Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000-2019 time-series Landsat data

Measuring Temporal Change in Scrub Vegetation Cover Using UAV-Derived Height Maps: A Case Study at Two UK Nature Reserves

Measuring the outcome of practical interventions and actions helps to inform conservation management objectives and assess progress towards objectives and targets. Measuring success also informs future management by identifying actions that are effective and those that are not. Scrub vegetation is an important habitat type in terrestrial ecosystems, providing important shelter and food resources for biodiversity and livestock. Much of practical land management in the UK involves the monitoring and management of scrub, and current drone-based methods of scrub collection requires expensive equipment or complex methods. A 2021 paper determined a cheap and simple way to determine scrub levels, and this could potentially be used to map temporal changes, as well as identify directional change in scrub. This study looks at whether the method outlined in the 2021 study could be used to measure temporal and directional changes in scrub cover on two nature reserves in the UK: Daneway Banks in Gloucestershire and Flat Holm Island in the Severn Estuary. Scrub levels at Daneway Banks increased from 14.63% in 2015 to 16.52% in 2017, before decreasing to 14.89% in 2021 due to managed cutting and clearing. Scrub cover at Flatholm Island decreased from 10.18% in 2019 to 8.71% in 2021. The exact locations of scrub growth and loss for each site was also calculated and mapped. This approach was found to be a viable way of measuring temporal and directional change in scrub levels. The data can also be used to reframe changes in scrub levels as a shift towards vegetation succession or reduction, to better visualise how changes in scrub levels affect overall site management goals, and is a cheaper, more accessible alternative to current methods of measuring temporal vegetation changes.

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data

Wetlands in arid and semi-arid regions are characterized by dry- and wet-phase vegetation expression which responds to variable water resources. Monitoring condition trends in these wetlands is challenging because transitions may be rapid and short-lived, and identification of meaningful condition change requires longitudinal study. Remotely-sensed data provide cost effective, multi-decadal information with sufficient temporal and spatial scale to explore wetland condition. In this study, we used a time series of Enhanced Vegetation Index (EVI) derived from 34 years (1988-2021) of Landsat imagery, to investigate the long-term condition dynamics of six broad vegetation groups (communities) in a large floodplain wetland system, the Macquarie Marshes in Australia. These communities were persistently mapped as River Red Gum wetland, Black Box/Coolibah woodland, Lignum shrubland, Semi-permanent wetland, Terrestrial grassland and Terrestrial woodland. We used generalized additive models (GAM) to explore the response of vegetation to seasonality, river flow and climatic conditions. We found that EVI was a useful metric to monitor both wetland condition and response to climatic and hydrological drivers. Wetland communities were particularly responsive to river flow and seasonality, while terrestrial communities were responsive to climate and seasonality. Our results indicate asymptotic condition responses, and therefore evidence of hydrological thresholds, by some wetland communities to river flows. We did not observe a long-term trend of declining condition although an apparent increase in condition variability towards the end of the time series requires continued monitoring. Our remotely-sensed, landscape-scale monitoring approach merits further ground validation. We discuss how it can be used to provide a management tool which continuously assesses short and long-term wetland condition and informs conservation decisions about water management for environmental flows.

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands' protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands' dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000-2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005-2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands' variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands' resources and an accurate decision-making on the ecological environment's security.

Spatial and temporal variations of vegetation cover and its influencing factors in Shandong Province based on GEE

