Spatial Patterns Of Vegetation Research Articles

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

Interactions of cross-diffusion and nonlocal delay induce spatial vegetation patterning in semi-arid environments

Interactions of cross-diffusion and nonlocal delay induce spatial vegetation patterning in semi-arid environments

Spatial patterns transformation due to deforestation in the Paraguayan Pantanal Ecoregion from 1987 to 2020

Understanding the effects of land use on landscape configuration as a proxy for ecological connectivity is necessary to envision territorial management and planning and promote natural resource conservation. This study aimed to identify the landscape configurations that resulted from land-cover change processes within the Paraguayan Pantanal Ecoregion (PPE), one of the largest and most biodiverse wetlands, in 1987, 2004, and 2020, with a special emphasis on forest and aquatic vegetation spatial patterns. Forest fragmentation between 1987 and 2020 was determined using landscape ecology metrics. In 1987, natural vegetation was highly connected, with two main forest patches of 12 887 and 15 910 km2. However, in 2004 and 2020, several categories of forest patches were identified, with the largest measuring 9197 and 2461 km2, respectively. Although forest and aquatic vegetation remnants remained viable in 2020, appropriate measures to reduce forest loss should be adopted to ensure the long-term viability of PPE biodiversity.

Multiple sources forcing driving changes in spatial patterns of coastal wetland vegetation

Multiple sources forcing driving changes in spatial patterns of coastal wetland vegetation

Monitoring vegetation patterns and their drivers to infer resilience: Automated detection of vegetation and megaherbivores from drone imagery using deep learning

Ecological pattern theory has highlighted spatial vegetation patterns that can be used as indicators of ecosystem resilience. Combining this spatial pattern theory with aerial imagery from drones and automated image processing with deep learning methods, we show how monitoring of natural ecosystems can be enhanced through quantifying vegetation spatial patterns. We demonstrate this approach in a tropical seagrass ecosystem with a high abundance of turtles that generate vegetation patches when grazing. Past field observations suggest that patch size and density reflect the seagrass meadow resilience, but understanding the natural variation in vegetation patchiness is crucial. Employing the deep learning methods of semantic segmentation and object detection, we quantify vegetation patchiness metrics and turtle distribution across 12 ha of seagrass meadow in the years 2012 and 2022. The resulting output facilitates spatial and temporal comparisons, revealing areas of low resilience. In 2012, turtle grazing across the entire site yielded vegetation patch sizes averaging 2 ± 0.2 m2 (95% confidence interval). Reduced patch sizes of 0.24 ± 0.05 m2 and 0.67 ± 0.6 m2 at the reef edge and beach slope respectively, in conjunction with a reduced patch density, indicated lower resilience at the seagrass meadow edges. Analysis of the 2022 dataset indicates a general decrease in patch size over time, suggesting declining resilience. A retraining experiment of the semantic segmentation model was conducted where the initial model was retrained on the 2022 dataset and demonstrated the adaptability of the deep learning methods. Despite using different equipment, the model achieved high accuracy with only 5–10 additional training images. By providing the tools to conduct these analyses, we aim to stimulate the uptake of deep learning for enhancing the data obtained from aerial imagery to improve the monitoring and conservation of natural ecosystems.

Reconstruction of zonal vegetation of East Asia in the early Eocene

For the fi rst time, unifi ed types of zonal vegetation are established for the early Eocene in east of Asia and their changes in space are traced. The reconstruction is based on the analysis of 48 palynofl oras from the early Eocene localities situated from 75 to 19° N using Integrated Plant Record (IPR) vegetation analysis. In the results, quantitative parameters of the relative proportions of vegetation components are obtained. According to our data, along the latitudinal gradient from north to south, there is an increase in evergreen plants and herbs, but a decrease in conifers and deciduous plants, and no changes in the proportions of xerophytic plants. Mixed mesophytic forest is a widespread and dominant type of zonal vegetation. In general, the main changes in plant biomes show a very shallow and weakly pronounced diff erentiation into two plant zones: mixed mesophytic forest, north of ~50° paleolatitude, and warmer types of zonal vegetation (broadleaved evergreen forest and an ecotone between mixed mesophytic and broadleaved evergreen forests), in addition to mixed mesophytic forest, south of ~50° paleolatitude. The resulting spatial patterns of vegetation are consistent with climatic inferences.

Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management

Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in enhancing the sustainability and livability of cities. However, monitoring urban vegetation and accurately estimating its status pose challenges due to the heterogeneous nature of the urban environment. In response to this, the current study proposes utilizing a remote sensing-based classification framework to enhance data availability, thereby improving practices related to urban vegetation management. The aim of the current research is to explore the spatial pattern of vegetation and enhance the classification of tree species within diverse and complex urban environments. This study combines various remote sensing observations to enhance classification capabilities. High-resolution colored rectified aerial photographs, LiDAR-derived products, and hyperspectral data are merged and analyzed using advanced classifier methods, specifically partial least squares-discriminant analysis (PLS-DA) and object-based image analysis (OBIA). The OBIA method demonstrates an impressive overall accuracy of 95.30%, while the PLS-DA model excels with a remarkable overall accuracy of 100%. The findings validate the efficacy of incorporating OBIA, aerial photographs, LiDAR, and hyperspectral data in improving tree species classification and mapping within the context of PLS-DA. This classification framework holds significant potential for enhancing management practices and tools, thereby optimizing the ecological services provided by urban vegetation and fostering the development of sustainable cities.

GIS-Based Ecological Restoration Planning for Densely Populated Areas of Hangjiahu River Network in the Context of the Internet of Things

Abstract In order to better protect the ecosystem of Hangjia Lake, repair ecological problems, and improve biodiversity, this article takes the ecosystem of Hangjia Lake area as the research object and proposes a geographic information system (GIS)-based ecological restoration planning method under the Internet of Things. The ArcGIS software was used to analyze the rainfall, soil pollution, water pollution, and land use in Hangjiahu area, and evaluate the current status of ecosystem. Moreover, the weight of the influencing factors of ecosystem was calculated. In addition, the ecological restoration was planned from the perspective of optimizing resources and reasonable allocation. Furthermore, Hangjiahu area was planned reasonably based on land suitability and vegetation spatial pattern. The potential and limitation of dense waterway net region of Hangjiahu were analyzed by the multi-factor evaluation method. Finally, the planning was carried out from the aspects of soil and water conservation, biodiversity, and vegetation selection. Experimental results show that the proposed method has good effect on soil and water conservation and many biological species, so it can realize the efficient restoration for the ecosystem in Hangjiahu area.

Self-organization as a mechanism of resilience in dryland ecosystems

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.

Phenotypic plasticity: A missing element in the theory of vegetation pattern formation

Regular spatial patterns of vegetation are a common sight in drylands. Their formation is a population-level response to water stress that increases water availability for the few via partial plant mortality. At the individual level, plants can also adapt to water stress by changing their phenotype. Phenotypic plasticity of individual plants and spatial patterning of plant populations have extensively been studied independently, but the likely interplay between the two robust mechanisms has remained unexplored. In this paper, we incorporate phenotypic plasticity into a multi-level theory of vegetation pattern formation and use a fascinating ecological phenomenon, the Namibian “fairy circles,” to demonstrate the need for such a theory. We show that phenotypic changes in the root structure of plants, coupled with pattern-forming feedback within soil layers, can resolve two puzzles that the current theory fails to explain: observations of multi-scale patterns and the absence of theoretically predicted large-scale stripe and spot patterns along the rainfall gradient. Importantly, we find that multi-level responses to stress unveil a wide variety of more effective stress-relaxation pathways, compared to single-level responses, implying a previously underestimated resilience of dryland ecosystems.

The Classification Characteristics and Dynamic Changes of Desert Vegetation Based on Unmanned Aerial Vehicle Remote Sensing

The common ground sample method of vegetation survey requires a lot of manpower and time. Meanwhile, many vegetation information may not be revealed because the scale of standard sample is usually only 100 m2. For this reason, this study developed a set of desert vegetation sample survey method based on UAV technology, and further developed a set of vegetation spatial distribution pattern analysis method based on it, and carried out an empirical study with typical shrub communities in Inner Mongolia as an example. It is expected to provide scientific basis for wind and sand control, vegetation restoration and ecological construction in the ecologically fragile area at the edge of Inner Mongolia.

