Heterogeneous Vegetation Cover Research Articles

1100-years history of transformation of the East European forest-steppe into arable land: Case study from Kursk region (Russia)

Large parts of the East European forest-steppe are covered by agricultural and pastoral landscapes with decreasing proportions of semi-natural meadow steppes and fragments of semi-natural woodland. Although numerous palynological records indicate that a total deforestation occurred in the last 500 years, the details of the transformation from natural vegetation into an agrarian landscape are still lacking as well as an evaluation of the political role in this process. This study focuses on the vegetation and fire history at the northern edge of the forest-steppe in the Kursk region (Russia) and aim to reconstruct the transformation process in its historical context. New pollen, non-pollen palynomorphs and charcoal records with decennial to centennial resolution obtained from the Seim River region enable a comprehensive reconstruction of the local and regional landscape history over the last 1,100 years. The palynological records provide unique insights into the heterogeneous vegetation cover of microregions and evidence spatial asynchronous deforestation and crop field creation. Our findings highlight a crucial role of political systems on the formation of agro-pastoral landscapes with small remaining forest patches of today. The heterogeneity of the natural vegetation distribution before major deforestation as well as the duration of human impact should be considered in ecosystem restoration projects. All raw data are submitted to PANGAEA database (https://www.pangaea.de/: Peny PDI-34573, Razdolye PDI-34620). All pictures of NPP are submitted to NPP-ID (http://non-pollen-palynomorphs.uni-goettingen.de/).

Combining bird tracking data with high-resolution thermal mapping to identify microclimate refugia

Elevated temperatures can have a range of fitness impacts, including high metabolic cost of thermoregulation, hence access to microclimate refugia may buffer individuals against exposure to high temperatures. However, studies examining the use of microclimate refugia, remain scarce. We combined high resolution microclimate modelling with GPS tracking data as a novel approach to identify the use and availability of cooler microclimate refugia (sites > 0.5 °C cooler than the surrounding landscape) at the scales experienced by individual animals. 77 little bustards (Tetrax tetrax) were tracked between 2009 and 2019. The 92,685 GPS locations obtained and their surrounding 500 m areas were characterised with hourly temperature and habitat information at 30 m × 30 m and used to determine microclimate refugia availability and use. We found that the semi-natural grassland landscapes used by little bustards have limited availability of cooler microclimate areas—fewer than 30% of the locations. The use of cooler microclimate sites by little bustards increased at higher ambient temperatures, suggesting that individuals actively utilise microclimate refugia in extreme heat conditions. Microclimate refugia availability and use were greater in areas with heterogeneous vegetation cover, and in coastal areas. This study identified the landscape characteristics that provide microclimate opportunities and shelter from extreme heat conditions. Little bustards made greater use of microclimate refugia with increasing temperatures, particularly during the breeding season, when individuals are highly site faithful. This information can help identify areas where populations might be particularly exposed to climate extremes due to a lack of microclimate refugia, and which habitat management measures may buffer populations from expected increased exposure to temperature extremes.

Decades of weathering improves coal waste biogeochemical properties by altering dissolved organic matter and microbial communities: A case‐study of a coal waste pile

AbstractThe spatial heterogeneity of vegetation cover in coal waste pile suggests that the geochemical and biological properties of coal waste are spatially heterogeneous, which contributes to vegetation colonization on tailings. However, whether and how altering the organic matter components and microbial community of coal waste via weathering affects biogeochemical characteristics and vegetation establishment mechanisms remain unclear. Thus, we explored the characterization of dissolved organic matter in coal waste and its biogeochemical implications. Our results highlight coal waste as primary succession for bacterial communities and provide new insights into the bacterial community assembly response to dissolved organic matter and geochemical properties at such sites. Biogeochemical parameters including pH, EC, nutrients, particle size, and enzymatic activities were affected differently by organic components. The electrical conductivity (EC) decreased after decades of natural weathering, whereas pH, moisture, available N and P, urease, phosphatase, soluble microbial byproduct‐like material, humic acid‐like organics, bacterial diversity, and richness increased significantly. Further taxonomical and co‐occurrence network analyses indicated that the primary succession of bacterial communities was strongly influenced by biogenically formed organic components, particle size, and available nutrients. Our results revealed that the humic‐like components facilitated the improved biogeochemical properties and succession of composition and structure of the bacterial community. It demonstrated that weathering‐induced organic matter degradation trigger biogeochemical property improvement and bacterial community succession, and further facilitate the transformation of coal waste into a plant growth substrate. Our results provide a reference for engineering and application practice of utilization and in‐situ remediation ecological reconstruction in coal waste deposits.

UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Understanding the impact of land use and land cover change on surface energy and water budgets is increasingly important in the context of climate change research. Eddy covariance (EC) methods are the gold standard for high temporal resolution measurements of water and energy fluxes, but cannot resolve spatial heterogeneity and are limited in scope to the tower footprint (few hundred meter range). Satellite remote sensing methods have excellent coverage, but lack spatial and temporal resolution. Long-range unmanned aerial systems (UAS) can complement these other methods with high spatial resolution over larger areas. Here we use UAS thermography and multispectral data as inputs to two variants of the Two Source Energy Balance Model to accurately map surface energy and water fluxes over a nutrient manipulation experiment in a managed semi-natural oak savanna from peak growing season to senescence. We use energy flux measurements from 6 EC stations to evaluate the performance of our method and achieve good accuracy (RMSD ≈ 60 W m−2 for latent heat flux). We use the best performing latent heat estimates to produce very high-resolution evapotranspiration (ET) maps, and investigate the drivers of ET change over the transition to the senescence period. We find that nitrogen and nitrogen plus phosphorus treatments lead to significant increases in ET (P < 0.001) for both trees (4 and 6%, respectively) and grass (12 and 9%, respectively) compared to the control. These results highlight that the high sensitivity and spatial and temporal resolution of a UAS system allows the precise estimation of relative water and energy fluxes over heterogeneous vegetation cover.

Combined effect of bridge piers and floodplain vegetation on main channel hydraulics

Different complex flow fields like down-flow, horseshoe vortex, wake vortices and bow waves are generated due to the interaction of approach flow with bridge piers. Scientific researches around bridge piers with floodplain vegetation presents the multilevel fluid dynamics. This allows understanding the interactions of bridge pier and heterogeneous vegetation cover on the river corridor with the flow field of a stream channel. The present study experimentally investigated a three-dimensional turbulent flow field in the main channel of the river with the combined effect of bridge pier and floodplain vegetation. Further, the presence of a single large diameter pier with floodplain vegetation increases the main channel velocity up to 36% compared to no-obstruction on the floodplain. For the same case, the length of the vortex is more, and its strength is two times more than the double pier of smaller diameter. This reveals that the secondary vortex grows its size and strength in the large diameter pier. Furthermore, the convergence induced contraction of the curved channel along with the bridge pier on its floodplain solely affects the turbulent structures in the mainstream of the channel. The experiments associated with the pier diameter and number of piers placed on the floodplain will be notable in understanding flow behavior. In addition, for river ecosystem restoration, the notion of pier diameter, number of piers, and floodplain vegetation should be included in the design practice.

Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric and morphological properties. In addition, SAR acquisitions are weather-independent, providing a major advantage over optical imaging during periods of cloud cover. Most often, vegetation properties are measured using vegetation indices, including simple ratios of backscatter intensities. However, this approach can miss the scattered wave purity from vegetation targets. The scattered wave characteristic is an essential parameter as it is susceptible to the complex random structure of vegetation. In this study, we propose a novel dual-polarimetric SAR vegetation descriptor based on the co-pol purity component of the wave. We use this descriptor within a semi-empirical vegetation model to estimate soil moisture. It is noteworthy that this proposed method to retrieve soil moisture uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Therefore, the proposed method has the potential for operational scale monitoring applications at a global scale. This study validated different crop types (viz. canola, oats, forage, maize, soybeans, sugar beet, wheat, winter wheat, winter barley) over two test sites using airborne SAR data. The Root Mean Square Error (RMSE) values for soil moisture are within the range of 4.3% to 7.7% with the Pearson correlation coefficient r greater than 0.6. This novel descriptor will facilitate the operational use of dual-polarimetric GRD SAR data for vegetation monitoring and soil moisture estimation.The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (Rs) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET.