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and -accumulation effects

Quantifying the contributions of climate change (CC) and human activities (HA) to vegetation change is crucial for making a sustainable vegetation restoration scheme. However, the effects of extreme climate and time-lag and -accumulation effects on vegetation are often ignored, thus underestimating the impact of CC on vegetation change. In this study, the spatiotemporal variation of fractional vegetation cover (FVC) from 2000 to 2019 in northern China (NC) as well as the time-lag and -accumulation effects of 15 monthly climatic indices, including extreme indices, on the FVC, were analyzed. Subsequently, a modified residual analysis considering the influence of extreme climate and time-lag and -accumulation effects was proposed and used to attribute the change in the FVC contributed by CC and HA. Given the multicollinearity of climatic variables, partial least squares regression was used to construct the multiple linear regression between climatic indices and the FVC. The results show that: (1) the annual FVC significantly increased at a rate of 0.0268/10a from 2000 to 2019 in all vegetated areas of NC. Spatially, the annual FVC increased in most vegetated areas (∼81.6 %) of NC, and the increase was significant in ∼54.6 % of the areas; (2) except for the temperature duration (DTR), climatic indices had no significant time-lag effects but significant time-accumulation effects on the FVC change. The DTR had both significant time-lag and -accumulation effects on the FVC change. Except for potential evapotranspiration and DTR, the main temporal effects of climatic indices on the FVC were a 0-month lag and 1-2-month accumulation; and (3) the contributions of CC and HA to FVC change were 0.0081/10a and 0.0187/10a in NC, respectively, accounting for 30.2 % and 69.8 %, respectively. HA dominated the increase in the FVC in most provinces of NC, except for the Qinghai and Neimenggu provinces.

Processes and mechanisms of vegetation ecosystem responding to climate and ecological restoration in China

Vegetation is an essential component of the earth's surface system and its dynamics is a clear indicator of global climate change. However, the vegetation trends of most studies were based on time-unvarying methods, cannot accurately detect the long-term nonlinear characteristics of vegetation changes. Here, the ensemble empirical mode decomposition and the Breaks for Additive Seasonal and Trend algorithm were applied to reconstruct the the normalized difference vegetation index (NDVI) data and diagnose spatiotemporal evolution and abrupt changes of long-term vegetation trends in China during 1982-2018. Residual analysis was used to separate the influence of climate and human activities on NDVI variations, and the effect of specific human drivers on vegetation growth was obtained. The results suggest that based on the time-varying analysis, high vegetation browning was masked by overall vegetation greening. Vegetation growth in China experienced an abrupt change in the 1990s and 2000s, accounting for 50% and 33.6% of the whole China respectively. Of the area before the breakpoint, 45.4% showed a trend of vegetation decrease, which was concentrated mainly in east China, while 43% of the area after the breakpoint also showed vegetation degradation, mainly in northwest China. Climate was an important driving force for vegetation change in China. It played a positive role in south China, but had a negative effect in northwest China. The impact of human activities on vegetation growthchanged from an initial negative influence to a positive one. In terms of human activities, an inverted-U-shaped relation was detected between CO2 emissions and vegetation growth; that is, the fertilization effect of CO2 had a certain threshold. Once that threshold was exceeded, it would hinder vegetation growth. Population density had a slight constraint on vegetation growth, and the implementation of ecological restoration projects (e.g., the Grain for Green Program) can promote vegetation growth to a certain extent.

Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine

Monitoring the ecological environment quality is an important task that is often connected to achieving sustainable development. Timely and accurate monitoring can provide a scientific basis for regional land use planning and environmental protection. Based on the Google Earth Engine platform coupled with the greenness, humidity, heat, and dryness identified in remote sensing imagery, this paper constructed a remote sensing ecological index (RSEI) covering northern Anhui and quantitatively analyzed the characteristics of the spatiotemporal changes in the ecological environment quality from 2001 to 2020. Geodetector software was used to explore the mechanism driving the characteristics of spatial differentiation in the ecological environment quality. The main conclusions were as follows. First, the ecological environment quality in northern Anhui declined rapidly from 2001 to 2005, but the rate of decline slowed from 2005 to 2020 and a trend of improvement gradually emerged. The ecological environment quality of Huainan from 2001 to 2020 was better and more stable compared with other regional cities. Bengbu and Suzhou showed a trend of initially declining and then improving. Huaibei, Fuyang, and Bozhou demonstrated a trend of a fluctuating decline over time. Second, vegetation coverage was the main influencing factor of the RSEI, while rainfall was a secondary factor in northern Anhui from 2001 to 2020. Finally, interactions were observed between the factors, and the explanatory power of these factors increased significantly after the interaction. The most apparent interaction was between vegetation coverage and rainfall (q = 0.404). In addition, we found that vegetation abundance had a positive impact on ecological environment quality, while population density and urbanization had negative impacts, and the ecological environment quality of wetlands was the highest. Our research will provide a theoretical basis for environmental protection and support the high-quality development of northern Anhui.

