Asynchronous local dynamics contributes to stability of a seagrass bed in Tokyo Bay

It is known that asynchronous temporal variations in local populations can contribute to the stability of metapopulations. However, studies evaluating the hierarchical organization of multiple spatial scales are rare for continuous marine landscapes, especially for marine vegetation such as seagrass beds. In this study, long‐term observation (26 yr) of temporal changes and nested spatial analyses were combined for an extensive seagrass meadow in Tokyo Bay, Japan, using remote sensing and geographic information system technologies. We examined how the dynamics at the whole‐bed scale (~1 km²) are related to those at a local scale (0.04 km²), and investigated the relationship between the seagrass dynamics and long‐term changes in environmental conditions using data on oceanography, water quality, and sediment dynamics. The seagrass bed size fluctuated between a maximum of 1.28 km² (in 1987) and a minimum of 0.39 km² (in 2001), with an average of 0.90 km². The temporal variation in seagrass bed size at the whole‐bed scale correlated with sand movement within the seagrass bed related to changes in the position of a sandbar. Seagrass bed size fluctuated asynchronously at a local scale. Multivariate analyses recognized clusters of local areas showing similar patterns of fluctuation. Temporal patterns in the various clusters responded differently to changes in environmental factors, e.g. the position of the sandbar was highly correlated with seagrass bed size in shallow habitats but not in deeper areas. The magnitudes of the temporal variations for the local clusters were greater than that of the entire bed, suggesting that asynchronous fluctuation in different areas of the bed plays an important role in the overall stability of the seagrass bed. The results of the present study also highlight the importance of physical processes in regulating the temporal dynamics of seagrass beds in shallow sedimentary landscapes.     

Spectral response of the seagrass Zostera noltii with different sediment backgrounds

The efficiency of vegetation indices (VIs) to estimate the above-ground biomass of the seagrass species Zostera noltii Hornem. from remote sensing was tested experimentally on different substrata, since terrestrial vegetation studies have shown that VIs can be adversely influenced by the spectral properties of soils and background surfaces. Leaves placed on medium sand, fine sand and autoclaved fine sand were incrementally removed, and the spectral reflectance was measured in the 400–900 nm wavelength range. Several VIs were evaluated: ratios using visible and near infrared wavelengths, narrow-band indices, indices based on derivative analysis and continuum removal. Background spectral reflectance was clearly visible in the leaf reflectance spectra, showing marked brightness and spectral contrast variations for the same amount of vegetation. Paradoxically, indices used to minimize soil effects, such as the Soil-Adjusted Vegetation Index (SAVI) and the Modified second Soil-Adjusted Vegetation Index (MSAVI₂) showed a high sensitivity to background effects. Similar results were found for the widely used Normalized Difference Vegetation Index (NDVI) and for Pigment Specific Simple Ratios (PSSRs). In fact, background effects were most reduced for VIs integrating a blue band correction, namely the modified specific ratio (mSR₍₇₀₅₎), the modified Normalized Difference (mND₍₇₀₅₎), and two modified NDVIs proposed in this study. However, these indices showed a faster saturation for high seagrass biomass. The background effects were also substantially reduced using Modified Gaussian Model indices at 620 and 675 nm. The blue band corrected VIs should now be tested for air-borne or satellite remote sensing applications, but some require sensors with a hyperspectral resolution. Nevertheless, this type of index can be applied to analyse broad band multispectral satellite images with a blue band.     

基于遥感图像不同辐射校正水平的植被覆盖度估算模型

选用南京市SPOT 5 HRG图像的地物反射率(PAC)、表观反射率(TOA)和灰度值（DN）影像,提取了4种植被指数（VI）,即归一化植被指数（NDVI）、转换植被指数（TVI）、土壤调节植被指数（SAVI）和修正的土壤调节植被指数（MSAVI）,与地面实测的植被覆盖度进行了回归分析,并建立了36个VI-VFC关系模型.结果表明：在所有模型中,基于PAC级影像提取的NDVI和TVI的3次多项式模型最优;其次为基于DN级影像提取的SAVI和MSAVI的3次多项式模型,在VFC>0.8时其精度略高于前两种模型.这4个模型在植被中等密集区域（VFC＝0.4～0.8）的精度高于植被稀疏区域（VFC=0～0.4）.所建模型可通过中间模型的联结,进行推广使用.在基于VI-VFC关系建模过程中,基于遥感影像不同辐射校正水平提取植被指数,有利于充分挖掘遥感影像信息,进而提高VFC估算的精度.     

