Changes In Vegetation Patterns Research Articles

A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence

Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. On the test set, for LST-Day data, CatBoost and XGBoost achieved Median Absolute Errors (MedAE) of approximately 1.434 °C and 1.425 °C, respectively. For LST-Night data, also on the test set, the MedAE values were approximately 1.186 °C for CatBoost and 1.176 °C for XGBoost. The second part explores the intricate relationships between climatic variables—LST, precipitation, and relative humidity—forest fire occurrences, and vegetation dynamics in various subregions. The findings revealed complex interactions, with high LST, reduced precipitation, and humidity associated with increased forest fire activity and subsequent changes in vegetation patterns, particularly in the Central Mixedwood, Dry Mixedwood, and Montane subregions. A notable potential association was identified between high LST, reduced precipitation and humidity, and increased forest fire activity in these areas. These climate change impacts and fire events were found to influence ecological processes, altering species composition, reducing biodiversity, and potentially disrupting ecosystem services such as carbon sequestration and nutrient cycling. These insights are crucial for informing adaptive forest management strategies aimed at understanding and mitigating the cascading effects of climate change on fire regimes and vegetation dynamics in Alberta's diverse landscapes.

Future land use/land cover and its impacts on ecosystem services: case of Aydın, Turkey

AbstractThis paper aims to reveal the impacts of land use/land cover changes on ecosystem services and to guide sustainable development decisions by modelling the future land use/land cover pattern and its ecosystem services in the case of Aydın province, Turkey. In this context, the study examined land use/land cover changes in Aydın province, predicted future land use/land cover patterns with three scenarios (natural development, ecosystem service-based development, and economy-based development) using the PLUS model, and determined the impact of land use/land cover changes on InVEST generated carbon storage and habitat quality ecosystem services. The key drivers of land use/land cover change in Aydın were agricultural expansion, the transformation of different vegetation types into each other, and, even on a small scale, urbanization. The study revealed that changes in the spatial pattern of land use/land cover types, together with the effect of changing vegetation patterns, have a significant impact on carbon storage and habitat quality. While reductions in shrubland and forests were detrimental, transformations from openspaces to them, particularly at their boundaries, enhanced the capacity of carbon storage and habitat quality. On the other hand, even though the scenarios of ecosystem service-based development and economy-based development were based on the economic value of ecosystem services and the value of products/services obtained from different land use/land cover types, respectively, the ecosystem service-based development scenario was characterized by more effective land use/land cover management in terms of maximizing both the economic and ecological benefits. This highlights the significance and emerging need for prioritizing ecological considerations in urban planning.

Intra-annual vegetation changes and spatial variation in China over the past two decades based on remote sensing and time-series clustering.

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies.

Reconstruction of NDVI based on Larix gmelinii tree-rings during June–September 1759–2021

Investigating the long-term dynamics in the canopy proves to be difficult due to the short observational records of the normalized difference vegetation index (NDVI). To explore the linkage between tree growth, NDVI dynamics and large-scale atmospheric circulation in the Greater Khingan Mountain, Northeast China, we established a chronology of Larix gmelinii tree ring width at three elevations (870–920 m, 1,100–1,150 m and 1,270–1,320 m) in the northern foothills of the mountain range. We then calculated the correlations between the tree ring chronologies and NDVI and climate factors, and reconstructed the NDVI time series from June to September 1759–2021 in the region based on the middle-elevation tree ring chronology. The results identify the positive effect of temperature (r = 0.56, p < 0.01) and the negative effect of precipitation (r = −0.44, p < 0.01) in the growing season as the main influencing factors of NDVI for the study period (1981–2019). The 11-year moving average of the reconstructed NDVI series reveals two periods of high canopy vigor (1898–1926 and 2009–2013) and three periods of low canopy vigor (1860–1962, 1882–1888 and 1968–1977) in the last 263 years. These periods correspond to drought events recorded in the historical literature. Wavelet analysis shows that the reconstructed sequences exhibited 11–13, 23–25, and 39–42 years period variations. Integrating this with spatial correlation analysis reveals that tree growth in the Mangui region was impacted by the combined effect of the North Pacific Decadal Oscillation and the North Atlantic Multidecadal Oscillation. The results of this paper provide a reference for the study of vegetation change patterns in the northern foothills of the Greater Khingan Mountains.

