Plants as indicators of snow layer duration in southern Norwegian mountains

The main aim of this paper was to study the responses of mountain plants in relation to the time of snowmelt. Three mountain areas situated along an oceanic–continental gradient were selected as study sites, and the sample plots ranged from 182 m below to 473 m above the climatic forest limit. In total, 185 quadrats (2 m × 2 m), stratified to include only oligotrophic and mesotrophic mountain vegetation types, were selected to represent a topographic range along altitudinal gradients. In each quadrat, the percentage groundcover of the species was recorded. From the beginning of April until July 2004, snow thickness was monitored, and the Julian day when the snow had completely melted was determined for all plots. The relationship between species abundances and Julian day of snowmelt were analysed by two different numerical methods: (1) relative values for species optimum and tolerance were given by Detrended Canonical Correspondence Analysis (DCCA) with Julian day of snowmelt as the constraining variable. (2) Species responses were modelled by Generalized Linear Models (GLM). For species with significant unimodal responses, optimum and tolerance were calculated. For species with significant linear models, different species response models were identified by the regression intercepts. One hundred and twenty six species (taxa) were tested, and 103 evidenced statistically significant (p < 0.05) distribution responses. Several common alpine plants had a distribution that appeared to be independent of snow. On the basis of the results of the numerical methods, the species were separated into nine Snow Indicator (SI) classes, as a parallel to the Ellenberg indicator values. The species’ SI values were used to calculate weighted average SI values to examine the relationships between previously described plant communities and vegetation transects which experience different snow conditions.     

Optimizing the bioindication of forest soil acidity,nitrogen and mineral nutrition using plant species

Soil moisture and nutritional characteristics are frequently assessed using plant species and community bioindication, e.g., the Ellenberg system of species indicator values. This method, based on complete inventories of plant species present in plots, is time-consuming, which could prevent its general use for forest or other natural land management. Our aim was to determine the impact of a reduction in the time spent to carry out a floristic inventory on the quality of soil characteristic assessment using plant bioindication. We compared the measurements of soil pH-H₂O (pH), organic carbon to total nitrogen ratio (C:N) and base saturation (BS) in the 0–5 cm soil layer of 470 plots with the same variables estimated from floristic inventories of increasing duration, using plant indicator values (IV) from the EcoPlant database. The performance of predictions was evaluated by the square of the linear correlation coefficient between measured and predicted values (R²) and the root mean square error (RMSE) of predictions.The number rather than the percentage of total plot species used for the estimations was determinant for the prediction of soil pH quality. Performance of bioindication of pH, BS and C:N reached the maximum R² using the first 20–25 species recorded per plot, corresponding to a 14-min-long floristic inventory in comparison to a mean of 28 min spent to carry out a complete floristic inventory. A precision of prediction of 80% of the maximal precision was obtained after 4–5 min (6–12 inventoried species) for the three studied variables. These results are independent of the nutritional capability of the soils and were similar at the national and local scales. In order to estimate soil nutritional resources by plant bioindication, it is feasible to significantly reduce the time spent on floristic inventories and, thus, their cost. This is especially useful when the goal is to map the soil quality for decision-making in forest management.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

Abandonment of traditionally managed mesic mountain meadows affects plant species composition and diversity

In recent years abandonment of traditional management of mountain grasslands has been observed throughout Central Europe. However, the impact of abandonment on vegetation of mountain grasslands is still unclear. In this study it was hypothesized that the cessation of traditional management of mesic mountain meadows causes changes in their species composition and a decrease in the biodiversity. In total, 260 plots were established in the Sudetes (SW Poland) on meadows with regular annual mowing, meadows with irregular mowing management, and abandoned meadows. Relevés (5 × 5 m) were performed, and the habitat properties were determined using Ellenberg indicator values. The study confirmed the hypothesis that the various ways of extensive management have an influence on species richness. The lowest species richness was observed on the irregularly managed meadows, while higher species numbers were found on the abandoned and regular managed meadows. The majority of patches on abandoned meadows exhibited degradation through the expansion of Solidago gigantea, Solidago canadensis, Lupinus polyphyllus, Heracleum sosnovsky, Calamagrostis epigejos, Deschampsia flexuosa, Festuca rubra and Hypericum maculatum. Meadows subjected to different management practices differed significantly in Ellenberg indicator values. The abandoned meadows had the highest values of the light index (L) and nitrogen availability (N), whereas the highest values of soil moisture (F) were noted on the irregularly managed meadows. The degradation of mountain mesic meadows requires regular mowing management, which stops ecological succession and preserves their high biodiversity.     

