Spatial Patterns Of Vegetation Research Articles

Global spatial distribution of vegetation and 20-years dynamic changes

Global spatial distribution of vegetation is determined jointly by climate, monsoon, ocean current and human activities. In recent decades, surface vegetation changes caused by global warming, extreme climate events and intensified human activities need a further understood. In this paper, we adopted available MODIS land cover dataset (MCD12Q1) to investigate the spatial distribution feature of global vegetation sand dynamics during the past 20 years. Results show that: (1) the spatial distribution pattern of global vegetation formed by the long-term ecosystem evolution exhibits stable with less variation in both latitude and vertical dimensions. Generally, the percent of forest, shrubland, grass and cropland respectively are 15.27%, 9.39%, 39.46% and 8.91% of surface covers, with a distinct land cover transition trend from rainforest at equator region to moss and lichen at the north and south pole. (2) In the past 20 years, the global forest land decreased by 3.0×105 km2, with an average annual decrease of 1.255%, open shrubs experienced slight fluctuations in 2001-2017 and increased sharply by 1.6×105 km2 in 2017-2018. And the grassland, croplands and cropland/natural vegetation mosaics had different degrees of growth rate, with an average annual dynamic increase of 0.021%, 0.019% and 0.502%, respectively. (3) There are about 4.2×106 km2 of forest changed into grassland in West Siberia and the Amazon, and 2.5×106 km2 of grassland into shrubs, as well as 1.9×106 km2 of grassland vegetation into farmland, and 9.4×104 km2 of forest into shrubs.

Spatial and temporal patterns of upland vegetation over the last 200 years in the northern pyrenees: Example from the Bassiès valley, Ariège, France

Quantitative estimates of past vegetation with a clearly defined spatial scale are a critical step to better understand the patterns and processes of changes in vegetation and land-cover over time and space. However, such estimates are difficult to obtain from pollen data due to their lack of spatial dimension and the inter-taxonomic differences in pollen production, dispersal, and deposition mechanisms. This study assesses the potential of the Landscape Reconstruction Algorithm (LRA) to reconstruct the spatial and temporal patterns of upland vegetation in the Bassiès Valley, northern Pyrenees over the last 200 years, a period characterised by a decline of traditional pastoral activities. We used well-dated pollen records from eight bogs and ponds and vegetation and land-cover data within a 1-km radius from each site to evaluate the LRA-based reconstruction of local land-cover. The LRA approach was then used to reconstruct the 200-year history of local land-cover dynamics at 10 to 20-year intervals around each site in relation to land-abandonment. Our study shows that in addition to allowing long-term reconstruction, LRA estimates (1) are more sensitive to vegetation composition changes over time and space compared to historical land-cover maps and pollen percentages, and (2) improve the reconstruction of past local vegetation compared to pollen percentages alone. The LRA results reveal site-specific land-cover trajectories within the small-sized study area over 200 years. Some sites exhibit changes in land-cover composition with heathland and tree cover increase (ESC, EM), while others are found to be more stable, dominated 1) exclusively by heathland (LEG, W1652), 2) heathland with intermediate proportions of grassland (FOUZ, SIG), or 3) exclusively by grassland (OT). This land-cover composition variability over space and time may be related both to local topography and past grazing activity. The quantification of past vegetation dynamics provides opportunities for bridging the gap between researchers, policy makers and practitioners, and should be taken into account to further investigate the effect of long-term grazing activities on the past and modern plant and landscape diversity.

Size distribution of the quasi-circular vegetation patches in the Yellow River Delta, China

Recent empirical studies have suggested that the patch-size distribution of vegetation can be fitted by a power law, truncated power law, or lognormal model to provide explanatory mechanisms for vegetation pattern formation in arid and semiarid regions. However, contradictory results have been reported. Therefore, additional empirical studies are necessary to test the patch-size distribution of vegetation over several regions before it can be considered as an indicator for assessing the discontinuous transition of ecosystems and understanding the mechanisms of vegetation pattern formation. Analogous to arid and semiarid regions of the world, vegetation patterns are characterized by a two-phase mosaic composed of dense vegetation patches interspersed with areas of bare soil, referred to as quasi-circular vegetation patches (QVPs), in the Yellow River Delta (YRD), China. However, research on the patch-size distribution of the QVPs reflecting vegetation patterns and ecosystem functioning is lacking. To fill this gap, for the first time, we examined the patch-size distribution of the QVPs using the fused IKONOS high-spatial-resolution image and evaluated the statistical distributions that better fit the patch size data of the QVPs in the YRD. We found that a power law, truncated power law, or lognormal distribution was not supported in the study area, whereas gamma distribution reasonably fits the size data of QVPs, implying that micro-depressions, combined with the water-limited and salinization environments had considerable effects on vegetation pattern formation. Our results provide helpful insights and suggest that further studies are needed to classify different types of QVPs. Additionally, more efficient approaches need to be used to fit the statistical distributions for elucidating the spatial vegetation patterns in the YRD.

