity, with affected trees showing reduced greenness in the following spring. Our approach reliably detects early wilting at the resolution of large individual crowns and links it to key environmental drivers. It provides a sound basis to monitor and forecast early-wilting responses that may follow the droughts of the coming decades.

Monitoring tropical forests under a functional perspective with satellite-based vegetation optical depth

Monitoring ecosystem functions in forests is a priority in a climate change scenario, as climate-induced events may initially alter the functions more than slow-changing attributes, such as biomass. The ecosystem functional properties (EFPs) are quantities that characterize key ecosystem processes. They can be derived by point observations of gas and energy exchanges between the ecosystems and the atmosphere that are collected globally at FLUXNET flux tower sites and upscaled at ecosystem level. The properties here considered describe the ability of ecosystems to optimize the use of resources for carbon uptake. They represent functional forest information, are dependent on environmental drivers, linked to leaf traits and forest structure, and influenced by climate change effects. The ability of vegetation optical depth (VOD) to provide forest functional information is investigated using 2011-2014 satellite data collected by the Soil Moisture and Ocean Salinity mission and using the EFPs as reference dataset. Tropical forests in Africa and South America were analyzed, also according to ecological homogeneous units. VOD jointly with water deficit information explained 93% and 87% of the yearly variability in both flux upscaled maximum gross primary productivity and light use efficiency functional properties, in Africa and South America forests respectively. Maps of the retrieved properties evidenced changes in forest functional responses linked to anomalous climate-induced events during the study period. The findings indicate that VOD can support the flux upscaling process in the tropical range, affected by high uncertainty, and the detection of forest anomalous functional responses. Preliminary temporal analysis of VOD and EFP signals showed fine-grained variability in periodicity, in signal dephasing, and in the strength of the relationships. In selected drier forest types, these satellite data could also support the monitoring of functional dynamics.

Forest Disturbances in Polish Tatra Mountains for 1985–2016 in Relation to Topography, Stand Features, and Protection Zone

For more than four centuries, the Tatra Mountains were affected by many factors, such as forest and pastoral management, mining and metallurgy, windthrows, snow avalanches, and bark beetle outbreaks. Due to the availability of the long-running Landsat program enabling acquisition of spatially and spectrally consistent information, it is possible to the use these data for forest disturbance analysis. The main aim of this study was to analyze the relationships between the frequency of disturbances detected over the period of 1985–2016 and selected topographic features, such as elevation, exposure, and slope, derived from a digital elevation model (DEM); stand features, such as vegetation community type, age, structure, and degree of naturalness of the stand; and the management protection zone, which was extracted from thematic layers of the Tatra National Park (TNP). Using the normalized difference moisture index (NDMI), we detected forest disturbances in each year and analyzed them in the context of these topographic features, forest stand characteristics, and the management protection zone. We observed that forest stands in the lower montane zone, slopes between 10°–30°, and eastern exposures were primarily affected by disturbances. These consisted of artificially planted spruce stands aged between 51 and 100 years old.

Assessing vegetation recovery in reclaimed opencast mines of the Teruel coalfield (Spain) using Landsat time series and boosted regression trees