NIRS meets Ellenberg's indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics

Ellenberg indicator values are widely used ecological tools to elucidate relationships between vegetation and environment in ecological research and environmental planning. However, they are mainly deduced from expert knowledge on plant species and are thus subject of ongoing discussion. We researched if Ellenberg indicator values can be directly extracted from the vegetation biomass itself. Mean Ellenberg “moisture” (mF) and “nitrogen” (mN) values of 141 grassland plots were related to nutrient concentrations, fibre fractions and spectral information of the aboveground biomass. We developed calibration models for the prediction of mF and mN using spectral characteristics of biomass samples with near-infrared reflectance spectroscopy (NIRS). Prediction goodness was evaluated with internal cross-validations and with an external validation data set. NIRS could accurately predict Ellenberg mN, and with less accuracy Ellenberg mF. Predictions were not more precise for cover-weighted Ellenberg values compared with un-weighted values. Both Ellenberg mN and mF showed significant and strong correlations with some of the nutrient and fibre concentrations in the biomass. Against expectations, Ellenberg mN was more closely related to phosphorus than to nitrogen concentrations, suggesting that this value rather indicates productivity than solely nitrogen. To our knowledge we showed for the first time that mean Ellenberg indicator values could be directly predicted from the aboveground biomass, which underlines the usefulness of the NIRS technology for ecological studies, especially in grasslands ecosystems.     

The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures

Retrieving leaf chlorophyll content at a range of spatio-temporal scales is central to monitoring vegetation productivity, identifying physiological stress and managing biological resources. However, estimating leaf chlorophyll over broad spatial extents using ground-based traditional methods is time and resource heavy. Satellite-derived spectral vegetation indices (VIs) are commonly used to estimate leaf chlorophyll content, however they are often developed and tested on broadleaf species. Relatively little research has assessed VIs for different leaf structures, particularly needle leaves which represent a large component of boreal forest and significant global ecosystems. This study tested the performance of 47 published VIs for estimating foliar chlorophyll content from different leaf and canopy structures (broadleaf and needle). Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species (Picea mariana and Acer saccharum) and a variety of canopy structures. Leaf reflectance data was collected using an ASD Fieldspec Pro spectroradiometer (400–2500 nm) for over 300 leaf samples. Canopy reflectance data was acquired from the medium resolution imaging spectrometer (MERIS). At the canopy level, with both leaf types combined, the DD-index showed the strongest relationship with leaf chlorophyll (R² = 0.78; RMSE = 3.56 μg/cm²), despite differences in leaf structure. For needleleaf trees alone the relationship with the top VI was weaker (D_[red], R² = 0.71; RMSE = 2.32 μg/cm²). A sensitivity study using simulated VIs from physically-modelled leaf (PROSPECT) and canopy (4-Scale) reflectance was performed in order to further investigate these results and assess the impacts of different background types and leaf area index on the VIs’ performance. At the leaf level, the MNDVI8 index showed a strong linearity to changing chlorophyll and negligible difference to leaf structure/type. At canopy level, the best performing VIs were relatively consistent where LAI ≥ 4, but responded strongly to differences in background at low canopy coverage (LAI = 2). This research provides comprehensive assessments for the use of spectral indices in retrieval of spatially-continuous leaf chlorophyll content at the leaf (MTCI: R² = 0.72; p < 0.001) and canopy (DD: R² = 0.78; p < 0.001) level for resource management over different spatial and temporal scales.     