Emergy benefit and radiation effect of multi-dimensional service function of vegetation ecosystem

Vegetation, as a multi-type and multi-functional green energy, plays an important role in regional carbon emission reduction and carbon neutrality. This study carried out the concept of green and sustainable development in depth and constructed an emergy quantification methodology system for the multidimensional service functions of vegetation ecosystems consisting of forests and grasslands based on the theory of emergy analysis and multidisciplinary integration methods. Using the theory of spatial correlation and breakpoints, we delineated the major ecological zones and investigated the radiation effects of typical regulating functions. Taking Luoyang, China, as an example, the results showed that the annual sequence of vegetation ecosystem service function (VES) emergy in Luoyang City showed a decreasing and then increasing trend with 2015 as the cut-off point. Early-stage Forest exploitation had profound effects, while increasing cultural benefits in later stages demonstrated national emphasis on forest research and conservation. The forest's high-quality ecological zone in Luoyang City could be found in the three southern counties of Luoning (LN), Luanchuan (LC), and Song (S). The radiation effect encompassed the entire city, resulting in an obvious impact with a total radiation of approximately 4.10E+20 sej. The high-quality ecological zone of the grassland did not appear until 2020 and is located in Yiyang (YY) county in central Luoyang. It benefited only the surrounding counties and had a total radiation of 1.32E+18sej. However, the development trend is optimistic. The spatial pattern of vegetation should be suitable for natural conditions, and the development strategy of localization as the driving force of the whole should be realized through the establishment of high-quality ecological zones, so as to promote harmonious coexistence between human and nature through green development.

Investigating the Relationship between Topographic Factors and Vegetation Spatial Patterns in the Alpine Plateau: A Case Study in the Southwestern Tibetan Plateau

Vegetation on the Southwestern Tibetan Plateau (SWTP) is critical to ensuring ecological security and promoting regional economic and social development. Here, we explored the relationship between topographic factors (elevation, slope, and aspect) and the spatial patterns in the normalized difference vegetation index (NDVI) in the SWTP over the past 20 years. The results found that the NDVI in the SWTP was primarily influenced by elevation and slope. The regions with significant variations in NDVI were concentrated between 4500 m to 5500 m and slopes ranging from 0° to 15°. Although the influence of aspect on NDVI was small, there was a decreasing trend in NDVI on sunny slopes and an increasing trend on shady slopes. Dominant topographic conditions were identified by considering 230 different combinations of elevation, slope, and aspect. The combination of topographic parameters indicated stronger patterns in NDVI variability, notably within sections of 0°–25°slopes and below 5000 m elevation. These findings highlight the relevance of topography, notably slope and aspect, for vegetation in alpine settings. The information gathered from this study about the prevailing topographic distribution and vegetation growth state in the SWTP can help with future ecological restoration and conservation efforts in the Tibetan Plateau and other comparable regions worldwide.

Influence of Climate, Topography, and Hydrology on Vegetation Distribution Patterns—Oasis in the Taklamakan Desert Hinterland

Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the spatial distribution of vegetation in the natural desert hinterland and to reveal how different environmental factors affect vegetation changes. Taking a Taklamakan Desert hinterland oasis as our research object, we analyzed the effects of different environmental factors on desert vegetation using a time-series normalized difference vegetation index (NDVI) combined with meteorological, topographic, and hydrological data, including surface water and groundwater data. Vegetation was distributed in areas with high surface water frequency, shallow groundwater levels, relatively flat terrain, and dune basins. NDVI datasets show greening trends in oasis areas over the past 20 years. The frequency of surface water distribution influences water accessibility and effectiveness and shapes topography, thus affecting the spatial distribution pattern of vegetation. In this study, areas of high surface water frequency corresponded with vegetation distribution. The spatial distribution of groundwater depth supports the growth and development of vegetation, impacting the pattern of vegetation growth conditions. Vegetation is most widely distributed in areas where the groundwater burial depth is 3.5–4.5 m. This study provides data for restoring riparian vegetation, ecological water transfer, and sustainable development.