Opencast mining is an activity that caters to many economic sectors; however, it has a large impact on society and the environment. After mining, the major concern is to restore the previous land cover, which was generally a natural vegetation cover. Establishing permanent vegetation cover can restore landscape connectivity and previous ecosystem functions, enhance aesthetic values and prevent off-side effects associated with post-mining landscapes. Opencast mining reclamation deals with these issues with several strategies that aim to develop a vegetation cover after mining activity has stopped. However, not all reclamation actions are effective, and assessing their efficiency by monitoring vegetation development at reclaimed sites is a time-consuming task because it usually involves extensive field work. In this study, we present a semi-automatic approach based on analysing satellite data (Landsat) time series and using a machine learning technique to identify suitable conditions for vegetation development at reclaimed opencast mines. We analysed the Teruel coalfield (Aragón, central-eastern Spain). This area is a representative Mediterranean-Continental region that is of particular interest due the diversity of reclamation actions that have been applied and the increase in drier conditions during the last decades. Conditions were described with topography derived variables, technical reclamation features and drought-occurrence variables as potential explanatory factors. The implemented approach allowed us to identify the main abiotic filters for vegetation of this geographic region: the water availability and soil retention (both controlled by the topographic slope), and the proximity to seed sources. The analysis evidenced the negative influence of drought occurrence on vegetation development, and different responses were found depending on the timescale at which drought is calculated. Our results indicate that the reclamation landform model is the main key factor influencing vegetation development. A model such as the smooth berm-slope increases water availability and controls soil erosion, and hence, improves vegetation development. In addition, we found that further than 500-600 m from the mine, the effect of seed source declines dramatically. Therefore, all these issues should be considered in future reclamation designs in a Mediterranean-Continental environment. Our methodology could be adapted to other geographic regions where spatial environmental data are available.

Precipitation-Sensitive Dynamic Threshold: A New and Simple Method to Detect and Monitor Forest and Woody Vegetation Cover in Sub-Humid to Arid Areas

Remote-sensing tools and satellite data are often used to map and monitor changes in vegetation cover in forests and other perennial woody vegetation. Large-scale vegetation mapping from remote sensing is usually based on the classification of its spectral properties by means of spectral Vegetation Indices (VIs) and a set of rules that define the connection between them and vegetation cover. However, observations show that, across a gradient of precipitation, similar values of VI can be found for different levels of vegetation cover as a result of concurrent changes in the leaf density (Leaf Area Index—LAI) of plant canopies. Here we examine the three-way link between precipitation, vegetation cover, and LAI, with a focus on the dry range of precipitation in semi-arid to dry sub-humid zones, and propose a new and simple approach to delineate woody vegetation in these regions. By showing that the range of values of Normalized Difference Vegetation Index (NDVI) that represent woody vegetation changes along a gradient of precipitation, we propose a data-based dynamic lower threshold of NDVI that can be used to delineate woody vegetation from non-vegetated areas. This lower threshold changes with mean annual precipitation, ranging from less than 0.1 in semi-arid areas, to over 0.25 in mesic Mediterranean area. Validation results show that this precipitation-sensitive dynamic threshold provides a more accurate delineation of forests and other woody vegetation across the precipitation gradient, compared to the traditional constant threshold approach.

Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index

Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.

Long-term land cover change in Zambia: An assessment of driving factors

Land cover change (LCC) has significant effects on the global ecosystem diversity and function. This topic has received increasing attention due, in part, to its relationship with climate change, and the availability of remotely-sensed imagery that is used to monitor LCC. However, studies analysing the factors that drive LCC at large spatial scales and over long temporal scales are uncommon. This study aimed to identify the factors driving long-term (44 years, 1972-2016) national level LCC in Zambia. Two analyses were conducted, with the first considering factors that led to any LCC. The second scenario identified factors associated with changes from forests to other land covers, and the reversion to forests from non-forested covers. Candidate factors considered in both analyses include accessibility, proximity, topography, climate, conservation and socioeconomics. A classification tree (CT) approach was used to relate the explanatory candidate factors to LCC. The results showed that the CT models predicted LCC with accuracies ranging from 71 to 85%. The first analysis showed that the major factors determining LCC were percentage of area under agriculture, distance to water bodies, change in crop yield, mean temperature and elevation. Meanwhile, the second analysis showed that primary, secondary and plantation forest cover losses were mainly influenced by human population density, crop yield per hectare and mean crop yield, respectively. Protection status was the most important factor for forest reversion and recovery, while a variety of factors including distance to the railway, elevation and total precipitation also influenced forest reversion and recovery. The findings from this study provide insights into the factors that influence LCC and are important for developing effective policies to reduce the negative impacts of LCC and to promote forest reversion and recovery through effective management of protected areas. While this study focused on factors associated with historical LCC, the findings will also help to predict and understand future LCC scenarios.

