Vegetation Height Research Articles

FOREST FEATURE LIDAR SLAM (F2-LSLAM) AND INTEGRATED SCAN SIMULTANEOUS TRAJECTORY ENHANCEMENT AND MAPPING (IS2-TEAM) FOR ACCURATE FOREST INVENTORY USING BACKPACK SYSTEMS

Abstract. Under-canopy mapping is desired to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume. The main challenge of such under-canopy mapping is the intermittent access to the global navigation satellite system (GNSS) signal, which is crucial to deriving accurately georeferenced mapping products. In this study, we propose two frameworks – Forest Feature LiDAR Simultaneous Localization and Mapping (F2-LSLAM) and Integrated Scan Simultaneous Trajectory Enhancement and mapping (IS2-TEAM) – for 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventory. In the F2-LSLAM strategy, ground/tree trunk features are extracted from individual LiDAR scans. On the other hand, when trajectory information provided by navigation sensors is available, these semantic features are derived from LiDAR points within several scans (i.e., integrated scan) for the IS2-TEAM strategy. Then, local/global least squares adjustment (LSA) using derived features is performed to register LiDAR scans to a common reference frame for both strategies. To evaluate the performance of the proposed strategies, three in-house developed backpack systems with varying specifications were used to collect data in complicated forest environments. Through the comparison with point clouds acquired by a commercial backpack LiDAR system, the proposed frameworks are capable of generating point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) for all backpack systems in natural forest areas with relatively flat terrain. However, for more challenging areas with dense undergrowth vegetation and/or large height differences, F2-LSLAM framework cannot extract sufficient features, while IS2-TEAM still exhibits good performance.

ACCURACY EVALUATION OF ICESAT-2 ATL08 IN FINLAND

Abstract. ATL08 is level-3A land and vegetation height product of ICESat-2 data, which recorded the terrain elevation and surface height parameters at 100 m fixed-length along the ground track. It has been used widely not only in vegetation monitoring at large scale, but also in improving the accuracy of satellite stereo mapping, so, the accuracy of ATL08 product is the precondition of its application. Although some studies have evaluated the terrain and canopy height retrieval accuracy of ATL08, it is still not comprehensive that most studies focus on the accuracy in vegetation area. In this study, the performance on terrain and surface height retrieval of ATL08 was evaluated both in forested and non-forested area based on DEM and ALS data in Finland. A total of 6,682,846 and 3,980,235 segments were used to evaluate the accuracy of terrain and surface height retrieval, respectively. The result showed that, firstly, terrain elevation retrieval from ICESat-2/ATLAS is accurate, e.g., sub-meter level both in forested and non-forested area and slope was an important factor affecting the accuracy of terrain retrieval, while, surface height retrieval was low accurate, e.g., the RMSE of surface height retrieval were more than 5 m. Secondly, the accuracy of terrain elevation retrieval in non-forested area was slightly higher than that in forested area showed by MAE and RMSE. Finally, the accuracy of surface height retrieval in forested area was higher than that in non-forested area, and it should be emphasized that there is a significantly difference on RMSE% between forested and non-forested area. The study indicated that the terrain elevation retrieval from ATL08 is relative reliable and recommended to use when the accuracy is not strictly required, while, the use of surface height should be considered carefully and avoid to use low accuracy observation.

Changes in Nutrient-Regulated Soil Microbial Communities in Soils Concomitant with Grassland Restoration in the Alpine Mining Region of the Qilian Mountains

In response to the significant ecological damage caused by unsustainable mining practices in the Qilian Mountains, ecological restoration projects have been undertaken in recent years. Analyzing the changes in soil microbial communities during the restoration process of mine meadows helps to reveal the mechanism of the restoration process in alpine mining areas. To explore the characteristics of soil microbial community distribution and their relationships with soil environmental factors during the restoration of alpine grasslands in the Qilian Mountains, we conducted surveys and analyses in two restoration levels low restoration (LR) and high restoration (HR) in the eastern Qilian Mountains, along with an undisturbed natural grassland control (NG). We found that as the degree of high-altitude mining area recovery increases, there were significant increases in vegetation cover, vegetation height, above-ground biomass, vegetation Shannon–Wiener index, soil organic carbon (SOC), soil water content (SWC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), and available nitrogen (AN). Conversely, soil pH and electrical conductivity (EC) significantly decreased, with soil pH decreasing from 6.93 to 4.13. Restoration of high-altitude mining area grasslands significantly alters the distribution and composition of soil bacteria and fungi, while the impact on soil microbial community changes was not significant. Notably, with increasing recovery level, the dominant bacterial phyla are Acidobacteria and Proteobacteria, while the dominant fungal phyla are Ascomycota and Basidiomycota. These results indicate that changes in vegetation and soil properties both affect the composition of soil microbial communities, with soil properties having a greater influence. Soil fertility and nutrient levels emerge as the primary drivers influencing soil microbial composition communities and the degree of high-altitude mining area grassland recovery.