How do productivity gradient and diffusion shape patterns in a plant–herbivore grazing system?

Natural systems show heterogeneous patchy distributions of vegetation over large landscapes. Reaction–diffusion systems can demonstrate such heterogeneity of species distributions. Here, we analyse a reaction–diffusion model of plant–herbivore interactions in two-dimensional space to illustrate non-homogeneous distributions of plants and herbivores. The non-spatial system shows bottom-up control, where herbivore density is low under low and high primary productivity but increased at intermediate productivity. In addition, the non-spatial system provides bistability between a dense vegetation state devoid of herbivores and a coexisting state of plants and herbivores. In the spatiotemporal model, we give analytical conditions of occurring diffusion-driven (Turing) instability, where a novel point in our model is the relative dispersal of herbivores, which represents the movement of herbivores from a higher to a lower vegetation state in addition to the self-diffusion of both species. It is shown that heterogeneity in the population distribution does not occur if the relative dispersal of herbivores is low, but it appears in the opposite case. Due to bistability in the underlying non-spatial system, the spatiotemporal model produces initial value-dependent patterns. The two initial values make different patterns despite having the same primary productivity and relative dispersal rate. As productivity increases with a given relative herbivore dispersal, pattern transition occurs from a blend of stripes and spots of low vegetation state to a predominantly low-density vegetation state with smaller patches of densely vegetated states with one initial value. On the contrary, a discernible change in vegetation patterns from cold spots in the dense vegetation to hot stripes in the primarily low-vegetated state is noticed under the other initial population value. Furthermore, the population distributions of plants and herbivores in the entire domain after a long period are heterogeneous for both initial values, provided the relative herbivore dispersal is substantial. We estimated mean population densities to observe species fitness in the whole domain under variable productivity. When productivity is high, the mean population density of plants may go up or down, depending on the herbivore’s relative dispersal rate. In contrast to the bottom-up control dynamics of the non-spatial system, the system exhibits a top-down control under high relative dispersal, where the herbivore regulates vegetation growth under high productivity. On the other hand, herbivores are extinct under high productivity if the relative dispersal is low.

Paleopalynological Analysis of Primulaceae Family Evolution during the New Holocene Period in Elbasan, Albania

This paper presents pioneering paleopalynological data concerning the Primulaceae family during the New Holocene period in Elbasan, Albania. Fossil pollen data were extracted from soil deposits dating back to the last 20th centuries, shedding light on the evolutionary trajectory of Primulaceae Family plants in the region. Soil samples, primarily weighing approximately 0.5 kg and retrieved from depths ranging from 4 m to the surface, were collected at five stations in the Elbasan area using a dry rotary drilling probe between October and November 2023. The study aims to unveil the evolutionary dynamics of Primulaceae plants during the New Holocene, offering novel insights into their paleoenvironmental interactions. The Holocene epoch marks a critical period characterized by significant shifts in climate and ecological conditions. Understanding plant evolution during this era provides valuable insights into environmental dynamics and human impact on vegetation. A total of 485 Primulaceae palynomorphs were identified across all analysed soil samples, indicating consistent presence throughout the New Holocene period. The abundance and distribution of Primulaceae pollen suggests enduring resilience and adaptation within the local ecosystem. Furthermore, the study highlights a significant correlation between changes in vegetation patterns and human cultivation practices, underscoring anthropogenic influences on regional flora dynamics. The prevalence of Primulaceae palynomorphs across soil layers underscores the plants adaptability to varying environmental conditions. Human activities, particularly cultivation practices, emerge as key drivers shaping vegetation dynamics in the Elbasan region. The consistent presence of Primulaceae palynomorphs underscores their resilience amidst changing environmental dynamics. Human intervention emerges as a significant determinant of regional vegetation patterns, highlighting the intertwined relationship between human activities and plant evolution.

