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Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world’s most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic–abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security1, ecosystem integrity and societies in general2,3,4,5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment6. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa’s geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage7,8. X. fastidiosa can infect more than 350 plant species worldwide9, and early detection is critical for its eradication8. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.

Climate change and land-use activities are increasing fire activity across much of the Siberian boreal forest, yet the climate feedbacks from forest disturbances remain difficult to quantify due to limited information on forest biomass distribution, disturbance regimes and post-disturbance ecosystem recovery. Our primary objective here was to analyse post-fire accumulation of Cajander larch (Larix cajanderi Mayr.) aboveground biomass for a 100 000 km2 area of open forest in far northeastern Siberia. In addition to examining effects of fire size and topography on post-fire larch aboveground biomass, we assessed regional fire rotation and density, as well as performance of burned area maps generated from MODIS satellite imagery. Using Landsat imagery, we mapped 116 fire scar perimeters that dated c. 1966–2007. We then mapped larch aboveground biomass by linking field biomass measurements to tree shadows mapped synergistically from WorldView-1 and Landsat 5 satellite imagery. Larch aboveground biomass tended to be low during early succession (≤ 25 yr, 271 ± 26 g m−2, n = 66 [mean ± SE]) and decreased with increasing elevation and northwardly aspect. Larch aboveground biomass tended to be higher during mid-succession (33–38 yr, 746 ± 100 g m−2, n = 32), though was highly variable. The high variability was not associated with topography and potentially reflected differences in post-fire density of tree regrowth. Neither fire size nor latitude were significant predictors of post-fire larch aboveground biomass. Fire activity was considerably higher in the Kolyma Mountains (fire rotation = 110 yr, fire density = 1.0 ± 1.0 fires yr−1 × 104 km−2) than along the forest-tundra border (fire rotation = 792 yr, fire density = 0.3 ± 0.3 fires yr−1 × 104 km−2). The MODIS burned area maps underestimated the total area burned in this region from 2000–2007 by 40%. Tree shadows mapped jointly using high and medium resolution satellite imagery were strongly associated (r2 0.9) with field measurements of forest structure, which permitted spatial extrapolation of aboveground biomass to a regional extent. Better understanding of forest biomass distribution, disturbances and post-disturbance recovery is needed to improve predictions of the net climatic feedbacks associated with landscape-scale forest disturbances in northern Eurasia.

Deforestation contributes 6–17% of anthropogenic carbon dioxide emissions. However, much uncertainty in the calculation of deforestation emissions stems from the inadequacy of forest carbon-density and deforestation data. Now an analysis provides the most-detailed estimate so far of the carbon density of vegetation and the associated carbon dioxide emissions from deforestation for ecosystems across the tropics. Deforestation contributes 6–17% of global anthropogenic CO 2 emissions to the atmosphere 1 . Large uncertainties in emission estimates arise from inadequate data on the carbon density of forests 2 and the regional rates of deforestation. Consequently there is an urgent need for improved data sets that characterize the global distribution of aboveground biomass, especially in the tropics. Here we use multi-sensor satellite data to estimate aboveground live woody vegetation carbon density for pan-tropical ecosystems with unprecedented accuracy and spatial resolution. Results indicate that the total amount of carbon held in tropical woody vegetation is 228.7 Pg C, which is 21% higher than the amount reported in the Global Forest Resources Assessment 2010 (ref.  3 ). At the national level, Brazil and Indonesia contain 35% of the total carbon stored in tropical forests and produce the largest emissions from forest loss. Combining estimates of aboveground carbon stocks with regional deforestation rates 4 we estimate the total net emission of carbon from tropical deforestation and land use to be 1.0 Pg C yr −1 over the period 2000–2010—based on the carbon bookkeeping model. These new data sets of aboveground carbon stocks will enable tropical nations to meet their emissions reporting requirements (that is, United Nations Framework Convention on Climate Change Tier 3) with greater accuracy.