Exploring and attributing change to fractional vegetation coverage in the middle and lower reaches of Hanjiang River Basin, China

The middle and lower reaches of Hanjiang River Basin (MLHB), areas that have an important ecological function in China, have experienced great changes in the vegetation ecosystem driven by natural environmental change and human activity. Here, we explored the spatio-temporal dynamics of fractional vegetation coverage (FVC) and quantitatively analyzed its driving factors to advance current understanding of how the ecological environment has changed. Specifically, we used the dimidiate pixel model to calculate the FVC of the MLHB from 2001 to 2018 based on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data. We then used Theil-Sen median slope (Sen's slope) and coefficient of variation (CV) to explore spatial and temporal variations, as well as characteristics in fluctuations. Finally, we utilized a geographical detector model (with spatial scale effects and spatial data discretization tests) to quantify the influence of the detected natural and human factors. Results showed that average annual FVC was 0.30-0.75 for ~90% of the study area over the 19-year study period with a heterogeneous spatial distribution. FVC variation trend displayed stability and improvement. Areas with higher FVC displayed greater stability. All 10 detected natural and anthropogenic factors were responsible for changes in FVC. The primary factors causing FVC to change were precipitation (in 2001) and slope (in 2018), followed by landform type, distance to water, and nighttime light (NTL) (in 2018). Precipitation and slope consistently displayed the largest interaction across all years. The interaction between human and topographical factors had gradually increasing significance on changes in FVC over the research period. The range and type of factors suitable for promoting vegetation growth were detected in the study area. Results of this study can provide a scientific basis for developing effective strategies for local vegetation protection, restoration, and land resource management.

Aboveground biomass of marshes in Northeast China: Spatial pattern and annual changes responding to climate change

Examining vegetation aboveground biomass (AGB) changes is important to understanding wetland carbon sequestration. Here, we combined the field-measured AGB data (458 samples) from 2009 to 2021, moderate resolution imaging spectroradiometer reflectance products, and climatic data to reveal the AGB variations of marshes in Northeast China by comparing various models driven by different indicators. The results indicated that random forest model driven by six vegetation indices, land surface temperature, and land surface water index achieved accurate marsh AGB estimation with R2 being 0.78 and relative error being 16.71%. The mean marsh AGB in Northeast China from 2000 to 2021 was 682.89 ± 31.69 g·m−2, which generally increased from north to south in space. Temporally, annual marsh AGB declined slowly at a rate of 3.45 g·m−2·year−1 during the past 21 years driven mainly by the decrease in summer mean temperature that was characterized by a significantly positive correlation between them. Nevertheless, we highlighted that the temporal changes of marsh AGB spatially varied in response to inconsistent climate change, thus place-based measures are required for sustainable management of marshes.

Spatio-temporal monitoring of marsh vegetation phenology and its response to hydro-meteorological factors using CCDC algorithm with optical and SAR images: In case of Honghe National Nature Reserve, China

Vegetation phenology is a sensitive indicator which can comprehensively reflect the response of wetland vegetation to external environment changes. However, the time-series monitoring wetland vegetation phenological changes and clarifying its response to hydrology and meteorology still face great challenges. To fill these research gaps, this paper proposed a novel time-series approach for monitoring phenological change of marsh vegetation in Honghe National Nature Reserve (HNNR), Northeast China, using continuous change detection and classification (CCDC) algorithm and Landsat and Sentinel-1 SAR images from 1985 to 2021. We evaluated the spatio-temporal response relationship of phenological characteristics to hydro-meteorological factors by combining CCDC algorithm with partial least squares regression (PLSR). Finally, this study further explored the intra-annual loss and restoration of marsh vegetation in response to hydro-meteorological factors using the transfer entropy (TE) and CCDC-MLSR model constructed by CCDC and Multiple Linear Stepwise Regression (MLSR) algorithms. We found that the bimodal trajectory of phenology reflects two growth processes of marsh vegetation in one year, and high-frequency and high-amplitude loss occurred in shallow-water and deep-water marsh vegetation from April to October, resulting in the loss area within the year was significantly greater than the recovery area. We confirmed that the CCDC algorithm could track the evolution trajectory of time-series phenology of marsh vegetation. We further revealed that precipitation, temperature and frequency of water-level changes are the main driving factors for the spatio-temporal phenological evolution of different marsh vegetation. This study verified the effect of alternative changes of hydrology and climate on loss and recovery of marsh vegetation in each growth stage. The results of this study provide a scientific basis for wetland protection, ecological restoration, and sustainable development.