Important role of forest disturbances in the global biomass turnover and carbon sinks

Forest disturbances leading to replacement of whole tree stands are a cornerstone of forest dynamics, with drivers including fire, wind-throw, biotic outbreaks and harvest. The frequency of disturbances may change over the next century, impacting the age, composition and biomass of forests. However, the variation in disturbance return time, i.e. the mean interval between disturbance events, across the world's forested biomes remains poorly characterised, hindering quantification of their role in the global carbon cycle. Here we present the global distribution of stand-replacing disturbance return time inferred from satellite-based observations of forest loss. Prescribing this distribution within a vegetation model with a detailed representation of stand structure, we quantify the importance of stand-replacing disturbances for biomass carbon turnover globally over 2001-2014. Return time varied from less than 50 years in heavily-managed temperate ecosystems to over 1000 years in tropical evergreen forests. Stand-replacing disturbances accounted for 12.3% (95% confidence interval, 11.4-13.7%) of annual biomass carbon turnover due to tree mortality globally, and in 44% of forested area biomass stocks are strongly sensitive to changes in disturbance return time. Relatively small shifts in disturbance regimes in these areas would substantially influence the forest carbon sink, that currently limits climate change by offsetting emissions.

Direct, ECOC, ND and END Frameworks—Which One Is the Best? An Empirical Study of Sentinel-2A MSIL1C Image Classification for Arid-Land Vegetation Mapping in the Ili River Delta, Kazakhstan

To facilitate the advances in Sentinel-2A products for land cover from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery, Sentinel-2A MultiSpectral Instrument Level-1C (MSIL1C) images are investigated for large-scale vegetation mapping in an arid land environment that is located in the Ili River delta, Kazakhstan. For accurate classification purposes, multi-resolution segmentation (MRS) based extended object-guided morphological profiles (EOMPs) are proposed and then compared with conventional morphological profiles (MPs), MPs with partial reconstruction (MPPR), object-guided MPs (OMPs), OMPs with mean values (OMPsM), and object-oriented (OO)-based image classification techniques. Popular classifiers, such as C4.5, an extremely randomized decision tree (ERDT), random forest (RaF), rotation forest (RoF), classification via random forest regression (CVRFR), ExtraTrees, and radial basis function (RBF) kernel-based support vector machines (SVMs) are adopted to answer the question of whether nested dichotomies (ND) and ensembles of ND (END) are truly superior to direct and error-correcting output code (ECOC) multiclass classification frameworks. Finally, based on the results, the following conclusions are drawn: 1) the superior performance of OO-based techniques over MPs, MPPR, OMPs, and OMPsM is clear for Sentinel-2A MSIL1C image classification, while the best results are achieved by the proposed EOMPs; 2) the superior performance of ND, ND with class balancing (NDCB), ND with data balancing (NDDB), ND with random-pair selection (NDRPS), and ND with further centroid (NDFC) over direct and ECOC frameworks is not confirmed, especially in the cases of using weak classifiers for low-dimensional datasets; 3) from computationally efficient, high accuracy, redundant to data dimensionality and easy of implementations points of view, END, ENDCB, ENDDB, and ENDRPS are alternative choices to direct and ECOC frameworks; 4) surprisingly, because in the ensemble learning (EL) theorem, “weaker” classifiers (ERDT here) always have a better chance of reaching the trade-off between diversity and accuracy than “stronger” classifies (RaF, ExtraTrees, and SVM here), END with ERDT (END-ERDT) achieves the best performance with less than a 0.5% difference in the overall accuracy (OA) values, but is 100 to 10000 times faster than END with RaF and ExtraTrees, and ECOC with SVM while using different datasets with various dimensions; and, 5) Sentinel-2A MSIL1C is better choice than the land cover products from MODIS and Landsat imagery for vegetation species mapping in an arid land environment, where the vegetation species are critically important, but sparsely distributed.