Effect of farmland type and vegetation height on habitat use and breeding success of Northern Lapwings in southeastern Norway

Effect of farmland type and vegetation height on habitat use and breeding success of Northern Lapwings in southeastern Norway

Facets of functional diversity support niche‐based explanations for Australian biodiversity gradients

AbstractAimThere is widespread support that species richness increases with the available energy of an ecosystem, but the mechanisms underlying this driver of biodiversity patterns remain elusive. We evaluated gradients of functional diversity to test whether the higher species richness of productive, structurally diverse environments is due to a greater range of niches being supported by the abiotic conditions present (environmental filtering), greater availability of biotic resource and habitat niches (more niches) or increasing functional similarity of species (niche packing).LocationAustralia.TaxonBirds and mammals.MethodsWe used structural equation modelling to evaluate the relative contributions of climatic harshness (actual evapotranspiration, AET) and the availability of resource (gross primary productivity, GPP) and habitat (tree height) niches on taxonomic richness and functional richness, dispersion and evenness. We performed parallel analyses within 15 bioclimatic zones and continentally to evaluate the scaling of biodiversity gradients and the shifting balance between niche‐based mechanisms along environmental gradients.ResultsAll continental diversity gradients were primarily associated with energy variables, but while species richness of both taxa and all functional diversity measures of bird assemblages increased with AET, mammal functional diversity was more strongly associated with GPP gradients. Results were more variable at the regional scale, but species richness gradients along tree height (birds and mammals) and GPP (mammals) within bioclimatic zones tended not to be paralleled by increases in functional richness or dispersion.Main ConclusionsThe niche‐based explanations of biodiversity gradients varied in importance with scale, position on environmental gradients and taxonomic group. At the continental extent, bird biodiversity gradients were structured by environmental filtering by climatic harshness, while mammal biodiversity was related to the increasing availability of resource niches with increasing productivity. Niche packing was more prominent at the regional scale, especially in bioclimatic zones where productivity and vegetation height were less limiting, and in mammal assemblages, suggesting that biodiversity patterns scale differently for birds and mammals.

Temporal and vertical dynamics of carbon accumulation potential under grazing-excluded grasslands in China: The role of soil bulk density

Despite the progress made in understanding relevant carbon dynamics under grazing exclusion, previous studies have underestimated the role of soil bulk density (BD), and its implications for potential accumulation of soil organic carbon (SOC), especially at regional scale over long term. In this study, we first constructed a database covering a vast majority of the grasslands in northwestern China based on 131 published literatures. A synthesis was then conducted by analyzing the experimental data to comprehensively investigate the mechanisms of vegetation recovery, carbon-nitrogen coupling, and the importance of changed soil BD in evaluating SOC sequestration potential. The results showed that although the recovery of vegetation height and cover were both critical for improving vegetation biomass, vegetation height required a longer recovery period. While the SOC accumulation was found to be greater in surface layers than deeper ones, it exhibited a reduced capacity for carbon sequestration and an increased risk of SOC loss. Grazing exclusion significantly reduced soil BD across different soil profiles, with the rate of change influenced by soil depth, time, geographical and climatic conditions. The potential for SOC accumulation in the top 30 cm of soil based on data of 2003–2022 was 0.78 Mg ha−1 yr−1 without considering BD effects, which was significantly underestimated compared to that of 1.16 Mg ha−1 yr−1 when BD changes were considered properly. This suggests that the efficiency of grazing exclusion in carbon sequestration and climate mitigation may have been previously underreported. Furthermore, mean annual precipitation represented the most relevant environmental factor that positively correlated to SOC accumulation, and a wetter climate may offer greater potential for carbon accumulation. Overall, this study implies grazing exclusion may play an even more critical role in carbon sequestration and climate change mitigation over long-term than previously recognized, which provides essential scientific evidence for implementing stepwise ecological restoration in grasslands.

