Terrestrial Surface Research Articles

Modeling of input signals reflected from coastal zones and observed by small-sized radar systems from UAVs

When developing modern unmanned aerial vehicle-based onboard small-sized radar systems, which are used for monitoring of coastal zones for their operational and environmental purposes, adequate mathematical models are needed of reflections from the corresponding resolution elements of the underlying surfaces, located on the water–land interface. We consider the development of some mathematical models for such applications. We analyze two modern approaches used in developing mathematical models for input signals reflected both separately from the terrestrial and marine surfaces and simultaneously from two aforementioned surfaces at once. A statistical approach and two families of distributions are chosen for modeling the signals, which enable synthesis of efficient algorithms for modeling the fluxes of random variables. The input signals of sea surface reflections are approximated by the log-normal law, while the reflections from the land surface are approximated by the Weibull distribution. For edge-coastal modeling, it is proposed to use the law of distribution of the vector sum of the flux of random variables comprising the input signals reflected from elementary land and sea areas that simultaneously fall within the corresponding resolution element. A highlight of the models developed is the consideration of both the probability distribution laws of the reflected signals and their correlation-spectral characteristics between resolution elements, as well as the anisotropy of reflections when monitoring surfaces from several angles. The presented mathematical models are consistent with experimental data sets of reflections, obtained from coastal zones. The development of coastal reflection models and corresponding modeling algorithms reduces the required number of actual field tests and saves time for implementation of monitoring systems, and hence, limits the overhead of this implementation.

Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy and Aerosol Loaded Conditions: Part 1–Application to RGB Image Restoration Over Land With GOME-2

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

Full-Waveform Classification and Segmentation-Based Signal Detection of Single-Wavelength Bathymetric LiDAR

Single-wavelength bathymetric LiDAR (532 nm) can provide seamless meter- and submeter-scale DEMs of both the terrestrial surface and seafloor. However, the mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and non-terrestrial waveforms from the original waveforms without auxiliary information, and flexible detection of peaks based on a new FW theoretical model. A novel FW signal-detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FW are divided into 5 categories for subsequent signal detection by utilizing a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition and a flexible signal detection framework. In order to search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than a generalized Gaussian model optimized using the Levenberg–Marquardt algorithm (LM-GGM) and RLD algorithm.

The Common Mycelial Network (CMN) of Forests

Permeating the organic layer of the forest floor is a thick, interwoven matrix composed of fine white threads of fungal hyphae collectively known as mycelium. The majority of fungal biomass dwells underground in the form of mycelium, twisting and twining through an ocean of soil. Though most people associate the word fungi with mushrooms, the sight of a mushroom marks the mere tip of the mycelial iceberg that dwells below the terrestrial surface, intermingling with plant roots, soil fauna and microbiota. It may come as a surprise that the largest organism in the world is a fungus whose mycelium spans hundreds of square miles in eastern Oregon (Stamets 2005). Despite their enormous size, even such large fungi remain invisible to the average viewer until they produce an aboveground fruiting body, often in the form of a mushroom. As such, it can be easy to overlook the essential role fungi play in the healthy functioning of every ecosystem that plants inhabit. One such role is that of the Common Mycelial Network, or Common Mycorrhizal Network (CMN). In a CMN, the root systems of plants and trees interconnect belowground via a mycelial net of symbiotic fungal partners, extending as wide as the mycelium can grow. The CMN unites multiple plant species and mycorrhizal fungi in an internet of individuals, enabling transfer of isotopic carbon, nitrogen, phosphorous, water and chemical messages between plants and fungi across species, space, and time (Simard and Durall 2004).

Grazing intensity in grassland ecosystems: implications for carbon storage and functional properties