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

Fragmentation, forest structure, and topography modulate impacts of drought in a tropical forest landscape

Climate models predict increases in drought conditions in many parts of the tropics. Yet the response of tropical forests to drought remains highly uncertain, especially with regards to the factors that generate spatial heterogeneity in drought response across landscapes. In this study, we used Landsat imagery to assess the impacts of a severe drought in 2015 across an ~80,000-ha landscape in Puerto Rico. Specifically, we asked whether drought effects varied systematically with topography and with forest age, height, and fragmentation. We quantified drought impacts using anomalies of two vegetation indices, the enhanced vegetation index (EVI) and normalized difference water index (NDWI), and fit random forest models of these metrics including slope, aspect, forest age, canopy height, and two indices of fragmentation as predictors. Drought effects were more severe on drier topographic positions, that is, steeper slopes and southwest-facing aspects, and in second-growth forests. Shorter and more fragmented forests were also more strongly affected by drought. We also assessed which factors were associated with stronger recovery from drought. Factors associated with more negative drought anomalies were also associated with more positive postdrought anomalies, suggesting that increased light availability as a result of drought led to high rates of recovery in forests more severely affected by drought. In general, recovery from drought was rapid across the landscape, with postdrought anomalies at or above average across the study area. This suggests that forests in Puerto Rico might be resilient to a single-year drought, though vulnerability to drought varies depending on forest characteristics and landscape position.

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.

Evapotranspiration and water yield of a pine-broadleaf forest are not altered by long-term atmospheric [CO2 ] enrichment under native or enhanced soil fertility

Changes in evapotranspiration (ET) from terrestrial ecosystems affect their water yield (WY), with considerable ecological and economic consequences. Increases in surface runoff observed over the past century have been attributed to increasing atmospheric CO₂ concentrations resulting in reduced ET by terrestrial ecosystems. Here, we evaluate the water balance of a Pinus taeda (L.) forest with a broadleaf component that was exposed to atmospheric [CO₂ ] enrichment (ECO₂ ; +200 ppm) for over 17 years and fertilization for 6 years, monitored with hundreds of environmental and sap flux sensors on a half-hourly basis. These measurements were synthesized using a one-dimensional Richard's equation model to evaluate treatment differences in transpiration (T), evaporation (E), ET, and WY. We found that ECO₂ did not create significant differences in stand T, ET, or WY under either native or enhanced soil fertility, despite a 20% and 13% increase in leaf area index, respectively. While T, ET, and WY responded to fertilization, this response was weak (<3% of mean annual precipitation). Likewise, while E responded to ECO₂ in the first 7 years of the study, this effect was of negligible magnitude (<1% mean annual precipitation). Given the global range of conifers similar to P. taeda, our results imply that recent observations of increased global streamflow cannot be attributed to decreases in ET across all ecosystems, demonstrating a great need for model-data synthesis activities to incorporate our current understanding of terrestrial vegetation in global water cycle models.

Response of carbon uptake to abiotic and biotic drivers in an intensively managed Lei bamboo forest

Lei bamboo (Phyllostachys praecox) is widely distributed in southeastern China. We used eddy covariance to analyze carbon sequestration capacity of a Lei bamboo forest (2011-2013) and to identify the seasonal biotic and abiotic determinants of carbon fluxes. A machine learning algorithm called random forest (RF) was used to identify factors that affected carbon fluxes. The RF model predicted well the gross ecosystem productivity (GEP), ecosystem respiration (RE) and net ecosystem exchange (NEE), and displayed variations in the drivers between different seasons. Mean annual NEE, RE, and GEP were -105.2 ± 23.1, 1264.5 ± 45.2, and 1369.6 ± 52.5 g C m^-2, respectively. Climate warming increased RE more than GEP when water inputs were not limiting. Summer drought played little role in suppressing GEP, but low soil moisture contents suppressed RE and increased the carbon sink during drought in the summer. The most important drivers of NEE were soil temperature in spring, summer, and winter, and photosynthetically active radiation in autumn. Air and soil temperature were important drivers of GEP in all seasons.