Shape matters in sampling plant diversity: Evidence from the field

The identification of shape and size of sampling units that maximises the number of plant species recorded in multiscale sampling designs has major implications in conservation planning and monitoring actions. In this paper we tested the effect of three sampling shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded species. We used a large dataset derived from the network of protected areas in the Siena Province, Italy. This dataset is composed of plant species occurrence data recorded from 604 plots (10 m × 10 m), each divided in a grid of 16 contiguous subplot units (2.5 m × 2.5 m). Moreover, we evaluated the effect of plot orientation along the main environmental gradient, to examine how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. A significantly higher species richness was recorded by the random arrangement of 4 subplots within each plot in comparison to the ‘rectangle’ and ‘square’ shapes. Although the rectangular shape captured a significant larger number of species than squared ones, plot orientation along the main environmental gradient did not show a systematic effect on the number of recorded species. We concluded that the choice of whether or not using elongated (rectangular) versus squared plots should dependent upon the objectives of the specific survey with squared plots being more suitable for assessing species composition of more homogeneous vegetation units and rectangular plots being more suited for recording more species in the pooled sample of a large area.     

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.     

Interrill erosion at disturbed alpine sites: Effects of plant functional diversity and vegetation cover

It has been hypothesized that a diverse vegetation cover with a high number of plant species and plant functional groups may be more effective at governing soil erosion processes than a vegetation cover with few species and fewer different plant functional groups.We investigated the influence of plant cover and diversity on interrill erosion on a disturbed alpine site. Rainfall simulations were conducted on micro-scale plots (25 × 25 cm) with different degrees of vegetation cover and plant functional group diversity. We selected plots with 10%, 30% and 60% of vegetation cover containing different plant functional groups: (i) grasses, (ii) forbs, (iii) cryptogams (moss and/or lichens), and all possible combinations of these three groups. On each plot a rain intensity of 375 ml min^?1 (30 mm) was applied for 5 min. The degree of vegetation cover had the largest effect on interrill erosion. At 60% vegetation cover, the sediment yield was reduced by 83% in comparison to the un-vegetated ground. In the plots with 60% vegetation cover, an increase in functional group diversity decreased the sediment yield significantly. Sediment yield was three times lower in the presence of three plant functional groups than in systems with one plant functional group. Combinations of plant functional groups including grasses reduced the sediment yield more than other combinations.The findings of this study support the view that beside the re-establishment of a closed vegetation cover, a high plant functional diversity can be a relevant factor to further reduce interrill erosion at disturbed sites in alpine ecosystems.     

Experimental evidence for a delayed response of the above-ground vegetation and the seed bank to the invasion of an annual exotic plant in deciduous forests

Invasions by alien plants significantly affect native biodiversity and ecosystem functioning. We conducted a 5-year field experiment to investigate potential effects of the annual invasive plant Impatiens glandulifera on both the native above-ground vegetation and the soil seed bank in a deciduous forest in Switzerland. Eight years after the establishment of I. glandulifera, we set up plots in patches invaded by the alien plant, in plots from which the invasive plant had been manually removed and in plots which were not yet colonized by the invasive plant. We examined plant species richness, diversity and plant species composition in the above-ground vegetation and soil seed bank in all plots one year and five years after the initiation of the experiment. The 36 plots (3 plot types × 6 replicates × 2 sites) were equally distributed over two forest sites. Neither the native above-ground vegetation nor the soil seed bank was influenced by the presence of I. glandulifera one year after the start of the field experiment. After five years, however, plant species richness of both the above-ground vegetation and the soil seed bank was reduced by 25% and 30%, respectively, in plots invaded by the alien plant compared to plots from which I. glandulifera had been removed or uninvaded plots. Furthermore, plots invaded by the alien plant had a lower total seedling density (reduction by 60%) and an altered plant species composition in the soil seed bank compared to control plots. Our field experiment indicates that negative effects of the annual invasive plant on the native above-ground vegetation and soil seed bank of deciduous forests become visible with a delay of several years.     

