Bare Ground Research Articles

Large eddy simulation of near-surface boundary layer dynamics over patchy snow

The near-surface boundary layer over patchy snow is highly heterogeneous and dynamic. Layers of opposing stability coexist within only a few horizontal meters. Conventional experimental methods to investigate this layer suffer from limitations related to the fixed positions of eddy covariance sensors. To overcome these difficulties, we set up a centimeter-resolution large eddy simulation of flow across an idealised transition from bare ground to snow. We force the simulation with high-frequency eddy covariance data recorded during a field campaign. We show that the model can represent the real flow by comparing it to independent eddy covariance data. However, the simulation underestimates vertical wind speed fluctuations, especially at high frequencies. Sensitivity analyses show that this is influenced by grid resolution and surface roughness representation but not much by subgrid-scale parameterization. Nevertheless, the model can reproduce the experimentally observed plumes of warm air intermittently detaching from bare ground and being advected over snow. This process is highly dynamic, with time scales of only a few seconds. We can show that the growth of a stable internal boundary layer adjacent to the snow surface can be approximated by a power law. With low wind speeds, deeper stable layers develop, while strong wind speeds limit the growth. Even close to the surface, the buoyancy fluxes are heterogeneous and driven by terrain variations, which also induce the frequent decoupling of a thin layer adjacent to the snow surface. Our simulations point the path towards generalizing point-based and aerial measurements to three dimensions.

Effects of multiple mammalian herbivores and climate on grassland-shrubland transitions in the Chihuahuan Desert.

The replacement of grasses by shrubs or bare ground (xerification) is a primary form of landscape change in drylands globally with consequences for ecosystem services. The potential for wild herbivores to trigger or reinforce shrubland states may be underappreciated, however, and comparative analyses across herbivore taxa are sparse. We sought to clarify the relative effects of domestic cattle, native rodents, native lagomorphs, and exotic African oryx (Oryx gazella) on a Chihuahuan Desert grassland undergoing shrub encroachment. We then asked whether drought periods, wet season precipitation, or interspecific grass-shrub competition modified herbivore effects to alter plant cover, species diversity, or community composition. We established a long-term experiment with hierarchical herbivore exclosure treatments and surveyed plant foliar cover over 25 years. Cover of honey mesquite (Prosopis glandulosa) proliferated, responding primarily to climate, and was unaffected by herbivore treatments. Surprisingly, cattle and African oryx exclusion had only marginal effects on perennial grass cover at their current densities. Native lagomorphs interacted with climate to limit perennial grass cover during wet periods. Native rodents strongly decreased plant diversity, decreased evenness, and altered community composition. Overall, we found no evidence of mammalian herbivores facilitating or inhibiting shrub encroachment, but native small mammals interacting with climate drove dynamics of herbaceous plant communities. Ongoing monitoring will determine whether increased perennial grass cover from exclusion of native lagomorphs and rodents slows the transition to a dense shrubland.

An automated system for 2D building detection from UAV-based geospatial datasets

The focus of this manuscript is on integrating optical images and laser point clouds carried on low-cost UAVs to create an automated system capable of generating urban city models. After pre-processing both datasets, we co-registered both datasets using the DLT transformation model. We estimated structure heights from the LiDAR dataset through a progressive morphological filter followed by removing bare ground. Unsupervised and supervised image classification techniques were applied to a six-band image created from the optical and LiDAR datasets. After finding building footprints, we traced their edges, outlined their borderlines, and identified their geometric boundaries through several image processing and rule-based feature identification algorithms. Comparison between manually digitized and automatically extracted buildings showed a detection rate of about 92.3 % with an average of 7.4 % falsely identified areas with the six-band image in contrast to classifying only the RGB image that detected about 63.2 % of the building pixels with 25.3 % pixels incorrectly identified. Moreover, our building detection rate with the 6-band image was superior to that attained by performing traditional image segmentation for only the LiDAR DEM. Shifts in the horizontal coordinates between corner points identified by a human operator and those detected by the proposed system were in the range of 10–15 cm. This is an improvement over traditional satellite and manned-aerial large mapping systems that have lower accuracies due to sensor limitations and platform altitude. These findings demonstrate the benefits of fusing multiple UAV remote sensing datasets over utilizing a single dataset for urban area mapping and 3D city modeling.