Remote sensing metrics to assess exposure to residential greenness in epidemiological studies: A population case study from the Eastern Mediterranean

Introduction/aimsApplication of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover. MethodsNDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method. ResultsFor the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas. ConclusionsLSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in dense urban areas. Therefore, LSU potentially improves exposure assessment precision, implying reduced exposure misclassification.

Investigation of land surface phenology detections in shrublands using multiple scale satellite data

Shrublands occupy about 13% of the global land surface, contain about one-third of the biodiversity, store about half of the global terrestrial carbon, and provide many ecosystem services to a large amount of world's human population and livestock. Because phenology is a sensitive indicator of the response of shrubland ecosystems to climate change, the alteration of ecosystems following species invasions, and the dynamics of shrubland ecosystem function, biodiversity, and carbon budgets, it is critical to monitor and assess phenological dynamics in shrubland ecosystems. However, most current land surface phenology (LSP) products derived from satellite data do not provide phenology detections in some semiarid shrublands where the amplitude of seasonal vegetation greenness is small. Therefore, we investigated the LSP detection using multiple spatial resolution satellite data and examined the impacts of spatial scales and shrubland ecosystem components (shrub and herb cover) on LSP detections over the western United States. Specifically, greenup onset date (GUD) in shrublands was detected from 30 m Harmonized Landsat and Sentinel-2 (HLS) data and 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) data to quantify scale effects. The GUD spatial patterns were explored with 30 m pixel variations in shrubland ecosystem components. The results show that GUD values varied with percent vegetation cover and shifted to earlier dates with increasing vegetation cover, demonstrating that satellite observations were not able to capture greenup onset until a threshold of green vegetation cover is reached. GUD was mostly undetectable from both HLS and VIIRS pixels with vegetation cover less than 10% and became fully detectable with vegetation covers larger than 50%. Similarly, the differences of GUD between HLS and VIIRS detections also decreased with increased vegetation cover. As a result of high shrubland heterogeneity, GUD from 30 m HLS pixels could be partially detected within a 500 m pixel despite GUD being undetectable from VIIRS time series. Moreover, vegetation cover heterogeneity also made it difficult for GUD at 30 m to be aggregated to coarse scales (such as to 500 m VIIRS pixels). These findings have significant implications to the detection and characterization of shrubland LSP responses to environmental and climate changes.

Assessing the effect of fire severity on sediment connectivity in central Chile

Chilean territory is recurrently affected by severe wildfires, which drastically reduce the forest cover and promote runoff, soil erosion and slope instabilities. To understand how the geomorphic system responds to wildfires in terms of sediment dynamics, the assessment of sediment connectivity, i.e. the property describing the relationships between compartments of a geomorphic system, is crucial. This study aims to quantify the spatial linkages between fire severity and sediment connectivity to identify common patterns and driving factors. The compound use of field data and open-source satellite imagery helped to apply the Relative differenced Normalized Burn Ratio (RdNBR) and the Index of Connectivity (IC) in the context of two consecutive wildfires, occurred in 2002 and 2015, in the Rio Toro catchment (Chile). The fire severity assessment showed that the 2002 event affected 90% of the catchment, with high severity areas representing around 70%. The 2015 wildfire instead, affected 76% of the catchment with moderate severity around 42%. Accordingly, as result of the sudden reduction in forest cover in severely affected areas, the IC changed after both wildfires with an overall increase of 1.07 and 0.54, respectively. However, only for the second disturbance, it was possible to observe a clear relationship between the RdNBR and the IC variations. The different degree of vegetation cover heterogeneity between the two pre-wildfire scenarios contributed to different fire severity and IC variability between the two disturbances. The use of open-source satellite data and the development of a weighting factor (W), to be used in IC and able to capture the land cover change driven by the wildfires, could make the application of this approach straightforward, promoting its reproducibility in other catchments for land management and risk mitigation purposes.