Appropriate vegetation indices for measuring the impacts of deer on forest ecosystems

Increasing deer density can cause serious degradation of forests in the Americas, Europe, and Asia. To manage deer impacts, evaluating their current impacts on forest ecosystems is necessary, usually via vegetation indices. However, the relationship between vegetation indices and absolute deer density, while taking into account tree size, snow depth, light condition, and the type of understory vegetation, has never been investigated. We examined the relationship between various vegetation indices and absolute deer density in 344 study plots in the deciduous broad-leaved forest of Yamanashi Prefecture, central Japan. In each plot, debarking and browsing, along with the coverage and maximum height of understory vegetation, were surveyed. Estimated deer densities for 82 5 × 5-km mesh units ranged from 0.8 deer/km² to 32.7 deer/km². The percentages of debarked trees within a plot ranged from 0 to 84%. Debarking was promoted by high deer density, small tree size, and thick snow. The effect of tree size on debarking was stronger than that of deer density. Occurrence of browsing on understory vegetation was higher at higher deer densities, and where understory vegetation was dominated by evergreen dwarf bamboo. Coverage and maximum height of understory vegetation were unaffected by deer density but increased with canopy openness and the dominance of dwarf bamboo in the understory. Overall, we predict that debarking of small trees living in heavy snow areas should occur even at low deer densities (<10 deer/km²). Browsing on dwarf bamboo should occur at intermediate deer densities (10–30 deer/km²), while debarking of thick trees living in low snow areas should occur only at high deer densities (≥30 deer/km²). Our study shows that debarking and browsing on understory vegetation are appropriate indices for evaluating deer impacts on forest ecosystems, but that tree size, snow depth, and the type of understory vegetation should also be considered.     

Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress

Maize is one of the most widespread grain crops in the world; however, more than 70% of corn in China suffers some degree of drought disaster every year. Leaf area index (LAI) is an important biophysical parameter of the vegetation canopy and has important significance for crop yield estimation. Using the data of canopy spectral reflectance and leaf area index (LAI) for maize plants experiencing different levels of soil moisture from 2011 to 2012, the characteristics of the canopy reflective spectrum and its first derivative, and their relationships to leaf area index, were analyzed. Soil moisture of the control group was about 75% while that of the drought stress treatment was about 45%. In addition, LAI retrieval models for maize were established using vegetation indices (VIs) and principal component analysis (PCA) and the models were tested using independent datasets representing different soil water contents and different developmental stages of maize. The results showed that canopy spectral reflectances were in accordance with the characteristics of green plants, under both drought stress and at different developmental stages. In the visible band, canopy reflectance for both healthy and damaged vegetation had a green-wavelength peak and a red-wavelength valley; reflectance under drought stress, especially in the green peak (about 550 nm) and the red valley (about 676 nm) was higher than in the control group. In the near-infrared band, the canopy spectral reflectance decreased substantially between 780 and 1350 nm under drought stress. Moreover, the red edge of the spectrum was shifted toward blue wavelengths. The first derivative spectrum showed a double peak phenomenon at the edge of the red band at different developmental stages: the main peak appeared between 728 and 732 nm and the minor peak at about 718 nm. The double peaks become more obvious through the growth and development of the maize, with the most notable effect during the silking and milk stages, after which it gradually decreased. During maize growth, the LAI of all plants, regardless of soil moisture conditions, increased, and the largest LAI also occurred during the silking and milk stages. During those stages, the LAI of plants under different drought stress levels was significantly lower (by 20% or more) than in normal plants with sufficient water supplies. The LAI was highly significantly correlated with canopy spectral reflectance in the bands from 350 nm to 510 nm, from 571 nm to 716 nm, and from 1450 nm to 1575 nm. Also, the LAI was significantly correlated with red edge parameters and several VIs. The Perpendicular Vegetation Index (PVI) had the best correlation with LAI, with a coefficient of determination (R²) of 0.726 for the exponential correlation. Using dependent data, a LAI monitoring model for the maize canopy was constructed using PCA and VI methods. The test results showed that both the VI and PCA methods of monitoring maize LAI could provide robust estimates, with the predicted values of LAI being significantly correlated with the measured values. The model based on PVI showed higher precision under the drought stresses, with a correlation coefficient of 0.893 (n = 27), while the model based on PCA was more precise under conditions of adequate soil moisture, with a correlation coefficient of 0.877 (n = 32). Therefore, a synthesis of the models based on both VI and PCA could be more reliable for precisely predicting LAI under different levels of drought stresses in maize.     