Effective Screening and Texture Segmentation of Green Vegetation Cover Based on UAV Images

As an important part of the ecosystem, green vegetation coverage is crucial to people’s sensory and mental health. Using reliable data sets to classify and identify the green vegetation cover on the land surface and explore its spatial distribution law can provide important reference for the work of regional ecosystem managers and urban planners. The optimization of effective screening methods for green vegetation coverage areas is an important requirement to measure the surface vegetation status. UAV aerial images feature high definition, large scale, small area and high up-to-dateness. However, at present, there are few studies based on the reliable UAV aerial image system to identify green vegetation cover and further explore its spatial changes. In this study, 701 residential neighborhoods in Beijing were taken as the research objects, and the green vegetation of 7,695 sample points was identified by UAV. The green vegetation coverage was measured, and the spatial distribution pattern of green vegetation in different land surface areas was quantitatively compared. The results show that the image processing method proposed in this paper can effectively detect the boundary of green vegetation cover area from UAV aerial images, the correlation of texture segmentation is good, and the segmentation performance is better than other methods. The distribution of green vegetation cover in the research target area is uneven, with 63.79% of the research area having relatively low (Level 2) and medium (Level 3) green vegetation coverage, which indicates that the green vegetation coverage area in the research area is insufficient to meet the needs of regional ecosystem development. The characteristics of green vegetation cover in 16 districts in the study area are different, showing different spatial distribution patterns; except Xicheng District, there are 211 points without landscape in the area covered by green vegetation in 15 districts. The results can provide support for urban land surface planning and management.

Recent variations in soil moisture use efficiency (SMUE) and its influence factors in Asian drylands

Soil moisture use efficiency (SMUE) is an important indicator for evaluating the suitability of plant growth. This study synthetically assesses changes in SMUE in different vegetation types, fraction of vegetation cover, and altitude in the Asian drylands from 2001 to 2019. It also illustrates the inherent mechanisms by which soil moisture(SM) and net primary production (NPP), which are both affected by CO2 and relative humidity (f), influence SMUE. The results show an increase in SMUE over the study period, with annual mean values ranging from 0 to 0.59 (g C/mm). Forested areas have the highest SMUE value, while shrub land has the lowest value. These changes in SMUE are dominated by the three vegetation spatial combination patterns of NPP, SM, and the coupling relationship between NPP and SM. Forests and cropland are dominated by NPP, while wetlands, grassland and shrub land are dominated by SM. NPP is mainly affected by CO2 and the greenhouse effect, whereas SM is mainly affected by f. The results obtained from this study provide scientific data to support the ecological security and development of agriculture and animal husbandry in the Asian drylands, which is critical for maintaining regional ecological stability and sustainable economic and social development.

Inferring plant–plant interactions using remote sensing

Abstract Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine‐scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant‐based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close‐range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data‐driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.

Effects of vegetation spatial pattern on erosion and sediment particle sorting in the loess convex hillslope

To address the problem of serious soil erosion on the Loess Plateau, under the conditions of limited vegetation measures, the runoff erosion characteristics and erosion sediment sorting characteristics of vegetation at different positions on the upper slope of convex hillslopes are investigated, and the optimal vegetation spatial pattern is proposed according to the benefits of water storage and sediment reduction at different vegetation positions. The fluctuation degree of flow discharge per unit area of different vegetation spatial patterns is small, and the variation process of sediment discharge per unit area of each vegetation spatial pattern fluctuated sharply with the increase of runoff time. After planting vegetation on the slope, the total runoff yield and sediment yield were reduced. The runoff yield reduction benefit was 19.65% when the grass belt was 6 m away from the slope top; and the sediment yield reduction benefit was more than 70% when the grass belt was 2 m away from the slope top. Under the condition of hydraulic erosion on the slope covered with vegetation, the erosion particles are mainly fine particles, with high silt content and relatively small sand content. The farther the vegetation is arranged from the slope top, the more easily silt of size 0.002–0.05 mm is eroded. The higher effectiveness in terms of reductions of both runoff and sediment yields were obtained when the vegetation is planted in the proximity of the end of the length of the slope.