Modelling above-ground biomass estimation using combination of vegetation height and forest canopy density at secondary peat swamp forest of Kalimantan

Rapid estimation of carbon stock, particularly carbon from Above-Ground Biomass (AGB) in a high-potential carbon forest, is required to assess potential carbon emission projects. Satellite imagery has been widely used as the main data source to rapidly estimate AGB. Canopy Height (CH) data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Forest Canopy Density (FCD) data from satellite Landsat 8 imagery are widely used to estimate AGB. An allometric model with multiple linear regression equations has been used to estimate the AGB value using both parameters of the FCD value and the CH value. 43 values of calculated AGB by non-destructive field survey located in a peat swamp forest area use as a training dataset to develop an allometric model. The CH and FCD indices performed several tests, and the test result met the requirements for developing allometric equations. The coefficient correlation (r) of CH and AGB from 43 sample plots is 0.546, indicating a moderate correlation. The coefficient correlation (r) for FCD and AGB is 0.267 which indicates a weak correlation. The validation test was carried out to assess AGB from the developed allometric model and AGB of field survey with a non-destructive method. The validation result of the allometric model has the value of coefficient correlation (r), RMSE, and R2, respectively 0.569; 22.235; and 0.324. In terms of allometric model validation, another forest with typical conditions, particularly basal area, the model’s AGB result, and the validated area have RMSE 46.506 and r value 0.664.

Microhabitat selection of meadow and steppe vipers enlightened by digital photography and image processing to describe grassland vegetation structure

AbstractUnderstanding animals' selection of microhabitats is important in both ecology and biodiversity conservation. However, there is no generally accepted methodology for the characterization of microhabitats, especially for vegetation structure. We studied microhabitat selection of three Vipera snakes by comparing grassland vegetation structure between viper occurrence points and random points in three grassland ecosystems: V. graeca in mountain meadows of Albania, V. renardi in loess steppes of Ukraine and V. ursinii in sand grasslands in Hungary. We quantified vegetation structure in an objective manner by automated processing of images taken of the vegetation against a vegetation profile board under standardized conditions. We developed an R script for automatic calculation of four vegetation structure variables derived from raster data obtained in the images: leaf area (LA), height of closed vegetation (HCV), maximum height of vegetation (MHV) and foliage height diversity (FHD). Generalized linear mixed models revealed that snake occurrence was positively related to HCV in V. graeca, to LA in V. renardi and to LA and MHV in V. ursinii, and negatively to HCV in V. ursinii. Our results demonstrate that vegetation structure variables derived from automated image processing significantly relate to viper microhabitat selection. Our method minimizes the risk of subjectivity in measuring vegetation structure, enables the aggregation of adjacent pixel data and is suitable for comparison of or extrapolation across different vegetation types or ecosystems.

Impact of Gibberellic Acid on Water Status, Growth, and Development of Cape Gooseberry in Newly Reclaimed Sandy Lands within Arid Regions

In newly reclaimed sandy lands, plants face substantial environmental challenges, affecting their productivity, yield, and quality. Gibberellic acid (GA3) is a plant growth regulator physiologically involved in plant responses to abiotic stresses. As agricultural activities expand in desert regions, applications of GA3 could help address adverse plant growth and developmental effects. Here, we investigated the impact of exogenously applied GA3 on the growth of Cape gooseberry (Physalis peruviana L.) in newly reclaimed sandy lands in the arid Nubaria region of the West Delta of Nile, Egypt. Different GA3 concentrations of 100, 150, 200, and 250 ppm were foliar-applied to the plants. The application of GA3 in our study significantly improved the vegetative growth, plant height, leaf and branch count, and the fresh weight and yield of Cape gooseberry plants. Fruit weight, quality soluble solids, and leaf chlorophyll content were also improved. The most pronounced effects were achieved with concentrations of GA3 at 200 and 250 ppm, with the 200-ppm concentration displaying superiority in most parameters. Notably, GA3 treatments enhanced relative water content (RWC), an indicator of water status in arid conditions. Maintaining optimal RWC is crucial for essential processes like photosynthesis, promoting growth, and productivity. This research underscores GA3’s potential in enhancing Cape gooseberry growth, yield, and quality, providing a practical strategy for mitigating environmental challenges in arid regions, a concern exacerbated by climate change.