Abstract Grazing lands cover more than one-third of the terrestrial land surface and are still expanding. The intensity of grazing is also growing with time due to the increase in the number of animals per unit of land or the duration of herbivory. Changes in grazing intensity exert strong pressure on ecosystem functioning, including carbon (C) sequestration. Grazing response to ecosystem C retention can be explained by the changes in biogeochemical processes that regulate C fluxes. Here, we summarized the ecosystem functional properties that affect C storage in grasslands as a result of changing grazing intensities. We synthesized 151 published studies globally and discussed the changes in different ecosystem functional property responses to grazing. High-intensity grazing mostly decreased C storage while light- to moderate-intensity grazing optimized the trade-offs between C sequestration and animal production. The synthesis showed that 86% of the studies reported either an increase or no change in SOC stocks under light grazing while under heavy grazing, 74% of the studies reported a significant decrease in SOC stocks. We also discussed the changes in ecosystem functional properties whereby grazing affects C sequestration. Some of them include: the changes in net primary productivity, soil organisms, enzymatic activity, soil respiration, and the complex interactions of these processes with climate change. Grassland management practices should be ecosystem-specific to ensure optimum production and enhance C sequestration. It is also important to understand the time factor (hysteresis), especially on how long it takes to sequester a certain amount of C from grassland management practices.

Predictors of taxonomic and functional composition of black spruce seedling ectomycorrhizal fungal communities along peatland drainage gradients.

Many trees depend on symbiotic ectomycorrhizal fungi for nutrients in exchange for photosynthetically derived carbohydrates. Trees growing in peatlands, which cover 3% of the earth's terrestrial surface area yet hold approximately one-third of organic soil carbon stocks, may benefit from ectomycorrhizal fungi that can efficiently forage for nutrients and degrade organic matter using oxidative enzymes such as class II peroxidases. However, such traits may place a higher carbon cost on both the fungi and host tree. To investigate these trade-offs that might structure peatland ectomycorrhizal fungal communities, we sampled black spruce (Picea mariana (Mill.)) seedlings along 100-year-old peatland drainage gradients in Minnesota, USA, that had resulted in higher soil nitrogen and canopy density. Structural equation models revealed that the relative abundance of the dominant ectomycorrhizal fungal genus, Cortinarius, which is known for relatively high fungal biomass coupled with elevated class II peroxidase potential, was negatively linked to site fertility but more positively affected by recent host stem radial growth, suggesting carbon limitation. In contrast, Cenococcum, known for comparatively lower fungal biomass and less class II peroxidase potential, was negatively linked to host stem radial growth and unrelated to site fertility. Like Cortinarius, the estimated relative abundance of class II peroxidase genes in the ectomycorrhizal community was more related to host stem radial growth than site fertility. Our findings indicate a trade-off between symbiont foraging traits and associated carbon costs that consequently structure seedling ectomycorrhizal fungal communities in peatlands.

A CNN-Based Self-Supervised Synthetic Aperture Radar Image Denoising Approach

Synthetic aperture radar (SAR) plays an essential role in earth observation and projection due to its capability to penetrate clouds, which makes it possible to monitor terrestrial surfaces under all weather conditions. Multiplicative noise often occurs in the SAR signal, hampering the retrieval of information from SAR imagery. Convolutional neural networks (CNNs) have been used in many computer vision tasks and are helpful in image denoising. However, current CNN-based denoising approaches inevitably lead to a ``washed out'' effect that loses spatial details. Another limitation is that most typical CNN-based denoising models require a noise-free image for training. To address these issues, we propose a novel end-to-end self-supervised SAR denoising model: Enhanced Noise2Noise (EN2N), which can be trained without a noise-free image. To enhance the quality of the result images, the perceptual features from a pre-learned CNN are introduced to restore the spatial details by a hybrid loss function. Experiments show that our proposed method outperforms the typical denoising methods in terms of noise reduction and feature preservation based on image quality metrics. Also, the new hybrid loss could enhance the spatial details significantly. The good performance maintains the robustness throughout time, which reduces the uncertainty in time-series SAR caused by random noise. Benefiting from optimization of graphics processing unit (GPU) and multi-threading, the proposed method has higher computation efficiency than traditional methods. This study demonstrates the great potential of using our self-supervised deep learning approaches for SAR image denoising in the future.