Vegetation changes in Yellow River Delta wetlands from 2018 to 2020 using PIE-Engine and short time series Sentinel-2 images

Vegetation is the functional subject in the wetland ecosystem and plays an irreplaceable role in biodiversity conservation. It is of great significance to monitor wetland vegetation for scientific assessment of the impact of vegetation on ecological environment and biodiversity. In this paper, a method for extracting wetland vegetation based on short time series Normalized Difference Vegetation Index (NDVI) data set was constructed. First, time series NDVI data were constructed using Sentinel-2 images. Then, the Support Vector Machine (SVM) classifier was used to classify the wetland vegetation types. The distributions of the main wetland vegetation in the study area in 2018 and 2020 were got. Finally, the land cover transfer matrix was calculated to analyze the spatial pattern and change of wetland vegetation emphatically from 2018 to 2020. Based on 46 Sentinel-2 images acquired in 2018 and 2020, the spatial pattern and change of vegetation in the Yellow River Delta wetlands were extracted and analyzed in this paper. The results show that: (1) The method for extracting wetland vegetation in estuary delta based on PIE-Engine platform and short time series NDVI data constructed in this paper can effectively extract the wetland vegetation information. The overall accuracy of the classification results reached 90.47% in 2018 and 80.30% in 2020. The Kappa coefficient of the classification results are 0.874 in 2018 and 0.739 in 2020 respectively. Compared with the results from the random forest classification method and the maximum likelihood classification method, the accuracy is improved by 6.40% and 13.04%, and the Kappa coefficient is improved by 0.055 and 0.069. (2) There were significant changes in vegetation coverage in the Yellow River Delta wetlands from 2018 to 2020. The Spartina alterniflora increased by 3.74km2. The Suaeda salsa degraded seriously, and the total area decreased by 20.38km2. In addition, the increase of Spartina alterniflora effectively guaranteed the stability of the coastline in the study area. This study can provide a theoretical basis for wetlands vegetation classificaton, and the classificaton results can provide scientific reference for protecting the ecological environment of wetlands and maintaining ecological stability.

Can remotely sensed vegetation patterns signal dryland restoration success?

Active restoration is frequently implemented to restore degraded drylands globally, yet predicting the overall success of these restoration projects remains challenging. Here, we aim to explore if vegetation spatial patterns can be used to monitor ecosystem recovery and anticipate the success of restoration practices. We combined field surveys and high‐resolution drone images to analyze how vegetation spatial patterns change after the large‐scale straw checkerboards restoration projects in the sand dune systems of the Tengger Desert in northern China. We found that vegetation cover, plant species richness, and diversity increased rapidly, approaching the level of the naturally vegetated sand dunes, after 5 years of restoration. Soil fertility remained low despite the positive vegetation change. Along with the recovery process, we found a larger diversity of patch sizes (i.e. spatial variance) with an increasing proportion of large‐size patches. These patterns, combined with a constant positive recovery rate in vegetation cover, are consistent with theoretical predictions used to anticipate transitions to alternative ecosystem states. Although some indicators may be masked by the artificial planting scheme and community succession process during recovery, our results show that vegetation spatial patterns can be used to forecast and monitor the possible recovery of drylands.