The experience of developing large-scale geobotanical maps based on field and remote sensing data

The article discusses the experience of compiling large-scale vegetation maps, the advantages of using aerial photography data from a radio-controlled unmanned aerial vehicle (UAV) and the technology of geo-information systems in combination with ground-based traditional geobotanical mapping methods. The use of remote sensing data is one of the ways to quickly obtain data for vegetation mapping. Currently this method is actively used and developed. However, satellite data has several disadvantages. The use of UAV is a promising direction for timely and cost-effective environmental monitoring. Using the aerial photo obtained in this way, it is possible, on the one hand, to trace the dynamics of vegetation during one season, and on the other, to identify and map both different plant communities and populations of individual species that do not coincide in the phenological phase of development with each other. The aim of the study was to reflect the heterogeneity of vegetation cover on a detailed geobotanical map in a simple way, which will act as the basis for further research. The map constructed by the described method is the most objective reflection of the state of the vegetation cover.

Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds

Digital Elevation Models (DEMs) are 3D representations of the Earth’s surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM).

Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data

ABSTRACTMapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.

Contrasting effects of mosaic structure on alpha and beta diversity of bird assemblages in a human‐modified landscape

Habitat loss and fragmentation are key processes causing biodiversity loss in human‐modified landscapes. Knowledge of these processes has largely been derived from measuring biodiversity at the scale of ‘within‐habitat’ fragments with the surrounding landscape considered as matrix. Yet, the loss of variation in species assemblages ‘among’ habitat fragments (landscape‐scale) may be as important a driver of biodiversity loss as the loss of diversity ‘within’ habitat fragments (local‐scale). We tested the hypothesis that heterogeneity in vegetation cover is important for maintaining alpha and beta diversity in human‐modified landscapes. We surveyed bird assemblages in eighty 300‐m‐long transects nested within twenty 1‐km2 vegetation ‘mosaics’, with mosaics assigned to four categories defined by the cover extent and configuration of native eucalypt forest and exotic pine plantation. We examined bird assemblages at two spatial scales: 1) within and among transects, and 2) within and among mosaics. Alpha diversity was the mean species diversity within‐transects or within‐mosaics and beta diversity quantified the effective number of compositionally distinct transects or mosaics. We found that within‐transect alpha diversity was highest in vegetation mosaics defined by continuous eucalypt forest, lowest in mosaics of continuous pine plantation, and at intermediate levels in mosaics containing eucalypt patches in a pine matrix. We found that eucalypt mosaics had lower beta diversity than other mosaic types when ignoring relative abundances, but had similar or higher beta diversity when weighting with species abundances. Mosaics containing both pine and eucalypt forest differed in their bird compositional variation among transects, despite sharing a similar suite of species. This configuration effect at the mosaic scale reflected differences in vegetation composition among transects. Maintaining heterogeneity in vegetation cover could help to maintain variation among bird assemblages across landscapes, thus partially offsetting local‐scale diversity losses due to fragmentation. Critical to this is the retention of remnant native vegetation.

Reptiles and frogs use most land cover types as habitat in a fine‐grained agricultural landscape

