The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the ...uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ~0.84; Root Mean Square Error (RMSE)~1.8g C m−2 d−1). However, when predictions were based on only remotely sensed data the accuracy was close to the optimum (ρ~0.8; RMSE~1.9g C m−2 d−1) and to a commonly used light use efficiency model (MOD17) with parameters optimised for the applied study sites (the MOD17+, ρ~0.79; RMSE~2.04g C m−2 d−1). Remotely sensed data were key drivers for the accurate prediction of GPP in ecosystems with high variability of green biomass over the phenological cycle (e.g., deciduous broad-leaved forests) or highly affected by the human management (e.g. croplands). In contrast, in the ecosystems with low variability of greenness (e.g., evergreen broad-leaved forests), the predictions were poor when meteorological information were not used. At a European spatial scale, when modelled grids of meteorological, land cover and fPAR data were used as inputs, the propagation of their uncertainty, not accounted in the models training, had significant effects on the uncertainty of the mean annual GPP. At this scale, the effects of meteorological uncertainty were higher than the misclassification error. These findings suggested that a strategy based on satellite-measured data could be a favourable improvement for the spatial upscaling of GPP, because avoiding the propagation of the uncertainties of the modelled grids.
•We train 10 Random Forest (RF) to spatial upscale Gross Primary Production (GPP).•RF that uses only remote sensing (RS) data has a performance similar to the best RF.•At European scale the uncertainty of prediction due to modelled drivers is high.•The uncertainty of European GPP is mainly due to the meteorological reanalysis.•Model driven by only measured RS data avoids the uncertainty of modelled drivers.
The modification of the surface radiation and energy balance in urban areas causes the temperatures in these areas to exceed those of the surrounding countryside. It has thus been suggested that ...urban environments may serve as field laboratories for studying the effects of a warming climate on biota in a space-for-time substitution. Here we investigated changes in the timing of plant phenology and temperature across study sites that differed in the degree of urbanization using publicly available pan-European datasets for the period 1981-2010. We found a significant advancement in the phenological phases of leaf development, flowering and fruiting with higher degrees of urbanization, whereas a significant delay was observed for phenological phases of leaf senescence. In addition to these phenological changes, an increase in air temperature with higher degrees of urbanization was observed. This increase was largest during the periods of leaf development, flowering and fruiting and smallest during the period of leaf senescence. On the basis of these results, we show that the apparent temperature sensitivity of phenological phases to urban warming is either significantly dampened (leaf development, flowering and fruiting) or reversed (leaf senescence) compared with the temperature sensitivity inferred from temporal changes in phenology and temperature. We conclude that gradients in urbanization represent a poor analogue for the temporal changes in plant phenology, apparently owing to confounding factors associated with urbanization.
•Horizontal net radiation measurements are standard.•In sloping terrain these do not reflect the available energy.•We propose a footprint-weighted correction.•The correction improves the energy ...balance closure.•A simpler approach works provided inclination and aspect reflect main footprint.
In complex, sloping terrain, horizontal measurements of net radiation are not reflective of the radiative energy available for the conductive and convective heat exchange of the underlying surface. Using data from a grassland site on a mountain slope characterised by spatial heterogeneity in inclination and aspect, we tested the hypothesis that a correction of the horizontal net radiation measurements which accounts for the individual footprint contributions of the various surfaces to the measured sensible and latent heat eddy covariance fluxes will yield more realistic slope-parallel net radiation estimates compared to a correction based on the average inclination and aspect of the footprint. Our main result is that both approaches led to clear, but very similar improvements in the phase between available energy and the sum of the latent and sensible heat fluxes. As a consequence the variance in the sum of latent and sensible heat flux explained by available radiation improved by>10%, while energy balance closure improved only slightly. This is shown to be mainly due to the average inclination and aspect corresponding largely with the inclination and aspect of the main flux source area in combination with a limited sensitivity of the slope correction to small angular differences in, particularly, inclination and aspect. We conclude with a discussion of limitations of the present approach and future research directions.
The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce ...bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks.
•Phenopix is a new R package for phenology from digital images of vegetation.•It contains the most up-to-date processing techniques along with novelties.•Novelties include the combination of ...different fit/phenophase extraction methods.•Pixel-by-pixel analysis allows for the extraction of spatially explicit phenology.•The software is open source and freely available at the R-forge website.
In this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties.
The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network (http://phenocam.sr.unh.edu/), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel-based image analysis available in the phenopix R package.
Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to ...protect it. Remote sensing provides consistent quality and standardized data, which can be used to estimate different aspects of biodiversity. The Height Variation Hypothesis (HVH) represents an indirect method for estimating species diversity in forest ecosystems from the LiDAR data, and it assumes that the higher the variation in tree height (height heterogeneity, HH), calculated through the ’Canopy Height Model’ (CHM) metric, the more complex the overall structure of the forest and the higher the tree species diversity. To date, the HVH has been tested exclusively with CHM data, assessing the HH only with a single heterogeneity index (the Rao’s Q index) without making use of any moving windows (MW) approach. In this study, the HVH has been tested in an alpine coniferous forest situated in the municipality of San Genesio/Jenesien (eastern Italian Alps) at 1100 m, characterized by the presence of 11 different tree species (mainly Pinus sylvestris, Larix decidua, Picea abies followed by Betula alba and Corylus avellana). The HH has been estimated through different heterogeneity measures described in the new R rasterdiv package using, besides the CHM, also other LiDAR metrics (as the percentile or the standard deviation of the height distribution) at various spatial resolutions and MWs (ALS LiDAR data with mean point cloud density of 2.9 point/m2). For each combination of parameters, and for all the considered plots, linear regressions between the Shannon’s H′ (used as tree species diversity index based on field data) and the HH have been derived. The results showed that the Rao’s Q index (singularly and through a multidimensional approach) performed generally better than the other heterogeneity indices in the assessment of the HH. The CHM and the LiDAR metrics related to the upper quantile point cloud distribution at fine resolution (2.5 m, 5 m) have shown the most important results for the assessment of the HH. The size of the used MW did not influence the general outcomes but instead, it increased when compared to the results found in the literature, where the HVH was tested without MW approach. The outcomes of this study underline that the HVH, calculated with certain heterogeneity indices and LiDAR data, can be considered a useful tool for assessing tree species diversity in considered forest ecosystems. The general results highlight the strength and importance of LiDAR data in assessing the height heterogeneity and the related biodiversity in forest ecosystems.