The Lake Effect: Lake Michigan, Landscape Evolution, and the Archaeological Record of the Chicago Region

Abstract Lake Michigan continues to have a major impact on the environment in the Chicago region, and changes in lake levels were a major driver of landscape evolution throughout the late Pleistocene and Holocene. Fluctuating lake levels reduced or increased the extent and character of terrestrial resources. The landscape has also been altered by erosional and aggradational processes from near-shore currents and wave action. Interpretation of archaeological site and settlement patterns in this region requires an understanding of the factors that have shaped and continue to shape this landscape. In the PaleoIndigenous and Archaic periods, foraging groups adapted to rapidly changing vegetation patterns and episodes of lake transgression and recession that opened or closed new habitats. In the Woodland and Upper Mississippian periods, lake levels stabilized, but the landscape continued to evolve, with erosion and deposition of sediments along Lake Michigan shorelines. Evidence for lake-level fluctuations over the last 12,000 years is available from a variety of data sets, including those for deep lacustrine sediments, ostracods, abandoned shorelines, and sand dunes. By synthesizing these data and utilizing GIS to reconstruct past lakeshores, we seek to better understand how people interacted with and adjusted to this dynamic landscape.

Assessment of flora diversity and population structure in Lagos-Sagamu-Abeokuta Expressway, Southwestern Nigeria

The Lagos-Shagamu-Abeokuta Expressway is a globally important biodiversity hotspot and is facing rapid loss in floristic diversity and changing patterns of vegetation due to various biotic and abiotic factors. This has necessitated the qualitative and quantitative assessment of floral diversity and population structure. The vegetation survey along this route was conducted using the systematic sampling methods. Three sample plots of 50 m x 50 m were laid in alternate side at 100m interval. In each sample plot, all living trees (with GBH at 1.3 m of trees) greater than or equal to 3 m high at midpoint were measured. A total of 4212 individuals representing 134 species, 117 genera, and 48 families were recorded. Fabaceae was the dominant family in this route with 22 species, followed by Euphorbiaceae (8 species), Apocynaceae (8 species), and Poaceae (5 species). Among genera, Senna was followed by Ficus, Terminalia, Cola, Clerodendrum, Albizia, and Alchornea. The population structure of woody species based on diameter class distribution reflected reversed J-shape. The species diversity indexes for dominance (0.02, 0.06), Simpson index value (0.97, 0.93), Shannon–Weiner (3.91, 3.25), evenness (0.59, 0.52) and Margalef (11.22, 6.15) were recorded for arboreal and non-arboreal species respectively. Results obtained revealed high diversity of woody species in the vegetation along this route. The non-arboreal species along this route is threatened by continuous animal grazing, intensification of commercialized farming and invasive species. The information on tree species structure and function can provide baseline information for the conservation of the biodiversity of the tropical forest in this area.

Agro-Pastoral Expansion and Land Use/Land Cover Change Dynamics in Mato Grosso, Brazil

Large-scale land use/land cover changes have occurred in Mato Grosso State (hereafter MT), Brazil, following the introduction of extensive mechanized agriculture and pastoral activities since the 1980s. Author investigated what kind of agro-pastoral activities which are both cattle ranching and top five crops (soybean, sugarcane, corn, cotton and rice) that are closely related to land use change on lands experiencing conversion land use change (such as deforestation and the increase in deeply anthropogenically influenced areas) at each municipal district in MT. Then, this study identifies the volume of exports including contribution ratio by municipal districts where land use changed due to agro-pastoral activities. The patterns of vegetation change indicated that cattle ranching, corn, cotton, rice croplands in the northwest, and soybean and sugarcane fields in the central areas are the main contributors to deforestation. It is shown that land use change due to soybean or corn cultivation occurs mainly in the west and the southeast, respectively. Corn cultivation is associated with a greater increase in anthropogenically influenced areas than soybean cultivation. The municipal districts that export each agro-pastoral product with land use change are limited. Exports of soybeans, corn, and cotton in the municipal districts associated with deforestation had increased dramatically after experienced land use change. For example, Sapezal, which has experienced deforestation, was the only municipal district associated with export of corn to only Switzerland. Since 2007, the number of export partners has increased to 56 countries with the export volume increased 2300 times. These findings highlight the overall non-sustainability of environmental resource development activities in MT.