基于湿地植物光谱的水体总氮估测

利用再生水补充城市湿地是目前湿地恢复与重建的主要方向,然而再水中高浓度的氮、磷含量极易导致水体富营养化。遥感技术已成为富营养化监测的重要手段,但对于植被覆盖水域的富营养化直接探测存在一定的局限性。以北京市典型再生水补水湿地奥林匹克公园南园湿地为研究区,利用湿地植物光谱进行水体富营养化主控因子总氮的遥感探测。测定芦苇(Phragmites australis)和香蒲(Typha angustifolia)的叶片光谱及水体总氮含量,在对数据进行预处理的基础上建立二者的关系模型,包括单变量模型(比值光谱指数(SR)模型和归一化差值光谱指数(ND)模型),与多变量模型(逐步多元线性回归(SMLR)模型和偏最小二乘回归(PLSR)模型),并利用交叉验证决定系数(R2cv)和均方根误差(RMSEcv)进行模型精度检验。结果表明,不同回归模型相比,多变量回归模型精度较高;多变量回归模型中,PLSR模型精度较高,R2cv可达0.72,RMSEcv仅为0.24,是建立湿地植物光谱与水体总氮含量关系的最优模型。不同湿地植物类型相比,利用芦苇反射光谱建立的各种预测模型的精度都高于香蒲。其他环境因子(总磷)也是影响TN含量与湿地植物反射光谱关系的重要因素。研究成果可以弥补现有水体富营养化遥感探测的不足,并为再生水利用的城市湿地水质监测与管理提供有力的科学依据。     

Genetics,novel weapons and rhizospheric microcosmal signaling in the invasion of Phragmites australis

Chemical communication and perception strategies between plants are highly sophisticated but are only partly understood. Among the different interactions, the suppressive interaction of a class of chemicals released by one plant through root exudates against the neighbouring plants (allelopathy) have been implicated in the invasiveness of many exotic weedy species. Phragmites australis (common reed) is one of the dominant colonizers of the North American wetland marshes and exhibits invasive behavior by virtually replacing the entire native vegetation in its niche. Recently, by adopting a systematic bioassay driven approach we elucidated the role of root derived allelopathy as one of the important mechanisms by which P. australis exerts its invasive behavior. Additionally, our recent preliminary data indicates the involvement of rhizobacterial signaling in the invasive success of P. australis. A better understanding of biochemical weaponry used by P. australis will aid scientists and technologists in addressing the impact of root secretions in invasiveness of weedy species and thus promote a more informed environmental stewardship.Key words: Phragmites australis, roots, phytotoxicity, reactive oxygen species, microtubules, microcosm     

Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

Monitoring soil respiration (R_s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R_s to the regional scales. In this study, we estimated the spatial distribution of R_s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R_s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R_s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R_s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R_s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R_s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R_s predictor model was validated by comparing it with the field measured R_s using an independent dataset. The TM-calibrated MODIS-estimated R_s was within an accuracy of field measured R_s with R² of 0.78 and root mean square error of 1.45 gC m⁻² d⁻¹. At the peak growing season of alpine grasslands, R_s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R_s in this study.     

Evaluation of Phragmites australis (Cav.) Trin. evapotranspiration in Northern and Southern Italy

