Spatial Patterns Of Vegetation Research Articles

An adaptive spatiotemporal tensor reconstruction method for GIMMS-3g+ NDVI

Satellite-derived normalized difference vegetation index (NDVI) is inevitably contaminated by clouds and aerosols, causing large uncertainties in depicting the seasonal and interannual variations of terrestrial ecosystems, and potentially misrepresents their responses to climate change and climate extremes. Although various methods have been developed to reconstruct NDVI time series using the similarity in time, space or their combination, they typically require known and accurate data quality information. It is still challenging to effectively reconstruct high-quality NDVI from Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3g+), which is one of the longest observation records but lacks reliable data quality information. This study introduces an adaptive spatiotemporal tensor reconstruction algorithm that leverages the spatial and temporal patterns of vegetation to produce high-quality long-term NDVI datasets without the need of data quality information. The reconstruction process employs two different tensor completion models to satisfy the low-rank constraints. These two models can effectively remove the high-frequency noises originating from atmospheric contamination, while preserving the abrupt or low-frequency changes attributable to disturbances such as drought, even in the absence of data quality information. The resultant NDVI shows good consistency with observations from geostationary satellites. Regions that show a strong correlation (r > 0.7) with geostationary satellite NDVI increased from 46.7 % (original GIMMS-3g+) to 62.2 % and 62.3 % (two reconstructions results) for East Asia, and from 41.4 % to 58.0 % and 59.0 % for Amazon. Our method also demonstrates superior performance to traditional methods such as Whittaker, HANTS, SG-filter, and comparable performance with the state-of-the-art ST-Tensor method when the fraction of contaminated observations is low. The proposed method can also be applied to other datasets such as EVI, LAI, etc., without additional data quality inputs. The resultant vegetation index dataset has the potential to improve plant phenology retrievals and evaluation of ecosystem responses to extremes.

Temporal and spatial patterns of riparian vegetation in the Colastiné Basin (Argentina) and riparian ecological quality estimation as tools for water management

ABSTRACT Information on the riverbanks can improve our ability to monitor water quality and generate adequate management strategies. This research seeks insights into the riverbanks of eight Colastiné River Basin (Argentina) streams, which have been influenced by intensive agricultural land use for decades. We aim to (a) describe the temporal and spatial distribution patterns of riparian vegetation, (b) assess their current riparian quality through riverbank quality indices, and (c) estimate whether the riparian quality is linked to the water quality. Results of the Normalized Difference Vegetation Index (NDVI) showed an increasing trend in the vegetation cover with seasonal periodicities during the last 22 years in only 2 streams. Overall, 41% of plant species registered were exotic although native species dominated in most streams. The overall riverbank quality, based on the mean values of four riverbank quality indexes, was regular-to-bad. The overall water quality of the streams was low and significantly correlated to the Riparian Quality Index, suggesting a link between both compartments. More studies are needed to determine the main variables that establish this connection. Further effort is also needed to generate appropriate indices for this region, as no current ones are still developed.

Uncovering the impact of climate and vegetation changes on runoff in karstic regions of southwest China

The vegetation-runoff relationship remains unclear in karstic regions. The karst landform in southwest China is a focal area where significant changes in vegetation have occurred in the past few decades, which may substantially impact water resources. To date, the effects of these changes on runoff remain uncertain. This study employed statistical analysis, numerical simulation, and scenario analysis to investigate the temporal and spatial patterns of runoff, climate, and vegetation in 20 typical catchments. The study also evaluated the response of runoff to vegetation and climate changes and the underlying factors. The findings revealed precipitation changes dominated changes in runoff in these catchments (mean contribution of 53.03%), whereas the contributions of vegetation and potential evapotranspiration changes were 23.16% and 23.82%, respectively. The study also revealed that the impacts of vegetation changes on runoff were significantly dependent on vegetation and climate factors (R2 = 0.60, P < 0.01). Furthermore, under the same climate change conditions, a higher distribution of natural vegetation (such as forest) in the catchment resulted in a larger decreasing trend in runoff. The results provide guidelines for the prediction of runoff variation in southwest China, and benefits to decision-making on ecological restoration and water resources development.