Global temperature is increasing, especially over Northern lands (>50 N), owing to positive feedbacks. As this increase is most pronounced in winter, temperature seasonality (ST)—conventionally defined as the difference between summer and winter temperatures—is diminishing over time, a phenomenon that is analogous to its equatorward decline at an annual scale. The initiation, termination and performance of vegetation photosynthetic activity are tied to threshold temperatures. Trends in the timing of these thresholds andcumulative temperatures above them may alter vegetation productivity, or modify vegetation seasonality (SV), over time. The relationship between ST and SV is critically examined here with newly improved ground and satellite data sets. The observed diminishment of ST and SV is equivalent to 4 and 7 (5 and 6 ) latitudinal shift equatorward during the past 30 years in the Arctic (boreal) region. Analysis of simulations from 17 state-of-the-art climate models4 indicates an additional ST diminishment equivalent to a 20 equatorward shift could occur this century. How SV will change in response to such large projected ST declines and the impact this will have on ecosystem services5 are not well understood. Hence the need for continued monitoring6 of northern lands as their seasonal temperature profiles evolve to resemble those further south.

Fire disturbance at high latitudes modifies a broad range of ecosystem properties and processes, thus it is important to monitor the response of vegetation to fire disturbance. This monitoring effort can be aided by lidar remote sensing, which captures information on vegetation structure, particularly canopy height metrics. We used lidar data acquired from the Geoscience Laser Altimetry System (GLAS) on ICESAT to derive canopy information for a wide range of burned areas across Alaska. The GLAS data aided our analysis of postfire disturbance and vegetation recovery by allowing us to derive returned energy height metrics within burned area perimeters. The analysis was augmented with MODIS reflectance data sets, which were used to stratify vegetation cover into cover type and density. We also made use of Landsat burn severity maps to further stratify the lidar metrics. Results indicate that canopy height decreases following fire, as expected, but height was not a good overall indicator of fire disturbance because many locations within the burned area perimeters either did not actually burn or experienced different levels of burn severity, typically leaving many standing trees or snags even after intensive burning. Because vegetation recovery following fire is differentially affected by burn severity, significantly greater height growth was documented in more severely burned areas due to a greater proportion of deciduous vegetation regrowth. When these factors were considered, GLAS height metrics were useful for documenting properties of regrowth in burned areas, thereby facilitating monitoring and mapping efforts following fire disturbance. A new satellite lidar sensor designed for vegetation studies would thus prove valuable information for improving ecosystem models that incorporate disturbance and recovery.

The majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. Plant trait relationships are thus generalizable to the edge of global trait-space, informing prediction of plant community change in a warming world.

Tropical forest degradation from selective logging, fire and edge effects is a major driver of carbon and biodiversity loss 1 – 3 , with annual rates comparable to those of deforestation 4 . However, its actual extent and long-term impacts remain uncertain at global tropical scale 5 . Here we quantify the magnitude and persistence of multiple types of degradation on forest structure by combining satellite remote sensing data on pantropical moist forest cover changes 4 with estimates of canopy height and biomass from spaceborne 6 light detection and ranging (LiDAR). We estimate that forest height decreases owing to selective logging and fire by 15% and 50%, respectively, with low rates of recovery even after 20 years. Agriculture and road expansion trigger a 20% to 30% reduction in canopy height and biomass at the forest edge, with persistent effects being measurable up to 1.5 km inside the forest. Edge effects encroach on 18% (approximately 206 Mha) of the remaining tropical moist forests, an area more than 200% larger than previously estimated 7 . Finally, degraded forests with more than 50% canopy loss are significantly more vulnerable to subsequent deforestation. Collectively, our findings call for greater efforts to prevent degradation and protect already degraded forests to meet the conservation pledges made at recent United Nations Climate Change and Biodiversity conferences. A global survey on the magnitude and persistence of moist forest cover change and canopy height following degradation using satellite remote sensing data finds that the effects are substantial and persist for decades.

Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).