A Synthetic Algorithm on the Skew-Normal Decomposition for Satellite LiDAR Waveforms

Full-waveform satellite LiDAR can be used to retrieve the terrestrial surface information by decomposing its received waveforms. However, it is challenging to accurately extract the parameters of each component from a non-Gaussian overlapped waveform, which happens in steep mountain or urban areas. Therefore, a synthetic algorithm with the boosted Richardson–Lucy (RL) deconvolution, layered extraction, and gradient descent is proposed to implement the skew-normal decomposition for the received waveforms. To validate the performance of the proposed algorithm, we developed waveform decomposition experiments for three types of data, including known-parameter waveforms, simulated waveforms, and Global Ecosystem Dynamics Investigation (GEDI) satellite LiDAR waveforms. Meanwhile, we figured out the evaluation metrics involving correlation coefficients (CCs); root mean square errors (RMSEs); extracted parameter errors; and successful, missing, and unwanted rates for the decomposed waveforms. Through comparing the decomposed results of the proposed algorithm and the classical direct Gaussian decomposition (DGD) algorithms, we discovered that 1) the average CC has a growth of 4% and the average RMSE has a reduction of 60%; 2) the average errors of extracted amplitude, peak position, and pulsewidth have mitigated with 3.9%, 2.2%, and 5.1%, respectively; and 3) the successful detection rate increases by 40% and the unwanted and the missing rate decrease by 5% and 35% for the 2000 groups of known-parameter waveforms. In addition, the average CCs have slight growth of 3% and 1.2%, and the average RMSEs have significant reductions of 43% and 49% for the simulated and GEDI LiDAR waveforms, respectively. This research provides a preferable waveform decomposing approach conductive to characterizing the terrestrial information from the overlapping skew-normal full waveforms.

Consistency Assessments of the Land Cover Products on the Tibetan Plateau

Land cover and land cover change information are essential data in terrestrial surface research. However, the land cover products are highly inconsistent, especially in the mountainous area. Most land cover assessment studies focused on the consistency of spatial patterns, while ignoring the consistency of change and elevation heterogeneity. In this study, four land cover products were assessed for spatiotemporal consistency on the Tibetan Plateau (TP), including the Moderate resolution Imaging Spectroradiometer land cover (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC), the 30-meter Global Land Cover (Globeland30) and the Multi-Period Land Use/Land Cover Remote Sensing Monitoring Database in China (CNLUCC). The impact of elevation on the consistency of multiple land cover products across spatiotemporal scales was further analyzed. The spatial consistency of the three and more products was about 70%, with higher consistency for grassland and bare land and lower consistency for wetland and shrubland on the TP. Globeland30 and CCI-LC were better than others for overall monitoring, with the inconsistency of less than 45% by google earth dataset validation. The temporal change consistency of the four land cover products was less than 10%. With increasing elevation, the average spatial consistency decreased and the land cover change area and temporal change consistency increased. There is a high inconsistency of land cover changes on the TP in existing commonly used products, demonstrating the need to develop high-quality land cover products in long time series.

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

Mercury Isotope Fractionation during the Exchange of Hg(0) between the Atmosphere and Land Surfaces: Implications for Hg(0) Exchange Processes and Controls.

Atmosphere-surface exchange of elemental mercury (Hg(0)) is a vital component in global Hg cycling; however, Hg isotope fractionation remains largely unknown. Here, we report Hg isotope fractionation during air-surface exchange from terrestrial surfaces at sites of background (two) and urban (two) character and at five sites contaminated by Hg mining. Atmospheric Hg(0) deposition to soils followed kinetic isotope fractionation with a mass-dependent (MDF) enrichment factor of -4.32‰, and negligible mass-independent fractionation (MIF). Net Hg(0) emission generated average MDF enrichment factors (ε202Hg) of -0.91, -0.59, 1.64, and -0.42‰ and average MIF enrichment factors (E199Hg) of 0.07, -0.20, -0.14, and 0.21‰ for urban, background, and Hg mining soils and cinnabar tailing, respectively. Positive correlations between ε202Hg and ambient Hg(0) concentration indicate that the co-occurring Hg(0) deposition (accounting for 10-39%) in a regime of net soil emission grows with ambient Hg(0). The MIF of Hg(0) emission from soils (E199Hg range -0.27 to 0.14‰, n = 8) appears to be overall controlled by the photochemical reduction of kinetically constrained Hg(II) bonded to O ligands in background soils, while S ligands may have been more important in Hg mining area soils. In contrast, the small positive MIF of Hg(0) emission from cinnabar ore tailing (mean E199Hg = 0.21‰) was likely controlled by abiotic nonphotochemical reduction and liquid Hg(0) evaporation. This research provides critical observational constraints on understanding the Hg(0) isotope signatures released from and deposited to terrestrial surfaces and highlight stable Hg isotopes as a powerful tool for resolving atmosphere-surface exchange processes.