Spatial distributions and edge relationships of plant communities in coastal barrens in Nova Scotia, Canada

Coastal barrens support varied vegetation that includes wetlands, dwarf shrublands, and small patches of forest. Forest expansion, sea-level rise and recreational trails affect plant communities but spatial vegetation patterns within barrens are unknown. Using high-resolution multispectral aerial imagery, we classified plant communities and other land cover types using 500 m x 500 m landcover maps at three coastal barrens sites on the Atlantic coast of Nova Scotia, Canada. Community patches were compared using size and shape metrics; shared edge length identified adjacent communities. Community distributions were modelled using environmental variables such as elevation and distance to coast. Forty distinct plant communities were detected, with shrublands (37.5 % total area), dwarf shrublands (23.3 %) and bog wetlands (13.9 %) the most abundant. Average patch size was 9.2 m2; average patch density was 951 patches/ha, indicating fine scale community variability. Recreational vehicle trails occurred primarily in bog wetlands. Dwarf shrublands and some wetland types were closest to the coastline; taller shrublands and tree islands occurred further from the coast. Edge relationships revealed a vegetation height gradient across the forest-barren ecotone: tree islands were mostly adjacent to tall shrub communities, followed by progressively shorter vegetation. Topographic variability and distance to coast were important predictors of community distribution. Edge relationships among communities allowed identification of those most at risk from trail disturbance, forest expansion and coastal squeeze.

Spatiotemporal Patterns of Vegetation Evolution in a Deep Coal Mining Subsidence Area: A Remote Sensing Study of Liangbei, China

This study aims to provide a comprehensive analysis of the impacts of high-intensity coal mining on vegetation in Liangbei Town, a typical deep coal mining area in central of China. Using Landsat remote sensing data from 2000 to 2023, processed by the Google Earth Engine (GEE) platform, the study calculates the Normalized Difference Vegetation Index (NDVI). Temporal and spatial distribution patterns of vegetation were assessed using LandTrendr algorithm, Sen’s slope estimation, the Mann–Kendall test, the coefficient of variation, and the Hurst index. Vegetation growth dynamics were further analyzed through transfer matrix and intensity analysis frameworks. Driving factors influencing vegetation trends were evaluated using local climate data and surface deformation variables from SAR imagery. Temporal Dimension: From 2000 to 2023, the annual NDVI in Liangbei Township showed an upward trend with a growth rate of 0.0894 (10a)−1, peaking at 0.51 in 2020. Spatial Dimension: The NDVI distribution in Liangbei Township displayed a pattern of being lower in the center and higher around the edges, with values concentrated between 0.4 and 0.51, covering 50.34% of the total area. Trend of Change: Between 2000 and 2023, 83.28% of the area in Liangbei Township experienced significant improvement in the NDVI, with vegetation growth trends shifting primarily from slight to significant improvement, encompassing a total area of 10.98 km². This shift exhibited a marked tendency. Driving Factors: Deep mining in Liangbei Township is concentrated in the eastern part, with SAR imagery indicating a maximum surface subsidence of 0.26 m. As surface subsidence increases, the NDVI significantly decreases. The findings suggest that in the future, 91.13% of the vegetation in Liangbei Township will display an antipersistent change trend. The study offers critical insights into the interaction between mining activities and vegetation cover can serve as a reference for environmental evolution and management in similar mining areas.

Spatial-Temporal Dynamics of Water Resources in Seasonally Dry Tropical Forest: Causes and Vegetation Response

Seasonally Dry Tropical Forests (SDTFs) are situated in regions prone to significant water deficits. This study aimed to evaluate and quantify the dynamics and spatial patterns of vegetation and water bodies through the analysis of physical–hydrological indices for a remnant of FTSD between 2013 and 2021. Basal area, biomass, and tree number were monitored in 80 permanent plots located in two areas of an SDTF remnant with different usage histories. To assess vegetation and water resource conditions, geospatial parameters NDVI, NDWIveg, NDWI, and MNDWI were estimated for the period from 2013 to 2021. The observed patterns were evaluated by simple linear regression, principal component analysis (PCA), and principal component regression (PCR). Area 2 presented higher values of basal area, biomass, and number of trees. In area 1, there was an annual increase in basal area and biomass, even during drought years. The NDVI and NDWIveg indicated the vulnerability of vegetation to the effects of droughts, with higher values recorded in 2020. NDWI and MNDWI detected the water availability pattern in the study area. Physical–hydrological indices in the dynamics of tree vegetation in dry forests are influenced by various factors, including disturbances, soil characteristics, and precipitation patterns. However, their predictive capacity for basal area, biomass, and tree number is limited, highlighting the importance of future research incorporating seasonal variability and specific local conditions into their analyses.

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.