Grassland albedo as a nature-based climate prospect: the role of growth form and grazing

Abstract Nature-based solutions for mitigating climate change focus largely on land management to reduce carbon emissions and enhance carbon sequestration. Tree planting, commonly advocated for carbon offset, threatens grassland biodiversity and may induce positive radiative forcing (warming) by lowering albedo. Before making decisions about land-use changes in grasslands, an understanding of the fine-scale albedo of grassy versus woody vegetation is needed. Existing satellite-based albedo products offer global coverage with temporally fine, but spatially coarse, resolution, whereas fine-scale in situ grassland albedo data are sparse. We examined the hypotheses that albedo varies seasonally between grass type patches, between shrub and grass patches, and with grazing at the patch scale. Using a tripod-mounted albedometer, we quantified albedo of seven distinct grassland patches in South Africa’s eastern Karoo during early and late dormancy and growing seasons. Patches included intensely-grazed grazing lawn (Cynodon dactylon), grazed and less-grazed red tussock grass (Themeda triandra), grazed and less-grazed white tussock grass (Eragrostis lehmanniana), shrub (Pentzia incana) encroached grass, and bare ground. Season influenced albedo in all patches and, additionally, we found strong differences for the same period between years due to varying rainfall and temperature patterns. For grass-dominated patches, albedo differences were most pronounced during early dormancy, likely due to an effect of grass inflorescences. Albedo of intensely-grazed grazing lawns was consistently higher than other patches, except during early dormancy when white tussock grass albedo was equally high. We found no albedo difference between grazed and less-grazed tussock patches of either red or white grass. Shrub-encroached patches exhibited consistently lower albedo than other patches. Our findings underscore the nuanced relationship between grassland patches and albedo, with shrub encroachment, proposed afforestation, and certain grasses possibly increasing warming potential through reduced albedo. As climate initiatives extend into grasslands, understanding these patterns is essential for climate change mitigation and grassland conservation.

Reducing CO2 emissions from rivers around bare land contributes to improving the carbon sink function of the Qinghai–Tibetan Plateau

River CO2 fluxes (FCO2) play an important role in the carbon cycle of the Qinghai‒Tibet Plateau (QTP). However, the carbon budget of river water is ignored in the assessment of the carbon sources and sinks of ecosystems on the QTP, which makes the assessment of the carbon sinks of ecosystems uncertain on the QTP. Here, we selected rivers around alpine steppe, alpine meadow, forestland and bare land areas on the QTP to determine their FCO2 values and control factors during thaw, rainy and freezing periods. The results revealed that river water around the alpine steppe absorbed CO2 during the thaw and freezing periods, making it a carbon sink. The influencing factors of FCO2 around different ecosystems significantly differed across the different periods. The pCO2 was the most direct factor affecting the FCO2 of river water. During the thaw period, the FCO2 of rivers around the alpine steppe and bare land was affected mainly by dissolved organic carbon (DOC) and precipitation, respectively. The FCO2 of rivers around the alpine meadow and forestland was affected by dissolved inorganic carbon (DIC). During the rainy period, the FCO2 of the rivers around the alpine steppe and bare land was affected mainly by total nitrogen (TN) and salinity, respectively. The FCO2 of rivers around the alpine meadow and forestland was affected by dissolved oxygen (DO) and DOC. During the freezing period, the FCO2 of the rivers around the alpine steppe and bare land was affected mainly by TN and pCO2, respectively. The FCO2 of rivers around the forestland and alpine meadow was affected by precipitation, runoff and DIC. The FCO2 in rivers around vegetated areas were affected by rock weathering, aquatic ecosystem biology and hydrological processes to varying degrees at different times. However, rivers around bare ground were less affected by these processes. The FCO2 values around the alpine steppe, alpine meadow, forestland and bare land were 1.40 g C/m2/d, 1.12 g C/m2/d, 2.29 g C/m2/d and 4.20 g C/m2/d, respectively. The river water around bare land released a large amount of CO2. Therefore, to reduce the CO2 emissions of rivers on the QTP, it is urgent that we restore bare-land vegetation and promote the transfer of bare land to steppe, which is an effective way to increase the carbon sink of the ecosystems on the QTP.