The design, operation, pollutant removal as well as hydraulic modeling of wetland systems for wastewater treatment can be improved by better understanding and simulating the evapotranspiration process. To this purpose, two experiments were carried out in Northern (Veneto region) and Southern (Sicily region) Italy to measure evapotranspiration (ET) and determine the crop coefficient of Phragmites australis (Cav.) Trin. using the FAO 56 approach. The experimental set-up consisted of a combination of vegetated and unvegetated plastic tanks (Veneto) or pilot sub-surface flow beds (Sicily). The ET values were obtained by measuring the amount of water needed to restore the initial volume in the tanks and in the beds after a certain period. All the needed climatic variables were measured and taken into account in the ET measurements. In the two experimental sites cumulative reference evapotranspiration (ET₀) was similar to the cumulative ET measured in the control tanks and beds (without vegetation, ET_con), while ET measured for P. australis (ET_phr) was significantly higher, underlining the strong effect of vegetation. From June 2009 to September 2009 the cumulative ET₀, ET_con and ET_phr in Veneto were 455, 424 and 3048 mm, in Sicily 653, 556 and 3899 mm, respectively. The plant coefficient trend of P. australis (K_p) estimated in Veneto was similar to that in Sicily, suggesting that the role of the plant in dispersing water is similar under different environmental conditions. Additional measurements made in the Veneto plant showed that K_p assumes different patterns and values in relation to plant age and growth stage. These results highlight the importance of the plants in regulating water losses from a wetland system, above all from small-scale constructed wetlands where the effect of the advection in ET rates is evident.     

Contrasting effects of habitat structure on the recruitment and mortality of an epibiotic macroalga

Field surveys over 2 years in contiguous beds of the seagrassesZostera capricorni andPosidonia australis showed that the green algaCodium duthieae was consistently more abundant inZ. capricorni than inP. australis. In 1 year, mature plants were also more abundant at the boundary between the seagrass beds than in either bed. Field experiments and programmes of sampling were used to investigate three potential explanations for the unusual distribution of this alga: (1) that the availability of substrata suitable for attachment of the alga differed between the two seagrass beds; (2) that mortality of matureC. duthieae differed between the seagrass beds; and/or (3) that the intensity of recruitment was different in the two seagrass beds.C. duthieae plants were exclusively epibionts of the bivalveAnadara trapezia. Detailed sampling showed that the abundance ofA. trapezia was similar in both seagrass beds and that the distribution of bivalves suitable as substrata forC. duthieae plants was not obviously related to proximity to the boundary between the beds. Two experiments investigated the survival ofC. duthieae plants in each bed. In the first, matureC. duthieae plants transplanted into theP. australis bed suffered similar rates of mortality to plants which were disturbed and moved within theZ. capricorni bed or which were left undisturbed in theZ. capricorni bed. Fewer of the host bivalves were recovered from theZ. capricorni bed, however, indicating that the mechanism of mortality differed between the beds, hosts being more frequently dislodged in theZ. capricorni bed. Removal of the leaves of the seagrasses had consistently greater effects on near-bottom current velocities in theZ. capricorni bed than in theP. australis bed and significantly increased mortality ofC. duthieae in theZ. capricorni bed. Survival of plants was greater in plots of artificial leaves ofP. australis placed in theZ. capricorni bed than in plots of the naturalZ. capricorni leaves or plots where the natural leaves were removed. Most mortality in theZ. capricorni bed was due to dislodgement of the alga and its bivalve substratum. Corresponding manipulations of leaves in theP. australis bed had consistently smaller effects on survivorship of both the alga and its host. Patterns in the recruitment of the alga most clearly reflected the distribution of adults.C. duthieae recruits were 5 times more abundant in theZ. capricorni bed and at the boundary between the two beds than in theP. australis bed. The results demonstrate how habitat structure, provided by the canopy of leaves of the two species of seagrass, can have contrasting effects on the recruitment and mortality of a macroalga. In the case ofC. duthieae, it appears that the differential pattern of recruitment is the primary determinant of the distribution of adult plants.     

Using unmanned aerial vehicle‐based multispectral,RGB and thermal imagery for phenotyping of forest genetic trials: A case study in Pinus halepensis