AbstractAgricultural landscapes comprise much of the earth's terrestrial surface. However, knowledge about how animals use and move through these landscapes is limited, especially for small and cryptic taxa, such as reptiles and amphibians. We aimed to understand the influence of land use on reptile and frog movement in a fine‐grained grazing landscape. We surveyed reptiles and frogs using pitfall and funnel traps in transects located in five land use types: 1) woodland remnants, 2) grazed pastures, 3) coarse woody debris added to grazed pastures, 4) fences in grazed pastures and 5) linear plantings within grazed pastures. We found that the different land cover types influenced the types and distances moved by different species and groups of species. Reptiles moved both within, and out of, grazed paddocks more than they did in woodland remnants. In contrast, frogs exhibited varying movement behaviours. The smooth toadlet (Uperoleia laevigata) moved more often and longer distances within remnants than within paddocks. The spotted marsh frog (Limnodynastes tasmaniensis) moved out of grazed pastures more than out of pastures with coarse woody debris added or fences and were never recaptured in plantings. We found that most recaptured reptiles and frogs (76.3%) did not move between trapping arrays, which added to evidence that they perceived most of the land cover types as habitat. We suggest that even simple fences may provide conduits for movement in the agricultural landscape for frogs. Otherwise, most reptile and frog species used all land cover types as habitat, though of varying quality. Reptiles appeared to perceive the woodland remnants as the highest quality habitat. This landscape is fine‐grained which may facilitate movement and persistence due to high heterogeneity in vegetation cover over short distances. Therefore, intensification and increasing the size of human land use may have negative impacts on these taxa.

Quantified moorland vegetation and assessment of the role of burning over the past five millennia

AbstractAimsTo apply the Landscape Reconstruction Algorithm (LRA) to pollen count data from multiple sites to estimate local vegetation abundance and compare with charcoal‐derived records of burning.LocationExmoor, southwest England, UK.MethodsPollen count data from 16 sites were transformed to estimates of distance‐weighted vegetation abundance using the LRA (REVEALS and LOVE models), correcting for bias in pollen production and dispersal. Charcoal concentration data from six sites were normalized using Box‐Cox transformation to produce z‐scores. Moving‐window correlation was undertaken to compare pollen percentage values for key taxa (Calluna, Poaceae) and localized burning. Estimates of distance‐vegetation abundance (LRA output) and time‐averaged charcoal z‐scores were compared to assess the role of burning as a driver for upland vegetation cover.ResultsComparison of pollen percentage and normalized charcoal z‐scores show little correlation between vegetation cover and burning. Estimates of distance‐weighted vegetation abundance and normalized charcoal data show relationships between vegetation change and burning at four of the six sites. The relationships are site‐specific: three sites suggest burning promoted grass‐dominated vegetation, at one site burning promoted heather‐dominated vegetation, and in two sites there is no apparent relationship.ConclusionsThe patterning of vegetation within uplands is a crucial part of ecosystem service delivery, and contemporary and future management benefits from understanding of ‘long‐term’ development, i.e. patterns over millennia. The correction of biases within pollen production and dispersal to produce local vegetation estimates has demonstrated spatial heterogeneity in vegetation cover on Exmoor that is not otherwise evident in the pollen percentage data (which retain a strong influence of the regional vegetation cover). The relationship between LRA‐derived vegetation cover and burning is not apparent in comparisons between pollen percentage data and charcoal records. This implies that studies that use pollen proportional data alone can misrepresent the relationship between vegetation cover and fire. This study demonstrates that fire has been an important part of the development of this cultural landscape.

Пространственное варьирование температуры корнеобитаемого слоя почв северной тайги Западной Сибири