Assessing Spectral Band, Elevation, and Collection Date Combinations for Classifying Salt Marsh Vegetation with Unoccupied Aerial Vehicle (UAV)-Acquired Imagery

New England salt marshes provide many services to humans and the environment, but these landscapes are threatened by drivers such as sea level rise. Mapping the distribution of salt marsh plant species can help resource managers better monitor these ecosystems. Because salt marsh species often have spatial distributions that change over horizontal distances of less than a meter, accurately mapping this type of vegetation requires the use of high-spatial-resolution data. Previous work has proven that unoccupied aerial vehicle (UAV)-acquired imagery can provide this level of spatial resolution. However, despite many advances in remote sensing mapping methods over the last few decades, limited research focuses on which spectral band, elevation layer, and acquisition date combinations produce the most accurate species classification mappings from UAV imagery within salt marsh landscapes. Thus, our work classified and assessed various combinations of these characteristics of UAV imagery for mapping the distribution of plant species within these ecosystems. The results revealed that red, green, and near-infrared camera image band composites produced more accurate image classifications than true-color camera-band composites. The addition of an elevation layer within image composites further improved classification accuracies, particularly between species with similar spectral characteristics, such as two forms of dominant salt marsh cord grasses (Spartina alterniflora) that live at different elevations from each other. Finer assessments of misclassifications between other plant species pairs provided us with additional insights into the dynamics of why classification total accuracies differed between assessed image composites. The results also suggest that seasonality can significantly affect classification accuracies. The methods and findings utilized in this study may provide resource managers with increased precision in detecting otherwise subtle changes in vegetation patterns over time that can inform future management strategies.

Characteristics of Vegetation Change and Its Climatic and Anthropogenic Driven Pattern in the Qilian Mountains

The Qilian Mountains (QLM) are an essential ecological security barrier in northwest China. Identifying the driven pattern of vegetation change is crucial for ecological protection and restoration in the QLM. Based on high-resolution vegetation coverage (VC) data in the QLM from 1990 to 2018, linear trend analysis was employed to examine the spatiotemporal dynamics of VC in the QLM, while correlation analysis was utilized to establish relationships between VC change and environmental factors. Multiple correlation analysis and residual analysis were adopted to recognize the climatically and anthropogenically driven pattern of VC change. The results showed that VC in the QLM presented a remarkable upward trend in volatility from 1990 to 2018. The significant increase areas accounted for 59.32% of the total, mainly distributed in the central and western QLM, and the significant decrease areas accounted for 9.18%, mostly located in the middle and eastern QLM. VC change showed a significant positive correlation with precipitation change and annual average temperature, while it exhibited a significant negative correlation with annual average precipitation, current VC status, livestock density, and slope. Climate change played a leading role in the increase of VC, and the impact of precipitation was significantly higher than that of temperature. Affected by climate change, the VC of alpine steppes and temperate steppes increased the most. Under the human interference, VC decreased significantly in 9.2% of the region, of which shrubs fell the most, followed by alpine meadows and forests. This study can provide certain guidance for local ecological protection and restoration efforts.

Past and projected future patterns of fractional vegetation coverage in China

With the intensifying climate change and the strengthening ecosystem management, quantifying the past and predicting the future influence of these two factors on vegetation change patterns in China need to be analyzed urgently. By constructing a framework model to accurately identify fractional vegetation coverage (FVC) change patterns, we found that FVC in China from 1982 to 2018 mainly showed linear increase (29.5 %) or Gaussian decrease (27.4 %). FVC variation was mainly affected by soil moisture in the Qi-North region and by vapor pressure deficit in other regions. The influence of environmental change on FVC, except for Yang-Qi region in the southwest (−2.0 %), played a positive role, and weakened from the middle (Hu-Yang region: 2.7 %) to the northwest (Qi-North region: 2.4 %) to the east (Hu-East region: 0.8 %). Based on five machine learning algorithms, it was predicted that under four Shared Socioeconomic Pathways (SSPs, including SSP126、SSP245、SSP370、SSP585) from 2019 to 2060, FVC would maintain an upward trend, except for the east, where FVC would rapidly decline after 2039. FVC in the eastern region experienced a transition from past growth to future decline, suggesting that the focus of future ecosystem management should be on this region.