The assessment of genetic differentiation in functional traits is fundamental towards understanding the adaptive characteristics of forest species. While traditional phenotyping techniques are costly and time‐consuming, remote sensing data derived from cameras mounted on unmanned aerial vehicles (UAVs) provide potentially valid high‐throughput information for assessing morphophysiological differences among tree populations. In this work, we test for genetic variation in vegetation indices (VIs) and canopy temperature among populations of Pinus halepensis as proxies for canopy architecture, leaf area, photosynthetic pigments, photosynthetic efficiency and water use. The interpopulation associations between vegetation properties and above‐ground growth (stem volume) were also assessed. Three flights (July 2016, November 2016 and May 2017) were performed in a genetic trial consisting of 56 populations covering a large part of the species range. Multispectral (visible and near infrared wavelengths), RGB (red, green, blue) and thermal images were used to estimate canopy temperature and vegetation cover (VC) and derive several VIs. Differences among populations emerged consistently across flights for VC and VIs related to leaf area, indicating genetic divergence in crown architecture. Population differences in indices related to photosynthetic pigments emerged only in May 2017 and were probably related to a contrasting phenology of needle development. Conversely, the low population differentiation for the same indices in July 2016 and November 2016 suggested weak interpopulation variation in the photosynthetic machinery of mature needles of P. halepensis. Population differences in canopy temperature found in July 2016 were indicative of variation in stomatal regulation under drought stress. Stem volume correlated with indices related to leaf area (positively) and with canopy temperature (negatively), indicating a strong influence of canopy properties and stomatal conductance on above‐ground growth at the population level. Specifically, a combination of VIs and canopy temperature accounted for about 60% of population variability in stem volume of adult trees. This is the first study to propose UAV remote sensing as an effective tool for screening genetic variation in morphophysiological traits of adult forest trees.     

Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R² = 0.39) and plant species richness (R² = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R² = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R² = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.     

Biological connectivity and its driving mechanisms in the Liaohe Delta wetland,China

Biological connectivity plays a cricial role in maintaining biodiversity and ecosystem function. However, it is poorly understood how to quantify biological connectivity and investigate its mechanisms in estuarine wetlands. In order to address this issue, this study aims to quantify biological connectivity in the Liaohe Delta wetland by utilizing three complementary approaches: the habitat quality simulation, the vegetation connectivity index, and the maximum entropy model. These approaches focused on habitat quality, vegetation, and bird species, respectively. The established criteria for assessing vegetation connectivity blockage and the jackknife method were utilized to identify the primary drivers of biological connectivity. The study found that (1) Habitat quality declined from 1976 to 2020, with a total decrease of 3.2 × 10⁶. (2) Vegetation patches have more fragmented and less connected over the last 45 years. There was also a higher vegetation probability density of Phragmites australis than Suaeda salsa. The area of unchanged P. australis was concentrated within the nature reserve, while the area of unchanged S. salsa was 0, indicating that P. australis was more affected than S. salsa. The vegetation connectivity blockage was 33.16%, with human activities having a greater impact compared to natural succession. (3) The area of suitable habitat for birds constituted 27.48% of the study area. Distance to S. salsa was the main driving factor, followed by the distance to intertidal muds, paddy fields, P. australis, and water sources. The results demonstrate that biological connectivity has generally declined over time, with human activities being a significant contributor. Our efforts to quantify biological connectivity provided clear spatial and temporal information on the trends and drivers of biological connectivity in the Liaohe Delta wetland. The results of this study can provide valuable information for conservation efforts aimed at preserving wetland biodiversity.     

Using hyperspectral vegetation indices as a proxy to monitor soil salinity

The spectral bands most sensitive to salt-stress across diverse plants have not yet been defined; therefore, the predictive ability of previous vegetation indices (VIs) may not be satisfied for salinization monitoring. The hyperspectra of seven typical salt-sensitive/halophyte species and their root-zone soil samples were collected to investigate the relationship between vegetation spectra and soil salinity in the Yellow River Delta (YRD) of China. Several VIs were derived from the recorded hyperspectra and their predictive power for salinity was examined. Next, a univariate linear correlogram as well as multivariate partial least square (PLS) regression was employed to investigate the sensitive bands. VIs examination and band investigation confirmed that the responses of the vegetation differed from species to species, which explained the vibrations of the VIs in many study cases. These differences were primarily between salt-sensitive and halophyte plants, with the former consistently having higher sensitivity than the latter. With the exception of soil adjusted vegetation index (SAVI), most VIs were found to have weak relationships with soil salinity (with average R² of 0.28) and some were not sensitive to all species [e.g. photochemical reflectance index (PRI) and red edge position (REP)], which verified that most currently available VIs are not adequate indicators of salinity for various species. PLS was validated as a more useful tool than linear correlogram for identification of sensitive bands due to well dealing with multicollinear spectral variables. From PLS, wavelengths at 395–410, 483–507, 632–697, 731–762, 812–868, 884–909, and 918–930 nm were determined to be the most sensitive bands. By combining the most sensitive bands in a SAVI form, we finally proposed four soil adjusted salinity indices (SASIs) for all species. Satisfactory relationships were observed between ECe and four SASIs for all species, with largely improved R² values ranging from 0.50 to 0.58. Our findings indicate the potential to monitor soil salinity with the hyperspectra of salt-sensitive and halophyte plants.     

Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area

Frequent and continuous time series is required for the detection of plant phenology and vegetation succession. The launch of novel remote sensor MODIS (moderate resolution imaging spectroradiometer) provided us with an opportunity to make a new trial of studying the rapid vegetation succession in estuarine wetlands. In this study, the spatiotemporal variations of vegetation cover and tidal flat elevation along a transect (covering 6 pixels of MODIS) of an estuarine wetland at Dongtan, Chongming Island, in Yangtze River estuary, China were investigated to assess its rapid vegetation succession and physical conditions. By combining the field data collected, the time series of MODIS-based VIs (vegetation indices), including NDVI (normalized difference vegetation index), EVI (enhanced vegetation index) and MSAVI (modified soil adjusted vegetation index), and a water index, LSWI (land surface water index) were utilized to characterize the rapid vegetation succession between 2001 and 2006. We found that NDVI, EVI and MSAVI exhibited significant spatial and temporal correlations with vegetation succession, while LSWI behaved in a positive manner with surface water and soil moisture along with the successional stages. In order to take the advantages of both VIs and water index, a composite index of VWR (vegetation water ratio) combining LSWI and EVI or MSAVI was proposed in this paper. This index facilitates the identification of vegetation succession by simply comparing the values of VWR at different stages, and therefore it could track vegetation succession and estimate community spread rate. Additionally, this study presented an attempt of using MODIS datasets to monitor the change of tidal flat elevation, which demonstrated a potential remote sensing application in geodesy of coastal and estuarine areas.     

Interspecific Interactions between Phragmites australis and Spartina alterniflora along a Tidal Gradient in the Dongtan Wetland,Eastern China

The invasive species Spartina alterniora Loisel was introduced to the eastern coast of China in the 1970s and 1980s for the purposes of land reclamation and the prevention of soil erosion. The resulting interspecific competition had an important influence on the distribution of native vegetation, which makes studying the patterns and mechanisms of the interactions between Spartina alterniora Loisel and the native species Phragmites australis (Cav.) Trin ex Steud in this region very important. There have been some researches on the interspecific interactions between P. australis and S. alterniora in the Dongtan wetland of Chongming, east China, most of which has focused on the comparison of their physiological characteristics. In this paper, we conducted a neighbor removal experiment along a tidal gradient to evaluate the relative competitive abilities of the two species by calculating their relative neighbor effect (RNE) index. We also looked at the influence of environmental stress and disturbance on the competitive abilities of the two species by comparing interaction strength (I) among different tidal zones both for P. australis and S. alterniora. Finally, we measured physiological characteristics of the two species to assess the physiological mechanisms behind their different competitive abilities. Both negative and positive interactions were found between P. australis and S. alterniora along the environmental gradient. When the direction of the competitive intensity index for P. australis and S. alterniora was consistent, the competitive or facilitative effect of S. alterniora on P. australis was stronger than that of P. australis on S. alterniora. The interspecific interactions of P. australis and S. alterniora varied with environmental conditions, as well as with the method used, to measure interspecific interactions.     