Response of vegetation variation to climate change and human activities in semi-arid swamps

Vegetation is a sensitive factor in marsh ecosystems, which can provide nesting sites, foraging areas, and hiding places for waterfowl and can affect their survival environment. The Jilin Momoge National Nature Reserve, which consists of large areas of marshes, is located in the semi-arid region of northeast China and is an important stopover site for the critically endangered species of the Siberian Crane (Grus leucogeranus). Global climate change, extreme droughts and floods, and large differences in evaporation and precipitation in this region can cause rapid vegetation succession. In recent years, increased grain production and river-lake connectivity projects carried out in this area to increase grain outputs and restore wetlands have caused significant changes in the hydrological and landscape patterns. Therefore, research on the response of variation trends in vegetation patterns to the main driving factors (climate change and human activities) is critical for the conservation of the Siberian Crane. Based on the Google Earth Engine (GEE) platform, we obtained and processed the Normalized difference vegetation index (NDVI) data of the study area during the peak summer vegetation period for each year from 1984 to 2020, estimated the annual vegetation cover using Maximum value composites (MVC) method and the image dichotomy method, calculated and analyzed the spatial and temporal trends of vegetation cover, explored the response of vegetation cover change in terms of climate change and human activities, and quantified the relative contribution of both. The results revealed that first, from the spatial and temporal changes, the average annual growth rate of regional vegetation was 0.002/a, and 71.14% of the study area was improved. The vegetation cover showed a trend of degradation and then recovery, in which the percentage of high vegetation cover area decreased from 51.22% (1984-2000) to 28.33% (2001-2005), and then recovered to 55.69% (2006-2020). Second, among climate change factors, precipitation was more correlated with the growth of vegetation in the study area than temperature, and the increase in precipitation during the growing season could promote the growth of marsh vegetation in the Momoge Reserve. Third, overall, human activities have contributed to the improvement of vegetation cover in the study area with the implementation of important ecological projects, such as the return of farmland to wetlands, the return of grazing to grass, and the connection of rivers and lakes. Fourth, climate change and human activities jointly drive vegetation change, but the contribution of human activities in both vegetation improvement and degradation areas (85.68% and 78.29%, respectively) is higher than that of climate change (14.32% and 21.71%, respectively), which is the main reason for vegetation improvement or degradation in the study area. The analysis of vegetation pattern change within an intensive time series in semi-arid regions can provide a reference and basis for studying the driving factors in regions with rapid changes in vegetation and hydrological conditions.

Evolution of Ecological Patterns of Poyang Lake Wetland Landscape over the Last One Hundred Years Based on Historical Topographic Maps and Landsat Images