Global quasi-daily fractional vegetation cover estimated from the DSCOVR EPIC directional hotspot dataset

Fractional Vegetation Cover (FVC) represents the planar fraction of the land-surface covered by green foliage, and its dynamics are important for an enhanced understanding of ecosystems especially how they respond to climate change. The lack of global near-real-time satellite-based products restricts the application of FVC in ecosystem modeling, climate change, and vegetation phenology studies. Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR) spacecraft provides daily spectral reflectance of the entire sunlit Earth in the near Hotspot directions. Hotspot observations (i.e., observation in Hotspot direction which has the peak backscattering reflected radiation) with only sunlit vegetation and sunlit soil components are more suitable for FVC estimation with a two-endmember mixture model as such observations exclude contributions from shaded vegetation and soil components. In this study, an algorithm for retrieving quasi-daily FVC from EPIC based on two-endmember mixture and gap fraction models is developed. Analyses of its performance predict that the average Root-Mean-Square Deviations (RMSDs) of retrievals in FVC units is below 0.050 when compared with reference values. The RMSD is 0.043 when compared to field-based Landsat reference FVC, which confirms lower retrieval uncertainty than FVC retrieved from Low-Earth-Orbit (LEO) satellite products such as MODIS, VIIRS, and GEOV2 with RMSDs 0.049– 0.087. The comparison analyses suggest a good consistency between EPIC FVC and FVC products from LEO and geostationary (GEO) satellites sensor, SEVIRI, with RMSD values less than 0.129. EPIC allows for quasi-daily FVC estimation across the global terrestrial surface at 10 km resolution, which is an important development for numerous biophysical applications.

Evaluation of atmosphere clearness and cloudiness parameters in the southern regions of Ukraine using statistical analysis

Introduction. This paper deals with the specific aspects of insolation of the terrestrial surface in the southern regions of Ukraine, namely the clearness index and diffuse fraction of the atmosphere. The study was based on satellite data of the average daily insolation and total cloudiness in the nodes of the two-degree grid for the domain with coordinates 48°-45° N and 29°-39° E for the period of 1981-2020. The purpose of article. The purpose was to develop statistic models of horizontal surface insolation for various locations of study domain. Main focus was put on special characteristics in conditions of fixed cloudiness. Satellite data for the summer season had been used to evaluate the maximum solar energy potential of Ukraine. Methods. Application statistical analysis and means of cartographic data layout were used in the paper. Results. It was found that with the highest (more than 50%) frequency the total cloud cover can be characterized by the atmosphere clarity corresponding to a clear sky condition. The significance of irradiation of the terrestrial surface with diffuse solar radiation has been observed, with the share of such radiation in the global irradiation (diffuse ratio) being closely inversely related to the clearness index (correlation about -0.97). In turn, both diffuse ratio and clearness index are statistically dependent on the sky clarity, that allowed deriving analytical functions - diffuse ratio and clearness index - of the sky clarity, which appeared to be S- and Z-shaped curves, respectively. Dispersion of the clearness index ( ) and the diffuse fraction ( ) values and the strength of their statistical relationship significantly depend on the sky clarity. The empirical distribution of the two-dimensional random variable ( ; ) well meets the Gaussian distribution, and the obtained dispersion ellipses allowed calculating the confidence intervals of the two-dimensional random variable (clearness index: diffuse fraction) for a given confidence level. The spatial distribution of the clearness index and diffuse fraction of the atmosphere in the southern regions of Ukraine revealed a significant dependence of these indices on the latitude and the type of underlying surface. At the end of the summer a seasonal effect has been observed in the spatial distribution of the clearness index and diffuse fraction, which can be explained by the specific seasonal features of atmospheric circulation, caused by the spreading out of the eastern ridge of the Azores anticyclone and the general situation with blocking developments in the Atlantic-European sector of the Northern Hemisphere. The scientific novelty. Correlation and regression models of special insolation characteristics in conditions of various cloudiness that are represented in this paper are new to Ukraine. Analysis of two-dimensional random value spread (clearness index: cloudiness index) allowed to assess probabilities of integral solar radiation flows. The obtained analytical membership functions for monthly average values of clearness and cloudiness indices depending on the level of sky clarity proved to be applicable for determining respective indices for daily time scale. Practical significance. The obtained results are important for comprehensive assessment of the solar / photovoltaic resources of southern regions of Ukraine. Specifically, analytical dependences have practical values for the purpose of forecasting direct and diffuse solar radiation in various time scales based on publically available global records of solar radiation.