Assessing drivers of intra-seasonal grassland dynamics in a Kenyan savannah using digital repeat photography

Understanding grassland dynamics and their relationship to weather and grazing is critical for pastoralists whose livelihoods depend on grassland productivity. Studies investigating the impacts of climate and human factors on inter-seasonal grassland dynamics have focused mostly on changes to vegetation structure. Yet, quantifying the impact of these on the inter-seasonal dynamics of specific grassland communities is not known. This study uses digital repeat photography to examine how intra-seasonal grassland dynamics of different grassland communities are affected by precipitation, temperature, and grazing in a heterogeneous semi-arid savannah in Kenya. A low-cost digital repeat camera network allowed for fine-scale temporal and spatial variability analysis of grassland dynamics and grazing intensity. Over all grass communities, our results show precipitation driving mainly early-season and in some cases mid-season flushing, temperature driving end-of-season senescence, and grazing influencing mid-season declines. Yet, our study quantifies how these three drivers do not uniformly impact grassland species communities. Specifically, Cynodon and Cynodon/Bothriochloa communities are rapidly and positively associated with precipitation, where mid-season declines in Cynodon communities are associated with grazing and late-season declines in Cynodon/Bothriochloa communities are associated with temperature increases. Setaria communities, on the other hand, have weaker associations with the drivers, with limited positive associations with precipitation and grazing. Kunthii/Digitaria diverse communities had no association with the three drivers. Highly diverse mixed communities were associated with increased precipitation and temperature, as well as lower intensity grazing. Our research sheds light on the complex interactions between plants, animals, and weather. Furthermore, this study also demonstrates the potential of digital repeated photography to inform about fine-scale spatial and temporal patterns of semi-arid grassland vegetation and grazing, with the goal of assisting in the formulations of management practises that better capture the intra-annual variability of highly heterogeneous dryland systems.

Study on Spatiotemporal Variation Pattern of Vegetation Coverage on Qinghai–Tibet Plateau and the Analysis of Its Climate Driving Factors

As one of the most sensitive areas to global environmental change, especially global climate change, the Qinghai–Tibet Plateau is an ideal area for studying global climate change and ecosystems. There are few studies on the analysis of the vegetation’s driving factors on the Qinghai–Tibet Plateau based on large-scale and high-resolution data due to the incompetence of satellite sensors. In order to study the long-term vegetation spatiotemporal pattern and its driving factors, this study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to improve the spatial resolution of the GIMMS NDVI3g (8 km) data of the Qinghai–Tibet Plateau in 1990 and 1995 based on the MODIS NDVI (500 m) data. The research on the spatiotemporal pattern and driving factors of vegetation on the Qinghai–Tibet Plateau from 1990 to 2015 was carried out afterward, with combined data including topographic factors, annual average temperature, and annual precipitation. The results showed that there was a strong correlation between the actual MODIS NDVI image and the fused GIMMS NDVI3g image, which means that the accuracy of the fused GIMMS NDVI3g image is reliable and can provide basic data for the accurate evaluation of the spatial and temporal patterns of vegetation on the Qinghai–Tibet Plateau. From 1990 to 2015, the overall vegetation coverage of the Qinghai–Tibet Plateau showed a degrading trend at a rate of −0.41%, and the degradation trend of vegetation coverage was the weakest when the slope was ≥25°. Due to the influence of the policy of returning farmland to forests, the overall degradation trend has gradually weakened. The significant changes in vegetation in 2010 can be attributed to the difference in the spatial distribution of climatic factors such as temperature and precipitation. The area with reduced vegetation in the west was larger than the area with increased vegetation in the east. The effects of temperature and precipitation on the distribution, direction, and degradation level of vegetation coverage were varied by the areal differentiation in different zones.

Progress of studies on satellite-based terrestrial vegetation production models in China

Vegetation production is an important variable in terrestrial ecosystems, playing crucial roles in sustaining carbon balance, reducing atmospheric CO2 concentration, and mitigating global climate change. Satellite-based models, which benefit from spatially and temporally continuous remote sensing observations of vegetation growth conditions, are widely used for quantifying regional and global vegetation production. Satellite-based vegetation production models were initially simple statistical models, but later, process-based light use efficiency models were developed. The latest models are based on the relationship between solar-induced chlorophyll fluorescence and vegetation production. An increasing number of satellite-based studies are being conducted by Chinese scientists, who are developing and implementing plant production models, particularly by self-developing a number of light consumption efficiency models and establishing a long-term worldwide dataset of vegetation production. Furthermore, Chinese scientists have investigated the spatial and temporal patterns of vegetation using diverse models, significantly improving our understanding of terrestrial functions and structures. However, current models and estimation techniques need further improvement, and Chinese scientists had the opportunity to improve model capability and our understanding of vegetation production patterns and their regulating elements.