Understanding the impacts of climate extremes on the carbon cycle is important for quantifying the carbon-cycle climate feedback and highly relevant to climate change assessments. Climate extremes ...and fires can have severe regional effects, but a spatially explicit global impact assessment is still lacking. Here, we directly quantify spatiotemporal contiguous extreme anomalies in four global data sets of gross primary production (GPP) over the last 30 years. We find that positive and negative GPP extremes occurring on 7% of the spatiotemporal domain explain 78% of the global interannual variation in GPP and a significant fraction of variation in the net carbon flux. The largest thousand negative GPP extremes during 1982-2011 (4.3% of the data) account for a decrease in photosynthetic carbon uptake of about 3.5 Pg C yr−1, with most events being attributable to water scarcity. The results imply that it is essential to understand the nature and causes of extremes to understand current and future GPP variability.
Grasslands cover up to 40% of the mountain areas globally and 23% of the European Alps and affect numerous key ecological processes. An increasing number of optical sensors offer a great opportunity ...to monitor and address dynamic changes in the growth and status of grassland vegetation due to climatic and anthropogenic influences. Vegetation indices (VI) calculated from optical sensor data are a powerful tool in analyzing vegetation dynamics. However, different sensors have their own characteristics, advantages, and challenges in monitoring vegetation over space and time that require special attention when compared to or combined with each other. We used the Normalized Difference Vegetation Index (NDVI) derived from handheld spectrometers, station-based Spectral Reflectance Sensors (SRS), and Phenocams as well as the spaceborne Sentinel-2 Multispectral Instrument (MSI) for assessing growth and dynamic changes in four alpine meadows. We analyzed the similarity of the NDVI on diverse spatial scales and to what extent grassland dynamics of alpine meadows can be detected. We found that NDVI across all sensors traces the growing phases of the vegetation although we experienced a notable variability in NDVI signals among sensors and differences among the sites and plots. We noticed differences in signal saturation, sensor specific offsets, and in the detectability of short-term events. These NDVI inconsistencies depended on sensor-specific spatial and spectral resolutions and acquisition geometries, as well as on grassland management activities and vegetation growth during the year. We demonstrated that the combination of multiple-sensors enhanced the possibility for detecting short-term dynamic changes throughout the year for each of the stations. The presented findings are relevant for building and evaluating a combined sensor approach for consistent vegetation monitoring.
Model parameterization and validation of earth–atmosphere interactions are generally performed using a single timescale (e.g., nearly instantaneous, daily, and annual), although both delayed ...responses and hysteretic effects have been widely recognized. The lack of consideration of these effects hampers our capability to represent them in empirical‐ or process‐based models. Here we explore, using an apple orchard ecosystem in the North of Italy as a simplified case study, how the considered timescale impacts the relative importance of the single environmental variables in explaining observed net ecosystem exchange (NEE) and evapotranspiration (ET). Using 6 years of eddy covariance and meteorological information as input data, we found a decay of the relative importance of the modeling capability of photosynthetically active radiation in explaining both NEE and ET moving from half‐hourly to seasonal timescale and an increase in the relative importance of air temperature (T) and VPD. Satellite NDVI, used as proxy of leaf development, added little improvement to overall modeling capability. Increasing the timescale, the number of variables needed for parameterization decreased (from 5 to 1), while the proportion of variance explained by the model increased (r2 from 0.56–0.78 to 0.85–0.90 for NEE and ET respectively). The wavelet coherence and the phase analyses showed that the two variables that increased their relative importance when the scale increased (T, VPD) were not in phase at the correlation peak of both ET and NEE. This phase shift in the time domain corresponds to a hysteretic response in the meteorological variables domain. This work confirms that the model parameterization should be performed using parameters calculated at the appropriate scale. It suggests that in managed ecosystems, where the interannual variability is minimized by the agronomic practices, the use of timescales large enough to include hysteretic and delayed responses reduces the number of required input variables and improves their explanatory capacity.
Model parameterization and validation of earth–atmosphere interactions are generally performed using a single timescale, although both delayed responses and hysteretic effects have been widely recognized. We quantified the relative importance of a set of environmental drivers influencing NEE and ET in an apple orchard, and we observed that it was changing over time. The relative importance of PAR was reducing increasing the timescale, in favor of T and VPD, and their explanatory capability increased. This study suggests that in agricultural ecosystems when parameterizing variables showing hysteretic effects with ecosystem fluxes it is convenient to enlarge the integration period up to the timescale of interest.
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