A free-geometry geodynamic modelling of surface gravity changes using Growth-dg software

Globally there is abundant terrestrial surface gravity data used to study the time variation of gravity related to subsurface mass and density changes in different geological, geodynamical and geotechnical environments. We present here a tool for analysing existing and newly acquired, 4D gravity data, which creates new findings from its reuse. Our method calculates in an almost automatic way the possible sources of density change responsible for the observed gravity variations. The specifics of the new methodology are: use of a low number of observation points, relatively small source structures, low signal/noise ratio in the data, and a free 3D source geometry without initial hypothesis. The process is based on the non-linear adjustment of structures defined by aggregation of small cells corresponding to a 3D section of the sub-floor volume. This methodology is implemented in a software tool, named GROWTH-dg, which can be freely downloaded for immediate use, together with a user manual and application examples.

Distinct roles of land cover in regulating spatial variabilities of temperature responses to radiative effects of aerosols and clouds

Surface temperature responses to radiative perturbations due to aerosols and clouds are complicated by the land surface properties. To disentangle these complexities, this study, from a terrestrial surface energy budget perspective, isolates the underlying biophysical processes from the instantaneous radiative effects of aerosols and clouds on surface temperature using the National Center for Atmospheric Research Community Earth System Model version 1.2.1. It is found that in comparison with the global heterogeneous distributions of instantaneous radiative perturbations at the surface induced by aerosols and clouds, the spatial variations of the corresponding surface temperature responses to aerosol direct radiative effects (DRE) during the daytime and cloud radiative effects (CRE) during the nighttime are amplified. It is because of the consistent global distribution of the local surface climate sensitivity (a function of land cover properties such as surface roughness and Bowen ratio) with those of daytime DRE and nighttime CRE. By applying identical anthropogenic aerosol and precursor emissions over eight major past, present and projected future anthropogenic aerosol emitting regions (i.e. Brazil, China, East Africa, India, Indonesia, South Africa, United States and Western Europe), surface temperature responses to aerosol radiative cooling in the daytime and cloud radiative warming in the nighttime over these regions positively regulated by local surface climate sensitivities are prominent.

Impact of snow‐albedo feedback termination on terrestrial surface climate at midhigh latitudes: Sensitivity experiments with an atmospheric general circulation model

AbstractTo examine the effect of the snow‐albedo feedback (SAF) on the current surface climate across terrestrial regions at mid–high latitudes, we perform a series of idealized sensitivity experiments using an atmospheric general circulation model (AGCM), in which the snow albedo is assumed to be considerably smaller than that in the control experiment. When the snow albedo is reduced, the fall and spring surface air temperatures (SATs) in northern Eurasia are considerably higher than those in the control experiment. Particularly, the spring SAT at terrestrial high latitudes rises by more than 10°C, due to low snow albedo. Despite the small surface snow albedo during both fall and spring, the fall temperature difference between the two experiments is smaller than that during spring. The feedback with the hydrological effect, which accelerates snowmelt, is either nonexistent or weak during periods of seasonally low air temperatures. This result indicates that the hydrological effect significantly influences the SAF, and different magnitudes of the hydrological effect including cloud differences are a possible source of SAF uncertainty between models. The radiative albedo effect also impacts the evolution of the snow area in central Asia and eastern Europe through changes in lateral water vapour transport. Furthermore, the experiments indicate that the cooling effect of snow on land is necessary for the strength of the Siberian High and the pattern of Arctic Oscillation, suggesting that snow cover may also significantly extend the duration of Arctic Oscillation effects.