An empirical assessment of the potential of post-fire recovery of tree-forest communities in Mediterranean environments

The accumulation of fuel and the homogenization of the landscape in Mediterranean forests are leading to an increasingly hazardous behavior of wildfires, fostering larger, more intense, severe, and frequent wildfires. The onset of climate change is intensifying this behavior, fostering the occurrence of extreme forest fires threatening the persistence of forest communities.In this study we present an assessment of the post-fire recovery potential of the most representative tree-forest communities affected by fire in Spain: Pinus halepensis, Pinus nigra, Pinus pinaster and Quercus ilex. A large database of field data collected during specific campaigns -carried out 25 years after the fire- is used in combination with remote sensing, forest inventory and geospatial data to build an empirical model capable of predicting the chances of recovery. The model, calibrated using Random Forest, combines information on burn severity (remote sensing estimates of the Composite Burn Index), local topography (slope and terrain aspect) and climatic data (mean values and trends of temperature and precipitation) to provide information on the degree of similarity (vegetation height, horizontal cover of the vegetation layer along vertical strata, aboveground biomass and species diversity) between the plots burned in the summer of 1994 and the unburned control.Overall, only 33 out of the 131 burned plots could be considered as recovered, that is, reaching a similar state to unburned stands in neighboring areas. Our results suggest a primary role played by burn severity (the higher the severity the lower the probability of recovery), but strongly modulated by local topographic features (higher probability of recovery on steep north-facing slopes). In turn, increasingly warm and wetter conditions increased the chance of recovery.

Reactive response to predation risk affects foraging time of hares, yet not their phosphorus intake

Antipredator responses could affect nutrient intake, which could lead to nutritional deficits. However, little is known about the antipredator response of small herbivores because most are nocturnal or crepuscular and therefore very difficult to study in the field. Therefore, we experimentally assessed the effect of a reactive response to predation risk on the nutrient (i.e., phosphorous) intake of the European hare (Lepus europaeus) using three different playback sounds. Additionally, we studied the time spent being costly vigilant, the time spent foraging, and the vegetation height in which the hares were present using accelerometers and GPS. Our results showed that elevated predation risk from our playback experiment did not affect the (1) phosphorus intake, (2) time spent being costly vigilant, and (3) time spent in tall vegetation. However, elevated predation risk did increase the time spent foraging. Possibly hares spent more time foraging with an increased predation risk because hares cannot seek refuge from predators. Additionally, the effect on phosphorus intake could be weak because phosphorous intake does not benefit a flight escape, while the reactive response acts late in the predation sequence limiting the effect on hare ecology. Prey anti-predator responses seem strongly related to the escape tactics of prey species that can differ between different habitats and the time of the day. More detailed field studies are necessary to get a better insight into species’ anti-predator-food tactics.

Characterizing the channel dependence of vegetation effects on microwave emissions from soils

ABSTRACT The two vegetation transfer parameters of τ (Vegetation Optical Depth,VOD) and ω (Omega) could vary significantly across microwave channels in terms of frequencies, polarizations, and incidence angles, and their channel-dependent characteristics have not yet been fully investigated. In this study, we investigate the channel dependence of vegetation effects on microwave emissions from soils using a higher-order vegetation radiative transfer model of Tor Vergata. Corn was selected as the subject of investigation, and a corn growth model was developed utilizing field data collected from the multifrequency and multi-angular ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River (SMELR). Upon compilation of the simulation dataset of microwave emissions of the corn field, the effective scattering albedo across different channels were calculated using the Tor Vergata model. Results show that vertical polarization of the vegetation optical depth is more affected by incidence angle changes, while horizontal polarization exhibits lower variations in vegetation optical depth due to incidence angle adjustments. The channel dependence of vegetation optical depth can be described as the polarization dependence parameter ( C P ) and the frequency dependence parameter ( C f ). These two parameters enable the calculation of vegetation optical depth at any channel under three adjacent frequencies (L-band, C-band and X-band). The effective scattering albedo of vegetation does not vary significantly with vegetation height or angle. It primarily depends on frequency and polarization, showing an overall increasing trend with increasing frequency. The effective scattering albedo with vertical polarization is slightly higher than that with horizontal polarization at higher frequencies, while both are lower in the L-band. This investigation is helpful for understanding the vegetation effects on microwave emissions from soils, ultimately advancing the accuracy of large-scale soil moisture retrieval in vegetated areas.