Ecoclimates

One of the greatest challenges to mankind is understanding the underlying principles of climate change. Over the last years, the role of forests in climate change has received increased attention. This is due to the observation that not only the atmosphere has a principal impact on vegetation growth but also that vegetation is contributing to local variations of weather resulting in diverse microclimates. The interconnection of plant ecosystems and weather is described and studied as ecoclimates. In this work we take steps towards simulating ecoclimates by modeling the feedback loops between vegetation, soil, and atmosphere. In contrast to existing methods that only describe the climate at a global scale, our model aims at simulating local variations of climate. Specifically, we model tree growth interactively in response to gradients of water, temperature and light. As a result, we are able to capture a range of ecoclimate phenomena that have not been modeled before, including geomorphic controls, forest edge effects, the Foehn effect and spatial vegetation patterning. To validate the plausibility of our method we conduct a comparative analysis to studies from ecology and climatology. Consequently, our method advances the state-of-the-art of generating highly realistic outdoor landscapes of vegetation.

Observation‐Based Evaluation of Local Climate Effect of Terrestrial Vegetation in Temperate Zones

AbstractTerrestrial vegetation modulates the land‐atmosphere dynamics and is one of the fundamental forces for climatic variability. The vegetation feedback may enhance or offset a portion of ongoing global warming. However, it is challenging to disentangle the role of vegetation from the effect of other drivers, primarily due to lacking suitable reference without vegetation. Fortunately, China has recorded the land surface temperature (LST) on bare land in 2400s weather stations, providing a deal bare‐land reference. Here, we used satellite measurements of the vegetation's canopy temperature, contrasted with the surface temperature of bare land, to quantify the vegetation's effect on LST. Results show that vegetation has a cooling effect during daytime and a warming effect at night, and the magnitude of the former is significantly higher than that of the latter. Consequently, vegetation has a net cooling effect and reduces LST up to 2.18°C in China. Spatially, the cooling effect of vegetation decreases by 0.25°C for a unit increase of latitude. Based on land surface energy balance, the spatial pattern of vegetation's net effect is mainly driven by vegetation‐induced evapotranspiration and albedo. As latitude increases, the decrease of vegetation density significantly reduces the evapotranspiration‐induced cooling effect while having no distinct impact on the albedo‐induced warming effect. As a result, the net cooling effect of vegetation has an evident latitudinal gradient and a clear seasonal contrast. Additionally, the vegetation's effect on LST is sensitive to solar radiation and snow cover changes.

Spatial pattern variation of artificial sand-binding vegetation based on UAV imagery and its influencing factors in an oasis–desert transitional zone

The spatial pattern of vegetation can reflect the impacts of the environment on plants and the response of plants to the environment, which can promote a deep understanding of the potential driving mechanisms of vegetation evolution and community maintenance. A sand-binding vegetation system without irrigation has been implemented in the oasis–desert transitional zone since the 1970 s, where the annual precipitation has been approximately 120 mm. While the mobile dunes have been effectively stabilized, a patchy pattern of sand-binding vegetation has been observed. However, we do not understand why the pattern of sand-binding vegetation changed from the initial uniform distribution to the current patchy pattern. In this study, low-altitude UAV remote sensing technology and imaging-based quantification techniques were used to explore the effects of biotic and abiotic factors on the spatial patterns of sand-binding vegetation over 50 years. The spatial pattern of Haloxylon ammodendron changed gradually from a uniform distribution to an aggregated distribution, and the degree of patch fragmentation of H. ammodendron at the landscape scale gradually increased with the age of the sand-binding vegetation. The artificial sand-binding vegetation composed of H. ammodendron showed discontinuous change in which the system state reached a transition point after 30 years and changed to another state after 40 years. There were no significant correlations between the landscape indices and soil water content in the shallow layers (0–10 cm, 10–50 cm), while the soil water content in the 50–100 cm layers was significantly negatively correlated with the class area, percentage of landscape, largest patch index, percentage of like adjacencies and aggregation index and was positively correlated with the normalized landscape shape index. The soil water content in the 100–200 cm layers was positively correlated with the number of patches and patch density. Competition intensity at the individual level had a more significant effect on the area-type indices, and competition intensity at the population level had a more significant effect on the clustering-type indices. Finally, we found that the soil water content in deep layers and competition are the main drivers of the H. ammodendron spatial pattern change from a uniform pattern to a patchy pattern. These findings enrich theory on the self-organization of vegetation in arid and semiarid environments and have important theoretical and practical significance for the establishment and management of artificial vegetation in arid areas.