Solar forcing and ENSO regulated rates of change of ecosystems in Northeast China since the last deglaciation

Understanding long-term (centennial- to millennial-scale) ecosystem transformation and dynamics is a key factor in the prediction of ecosystems under ongoing climate change. However, the magnitude and patterns of vegetation change on this timescale are poorly understood. The rates and patterns of vegetation change and their driving mechanisms in Northeast China were studied, using a compilation of 33 fossil pollen and phytolith sequences from this region. Rates of change (ROC) of vegetation in Northeast China increased continuously during the last deglaciation, decreased during the early Holocene, reaching their lowest level during the middle Holocene, and increased again in the late Holocene. ROCs were different for forest and grassland during specific intervals, and vegetation stability varied from north to south and from west to east. Attribution analysis showed that climatic factors have been the dominant drivers of vegetation change since the last deglaciation. Natural fires were also important, particularly during the last deglaciation through the middle Holocene; human activities became increasingly important from the middle through late Holocene. ROC of forest vegetation exhibited ~1500-yr and ~ 350-yr periodicities; solar activity was likely the driving mechanism. Grassland ROC showed ~500-yr and ~ 270-yr periodicities, perhaps reflecting ENSO regulation of East Asian summer monsoon intensity.

Compositional shifts of alpine plant communities across the high Andes

AbstractAimClimate change is transforming mountain summit plant communities worldwide, but we know little about such changes in the High Andes. Understanding large‐scale patterns of vegetation changes across the Andes, and the factors driving these changes, is fundamental to predicting the effects of global warming. We assessed trends in vegetation cover, species richness (SR) and community‐level thermal niches (CTN) and tested whether they are explained by summits' climatic conditions and soil temperature trends.LocationHigh Andes.Time periodBetween 2011/2012 and 2017/2019.Major taxa studiedVascular plants.MethodsUsing permanent vegetation plots placed on 45 mountain summits and soil temperature loggers situated along a ~6800 km N‐S gradient, we measured species and their relative percentage cover and estimated CTN in two surveys (intervals between 5 and 8 years). We then estimated the annual rate of changes for the three variables and used generalized linear models to assess their relationship with annual precipitation, the minimum air temperatures of each summit and rates of change in the locally recorded soil temperatures.ResultsOver time, there was an average loss of vegetation cover (mean = −0.26%/yr), and a gain in SR across summits (mean = 0.38 species m2/yr), but most summits had significant increases in SR and vegetation cover. Changes in SR were positively related to minimum air temperature and soil temperature rate of change. Most plant communities experienced shifts in their composition by including greater abundances of species with broader thermal niches and higher optima. However, the measured changes in soil temperature did not explain the observed changes in CTN.Main conclusionsHigh Andean vegetation is changing in cover and SR and is shifting towards species with wider thermal niche breadths. The weak relationship with soil temperature trends could have resulted from the short study period that only marginally captures changes in vegetation through time.