Airborne and Spaceborne Lidar Reveal Trends and Patterns of Functional Diversity in a Semi-Arid Ecosystem

Assessing functional diversity and its abiotic controls at continuous spatial scales are crucial to understanding changes in ecosystem processes and services. Semi-arid ecosystems cover large portions of the global terrestrial surface and provide carbon cycling, habitat, and biodiversity, among other important ecosystem processes and services. Yet, the spatial trends and patterns of functional diversity in semi-arid ecosystems and their abiotic controls are unclear. The objectives of this study are two-fold. We evaluated the spatial pattern of functional diversity as estimated from small footprint airborne lidar (ALS) with respect to abiotic controls and fire in a semi-arid ecosystem. Secondly, we used our results to understand the capabilities of large footprint spaceborne lidar (GEDI) for future applications to semi-arid ecosystems. Overall, our findings revealed that functional diversity in this ecosystem is mainly governed by elevation, soil, and water availability. In burned areas, the ALS data show a trend of functional recovery with time since fire. With 16 months of data (April 2019-August 2020), GEDI predicted functional traits showed a moderate correlation (r = 41–61%) with the ALS predicted traits except for the plant area index (PAI) (r = 11%) of low height vegetation (<5 m). We found that the number of GEDI footprints relative to the size of the fire-disturbed areas (=< 2 km2) limited the ability to estimate the full effects of fire disturbance. However, the consistency of diversity trends between ALS and GEDI across our study area demonstrates GEDI’s potential of capturing functional diversity in similar semi-arid ecosystems. The capability of spaceborne lidar to map trends and patterns of functional diversity in this semi-arid ecosystem demonstrates its exciting potential to identify critical biophysical and ecological shifts. Furthermore, opportunities to fuse GEDI with complementary spaceborne data such as ICESat-2 or the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR), and fine scale airborne data will allow us to fill gaps across space and time. For the first time, we have the potential to monitor carbon cycle dynamics, habitats and biodiversity across the globe in semi-arid ecosystems at fine vertical scales.

Transforming landscapes and mindscapes through regenerative agriculture.

Agriculture occupies 38% of the planet’s terrestrial surface, using 70% of freshwater resources. Its modern practice is dominated by an industrial–productivist discourse, which has contributed to the simplification and degradation of human and ecological systems. As such, agricultural transformation is essential for creating more sustainable food systems. This paper focuses on discursive change. A prominent discursive alternative to industrial–productivist agriculture is regenerative agriculture. Regenerative discourses are emergent, radically evolving and diverse. It is unclear whether they have the potential to generate the changes required to shift industrial–productivist agriculture. This paper presents a literature-based discourse analysis to illustrate key thematic characteristics of regenerative agricultural discourses. The analysis finds that such discourses: situate agricultural work within nested, complex living systems; position farms as relational, characterised by co-evolution between humans and other landscape biota; perceive the innate potential of living systems as place-sourced; maintain a transformative openness to alternative thinking and practice; believe that multiple regenerative cultures are necessary for deeply regenerative agriculture; and depart from industrialism to varying degrees. The paper concludes by reviewing three transformative opportunities for regenerative discourses—discourse coalitions, translocal organising and collective learning.Supplementary InformationThe online version supplementary material available at 10.1007/s10460-021-10276-0.

Venatorbacter cucullus gen. nov sp. nov a novel bacterial predator

A novel Gram-stain negative, aerobic, halotolerant, motile, rod-shaped, predatory bacterium ASxL5T, was isolated from a bovine slurry tank in Nottinghamshire, UK using Campylobacter hyointestinalis as prey. Other Campylobacter species and members of the Enterobacteriaceae were subsequently found to serve as prey. Weak axenic growth on Brain Heart Infusion agar was achieved upon subculture without host cells. The optimal growth conditions were 37 °C, at pH 7. Transmission electron microscopy revealed some highly unusual morphological characteristics related to prey availability. Phylogenetic analyses using 16S rRNA gene sequences showed that the isolate was related to members of the Oceanospirillaceae family but could not be classified clearly as a member of any known genus. Whole genome sequencing of ASxL5T confirmed the relationship to members the Oceanospirillaceae. Database searches revealed that several ASxL5T share 16S rRNA gene sequences with several uncultured bacteria from marine, and terrestrial surface and subsurface water. We propose that strain ASxL5T represents a novel species in a new genus. We propose the name Venatorbacter cucullus gen. nov., sp. nov. with ASxL5T as the type strain.

Integrated spatial planning for biodiversity conservation and food production

Integrated spatial planning for biodiversity conservation and food production