Macrotermes termite mounds influence the spatial pattern of tree species in two African rainforest sites, in northern Congo. But were they really forests in the past?

AbstractTermite mounds have been poorly studied in tropical rainforest in contrast to those of savannahs where they create fertility hotspots and influence the spatial pattern of vegetation. An inventory of termite mounds and of 15 tree species with a diameter at breast height ≥ 10 cm was carried out in two 800-ha blocks, in two rainforest sites located in northern Congo. We used inhomogeneous and intertype K functions to study the spatial pattern of termite mounds and of tree species around mounds, respectively. We found that mounds in Loundoungou were over-dispersed within a radius of less than 70 m, while those in Mokabi were randomly spaced. Tree species within a 20-m radius around a mound were aggregated towards the mound, e.g. Entandrophragma cylindricum, randomly distributed, or even repulsed by the mound. The specific responses also differed in the two sites. These results suggest that (i) the mounds in Loundoungou were created by savannah termite species 3,000-2,000 years BP during the Late Holocene Rainforest Crisis and (ii) the mounds in Mokabi are characteristic of forest mounds. The impact of termite mounds on the spatial pattern of tree species is thus site-dependent, and these differences might be due to species seed dispersal strategies and to soil calcium concentrations.

Assessing the Impacts of Tidal Creeks on the Spatial Patterns of Coastal Salt Marsh Vegetation and Its Aboveground Biomass

The spatial distribution patterns of salt marsh plant communities and their biomass provide useful information for monitoring the stability and productivity of coastal salt marsh ecosystems in space and time. However, the spatial patterns of plant vegetation and its aboveground biomass (AGB) in a coastal salt marsh remain unclear. This study mapped the spatial distributions of salt marsh communities and their AGB based on image and LiDAR data acquired by an unmanned aerial vehicle (UAV) in the Yangtze River Estuary. The differences in vegetation structure and AGB at regions located at different distances from tidal creeks were also tested. The results show that biomass estimated through a random forest model is in good agreement (R2 = 0.90, RMSE = 0.1 kg m−2) with field-measured biomass. The results indicate that an AGB estimation model based on UAV-LiDAR data and a random forest algorithm with high accuracy was useful for efficiently estimating the AGB of salt marsh vegetation. Moreover, for Phragmites australis, both its proportion and AGB increased, while the proportion and AGB of Scirpus mariqueter, Carex scabrifolia, and Imperata cylindrica decreased with increasing distance from tidal creeks. Our study demonstrates that tidal creeks are important for shaping spatial patterns of coastal salt marsh communities by altering soil salinity and soil moisture, so reasonable and scientific measures should be taken to manage and protect coastal ecosystems.

Effects of Distributions of Grass Strips on Soil Erosion in Spoil Tips

The spatial distribution pattern of vegetation is of great significance to the prevention and control of soil erosion in spoil tips. The objective of this study was to evaluate the effects of spatial distributions of grass strips on soil erosion in spoil tips. A field runoff plot (20 m long, 1 m wide, and 0.5 m deep) was used for inflow simulation experiments under four inflow rate patterns (even, rising, falling, and rising–falling) and five grass strip patterns (patterns I–V). Results showed that the runoff reduction benefit (RRB) and soil loss reduction benefit (SLRB) of grass strip patterns were 12.23–49.62% and 12.92–80.54%, respectively. The optimal grass strip pattern was when the grass strips were distributed on a slope in bands (pattern V). In this pattern, the soil and water conservation effects were best, with RRB and SLRB of 43.87% and 58.09%, respectively. The grass strip patterns exhibited a significant time-limited effect on controlling soil erosion. The maximum amount of soil loss reduction for patterns II, III, IV, and V was 93.02, 84.30, 65.86, and 98.26 kg, respectively. Soil loss reduction caused by decreasing runoff (SRR) for grass strip patterns was the main factor controlling erosion. The efficiency coefficient of soil loss reduction caused by decreasing runoff for pattern V was higher than that for the other patterns. The grass strip pattern V (i.e., grass strips were distributed on a slope in bands) should be considered a priority in the prevention of soil erosion in spoil tips. This study can guide the configuration of vegetation control measures for soil and water loss in spoil tips.