Alleviating the adverse effects of NaCl stress in maize seedlings by pretreating seeds with salicylic acid and 24-epibrassinolide

The effects of bio-regulators salicylic acid (SA) and 24-epibrassinolide (EBL) as seed soaking treatment on the growth traits, content of photosynthetic pigments, proline, relative water content (RWC), electrolyte leakage percent (EC%), antioxidative enzymes and leaf anatomy of Zea mays L. seedlings grown under 60 or 120 mM NaCl saline stress were studied. A greenhouse experiment was performed in a completely randomized design with nine treatments [control (treated with tap water); 60 mM NaCl; 120 mM NaCl; 10^− 4 M SA; 60 mM NaCl + 10^− 4 M SA; 120 mM NaCl + 10^− 4 M SA; 10 μM EBL; 60 mM NaCl + 10 μMEBL or 120 mM NaCl + 10 μM EBL] each with four replicates. The results indicated that NaCl stress significantly reduced plant growth traits, leaf photosynthetic pigment, soluble sugars, RWC%, and activities of catalase (CAT), peroxidase (POX) as well as leaf anatomy. However, the application of SA or EBL mitigated the toxic effects of NaCl stress on maize seedlings and considerably improved growth traits, photosynthetic pigments, proline, RWC%, CAT and POX enzyme activities as well as leaf anatomy. This study highlights the potential ameliorative effects of SA or EBL in mitigating the phytotoxicity of NaCl stress in seeds and growing seedlings.     

Forest carbon storage along the north-south transect of eastern China: Spatial patterns,allocation, and influencing factors

Forests play an important role in sequestrating atmospheric CO₂; therefore, understanding the spatial variations and controlling mechanisms of forest carbon (C) storage is important. In this study, we collected data on forest C storage along a north-south transect of eastern China from literature published between 2004 and 2014. The collected data, which were from over 2000 plots, allowed us to explore the latitudinal patterns in forest C storage. The results showed that vegetation C storage decreased with increasing latitude, while soil C storage increased. This spatial pattern of vegetation C storage was more apparent for mature forests (forest age > 80 years). Furthermore, latitudinal patterns in forest C storage, both in vegetation and in soil, became stronger with increasing statistical scale, increasing from plot scale to latitudinal scale (2–5°). However, total forest C storage (vegetation + soil) had no apparent latitudinal pattern. Interestingly, the allocation ratios of forest C storage between vegetation and soil had a negative logarithmic relationship with latitude. These results suggest that in eastern China, climatic factors control latitudinal patterns in the forest C storage of vegetation and soil, albeit in different ways (positive for vegetation and negative for soil), and also control the allocation ratios of forest C storage between vegetation and soil. Furthermore, the latitudinal patterns of forest C storage were opposite for vegetation and soil, resulting from the different climatic controlling mechanism.     