Response of vegetation dynamics in environmentally sensitive and fragile areas to natural and anthropogenic factors: A case study in Inner Mongolia Autonomous Region, China

Vegetation dynamics is crucial for understanding vegetation ecosystem processes in arid and semi-arid regions. The Inner Mongolia Autonomous Region (IMAR) is a typical arid and semi-arid region in China, where vegetation has been significantly altered in response to multiple disturbances over recent decades. However, vegetation dynamics under changing environment and the integrated driving effects on natural and anthropogenic factors are unclear for environmentally sensitive and fragile areas. Therefore, Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation status to systematically analyze the temporal and spatial characteristics of the vegetation dynamics in the IMAR from 2000 to 2020, and we quantify the independent and integrated effects of natural and anthropogenic factors on vegetation changes through Geo-detector. Additionally, we quantitatively separate the driving factors of vegetation from the perspective of dry-wet zones, and explored the different regional vegetation dynamics and its relationship with natural and human activities. The results showed that: (1) Vegetation had generally shown an upward trend with an interannual variability of 0.0186 a−1 from 2000 to 2020. (2) The spatial pattern of vegetation had obvious differences. Most of the improvement was mainly concentrated in the east of IMAR covered 69.14%, and degradation in the western desert region. (3) Natural factors were more influential than anthropogenic factors, precipitation had the greatest explanatory power for the spatial heterogeneity of vegetation with a q value of 80.28%, and the integrated effects on vegetation changes were strongest for precipitation and other drivers. (4) The main drivers affecting NDVI changes are more variable in different wet-dry zones, and precipitation gradients determined explanatory power and the relative importance of natural and anthropogenic factors for vegetation changes. These results contributed more insight into the driving mechanisms underlying vegetation dynamics, while being critical for predicting and evaluating vegetation recovery and vegetation ecosystem stability in the context of global climate change, especially in environmentally sensitive and fragile areas.

Diversity of Indicator and Dominant Plant Species along Elevation Gradients in Prince Mohammad Bin Salman Nature Reserve, KSA

It is essential to protect and preserve biodiversity, especially in habitats in which natural resources are scarce. The differing flora and vegetation distribution on the broad, arid landscape at the Crown Prince Mohammed Bin Salman Nature Reserve has yet to be investigated. Based on GPS coordination and the transitional zone of plant communities, 48 symmetric plots of 50 × 50 m2 were distributed evenly over six elevations. In this study, we recorded 70 species in 33 families and elucidated floristic traits correlated with elevation. High species richness was recorded for the families Fabaceae, Poaceae, Asteraceae, and Chenopodiaceae. High numbers of chamaephyte and phanerophyte species were observed. In the chorotype, the mono-regional component contained 47% of the species, the bi-regional component 35.7%, and multi-regional and worldwide species comprised 10% and 7%, respectively. This study noted the growth habits of 23 herbs, 15 shrubs, 10 trees, and a single species of grass, vine, climber, and mistletoe. Diversity indices, indicator species, dominant plant communities, and soil profiles were compared for the defined zones of elevation. Alpha and beta diversity were high at elevations of ≥1000, 800, and ≤100 ma.s.l., compared to elevations of 600 m, 400 m, and 200 m. The highest species richness and species turnover were recorded at elevations of ≥1000, 800, and ≤100 m, while species evenness was greater at elevations of 600, 400, and 200 m. Vegetation analyses and indicator species (based on relative abundance) showed species variation with elevation. Species domination was influenced by physical soil structure and soil chemistry. Microclimates, including temperature and relative humidity variations, were found to be a significant driver in the ecosystem, resulting in varying plant diversity and species distribution at different elevations. Through canonical correspondence analysis (CCA), we used an autocorrelation of elevations, plant species, and soil properties to identify three phytogeographic categories that were presumed to be a proxy of microclimate change: Category I: elevations 1000 m and 800 m, including Retama raetam, Zilla Spinosa, and Vachellia gerrardii linked with sandy soil; Category II: elevations 600 m and 400 m, including species Haloxylon salicornicum, Rhazya stricta, and Leptadenia pyrotechnica linked with enriched soils containing CaCO3 and HCO3 and having a clay texture; and Category III: elevations 200 m and 100 m, including Zygophyllum coccineum, Tamarix nilotica, and Hyphaene thebaica, which thrived in salinity and silt soils. The spatial vegetation patterns of the xeric environment and its transition zones in Prince Mohammed Bin Salman Nature Reserve were also documented. It is recommended that microclimate effects on species nominated for vegetation restoration or afforestation be considered for the optimal management of this important nature reserve.