Do environmental variables and overstory communities affect the spatial pattern of understory vegetation? Lessons from Monotheca buxifolia (Falc.) A. DC. forests in Pakistan

The importance of understory vegetation cannot be denied as an integral component of forest ecosystems, but there is a dearth of studies to explore the effects of environmental variables and canopy species on its diversity and composition. Here we investigated the effects of environmental variables and overstory stand structure on the understory vegetation in old-growth Monotheca buxifolia dominated forests with considerable co-dominance of other broadleaved by using ecologically standardized data collection methods across Pakistan. Multivariate analyses were used to examine the vegetation composition and different species assemblages with multiple influencing variables. We found a total of 58 understory species belonging to 55 genera and 30 families mostly representing pluriregional (33.89 %) chorological affinities with therophytic (17 species) life-form dominance and microphyll (34.48 %) leaf-size spectrum. Likewise, significant differences were observed in species richness and diversity indices between different understory community types. Attributes such as elevation, aspect, soil properties, and tree canopy structure were most strongly correlated with the Redundancy Analysis (RDA-ordination), indicating that several factors exert the strongest influence and explained the spatial pattern of understory vegetation. The findings of this research can assist forestry resources managers, forest biologists and ecologists in restoration and conservation plans for understory vegetation in the region.

基于无人机影像的阿拉善东南部荒漠灌丛植被空间格局研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于无人机影像的阿拉善东南部荒漠灌丛植被空间格局研究 DOI: 10.5846/stxb202109142585 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41771101）；中国科学院"一带一路"生态水文学团队项目（Y929731） Spatial pattern of shrub in southeast Alxa Desert based on high resolution unmanned aerial vehicle images Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱区灌丛植被空间格局受多种物理和生态过程影响，能够指示生态系统的状态。研究通过量化灌丛斑块大小的空间分布来评估阿拉善高原东南部覆沙荒漠植被生态系统的状态，采用点格局分析法分析灌木种群的相互关系，以阐明不同灌木种在斑块格局形成中的作用，并结合土壤条件及下垫面粗糙度等指标验证评估的准确性，探讨灌丛空间格局差异的内在机理。结果表明，研究区样方2灌丛斑块大小符合截尾幂律分布，其他样方符合对数正态分布，前者的空间结构及生境条件均优于后者，说明植被空间格局可以准确表征生态系统状态。在局地尺度上灌木种内和种间呈现不同的相互关系，以竞争关系为主导是导致斑块破碎化的主要驱动机制。小灌木（如猫头刺）的种内互利关系有利于促进多样化斑块形态的形成，而大灌木（如沙冬青和蒙古扁桃）种间的互利作用则有利于形成异质性更强的复杂空间格局。基于灌丛斑块的空间格局评估生态系统状态，可为保护和恢复生态脆弱区受损植被提供重要的借鉴。 Abstract:The vegetation spatial pattern in drylands, which is dictated by various physical and ecological processes, could be interpreted as an indicator of the ecosystem state. According to the statistical analysis of shrub patch size distribution, we estimated the ecosystem state of desert vegetation in the southeast of the Alxa Plateau. The interactions among shrub populations were shown by point pattern analysis, to illustrate that different species might act as various ingredients in patchiness. Soil properties and roughness of underlying surface were surveyed to support the estimation. Ultimately, the internal mechanism of the differentiation of shrub spatial pattern was discussed. The results show that the truncated power law is the best fitting model for the patch sizes distribution in quadrat 2, while the lognormal distribution is the best for others. Assessment of ecosystem states is verified by the comparation of habitat conditions, which shows that quadrat 2 is better too. The interaction at small scales between shrubs varies with different species, competition dominated relationship is the main reason for the narrowing down of patch size distribution. The positive relationship within species of small patch size, such as Oxytropis aciphylla, is conducive to the formation of diversified patches, whereas species of large patch size with facilitation at small scales, such as Ammopiptanthus mongolicus and Prunus mongolica, give rise to a complex pattern. Sound management should consider to comprise assessment of ecosystem states through shrub spatial patterns, which will provide indications of conservation and restoration of vulnerable vegetation. 参考文献 相似文献 引证文献