Detecting soil salinity with MODIS time series VI data

Mapping of salinization using the satellite derived vegetation indices (VIs) remains difficult at broad regional scales due to the low classification accuracy. Satellite derived VIs from the Moderate Resolution Imaging Spectroradiometer (MODIS) have more potential because the MODIS balances the requirements of spatial detail, spectral and temporal density and tends to reflect vegetation responses through time. However, the relationship between MODIS data and salinity may be underestimated in previous studies because the MODIS time series data were not investigated thoroughly, especially regarding vegetation phenology. This study assessed the applicability of MODIS time series VI data for monitoring soil salinization with a series of MODIS pixels selected in the Yellow River Delta, China. The hidden information in vegetation phenology was investigated by improving the quality of VIs time series data with the Savitzky–Golay filter, extracting the phenological markers and differentiating VIs time series data based on vegetation types. The results showed that the quality of the enhanced vegetation index (EVI) time series data were improved by the Savitzky–Golay filter, which could provide more accurate thresholds of phenological stages than the empirical definition. The seasonal integral of EVI (EVI-SI) extracted from the smoothed EVI time series profile was verified as the best indicator of the degree of soil salinity. Additionally, the correlation of EVI-SI and soil salinity was highly dependent on land cover heterogeneity, and the ranges of correlation coefficients were as high as 0.59–0.92. EVI-SI was linearly correlated with EC_e in cropland with a high model fit (R² = 0.85). The relationship of EVI-SI and EC_e fit best with a binomial line and EVI-SI was able to explain 70% of the variance of EC_e. Despite the poor fit of the linear regression model in mixed sites limited by spatial resolution (R² = 0.32), MODIS time series VI data, as well as the extracted seasonal parameters, still show great potential to assess large-scale soil salinization.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

Wheat leaf traits monitoring based on machine learning algorithms and high-resolution satellite imagery

The leaf, which is a crucial indicator for evaluating crop status, plays an important role in plants' functions. Determining and monitoring leaf parameters can facilitate the detection and estimation of crop yield, which is essential for food security. Crop monitoring by remote sensing technology is critical to support crop production, especially over large scales. In this study, we developed a methodology to estimate leaf parameters based entirely on vegetation indices (VIs) from remotely sensed imagery in wheat under different management practices. Therefore, the current study aimed to examine the utility of VIs calculated from the sentinel-2 data in estimating the Leaf area index (LAI) and leaf parameters at wheat farms using machine learning algorithms. Leaf parameters included leaf dry weight (LDW), specific leaf area (SLA) and leaf specific weight (SLW), and machine learning algorithms were SVM (support vector machine), ANN (artificial neural network) and DNN (deep neural network). Leaf parameters were measured at several developmental stages of wheat in two contrasting environments in the southern Iran. The results demonstrated that the DNN algorithm could efficiently predict leaf parameters in the southern Iran with an overall precision of >72%, which assessed the potential of employing DNN to achieve the temporal and spatial distribution data of wheat based on the Sentinel-2 imagery. The validation of the DNN model generally showed high accuracy (R = 0.80, RMSE = 1.19, and MAE = 0.98) between observed and estimated LAI values when this model was used. NDVI was also highly sensitive to wheat LDW and SLA parameters, with a good correlation between field measurements and those predicted by the DNN model from sentinel-2 imagery, with the R values of 0.66 and 0.85, respectively. Further, NDVI and PVI (Perpendicular Vegetation Index) were linearly correlated with SLW across both temporal and spatial scales (R = 0.79). Among VIs considered from sentinel-2 imagery to predict wheat leaf parameters, NDVI was more sensitive than other VIs. This research, thus, indicated that using sentinel-2 data within a DNN model could provide a comparatively precise and robust prediction of leaf parameters and yield valuable insights into crop management with high temporal and spatial accuracy.     

Impact of landscape patterns on ecological vulnerability and ecosystem service values: An empirical analysis of Yancheng Nature Reserve in China