Analysing Thermal Land Surface Characteristics and Urban Expansion in Kenitra City, Morocco, using Remote Sensing and GIS

Abstract This research investigates how land use and cover the affect land surface temperature in Kenitra. It employs Geographic Information System techniques and remote sensing methods to monitor and control surface temperature changes. The present study integrates remote sensing and geographic information system to process satellite images. Furthermore, it focuses, also, on analyzing land surface temperature and its correlation with land cover changes over three time periods (1989, 2000, 2020) using landsat satellite data. Thus, supervised classification is employed to map these changes and derive urban heat islands from thermal band data. This study provides a comprehensive evaluation of the relationship between land use and land surface temperature. It utilizese a landscape dynamics assessment based on multi-source and multi-sensor remote sensing technologies. Specifically, the study uses landsat satellite data (TM for 1989 and OLI-8 for 2002 and 2020) to assess the effects of land use and land cover changes on land surface temperature distribution in the Kenitra city. Spatial and statistical analyses are performed by comparing maps from remotely sensed data generated using geographic information system. Therefore, these analyses reveal various changes in the kenitra region between 1989 and 2020. The primary changes observed include an increase in built-up areas and bare ground with a decrease alongside in natural areas (vegetation, etc.). The average temperatures in 1989, 2002, and 2020 were 29°C, 31.05°C, and 32°C, respectively. The obtained results are valuable for planning and managing climate scientists, land-use planners, and researchers focusing on sustainable urbanization areas. The study’s findings will assist urban planners and policymakers in adopting appropriate measures for sustainable planning in the city to mitigate the adverse effects of land surface temperature.

Burrowing Owls Require Mutualist Species and Ample Interior Habitat Space

Mitigating habitat loss of western burrowing owls (Athene cunicularia hypugaea) often involves relocation from California ground squirrel (Otospermophilus beecheyi) burrows to offsite nest boxes. Naval Air Station Lemoore (NASL), Kings and Fresno counties, California, initiated this approach to displace a regionally important population from airfield grasslands. We examined monitoring data of burrowing owls and fossorial mammals at NASL to assess mitigation options. Occupied nests increased by 33 (61%), with 47 nest box installations in 1997–2001, peaked at 87 in 1999, then declined by 50 through 2013. Although ≥13 nest boxes were occupied in 2000, none were occupied in 2003–2013. Within a 43.1 ha isolated grassland monitored for 13 years, nest site reuse in ground squirrel burrows averaged only 17% between any 2 consecutive years. Compared to the average density across grassland study areas, ground squirrel burrow systems/ha numbered 43% higher within 60 m of occupied nests and non-breeding-season burrows. Vegetation clearing to restore kangaroo rat (Dipodomys n. nitratoides) habitat preceded a 7.4-fold increase in ground squirrel burrow systems and a 4-fold increase in occupied nests, but drought-induced extirpation of ground squirrels eliminated occupied nests from the 43.1 ha grassland study area. Ground cover near occupied nests averaged 58% of the mean vegetation height and 67% of the mean percentage of bare ground in the field. Both nest sites and non-breeding-season burrows occurred >60 m interior to field edges 1.4 times more than expected. Non-breeding-season burrows averaged 328 m from same-year nest sites, and only 7% of non-breeding-season burrows were also used as nest sites. Mitigating habitat loss should be made more effective by fostering natural burrow construction by fossorial mammals on patches of short-stature vegetation that is sufficiently expansive to support breeding colonies of ≥12 pairs averaging ≥60 m from the field’s edge and a separation between non-breeding-season burrows and nest burrows minimally equal to mean nearest-neighbor distances among nests.

Land Use and Land Cover Change Detection Using Remote Sensing in the Kal River Basin, Raigad District, Maharashtra, India

Land use and land cover (LULC) are distinct yet interrelated concepts that describe the characteristics and utilization of land. Land cover refers to the physical surface, such as vegetation, water bodies, or man-made features, while land use denotes the purpose of land utilization. Changes in LULC significantly impact water resources, making it a critical component in water resource studies. This study aims to detect changes in LULC over a five-year period (2017–2021) in the Kal River basin, a sub-basin of the Savitri River in Maharashtra, India. Sentinel-2 satellite imagery with a 10 m resolution was used, and the supervised classification method, specifically Maximum Likelihood Classification (MLC), was applied to classify LULC into six categories: cropland, bare ground, built-up areas, trees, rangeland, and water bodies. The results show that in 2017, the areas under cropland, tree cover, bare ground, built-up areas, and water bodies were 35.20 km², 187.61 km², 0.133 km², 7.77 km², and 1.89 km² respectively. By 2021, cropland, tree cover and bare ground decreased by 3.82%, 4.85%, and 0.01% respectively while water bodies, rangeland, and built-up areas increased by 0.02%, 8.34%, and 0.32% respectively. The overall accuracy of LULC classification was 77% for 2017 and 91% for 2021 with validation using the Kappa coefficient indicating good to excellent accuracy. This study highlights the importance of monitoring LULC changes for understanding the impacts of human activities and climate on watershed development and water resource management. Such analysis provides valuable insights for decision-makers to plan for sustainable development, land conservation and environmental management, ensuring balanced growth while protecting natural resources in the Kal River basin.