Global biome patterns of the Middle and Late Pleistocene

AbstractOur primary aim was to assess the hypothesis that distinctive features of the patterns of vegetation change during successive Quaternary glacial–interglacial cycles reflect climatic differences arising from forcing differences. We addressed this hypothesis using 207 half‐degree resolution global biome pattern simulations, for time slices between 800 and 2 ka, made using the LPJ‐GUESS dynamic global vegetation model. Simulations were driven using ice‐core atmospheric CO2 concentrations, Earth's obliquity, and outputs from a pre‐industrial and 206 palaeoclimate experiments; four additional simulations were driven using projected future CO2 concentrations. Climate experiments were run using HadCM3. Using a rule‐based approach, above‐ground biomass and leaf area index of LPJ‐GUESS plant functional types were used to infer each grid cell's biome. The hypothesis is supported by the palaeobiome simulations. To enable comparisons with the climatic forcing, multivariate analyses were performed of global vegetation pattern dissimilarities between simulations. Results showed generally similar responses to glacial–interglacial climatic variations during each cycle, although no two interglacials or glacials had identical biome patterns. Atmospheric CO2 concentration was the strongest driver of the dissimilarity patterns. Dissimilarities relative to the time slice with the lowest atmospheric CO2 concentration show the log‐linear relationship to atmospheric CO2 concentration expected of an index of ecocarbon sensitivity. For each simulation, extent and total above‐ground biomass of each biome were calculated globally and for three longitudinal segments corresponding to the major continental regions. Mean and minimum past extents of forest biomes, notably Temperate Summergreen Forest, in the three major continental regions strongly parallel relative tree diversities, hence supporting the hypothesis that past biome extents played an important role in determining present diversity. Albeit that they reflect the climatic consequences only of the faster Earth system components, simulated potential future biome patterns are unlike any during the past 800 ky, and likely will continue to change markedly for millennia if projected CO2 concentrations are realised.

Vegetation type change in California’s Northern Bay Area: A comparison of contemporary and historical aerial imagery

The North Bay area of California is a populous and ecologically diverse area that has experienced significant changes in the past century, as well as a series of recent wildfires, after over a century of fire suppression practices. While much research has been conducted quantifying drivers and patterns of vegetation change in conifer-dominated ecosystems, and how such changes have influenced current trends in fire behavior, studies of similar focus and scale are rarer in non-conifer ecosystems, including mixed-hardwood forests or shrub-dominated ecosystems in central and coastal Northern California. This is despite the fact that ecosystems other than conifer forests make up the majority of area burned in California wildfires. As such, expanding research focused on patterns of large-scale vegetation change as a possible driver of this trend in this area is a priority. In this study, we sought to map the overall extent and patch sizes of broad vegetation classes across a 52,000 ha study area based on historical (1948) and contemporary (2014) aerial imagery and to investigate shifts in vegetation patterns, as well as potential pathways and drivers of detected changes. We classified vegetation types through segmenting our imagery into homogenous polygons, and assigning broad vegetation categories using a random forest algorithm. We then analyzed patterns of change using spatial statistics and conditional inference tree analyses. We detected a large increase (12%) in the relative landscape proportion and average patch size of the forest class, characterized by dense tree canopy cover. Woodlands and shrub patches were most susceptible to type change, with the majority (57% and 65%, respectively) of converted areas subsequently identified as denser forest stands. By contrast, herbaceous and forest patches were most persistent. We additionally found that disturbance history, specifically whether an area burned or not, and topographic variables, including elevation and aspect, were important influences on the likelihood of vegetation persistence, while slope and water availability were not. Historical aerial imagery, which provides fine resolution and accurate data over a large spatial scale, is a useful tool for detecting landscape-scale vegetation shifts in ecosystems where widespread vegetation monitoring was not common historically. The marked increase in dense forest we detected, specifically due to the conversion of large areas of shrub and woodland vegetation may correspond to higher surface and ladder fuel load and continuity, and potentially higher wildfire risk. Fuel reduction treatments typically implemented in conifer-dominated forests may also be warranted in these mixed hardwood forests. However, more research is needed to understand drivers of change in non-conifer-dominated ecosystems in California and how such change influences wildfire behavior.

Quantifying the nonlinear response of vegetation greening to driving factors in Longnan of China based on machine learning algorithm