Studies of soil heat regime are commonly confined to one vertical soil profile. Some studies on the basis of short-term temperature data series reported significant horizontal variation of topsoil horizons due to spatial variation in tree stand densities, plant cover and soil cover heterogeneity. Introduction of programmed automated temperature data loggers facilitated studies of horizontal heterogeneity in soil temperature, i.e. so called temperature fields with long temporal data series. However horizontal variation of soil temperature has not practically been studied in most Russian ecosystems, including those in the West Siberian northern taiga zone. The study of soil temperature regime was carried out in the environs of Noyabrsk (Yamalo-Nenetsky Autonomous Region, Russia) in the four key study sites: climax larch (Larix sibirica Ledeb.) and Scots pine (Pinus sylvestris L.) forests, and flat palsa mire (separately palsa and adjacent hollow). On each study site automated temperature data loggers were used in 5 replicated subsites to measure soil temperature with 0.5°С and record it every 4 hours, i.e. at 1, 5, 9, 13, 17 and 21 o’clock, daily. The loggers were placed at 10 cm soil depth in the root zone. The data from the meteorological station in Noyabrsk were also used in the study. The standard deviation of temperature values, recorded in a study site at one time point, was used as a measure of temperature variation. In all study sites the maximal temperature variation was observed at the beginning of the warm season. In both forest sites soil temperature began varying already after snow thawed, which was most likely due to the heterogeneity of vegetation cover and forest litter; in the pine site soil the heterogeneity of direct solar radiation as a result of the crown shadowing also contributed to temperature variation. The biggest soil temperature variation during this period was observed at the pine forest site, with difference between simultaneous replicated measurements reaching at certain time points as much as 9.6oС. In the larch study site the corresponding difference in June did not exceed 7-8oС. The minimal spatial variation of soil temperature in the root zone was observed in hollows of the flat palsa mire site, most likely due to the absence of the microrelief influence and high water saturation. The highest standard deviation values were observed during snow thawing at the beginning of May, when temperature difference between replicated measurements could be as high as 9.5oС. In June soil temperature variation decreased, staying low during the rest of the year. The palsa soil temperature at the flat palsa mire site and its variation in winter time showed strong response to changing air temperature. This winter variability was most likely due to unevenness of the snow cover thickness, resulting from the pronounced surface microrelief. During show thawing period the root zone temperature varied less, apparently because of even penetration and distribution of snowmelt water throughout the studied soil layer. Rapid soil warming resulted in increased temperature variability, which remained high during entire summer and the beginning of autumn, with differences between replicates being as high as 10.1oС. Summer variability of soil temperature at this site could be attributed to the permafrost layer, which in different microrelief positions is located at different depth as related to the root zone, thus exerting different influence on the temperature of the soil layers above. The most pronounced effect of diurnal fluctuations of root-zone temperature on its spatial variability was observed in pine forest during entire summer period, whereas on the palsa mire ecosystem it was observed at the beginning of summer during intensive thawing of the seasonally frozen layer.

Shrub-induced snowpack variability alters wintertime soil respiration across a simulated tundra landscape

ABSTRACTAcross the arctic tundra, the expansion of tall shrubs is expected to alter soil microbial activity in winter through shrub effects on snow redistribution. Tall shrubs act as a windbreak, trapping deep snowdrifts that insulate the ground from extreme cold and elevate soil respiration within shrub patches. However, this windbreak effect may reduce both snow cover and soil respiration in open tundra areas outside shrub patches. The net impact of increasing shrub cover on soil respiration across landscapes with heterogeneous vegetation cover and snow depths remains unknown. Here, I use a set of tundra landscape simulations to address this knowledge gap. The simulations vary shrub cover, mean snow depth and the strength of shrub windbreak effect. I show that for mean snowfall depths 40 cm or greater, increasing shrub cover usually reduced landscape-level soil respiration due to greater heterogeneity in snow depth. These simulations suggest that there is no overarching positive relationship between shr...

Impact of Plant Functional Types on Coherence Between Precipitation and Soil Moisture: A Wavelet Analysis

AbstractMapping the spatiotemporal patterns of soil moisture within heterogeneous landscapes is important for resource management and for the understanding of hydrological processes. A critical challenge in this mapping is comparing remotely sensed or in situ observations from areas with different vegetation cover but subject to the same precipitation regime. We address this challenge by wavelet analysis of multiyear observations of soil moisture profiles from adjacent areas with contrasting plant functional types (grassland, woodland, and encroached) and precipitation. The analysis reveals the differing soil moisture patterns and dynamics between plant functional types. The coherence at high‐frequency periodicities between precipitation and soil moisture generally decreases with depth but this is much more pronounced under woodland compared to grassland. Wavelet analysis provides new insights on soil moisture dynamics across plant functional types and is useful for assessing differences and similarities in landscapes with heterogeneous vegetation cover.

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

Abstract. Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which better represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.