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

Delineating the climate change impacts on urban environment along with heat stress in the Indian tropical city

India experiences significant levels of temperature variance as a result of urbanization, deforestation, and industrial growth, all of which contribute to a progressive increase in heat island effects. Significant landform change has occurred in Bhubaneswar City, Odisha, in recent decades as a result of major plant loss and temperature unpredictability. Thus, during the summer and winter months, an examination of the long-term heat island impacts is essential for Bhubaneswar. The evolution of landforms and heat island impacts in Bhubaneswar are evaluated using Landsat images from 1991 to 2021 datasets and correlation analysis of heat islands and geospatial variables. Between 1991 and 2021 with a 10-year gap, the landforms studied showed an increase in built-up land (38.009 km2), a decrease in vegetation (13.875 km2), bare ground (16.306 km2), and water bodies (7.828 km2). The annual temperature variance in Bhubaneswar was 0.124 °C (summer), and 0.293 °C (winter). Surface temperatures varied by about 3.71 °C (summer) and 8.80 °C (winter). During the study years, heat islands increased from 2.89 to 4.27 (summer) and 2.30 to 3.87 (winter). In Bhubaneswar, there was an increase in ecological variation of 0.092 (summer), and 0.071 (winter). There is a statistically significant negative correlation between the R2 values and the temperature and the built-up indices. Using this dataset, administrative and development authorities can choose which measures to take to counteract the effects of variations in thermal variation. Finding out how man-made land use structures, industrial zones, and other areas contribute to the built-up area a direct consequence of heat islands was the aim of this study.

Evaluation of subsurface soil water content estimate methods: Maximum entropy vs. exponential filter

Profile soil water content (SWC) is a vital variable in the atmosphere-vegetation-soil system. Although remote sensing currently can provide reliable surface SWC data (∼5 cm depth), acquiring accurate subsurface SWC data from existing reanalysis products remain challenging. In this study, we evaluated two widely used methods in estimating subsurface SWC, namely the exponential filter (ExpF) and the principle of maximum entropy (POME). The evaluation is carried out in two distinct areas on the Tibetan Plateau using ground observations collected from two monitoring networks: the Maqu network area characterized by cold humid climate and grassland and the Shiquanhe network area characterized by cold arid climate and bare ground. The results indicate that POME generally performs better than ExpF in both areas, particularly in deeper soil layers. Specifically, the accuracy of estimated SWC using the ExpF method decreases with depth, while it increases with depth using the POME method. Additionally, both methods achieve commendable performance at a depth of 10 cm in both areas. The deficiency of ExpF is mainly reflected in underestimations for dry cases, which is amplified with increasing depth. Dry cases account for 51 % in the humid area and 68 % in the arid area throughout the study period. Consequently, the ExpF method yields higher root mean square differences (RMSD) by 30 % and 113 % in the humid area at depths of 20 and 40 cm, respectively, compared to the POME method. Similarly, it results in higher RMSD values by 220 % and 200 % in the arid area. As expected, the superior performance of POME in deeper soil layers is primarily attributed to the incorporation of additional bottom and profile mean SWC observations. However, it also potentially introduces uncertainties when integrated with satellite-based data, which inherently contains errors compared to ground observations. To assess the potential of these two methods in large-scale applications combined with satellite-based datasets, this study conducted further evaluation of both methods with required input data derived from the soil moisture active and passive mission (SMAP). The results demonstrate that the performance of both methods in estimating subsurface SWC is acceptable in both humid and arid areas, although some bias is transferred from the input data. They achieve average RMSD values of 0.034 and 0.055 m3/m−3(−|−) in the humid area for the ExpF and POME methods, respectively, and 0.021 and 0.014 m3/m−3(−|−) in the arid area.