Breeding habitat selection of three sympatric species of Mediterranean warblers in eastern Spain

Understanding habitat selection patterns in congeneric species has raised special interest due to its importance when designing conservation strategies for the species. We analysed reproductive habitat selection of three coexisting warbler species in the eastern Iberian Peninsula. We detected the individuals by their vocalizations at 40 listening points in two study areas (Carcaixent and Alcoy). We used satellite images to identify different vegetation types and their availability. Reproductive habitat selection was analysed by the Ivlev selectivity index and a compositional analysis. In addition, we determined the effect of different habitat variables (dominant vegetation, vegetation height, cover and site) on the presence of the species fitting a binomial GLM with logistic linking function. We found that the Sardinian Warbler ( Curruca Melanocephala) selected scrub-dominated areas; while the Dartford Warbler ( C. undata) preferred areas with low (<50%) vegetation cover; and the Subalpine Warbler ( C. cantillans) was present in areas with scrubs and low (<50%) vegetation cover, avoiding crops. The compositional analysis showed that the species used different vegetation types, in relation to their availability and that the selection differed between species. The Sardinian Warbler was the only species which showed spatial variation in reproductive habitat selection. Our results evidence that the studied warbler species select and avoid different types of vegetation in relation to their specific requirements and the present habitat structure. This represents valuable information to understand the composition of local communities and the reproductive habitat selection of the studied species.

Image to Image Deep Learning for Enhanced Vegetation Height Modeling in Texas

Vegetation canopy height mapping is vital for forest monitoring. However, the high cost and inefficiency of manual tree measurements, coupled with the irregular and limited local-scale acquisition of airborne LiDAR data, continue to impede its widespread application. The increasing availability of high spatial resolution imagery is creating opportunities to characterize forest attributes at finer resolutions over large regions. In this study, we investigate the synergy of airborne lidar and high spatial resolution USDA-NAIP imagery for detailed canopy height mapping using an image-to-image deep learning approach. Our main inputs were 1 m NAIP image patches which served as predictor layers and corresponding 1 m canopy height models derived from airborne lidar data, which served as output layers. We adapted a U-Net model architecture for canopy height regression, training and validating the models with 10,000 256-by-256 pixel image patches. We evaluated three settings for the U-Net encoder depth and used both 1 m and 2 m datasets to assess their impact on model performance. Canopy height predictions from the fitted models were highly correlated (R2 = 0.70 to 0.89), precise (MAE = 1.37–2.21 m), and virtually unbiased (Bias = −0.20–0.07 m) with respect to validation data. The trained models also performed adequately well on the independent test data (R2 = 0.62–0.78, MAE = 3.06–4.1 m). Models with higher encoder depths (3,4) and trained with 2 m data provide better predictions than models with encoder depth 2 and trained on 1 m data. Inter-comparisons with existing canopy height products also showed our canopy height map provided better agreement with reference airborne lidar canopy height estimates. This study shows the potential of developing regional canopy height products using airborne lidar and NAIP imagery to support forest productivity and carbon modeling at spatially detailed scales. The 30 m canopy height map generated over Texas holds promise in advancing economic and sustainable forest management goals and enhancing decision-making in natural resource management across the state.

Combining UAV thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest

AbstractMicroclimate and vegetation structure control evapotranspiration (ET) from land surfaces at stand and landscape scales. Tropical rainforests are among the most diverse and complex terrestrial ecosystems, harbouring vast plant and animal species throughout their dense multistory canopy. They contribute substantially to global precipitation through their high ET. However, there is little information about ET influences at very small spatial scales under given climatic conditions. In a tropical rainforest on Sumatra, we studied the relationship between pixel‐level ET as derived from high‐resolution (~10 cm), near‐surface thermography from an unmanned aerial vehicle (UAV) and canopy structure as derived from red–green–blue (RGB) image and three‐dimensional (3D) point cloud analyses. The 16 derived potential predictors encompassed vegetation height, height variability, vegetation density and reflectance variables. Using regression models, several of the studied variables had a significant linear relationship with ET, but the explained variance was only marginal. However, applying a random forest algorithm including forward feature selection and target oriented cross validation explained substantial parts of the pixel‐level variance in ET (R2 = 0.56–0.65), thus indicating multiple non‐linear relationships with interactions among predictor variables. Therein, green leaf index, leaf area density and vegetation height were often the most important variables for the prediction outcome, but their sequence varied among the four study plots. Overall, combining canopy structure variables derived from RGB photogrammetry explained relatively large parts of spatial ET variations. Our study thus indicates the large potential of combining UAV‐based thermography and photogrammetry techniques with machine learning approaches to better understand ET but also suggests that more work remains to be done in explaining ET patterns at very small spatial scales.