Ecological pattern evolution of Poyang Lake wetland, the largest freshwater lake in China, is critical for regional ecological protection and sustainable development of migratory bird habitats; however, this information is still not fully explored. In this study, we quantitatively reconstructed the spatial distribution and landscape ecological pattern of Poyang Lake wetlands in three periods in the past 100 years based on the military topographic map in the 1930s and the Landsat satellite remote sensing image data in 1979 and 2021. Further, use the Fragstats software to analyze the ecological pattern index of wetland reconstruction results. The results show that the wetland area in the Poyang Lake region has experienced a continuous reduction process over the past 100 years, and it decreased from 3857 km2 in the 1930s to 3673 km2 in the 1970s, and then to 3624 km2 in the 2020s. The current wetland area has decreased by about 6.04% compared with the 1930s. The general trend of changes in the spatial pattern of Poyang Lake wetlands is that the surface water decreases and the open land increases. Nevertheless, the trend has certain spatial differences as a large area of wetlands disappeared in the southwest and west of Poyang Lake and the areas with enlarged wetland density values mainly appeared in the northeastern and northern parts of the study area. The NP (number of patches) in the wetlands of Poyang Lake over the past 100 years showed a downward trend during the 1930s–1970s, and an increasing trend during the 1970s–2010s. Due to the increases of constructed wetlands, the number and density of patches also increased, and PD (patch density) reached a maximum value of 0.142 in 2020s. The LPI (largest patch index) has shown a gradual downward trend in the past 100 years. Compared with the 1930s, the wetlands in 2020s dropped by about 26.64%, and the wetlands further showed a trend of fragmentation. The AI index, which indicates the concentration of wetland patches, reached the maximum value in 2020s, but the LSI (landscape shape index) showed a downward trend in general, indicating that the shape of wetland patches has been simplified over the past 100 years. The research results can provide basic data and decision-making basis for Poyang Lake wetland protection, construction of migratory bird reserve and regional sustainable development.

A new method to map groundwater-dependent ecosystem zones in semi-arid environments: A case study in Chile

Groundwater (GW) use has intensified in recent decades, threatening the ecological integrity of groundwater-dependent ecosystems (GDEs). The study of GDEs is limited; therefore, integrated, interdisciplinary environmental approaches that guarantee their monitoring and management amid current climate and anthropogenic changes are needed. A new geospatial method with an integrated and temporal approach was developed through a multicriteria approximation, taking into account expert opinion, remote sensing-GIS, and fieldwork to map groundwater-dependent ecosystem zones (GDEZ). A survey of experts (N = 26) was conducted to assign degrees of importance to the various geospatial parameters, and the mapping was carried out using 14 parameters. The reclassified parameters were normalized on a scale of 1 to 5 according to the degree of probability of the presence of GDE. The validation was carried out through fieldwork and statistical analysis. Then, the spatio-temporal changes amid changing GW levels were assessed using the summer season normalized difference vegetation index (NDVI). Two GDEZ maps were obtained, for 2002 and 2017, between which the high- and very-high-probability zones of GDEs decreased by 31,887 ha (~ 38%). The most sensitive temporal parameters that most influenced the spatio-temporal changes on GDEs were precipitation and land use, with rain exerting a slightly the greatest influence. It was also demonstrated that identified ecosystems decreased in area or were affected by aquifer depletion (NDVI-GW, r Pearson ≥0.74). This validated method allows spatio-temporal changes in GDEs to be mapped and analyzed at an annual scale and is transferable to other arid and semi-arid environments.

Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels

Examining the characteristics of vegetation change and associated spatial patterns under different protection levels can provide a scientific basis for national park protection and management. Based on the dense time-series Landsat enhanced vegetation index (EVI) data between 1986 and 2020, we utilized the Wild Binary Segmentation (WBS) approach to detect spatial and temporal characteristics of abrupt, gradual, and total changes in Wuyishan National Park. The differences in vegetation change in three protection-level areas (strictly protected [Prots], generally protected [Prot], and non-protected [NP]) were examined, and the contributions to their spatial patterns were evaluated through Geodetector. The results showed the following: (1) The highest percentage of area without abrupt change was in Prots (39.89%), and the lowest percentage was in NP (17.44%). The percentage of abrupt change frequency (larger than three times) increased from 4.40% to 9.10% and 12.49% with the decreases in protection. The significance test showed that the difference in changed frequencies was not significant among these regions, but the interannual variation of abrupt change in Prots was significantly different from other areas. (2) The vegetation coverage of the Wuyishan National Park generally improved. The total EVI change (TEVI) showed that the positive percentage of Prots and Prot was 90.43% and 91.71%, respectively, slightly higher than that of NP (88.44%). However, the mean greenness change of NP was higher than that of Prots and Prot. (3) The park’s EVI spatial pattern in 1986 was the strongest factor determining the EVI spatial pattern in 2020; the explanatory power reduced as the protection level decreased. The explanation power (q value) of abrupt vegetation change was lower and increased as the protection level decreased. The interaction detection showed that EVI1986 and TEVI had the strongest explanatory powers, but the explanatory ability gradually weakened from 0.713 to 0.672 to 0.581 in Prots, Prot, and NP, respectively. This study provided a systematic analysis of vegetation changes and their impacts on spatial patterns.