Fine spatial scale assessment of structure and configuration of vegetation cover for northern bobwhites in grazed pastures

BackgroundMonitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration.ResultsEstimated heights from drone imagery had a significant relationship with the field height measurements in September (r2 = 0.83; growing season) and February (r2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p < 0.05).ConclusionsDrones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing. This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing.

Contribution of Landsat Multispectral Images to the Land Cover Dynamics of the Toéghin-Bangré Watershed for the Period 1998 to 2022 in Burkina Faso

Remote sensing plays an important role in the continuous observation of the earth's surface. It produces large databases that can be used to map and monitor natural resources. To this end, land-use maps are genuine planning and decision-making tools for natural resource management. The aim of this study is to use remote sensing products to show the dynamics of land use, to understand these dynamics and their impact on water resources in general and on hydrology in particular. Specifically, it is a question of identifying the similarities and differences that have occurred, and finally evaluating the rate of change. The methodology adopted is the use of the likelihood algorithm to produce three thematic classifications for the years 1998, 2010 and 2022. Overall accuracy is determined from the confusion matrix, which ranges from 80% to 91%. Supervised classification yielded seven occupancy classes: water bodies, cultivated areas, gallery forests, habitats/outcrops, grassy savannahs, wooded savannahs and bare ground. Quantification of change from 1998 to 2022, assessed by the rate of change (Tc), shows a progressive trend estimated at 31.82% (habitats/outcrops) and 43.40% (grassy savannah), and a regressive trend estimated at -17.92% (cultivated areas); -95.15% (gallery forests); -51.93% (water bodies) and -78.36% (bare ground). At the end of our study, diachronic analysis enabled us to quantify changes between the years 1998-2010, 2010-2022 and overall 1998-2022 with good precision.

Prescribed fire influences occupancy and abundance of grassland-nesting passerines overwintering in pine forests of Central Louisiana

We surveyed longleaf (Pinus palustris) and loblolly pine (P. taeda) stands burned within 0–7 years to examine the influence of prescribed fire on grassland-nesting passerines that overwinter in pine forests of central Louisiana, USA. We modeled occupancy and abundance of Bachman’s sparrows (Peucaea aestivalis), Henslow’s sparrows (Ammodramus henslowii), and sedge wrens (Cistothorus stellaris) in relation to stand type, years since burn, and site-scale vegetation conditions. We also used analysis of variance to compare vegetation conditions across study sites and detection points for these three species. Bachman’s sparrow occupancy was 3.7 times greater in longleaf stands compared to loblolly stands and decreased 30 % with every 10 % increase in canopy cover. We also found a positive relationship between Bachman’s sparrow density and the density of herbaceous plants in both stand types. Henslow’s sparrows used herbaceous vegetation within longleaf pine stands burned 0–2 years ago that was significantly taller than what we observed at the site scale, but the best fit models for Henslow’s sparrow occupancy (maximum vegetation height, bare ground, herbaceous cover) had overlapping confidence intervals. Henslow’s sparrow density was positively associated with bare ground and negatively associated with litter depth. Sedge wren occupancy decreased 50 % with every 10 % increase in canopy cover. Sedge wren density increased with increasing percent herbaceous cover, however, the 95 % confidence intervals for this relationship largely overlapped. Our study reiterated the importance of frequent (<3 years) prescribed fires to maintain habitat used by grassland-nesting passerines that overwinter in southeastern pine forests.

Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover

Abstract Traditionally, local-scale habitat-relationship models are developed over small spatial extents, limiting model transferability and inference outside the study area. Thus, habitat managers frequently lack fine-scale information regarding the influence of vegetation composition and structure on site suitability or species abundance. Gymnorhinus cyanocephalus (Pinyon Jay) represents one declining species for which managers have limited information regarding the influence that vegetation composition and structure have on abundance at broad scales. To address this need, we developed a hierarchical Bayesian abundance model using summertime bird and vegetation data collected under the Integrated Monitoring in Bird Conservation Regions program to explain jay abundance as a function of local conditions. Our G. cyanocephalus abundance model allowed abundance relationships with pinyon pine (Pinus edulis and P. monophylla) and juniper (Juniperus spp.) to vary by ecoregion, thereby accounting for potential regional differences in habitat associations. We found G. cyanocephalus abundance was generally positively associated with pinyon pine and juniper cover; however, habitat relationships varied by ecoregion. Additionally, we found positive associations between jay abundance and grass cover, sagebrush cover, and percent bare ground. Our results agree with prior research suggesting mechanical removal of pinyon pine and juniper trees for sagebrush restoration or fuel treatments may negatively affect G. cyanocephalus. Managers wishing to reduce pinyon and juniper tree cover without negatively affecting G. cyanocephalus may benefit from targeting sites where both large-scale distribution models and our local habitat relationships suggest G. cyanocephalus are likely to occur in low numbers. Additionally, our modeled relationships indicate restoration that increases grass cover, sagebrush cover, and bare ground, while maintaining pinyon and (or) juniper cover, may lead to increased local densities of G. cyanocephalus.