In this study, an ecological vulnerability assessment indicator system was constructed using the “exposure–climate sensitivity–adaptive capacity” framework according to the theory of ecological vulnerability. An improved ecosystem service value calculation model was proposed based on empirical parameters. Using Yancheng Nature Reserve in China as an example, we employed remote sensing data to conduct an empirical analysis of the changes in the spatiotemporal pattern, ecological vulnerability, and ecosystem services of typical landscape types over the period from 1987 to 2013. The statistical results derived from landscape pattern indices (LPIs) showed that during the investigation period, three natural wetland landscape types (i.e., Spartina alterniflora, Suaeda glauca, and Phragmites australis) showed gradually increased fragmentation in the study area. The ecological vulnerability scores of the major landscape types (in descending order) were P. australis (0.053), farmland (0.047), S. alterniflora (0.042), S. glauca (0.031), and bare mud flat (0.002). The results derived from the ecosystem service value fluctuation index (ESVFI) showed that from 1987 to 1997, the value of the ecosystem services provided by S. glauca and P. australis wetlands decreased, whereas that of S. alterniflora wetlands increased continuously. From 2002 to 2013, the value of the ecosystem services provided by all three types of natural wetland showed a downward trend. In conclusion, the combined effects of human and natural factors, including the expansion of farmland and the invasion of S. alterniflora, and changes in seawater erosion and deposition led to changes in the landscape patterns, ecological vulnerability, and ecosystem services of the Yancheng Nature Reserve during the investigation period. The results indicate that a “dual adaptive” management system based on natural succession and supplemented by appropriate manual intervention should be implemented, and the management efficiency and flexibility should be improved to meet the common needs of biodiversity conservation and economic development.     

黄河三角洲植被空间分布特征及其环境解释

为了解黄河三角洲地区植被空间分布与环境因子之间的关系,通过局地植被样方调查、区域遥感影像提取归一化植被指数(NDVI)及地形高度、地下水位埋深、表层土壤Cl~-含量等环境数据采集,综合样地植被与环境数据进行了除趋势对应分析(DCA)和除趋势典范对应分析(DCCA),并对区域NDVI与主要环境变量进行了单因子相关性分析和多元逐步回归分析。结果显示:DCA排序可将黄河三角洲植被分为翅碱蓬、柽柳-翅碱蓬、芦苇-柽柳、芦苇4个主要群落类型(群丛),DCCA与DCA排序图总体相似,但DCCA更清晰地表明其第一轴主要代表的是潜水Cl~-浓度等关键水盐因子,且随着水土环境系统盐分含量的减小,群落由翅碱蓬逐渐向芦苇演变。区域典型植被群落和NDVI分布格局与变化趋势受地下水位埋深和潜水Cl~-浓度2个环境因素影响较大(NDVI与2个环境变量间建立的二元回归方程R~2=0.57),而土壤Cl~-含量的植被效应实际上受地下水位埋深和潜水Cl~-浓度的影响。在区域地下水普遍浅埋条件下,地下水成为影响植被生长与分布的生态环境最敏感要素,而地下水位埋深和潜水Cl~-浓度是这一要素中的2个关键因子,尤其是后者梯度变化对天然植被分布格局起重要的控制作用。     

Local and regional disturbances associated with the invasion of Chesapeake Bay marshes by the common reed <Emphasis Type="Italic">Phragmites australis</Emphasis>

The invasion of wetlands by Phragmites australis is a conservation concern across North America. We used the invasion of Chesapeake Bay wetlands by P. australis as a model system to examine the effects of regional and local stressors on plant invasions. We summarized digital maps of the distributions of P. australis and of potential stressors (especially human land use and shoreline armoring) at two spatial scales: for 72 subestuaries of the bay and their local watersheds and for thousands of 500 m shoreline segments. We developed statistical models that use the stressor variables to predict P. australis prevalence (% of shoreline occupied) in subestuaries and its presence or absence in 500 m segments of shoreline. The prevalence of agriculture was the strongest and most consistent predictor of P. australis presence and abundance in Chesapeake Bay, because P. australis can exploit the resulting elevated nutrient levels to enhance its establishment, growth, and seed production. Phragmites australis was also positively associated with riprapped shoreline, probably because it creates disturbances that provide colonization opportunities. The P. australis invasion was less severe in areas with greater forested land cover and natural shorelines. Surprisingly, invasion was low in highly developed watersheds and highest along shorelines with intermediate levels of residential land use, possibly indicating that highly disturbed systems are uninhabitable even to invasive species. Management strategies that reduce nutrient pollution, preserve natural shorelines, and limit nearshore disturbance of soils and vegetation may enhance the resilience of shorelines to invasion.