Drivers and mechanisms of tree mortality in moist tropical forests

Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO₂ fertilization-induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large-scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.

Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence

Solar-induced fluorescence (SIF) observations from space have resulted in major advancements in estimating gross primary productivity (GPP). However, current SIF observations remain spatially coarse, infrequent, and noisy. Here we develop a machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) channels to reproduce SIF normalized by clear sky surface irradiance from the Global Ozone Monitoring Experiment-2 (GOME-2). The resulting product is a proxy for ecosystem photosynthetically active radiation absorbed by chlorophyll (fAPARCh). Multiplying this new product with a MODIS estimate of photosynthetically active radiation provides a new MODIS-only reconstruction of SIF called Reconstructed SIF (RSIF). RSIF exhibits much higher seasonal and interannual correlation than the original SIF when compared with eddy covariance estimates of GPP and two reference global GPP products, especially in dry and cold regions. RSIF also reproduces intense productivity regions such as the U.S. Corn Belt contrary to typical vegetation indices and similarly to SIF.

Research frontiers for improving our understanding of drought-induced tree and forest mortality

Accumulating evidence highlights increased mortality risks for trees during severe drought, particularly under warmer temperatures and increasing vapour pressure deficit (VPD). Resulting forest die-off events have severe consequences for ecosystem services, biophysical and biogeochemical land-atmosphere processes. Despite advances in monitoring, modelling and experimental studies of the causes and consequences of tree death from individual tree to ecosystem and global scale, a general mechanistic understanding and realistic predictions of drought mortality under future climate conditions are still lacking. We update a global tree mortality map and present a roadmap to a more holistic understanding of forest mortality across scales. We highlight priority research frontiers that promote: (1) new avenues for research on key tree ecophysiological responses to drought; (2) scaling from the tree/plot level to the ecosystem and region; (3) improvements of mortality risk predictions based on both empirical and mechanistic insights; and (4) a global monitoring network of forest mortality. In light of recent and anticipated large forest die-off events such a research agenda is timely and needed to achieve scientific understanding for realistic predictions of drought-induced tree mortality. The implementation of a sustainable network will require support by stakeholders and political authorities at the international level.

The future of subalpine forests in the Southern Rocky Mountains: Trajectories for Pinus aristata genetic lineages

Like many other high elevation alpine tree species, Rocky Mountain bristlecone pine (Pinus aristata Engelm.) may be particularly vulnerable to climate change. To evaluate its potential vulnerability to shifts in climate, we defined the suitable climate space for each of four genetic lineages of bristlecone pine and for other subalpine tree species in close proximity to bristlecone pine forests. Measuring changes in the suitable climate space for lineage groups is an important step beyond models that assume species are genetically homogenous. The suitable climate space for bristlecone pine in the year 2090 is projected to decline by 74% and the proportional distribution of suitable climate space for genetic lineages shifts toward those associated with warmer and wetter conditions. The 2090 climate space for bristlecone pine exhibits a bimodal distribution along an elevation gradient, presumably due to the persistence of the climate space in the Southern Rocky Mountains and exclusion at mid-elevations by conditions that favor the climate space of other species. These shifts have implications for changes in fire regimes, vulnerability to pest and pathogens, and altered carbon dynamics across the southern Rockies, which may reduce the likelihood of bristlecone pine trees achieving exceptional longevity in the future. The persistence and expansion of climate space for southern bristlecone pine genetic lineage groups in 2090 suggests that these sources may be the least vulnerable in the future. While these lineages may be more likely to persist and therefore present opportunities for proactive management (e.g., assisted migration) to maintain subalpine forest ecosystem services in a warmer world, our findings also imply heighted conservation concern for vulnerable northern lineages facing range contractions.

Extreme weather and climate events with ecological relevance: a review

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'.

Vegetation demographics in Earth System Models: A review of progress and priorities

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.