Perseverance of management is needed – Efficient long-term strategy of Reynoutria management

One of the most problematic invasive species in Europe are knotweeds from genus Reynoutria (Fallopia) which have significant negative impact on the native communities as well on human activities. Therefore, they are a target of many control programmes. Due to their high regeneration potential, their management is problematic, and only chemical treatment is reported to be sufficiently effective. The aim of this paper was to describe and analyse the patterns of Reynoutria invasion under long-term chemical treatment with glyphosate-based herbicide in The Morávka river floodplain, Czech Republic. The data covers 17 years of management which started with the European project “Preservation of alluvial forest habitats in the Morávka river basin”. We focus on (i) assessment of Reynoutria distribution during long-term management, (ii) analysis of the change of distribution according to the habitat, and (iii) discussion of the optimal management strategy based on the long-term data. Distribution data was obtained using GNSS field mapping. Before the start of the study in 2007, Reynoutria stands covered 29% of the study area (96.9 ha). As a result of systematic whole area chemical management, the extent decreased to 19.6% (65.3 ha) in 2009, and even reached 14.5% (48.2 ha) in 2013, three years after its end. Due to implementation of local chemical management in the following years, the area of Reynoutria was maintained at similar level, with minimum value 41.8 ha in 2018 and a slight increase in recent mapping in 2023. Beside the extent, the structure and coverage of invaded sites was analysed. There was a clear trend of fragmentation of larger polycormons with high coverage into many smaller and less dense ones as a result of chemical spraying. The average size of Reynoutria stand decreased from 0.61 ha in 2007 to half in 2013 (0.32 ha) to 0.15 ha in 2023. Testing of the effects of time, habitat, and biotope did not reveal significant differences of changes of extent and abundance over different environments (forest, open, bare ground), which indicates that there are no differences in reaction to management in the studied habitat and vegetation types. Our study provides a robust and unique overview of the invasion, reinvasion, and suppression dynamics for an important invasive species. If herbicide management is used, chemical treatment must be quite long-term as even three years of intensive glyphosate foliar spray application was not sufficient for the complete eradication of Reynoutria. Therefore, we propose the following procedure for effective chemical management of Reynoutria: 1) In largely infested sites, the first step is to reduce the distribution of Reynoutria stands to isolated polycormons. This phase can last 3–5 years. 2) After reaching the state of sparse distribution of Reynoutria, we recommend herbicide application only in periods of every 3–5 years depending on the local context and rate of regrowth. 3) At sites exposed to soil disturbances, where the soil is contaminated by fragments of Reynoutria rhizomes, there is a need to apply herbicide immediately to target newly resprouting individuals.

Comparative Study of the Impacts of Maize and Soybean on Soil and Water Conservation Benefits during Different Growth Stages in the Loess Plateau Region

Maize (Zea mays L.) and soybean (Glycine max L. Merr.) are prevalent summer crops planted widely in the Loess Plateau region of China, which is particularly susceptible to severe soil erosion on the sloping farmland. However, which crop exhibits superior soil and water conservation capabilities while maintaining economic viability, and how their performance in soil and water conservation is affected by slope gradient and rainfall intensity remains unclear. The objective of this study was to compare the impacts of maize and soybean on regulating runoff and sediment through rainfall simulation experiments, and explore the main control factors of soil and water conservation benefits. Five slope gradients (8.7, 17.6, 26.8, 36.4, and 46.6%) and two rainfall intensities (40 and 80 mm h−1) were applied at five respective crop growth stages. Both maize and soybean effectively reduced soil and water losses compared with bare ground, although increasing slope gradient and rainfall intensity weakened the vegetation effect. Compared with slope gradient and rainfall intensity, vegetation coverage was the main factor affecting the performance of maize and soybean in conserving soil and water. The average time delay benefit (TDB), runoff reduction benefit (RRB), and sediment reduction benefit (SRB) of soybean (246.48 ± 11.71, 36.34 ± 2.51, and 54.41 ± 3.42%) were significantly higher (p &lt; 0.05) than those of maize (100.06 ± 6.81, 25.71 ± 1.76, and 43.70 ± 2.91%, respectively) throughout growth. After planting, the increasing rates of vegetation coverage, TDB, RRB, and SRB with time were consistently higher with soybean than maize. Moreover, under the same vegetation coverage, the TDB, RRB, and SRB of soybean were also consistently higher than those of maize. In conclusion, these findings indicate that soybean outperformed maize in terms of soil and water conservation benefits under the experimental conditions, making it more suitable for cultivation on sloping farmland. This finding offers crucial guidance for the cultivation of dry farming in regions plagued by severe soil erosion, facilitating a balance between economic objectives and ecological imperatives.