Mapping and monitoring of vegetation regeneration and fuel under major transmission power lines through image and photogrammetric analysis of drone-derived data

The use of drones and remote sensing in combination with geospatial analysis is a cost-efficient way to monitor energy distribution networks, especially those in fire-prone areas. This study investigated the use of image and photogrammetric analysis together with segmentation algorithms to assess vegetation height and volume in power line corridors in Southeast Queensland, Australia. Various fuel reduction techniques, including mega-mulching, spot sprays and cool mosaic burns, were implemented, and drone-generated models were employed to evaluate their effectiveness. The fuel hazard reduction and regrowth in terms of vegetation height and volume were recorded and analysed. Importantly, the study demonstrates a robust correlation (R 2 = 0.9073; df = 1,16; F = 156; p < .001) between field observations and drone-derived models, affirming the efficacy of this method in assessing fuel heights. This validation suggests that the approach could represent a viable, cost-efficient option for future monitoring and management of energy distribution networks in fire-prone areas.

Contrasting impacts of short‐ and long‐term large herbivore exclusion on understory net CO2 exchange in a boreal forest

Across boreal forests, trees are the main living biomass carbon (C) stock, but the understory vegetation can contribute significantly to the C cycling and net forest carbon dioxide (CO2) balance. The patchy understory vegetation, which consists of sunlit (i.e. lichen‐like) and shaded habitats (i.e. dwarf shrub‐like), is often altered by ungulate grazers. Grazers may influence understory CO2 exchange and, consequently, the forest CO2 balance. Grazing affects differently the biomass of slow‐growing lichens compared to the faster‐growing mosses and dwarf shrubs, and therefore the effects of grazing on CO2 exchange in the patchy understory vegetation may vary temporally. We studied how excluding grazing for short and long periods affects the CO2 exchange and vegetation biomass in the understory of an oligotrophic Scots pine forest. We measured growing season (2019, 2020) CO2 exchange across sunlit and shaded habitats inside fences that had excluded large grazers for 0–1 and 25–26 years and in the adjacent grazed area. In addition, we measured the height of understory vegetation. We found that short‐term grazer exclusion increased ecosystem CO2 source fluxes only in the shaded habitats. However, long‐term exclusion of grazing decreased CO2 net release regardless of the habitat type. Furthermore, grazer exclusion increased moss depth immediately, which coincided with an abrupt intensification of CO2 net release. Considering the impacts of grazing over both short‐ and long‐term periods may help to forecast C fluxes more accurately, which may be relevant for informed climate solutions regionally and even on a larger scale.

Grassland extensification enhances nest densities of ground‐nesting wild bees

Abstract Ground‐nesting wild bees provide essential pollination services in agroecosystems, but they are jeopardized by intensive agricultural management. To mitigate such negative impacts, agri‐environment schemes have been implemented. While the success of enhancing floral food resources is relatively well studied, the role of agri‐environmental schemes in providing suitable nesting habitat remains underexplored. We studied the effectiveness of meadow extensification according to the Swiss agri‐environment scheme in promoting nesting of ground‐nesting bees. Using a paired design, we quantified their nests during four rounds (March–June) in pairs of nine randomly selected extensively (i.e. no fertilizer input, postponed first mowing) and nine intensively managed meadows with similar soil properties, slope, exposure and landscape context. Nest numbers and vegetation characteristics were surveyed in areas of 250 m2. Vegetation properties were also assessed in 0.5 × 0.5 m plots around nest locations and randomly selected locations without nests within each meadow to assess their role as drivers of nesting incidence (nest presence/absence) at this plot scale. We found substantially higher nest numbers of ground‐nesting bees in extensively (mean ± SE per sampling round = 46.8 ± 14.2) compared to intensively managed meadows (0.8 ± 0.3; no nests in three of nine intensively managed meadows). Extensively managed meadows harboured nests of several dominant crop pollinator species, including aggregations of, for example, Lasioglossum malachurum contributing to high nest densities in some of them. Number of nests was negatively related to grass cover and vegetation height, which were lower in extensively compared to intensively managed meadows. Plot‐level nesting incidence increased with bare ground and moss cover, and decreased with grass cover. Synthesis and applications. Our study shows that extensively managed meadows are better nesting habitats for ground‐nesting bees than intensively managed meadows, if reduced management intensity is associated with altered vegetation characteristics such as reduced grass cover and vegetation height, and small‐scale availability of bare ground, driving these effects. This highlights that maintaining and promoting extensive management of meadows can promote ground‐nesting wild bees, including dominant crop pollinators, not only by enhancing floral resources but also by improving nesting opportunities in agroecosystems.