Spatiotemporal dynamics of wetlands and their driving factors based on PLS-SEM: A case study in Wuhan

Globally, wetlands have been severely damaged due to natural environment and human activities. Understanding the spatiotemporal dynamics of wetlands and their driving forces is essential for their effective protection. This study proposes a research framework to explore the interaction between the natural environment and human activities and its impact on wetland changes, by introducing Partial Least Squares Structural Equation Modeling (PLS-SEM) and Geographically Weighted Regression (GWR) model, then applying the methodology in Wuhan, a typical wetland city in China. The validity and reliability evaluation indicated that the PLS-SEM model is reasonable. The results showed that the area of wetlands in Wuhan decreased by 10.98% in 1990-2018 and four obvious direct pathways of influence were found. Positive soil and terrain conditions are conducive to maintaining wetlands, while rapid urbanization drastically reduce the distribution of wetlands. It is remarkable that the impact of climate on wetlands is gradually shifting from positive to negative. Furthermore, four potential indirect impact pathways affecting wetland distribution shown that urbanization and climate enhance the negative impact of terrain on wetland distribution, while their impacts on soil weaken soil's direct positive impact. This study provides a quantitative methodology for determining the causes of wetland loss; it can also be applied to other cities or regions, which is essential for applying more effective measures to protect wetlands.

Responses of leaf hydraulic traits of Schoenoplectus tabernaemontani to increasing temperature and CO2 concentrations

BACKGROUND

Against the background of a changing climate, the responses of functional traits of plateau wetland plants to increasing temperatures and CO₂ concentrations need to be understood. Hydraulic traits are the key for plants to maintain their ecological functions and affect their growth and survival. However, few studies have comprehensively considered the response strategies of wetland plants' hydraulic traits to environmental changes in the context of water and matter transport, loss, and retention. According to the latest IPCC prediction results, we performed experiments under increased temperature (2 °C) and CO₂ levels (850 μmol/mol) in an artificial Sealed-top Chamber (STC) to investigate the responses of the hydraulic characteristics of Schoenoplectus tabernaemontani, the dominant species in plateau wetlands in China.

RESULTS

Compared with the CK group, net photosynthetic rate, transpiration rate, stomatal length, cuticle thickness, vascular bundle length, vascular bundle width, and vascular bundle area of S. tabernaemontani in the ET group were significantly reduced, whereas stomatal density and vein density increased significantly. Compared with the CK group, the hydraulic traits of S. tabernaemontani in the EC group were reduced considerably in stomatal length and cuticle thickness but increased dramatically in stomatal density, and there were no significant differences between other parameter values and the control group. Net photosynthetic rate was significantly positively correlated with stomatal length, cuticle thickness, and vascular bundle length, and stomatal conductance was significantly positively correlated with cuticle thickness. The transpiration rate was significantly positively correlated with cuticle thickness, epidermal cell area, vascular bundle length, vascular bundle width, and vascular bundle area. Regarding the hydraulic traits, there was a significant negative correlation between stomatal density and stomatal length, or cuticle thickness, and a significant positive correlation between the latter two. The epidermal cell area was significantly positively correlated with epidermal thickness, vascular bundle length, vascular bundle width, and vascular bundle area.

CONCLUSIONS

Increased temperature and CO₂ levels are not conducive to the photosynthetic activity of S. tabernaemontani. Photosynthetic rate, stomatal density and size, vein density, epidermal structure size, and vascular bundle size play an essential role in the adaptation of this species to changes in temperature and CO₂ concentration. In the process of adaptation, hydraulic traits are not isolated from each other, and there is a functional association among traits. This study provide a scientific basis for the management and protection of plateau wetlands.