Surface functionalized mesoporous activated carbon for the immobilization of acidic lipase and their application to hydrolysis of waste cooked oil: Isotherm and kinetic studies

This study deals with the surface functionalization of mesoporous activated carbon, using ethylenediamine and glutaraldehyde to facilitate the strong immobilization of acidic lipase (AL) onto MAC. The AL was produced from Pseudomonas gessardii by using slaughterhouse lipid waste as the substrate. The AL immobilized on functionalized mesoporous activated carbon (ALFMAC) was applied for the hydrolysis of waste cooked oil (WCO). The optimum conditions for the immobilization of AL onto functionalized mesoporous activated carbon (FMAC) were 90 min; pH 3.5; and 35 °C; which resulted at the maximum immobilization of 5440 U/g of FMAC (3.693 mg of AL/g of FMAC or the yield 2.7% or the expressed activity 103.7% or the activity per unit area of FMAC 1.08 mg of AL/m²). The ALFMAC showed better thermal and storage stabilities than the free AL. The ALFMAC retained a 98% and a 92% initial activity at 40 °C and 50 °C, respectively, while the AL showed the thermal stability (residual activities) 65% and 38%, respectively. The storage stability of ALFMAC at 4 °C showed 100% initial activity up to 15 days from the initial day of the storage, whereas AL showed only 88% initial activity up to 15 days. The FMAC and ALFMAC were characterized by using scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, and X-ray diffraction (XRD) analysis. The K_m values of the ALFMAC and AL were 0.112 mM and 0.411 mM, respectively. The v_max values of the ALFMAC and AL were 1.26 mM/min and 0.53 mM/min, respectively. Immobilization of AL onto FMAC obeyed the Freundlich and Redlich–Peterson isotherm models. The non-linear models of pseudo first, and second order, intra-particle diffusion, Bangham, and Boyd plot were also performed to understand the dynamic mechanism of immobilization. ALFMAC showed a 100% hydrolysis of WCO up to 21 cycles of reuse, and 60% up to 45 cycles. The hydrolysis of WCO was confirmed by using FT-IR spectra.     

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

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.     

Effect of nitrogen load on growth and photosynthesis of seedlings of the hybrid larch F1 (Larix gmelinii var. japonica × L. kaempferi) grown on serpentine soil

We studied the growth and photosynthesis of the hybrid larch F₁ (Larix gmelinii var. japonica × L. kaempferi) grown on serpentine soil and the effects of soil N load, to determine the performance of this species as reforestation material in serpentine regions. We prepared 16 experimental plots (2 m × 4 m each), eight on serpentine and eight on brown forest soil, and planted one-year-old cutting seedlings of the hybrid larch F₁ in each plot, in May 2007. Ammonium sulfate was supplied to half of the plots of each soil type in 2008 and 2009, at a load of 47 kg N ha⁻¹ year⁻¹. Although the growth and photosynthetic capacity of hybrid larch F₁ seedlings in the serpentine soil were limited, the rate of growth in serpentine soil was greater than that of Sakhalin spruce (Picea glehnii) that is dominant species in serpentine regions. There was significant interaction between soil type and N load for the growth and photosynthetic parameters. The N load adversely affected growth and photosynthetic parameters in the serpentine soil, while improved them in brown forest soil. Although the growth rate of hybrid larch F₁ without N loading showed high potential as an afforestation species in serpentine region, increasing deposition of N might be a threat to the growth and photosynthesis of the hybrid larch F₁ in serpentine soil.     

Optimization of fermentation parameters for the biomass and DHA production of Schizochytrium limacinum OUC88 using response surface methodology

Fermentation parameters for biomass and DHA production of Schizochytrium limacinum OUC88 in a fermenter (working volume 7 L) were optimized using Plackett–Burman and central composite rotatable design. Out of 10 factors studied by Plackett–Burman design, 4 influenced the biomass production significantly. Central composite rotatable design was used to optimize the significant factors and response surface plots were generated. Using these response surface plots and point prediction, optimized values of the factors were determined as follows temperature (°C) 23 °C, aeration rate 1.48 L min⁻¹ L⁻¹, agitation 250 rpm and inoculum cells in mid-exponential phase, the maximum yield of DCW and DHA were 24.1 and 4.7 g L⁻¹, respectively. These predicted values were also verified by validation experiments.