Land-use suitability assessment for urban development using Multi-Criteria Decision-Making Analysis in the Himalayan districts of Shimla, Nainital, and Darjeeling, India

After India gained independence in 1947, notable advancements in healthcare and socio-economic sectors resulted in a remarkable population surge, consequently driving substantial expansion in built-up areas throughout the country. The consequences of the rapid and unplanned expansion are adverse in the Himalayas where earthquake, landslides, flash floods are frequent. The already established hill stations saw a rapid surge in tourism and subsequent urbanization. This study delves into assessing the urban suitability of such hill stations namely Nainital, Shimla, and Darjeeling. The surge in tourism led to alarming population growth with haphazard urbanization which is often severely affected by growing natural hazards. The aim of this study is to assess the urbanization suitability of the regions based on multiple factors that affects the choice of suitable place for urban growth. We employed the GIS based Multi-Criteria Decision-Making Approach (MCDMA) considering geological, socio-economical, ecological, and prohibitive factors using the Analytical Hierarchy Process (AHP) method. The obtained land-use suitability maps for urbanization show that in Nainital the most suitable area for urbanization is the foothills of the Himalayas whereas in Shimla it is around the central and southern part. The northern part and the eastern boundary of Darjeeling are the most suitable area for urbanization. Upon comparing the already existing built-up areas with the AHP derived site suitability results we found that the Darjeeling district is most vulnerable as almost half of the built-up area (49%) lies within a very low to moderate suitable zone. Our findings show that Darjeeling has the highest amount of suitable land for urbanization as the bare ground in the region is not suitable for agriculture. Interestingly, the farmlands in Nainital show highest suitability when urbanization is considered. This brings out a serious problem in urbanization trend and it is observed throughout the world. Thus, more careful research is needed ensuring proper land use and food security for the growing population. This study lays a foundational understanding of challenges associated to urbanization in a tectonically active mountain range.

A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.

Seasonal Fluctuations in Plant-Parasitic Nematode Vertical Distributions and Their Interactions with Edaphic Factors in Vegetable Fields of South Georgia, U.S.A.

Plant-parasitic nematodes (PPNs) can be found deep in the soil profile, compounding nematode management decisions and detection. This study aimed to understand how seasonal fluctuations in edaphic factors are associated with the vertical distribution of PPNs in south Georgia's vegetable cropping systems. Five-core composite soil samples were taken monthly (March 2020 to February 2022) at three random locations in six vegetable fields. Fields represented three cropping systems (vegetable plasticulture, bare ground cucumber, and plastic bed watermelon rotation) and two regions (north and south) sampled from five 15-cm strata (0 to 15, 15 to 30, 30 to 45, 45 to 60, and 60 to 75 cm). Only soil temperatures and precipitation had seasonal fluctuations; the other edaphic factors were vertically stratified. Latitude and stratum had the strongest associations with nematode composition, showing a clear separation between the north and south regions. Variations in soil texture, porosity, moisture, and PPN vertical distribution suggested that the illuvial zone in the north region was the main factor in the differences observed between regions and strata. Seasonal fluctuations in vertical distribution were found among Meloidogyne incognita and Nanidorus spp. Higher abundances of deep-dwelling PPNs were limited to the south region in the winter for deep-dwelling M. incognita and summer and winter for Nanidorus spp., probably due to the illuvial zone barrier in the north region. Because most root-knot nematodes dwell at ≤30 cm during the summer, fumigant/nematicide applications are recommended during this season to minimize the risk of deep-dwelling escapees.