The Seismic Record of Wind in Alaska

ABSTRACT Seismic data contains a continuous record of wind influenced by different factors across the frequency spectrum. To assess the influences of wind on ground motion, we use colocated wind and seismic data from 110 stations in the Alaska component of the EarthScope Transportable Array. We compare seismic probability power spectral densities and wind speed and direction during 2018 to develop a quantitative measure of the seismic sensitivity to wind. We observe a pronounced increase in seismic energy as a function of wind speed for almost all stations. At frequencies below the microseism band, our observations agree with previous authors in finding that sensor emplacement and ground materials are important, and that much of the wind influence likely comes from associated changes in barometric pressure. Wind has the least influence in the microseism band, but that is only because its contribution to noise is much smaller than the ubiquitous microseism background. At frequencies above the microseism band, we find that wind sensitivity is correlated with land cover type, increasing with vegetation height. This sensitivity varies seasonally, which we attribute to snow insulation, the burial of vegetation and objects around the station, and potentially the role of frozen ground. Wind direction also manifests in seismic data, which we attribute to turbulent air on the lee side of station huts coupling with the ground and the seismometer borehole cap. We find some dependence on bedrock type, with a greater seismic response in unconsolidated sediment. These results provide guidance on site selection and construction, and make it possible to forecast seismic network performance under different wind conditions. When we examine the factors at work in a warming climate, we find reason to anticipate increasing seismic noise from wind in the Arctic over the decades to come.

Effects of Alfalfa Crop Rotation on Soil Nutrients and Loss of Soil and Nutrients in Semi-Arid Regions

Soil desertification is an important factor leading to soil degradation and environmental problems such as atmospheric or water pollution. Conservation agriculture, such as crop rotation and conservation tillage, can reduce soil erosion and nutrient loss caused by wind in semi-arid regions. However, the relationships between the loss of soil and nutrients and surface vegetation characteristics are frequently obtained according to a short-term simulation experiment, which makes the application of the conclusions limited. In this study, we conducted a 4-year field experiment continuously with three crop rotations, i.e., spring wheat (Triticum aestivum L.) cropping, alfalfa (Medicago sativa L.) cropping, and fallow when previous rapeseed crops (Brassica napus L.) were being harvested; measured the surface vegetation characteristics, soil nutrients, and loss of soil and nutrients caused by wind; and analyzed their variations and quantitative relations. The findings of this study indicated that the coverage, height, and biomass of the aboveground vegetation in three rotations in the spring and autumn had significant differences, and the rank order was fallow field &lt; wheat field &lt; alfalfa field. With the extension of growing years, the soil organic carbon (SOC) and total nitrogen (TN) of a 0–5 cm soil layer all increased to varying degrees and had significant differences among the rotations during the late stages of the experiment (p &lt; 0.05), while the changes in the total phosphorus (TP) and total potassium (TK) were small, and their values had decreasing trends. The available nitrogen (AN) and available phosphorus (AP) decreased first and then increased during the experimental period, while the available potassium (AK) had an increasing tendency. The increase in soil nutrients in the alfalfa field was the highest during the whole experiment period, while the loss of soil and nutrients (SOC, TN, TP, and TK) was the lowest, followed by the wheat and fallow fields. There were significant negative correlations between the surface vegetation characteristics and the loss of soil and nutrients (p &lt; 0.01), while the correlations between soil loss and the loss of soil nutrients were significantly positive. In summary, alfalfa crop rotation can obviously reduce the loss of soil and nutrients in semi-arid areas, which is conducive to the sustainability of agroecosystems.