Using the Alternative Stable States Framework to understand the persistence of secondary shrubland within a Mexican oak forest with livestock and agricultural legacies

Many forests exist in human-modified mosaic landscapes subject to logging, agriculture, and livestock grazing. These disturbances can reduce ecosystem resilience and ultimately result in crossing thresholds, leading to the transition of the ecosystem into new stable states with different plant communities. The interaction between these disturbances and forest succession is still poorly understood, despite the growing need to manage these human-modified landscapes for biodiversity and productivity. We studied abrupt changes in vegetation composition in a Central Mexican oak forest and explored how these changes could indicate the coexistence of various alternative stable states caused by agricultural and livestock legacies. We established 36 strip transects covering 9000 m2 in patches with various land use legacies where we sampled vegetation, soil characteristics, and human disturbance. We assessed future and past tendencies in vegetation spatial patterns by evaluating seedling composition and analyzed time series of NDVI signals recorded by LANDSAT images (1972–2019). We utilized NMDS, PERMANOVA, and ANOVA analyses to assess if stable states can be distinguished based on historical and current variations in vegetation composition and structure in patches with different management legacies. We then assess potential factors that may contribute to differences in plant community composition by fitting edaphic, topographic, and anthropogenic disturbance variables to the ordination analyses.We identified three distinct plant communities: oak forest (dominated by late-successional Oak species), secondary shrubland (dominated by Vachellia-Dodonaea association), and a transitional forest-shrub community (a mixture of the first two communities). Time-series analyses and seedling composition provide evidence of long-term community segregation. Variation partition analysis demonstrated that anthropogenic disturbance (particularly livestock browsing), had a greater impact on species composition differentiation than environmental variables.A state-and-transition model is proposed, where cattle browsing and agricultural legacies trigger the transition from oak forests to a stable secondary shrubland community. Three potential self-reinforcing and interacting feedbacks are suggested: (i) micro-environmental filtering, (ii) plant-trait mediated assembly process, and (iii) cascading effects on tree recruitment caused by cattle browsing. The proposed state-and-transition model explains forest successional dynamics in systems with multiple anthropogenic disturbances and provides a conceptual framework for understanding and guiding successional trajectories.

Climate Warming Dominates Vegetation Productivity in the Hanjiang River Basin, China

The Hanjiang River Basin (HJRB) encompasses the Danjiangkou Reservoir, a critical water source for the South-to-North Water Transfer project, the world’s largest such endeavor. Recent studies have highlighted that increased vegetation growth in the HJRB has led to reduced water availability in the region. To investigate the seasonal dynamics and spatial patterns of vegetation and their association with the local climate, we employed Gross Primary Productivity (GPP), a pivotal component of terrestrial carbon-water cycling, derived from the MODIS MOD17A2HGF dataset at a 500 m resolution. We combined this dataset with station meteorological data and the Standardized Precipitation Evapotranspiration Index (SPEI) to explore the complex relationship between vegetation productivity, climate fluctuations, and hydrothermal changes in the HJRB from 2000 to 2020. Our findings reveal that the rising trend in vegetation productivity in the HJRB is primarily attributable to climate warming. Different types of vegetation in the upstream and downstream areas exhibit varying water requirements. While the region has not experienced prolonged widespread drought conditions thanks to its excellent water conservation capabilities, there remains a certain level of drought risk in the downstream area as the climate continues to warm. Moreover, variables such as wind speed and sunshine duration significantly impact the hydrothermal conditions within the river basin, consequently influencing vegetation productivity. This study elucidates the mechanisms through which climate change affects vegetation productivity in the HJRB. Despite afforestation efforts in the upstream region and climate warming leading to increased greening, there may be implications for the water retention function of the HJRB. This understanding is crucial for water resource management and ecosystem sustainability in the HJRB.