The main influencing factors and their nonlinear effects on the changes of vegetation in China’s mountainous areas under the interaction of different factors are not yet clear, and comprehending the evolutionary trends and driving mechanisms of vegetation is crucial to reveal the changes in ecosystem structure and function. In this study, trend analysis (M−K, T-S and EEMD) combined with machine learning algorithm, namely Boosted Regression Tree model (BRT), were used to quantify the trends of nonlinear responses and thresholds for bioclimatic variables, topography, soil properties and anthropogenic factors for vegetation changes in Longnan. The results showed that the trend analysis clearly confirm the increasing trend of vegetation at multiple spatio-temporal scales. The BRT indicated that total precipitation (bio12, 15.22%), land use (LUCC, 12.68%), elevation (DEM, 11.20%), and population density (Pd, 9.20%) were the more important factors of dominant vegetation greening. Bioclimatic variables were found to revealed the effects of climate with vegetation more clearly. In addition, the BRT revealed that the selected factors have the different nonlinear response relationships to vegetation greening trend and specific thresholds. Among them, increasing of cropland, grassland and forestland can promote vegetation greening. However, GDP, Pd, DEM, bio12, mean diurnal range and temperature seasonality (bio2, bio4) exceed the threshold can significantly inhibit vegetation growth. The BRT combined with trend analysis revealed the nonlinear response relationships and thresholds of the drivers behind the vegetation change patterns, which have obvious effects in exploring the driving mechanisms of vegetation changes in mountainous areas. This study provided an important reference for better revealing the interaction mechanisms between vegetation changes and drivers in the semi-humid zone in East Asia even globally.

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.

Fast response of vegetation in East Asia to abrupt climatic events during the last deglaciation.

Climate changes had major impacts on the vegetation of East Asia during the last deglaciation. However, the rate and pattern of vegetation succession in response to large-scale climatic events during this interval are controversial. Here, we present well-dated decadal-resolution pollen records from annually laminated Maar Lake Xiaolongwan during the last deglaciation. The vegetation changes were rapid and near-synchronous with millennial-scale climatic events, including Greenland Stadial 2.1a (GS-2.1a), Greenland Interstadial 1 (GI-1), Greenland Stadial 1 (GS-1), and the early Holocene (EH). The vegetation responded in different ways to the different rates of climate change. Vegetation change was gradual [∼1 thousand years (kyr) response time] during the transition between GS-2.1a and GI-1, but it was faster (∼0.4 kyr response time) during the transitions between GI-1, GS-1, and the EH, resulting in different patterns of vegetation succession. Additionally, the amplitude and pattern of vegetation changes resembled those in the records of regional climate change based on long-chain n-alkanes δ13C and stalagmite δ18O, as well as in the mid-latitude Northern Hemisphere temperature record and the Greenland ice core δ18O record. Therefore, the rate and pattern of vegetation succession in the Changbai Mountain of Northeast Asia during the last deglaciation were sensitive to the characteristics of changes in the regional hydrothermal conditions and mid-latitude Northern Hemisphere temperature, which were linked to both high- and low-latitude atmospheric-oceanic dynamics. Overall, our findings reveal a close relationship between ecosystem succession and hydrothermal changes during these millennial-scale climatic events in East Asia during the last deglaciation.

There and back again: Forty years of change in vegetation patterns in Irish peatlands

Northern peatlands play a disproportionally large role in the global carbon balance due to the massive amount of carbon stored in peat and ongoing sequestration. Vegetation composition and structure are recognised indicators for carbon sequestration and storage potential in these ecosystems, but decadal dynamics and roles of autogenic succession, climate, and land-use herein remain poorly understood.We assessed vegetation changes in the least disturbed centre of twelve Irish peatland reserves sampled across a gradient in temperature and precipitation between 1978(1983)–2021, combining re-classified traditional high-resolution vegetation maps with recent very high-resolution drone imagery. Specifically, we tested whether microform proportions of open water, wet hollow, moist lawn, and dry hummock had changed and explored to what extent changes were related to climate and land-use drivers.Results revealed that the studied peatlands underwent an overall unidirectional surface drying trend, with dry hummocks expanding at the expense of open water and wet hollow, while moist lawns remained approximately equal in proportions. The degree of change varied between the studied peatlands, with western blanket bogs and unrestored raised bogs experiencing more surface drying than mountain blanket bogs and raised bogs under hydrological restoration. While our results indicated climate and/or land-use as drivers of surface drying, our sample size was too small to make definitive conclusions about their exact role.The overall change from wet to dry surface conditions between 1978 and 2021 occurred at a much faster rate than hitherto reported for these slow-changing ecosystems. This raises concern for the future resilience of peatlands to changes in climate and land-use, as well as their potential impact on the peatland carbon balance.