Dam periodic storage drives the spatial pattern of vegetation towards banded evolution

Emerging and fragile ecosystems such as reservoir riparian zones are of increasing concern with the increasing number of damming projects worldwide. Current knowledge of riparian vegetation in reservoirs focuses on the individual and community level, with little understanding of spatial patterns and ecological processes at the landscape level. Here, we demonstrate the evolution process, evolution direction, and evolution mechanism of vegetation spatial patterns under persistent and periodic inundation disturbances in the reservoir riparian zone, through coupled analyses of landscape evolution and ecological processes, using continuous years of drone observation data in the riparian zones of two major reservoirs, Xiaowan and Nuozhadu of the Lancang River. We found that vegetation landscape evolution is driven by seed dispersal, environmental factors fluctuations, species tolerance differences, and competition for resource availability, showing a clear trend towards banded evolution. Ecological niche processes such as periodic wet-dry environmental filtering and interspecific competition dominated the formation of vegetation banding patches, and neutral processes of random seed dispersal dominated the areas where banding patches were formed. The water requirements of plants during the exposing period lead to a greater influence of environmental factors that are closely related to water, such as net rainfall, sunshine hours, groundwater storage, and soil moisture. Water redistribution between vegetated and bare ground patches is key to their formation and stability in the upper part of the riparian zone, where drought stress is severe, while in the lower part, vegetation is mainly affected by inundation stress during flooding periods. Therefore, riparian vegetation restoration is critical to maintain a certain area of bare ground patches for drought areas in the exposing period, in contrast to the emphasis on vegetation patch coverage. And it is possible to carry out spatial planning based on the development trend of the belt distribution, taking into account the flood and drought tolerance of the species as well as the fluctuating rhythms of the main factors, such as the rainfall, which is worth exploring further.

Effects of Groundwater Depth on Vegetation Coverage in the Ulan Buh Desert in a Recent 20-Year Period

As a typical desert in the Inner Mongolia Autonomous Region, the Ulan Buh Desert has a dry climate and scarce precipitation all year round. Groundwater has become the main factor limiting the growth of vegetation in this region. It is of great significance to study the influence of groundwater depth on the spatial distribution pattern of vegetation in this region. Based on the PIE-Engine platform and using long-term time-series Landsat data, this paper analyzed the spatial–temporal distribution characteristics and trends in vegetation coverage in the Ulan Buh Desert in the last 20 years using a pixel dichotomy model and the image difference method. The Kriging interpolation method was used to interpolate the groundwater depth data from 106 monitoring wells in the Ulan Buh Desert over the past 20 years, and the spatial distribution characteristics of groundwater depth in the Ulan Buh Desert were analyzed. Finally, the correlation coefficient between changes in vegetation coverage and changes in groundwater depth was calculated. The results showed the following: (1) The vegetation coverage in the Ulan Buh Desert was higher in the periphery and lower in the center of the desert. The overall vegetation level showed an increasing trend year by year; the growth rate was 4.73%/10 years, and the overall vegetation cover showed an improving trend. (2) The overall groundwater depth in the Ulan Buh Desert was deep in the southwest and shallow in the northeast. In the past 20 years, the groundwater depth in the Ulan Buh area has become shallower, and the ecological condition has gradually improved. (3) On the whole, the vegetation coverage varied with the groundwater depth, and the shallower the groundwater depth, the greater the vegetation coverage. When the groundwater depth increased to more than 4 m, the change in the groundwater depth had a significant effect on the vegetation coverage. However, when the groundwater depth was greater than 6 m, the change in the groundwater depth had no significant effect on the change in vegetation coverage.