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.     

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.     

Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments

Leaf water status information is highly needed for monitoring plant physiological processes and assessing drought stress. Retrieval of leaf water status based on hyperspectral indices has been shown to be easy and rapid. However, a universal index that is applicable to various plants remains a considerable challenge, primarily due to the limited range of field-measured datasets. In this study, a leaf dehydration experiment was designed to obtain a relatively comprehensive dataset with ranges that are difficult to obtain in field measurements. The relative water content (RWC) and equivalent water thickness (EWT) were chosen as the surrogates of leaf water status. Moreover, five common types of hyperspectral indices including: single reflectance (R), wavelength difference (D), simple ratio (SR), normalized ratio (ND) and double difference (DDn) were applied to determine the best indices. The results indicate that values of original reflectance, reflectance difference and reflectance sensitivity increased significantly, particularly within the 350–700 nm and 1300–2500 nm domains, with a decrease in leaf water. The identified best indices for RWC and EWT, when all the species were considered together, were the first derivative reflectance based ND type index of dND (1415, 1530) and SR type index of dSR (1530, 1895), with R² values of 0.95 (p < 0.001) and 0.97 (p < 0.001), respectively, better than previously published indices. Even so, different best indices for different species were identified, most probably due to the differences in leaf anatomy and physiological processes during leaf dehydration. Although more plant species and field-measured datasets are still needed in future studies, the recommend indices based on derivative spectra provide a means to monitor drought-induced plant mortality in temperate climate regions.     

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.     

Estimation of big sagebrush leaf area index with terrestrial laser scanning

Accurate monitoring and quantification of the structure and function of semiarid ecosystems is necessary to improve carbon and water flux models that help describe how these systems will respond in the future. The leaf area index (LAI, m² m⁻²) is an important indicator of energy, water, and carbon exchange between vegetation and the atmosphere. Remote sensing techniques are frequently used to estimate LAI, and can provide users with scalable measurements of vegetation structure and function. We tested terrestrial laser scanning (TLS) techniques to estimate LAI using structural variables such as height, canopy cover, and volume for 42 Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis Beetle & Young) shrubs across three study sites in the Snake River Plain, Idaho, USA. The TLS-derived variables were regressed against sagebrush LAI estimates calculated using specific leaf area measurements, and compared with point-intercept sampling, a field method of estimating LAI. Canopy cover estimated with the TLS data proved to be a good predictor of LAI (r² = 0.73). Similarly, a convex hull approach to estimate volume of the shrubs from the TLS data also strongly predicted LAI (r² = 0.76), and compared favorably to point-intercept sampling (r² = 0.78), a field-based method used in rangelands. These results, coupled with the relative ease-of-use of TLS, suggest that TLS is a promising tool for measuring LAI at the shrub-level. Further work should examine the structural measures in other similar shrublands that are relevant for upscaling LAI to the plot-level (i.e., hectare) using data from TLS and/or airborne laser scanning and to regional levels using satellite-based remote sensing.     

Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data

A study was conducted to understand the potential of Landsat-8 in the estimation of gross primary production (GPP) and to quantify the productivity of maize crop cultivated under hyper-arid conditions of Saudi Arabia. The GPP of maize crop was estimated by using the Vegetation Photosynthesis Model (VPM) utilizing remote sensing data from Landsat-8 reflectance (GPP_VPM) as well as the meteorological data provided by Eddy Covariance (EC) system (GPP_EC), for the period from August to November 2015. Results revealed that the cumulative GPP_EC for the entire growth period of maize crop was 1871 g C m⁻². However, the cumulative GPP determined as a function of the enhanced vegetation index – EVI (GPP_EVI) was 1979 g C m⁻², and that determined as a function of the normalized difference vegetation index – NDVI (GPP_NDVI) was 1754 g C m⁻². These results indicated that the GPP_EVI was significantly higher than the GPP_EC (R² = 0.96, P = 0.0241 and RMSE = 12.6%). While, the GPP_NDVI was significantly lower than the GPP_EC (R² = 0.93, P = 0.0384 and RMSE = 19.7%). However, the recorded relative error between the GPP_EC and both the GPP_EVI and the GPP_NDVI was −6.22% and 5.76%, respectively. These results demonstrated the potential of the landsat-8 driven VPM model for the estimation of GPP, which is relevant to the productivity and carbon fluxes.     

Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido,Japan

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225 km²) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40 m³ ha^–1 and from 30.78 to 180.54 MgC ha^–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r² = 0.80, RMSE = 55.04 m³ ha^–1; aboveground tree carbon content: r² = 0.78, RMSE = 19.10 MgC ha^–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r² = 0.84, RMSE = 62.66 m³ ha^–1; aboveground tree carbon content: r² = 0.76, RMSE = 27.05 MgC ha^–1) or coniferous forests (volume: r² = 0.75, RMSE = 51.07 m³ ha^–1; aboveground tree carbon content: r² = 0.58, RMSE = 19.00 MgC ha^–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.     

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.     

Whole crop rice silage: Predictions of yield and content of metabolizable energy,metabolizable protein and other nutrients for dairy cows from crop maturity and botanical fractions at harvest

The study investigated the suitability of stage of maturity and botanical fractions of whole crop rice (WCR) to predict yield and nutritive value of ensiled WCR for dairy cows. Eight varieties of WCR (i.e., Akichikara, Fukuhibiki, Habataki, Hamasari, Hokuriku 168, Kusanami, Tamakei 96, Yumetoiro) were harvested at four stages of maturity (i.e., 10, 22, 34, 45 days after flowering [DAF]) and ensiled. Dry matter (DM) yield at each harvest was determined. Silage samples were fractionated into four botanical fractions being: leaf blade, leaf sheath, stem and head. Silage samples were also analyzed for chemical composition, fermentation characteristics, in situ DM and N disappearance. Metabolizable energy (ME) and metabolizable protein (MP) content of samples were estimated according to Terada et al. (1988) and AFRC (1993), respectively. Relationships between maturity or proportions of botanical fractions and contents of WCR silage in terms of DM, ME and MP, and their yields, were estimated by correlation and regression analysis. Stage of maturity was positively related (P<0.001) to ME content (R² = 0.46; y = 4.53 + 0.08X) and MP content (R² = 0.56; y = 22.26 + 0.76X), and DM yield (R² = 0.63; y = 9.21 + 0.12X), ME yield (R² = 0.68, y = 36931 + 1708X) and MP yield (R² = 0.72, y = 161.0 + 14.15X) of WCR. Proportion of leaf was negatively related to yields and nutritive value of ensiled WCR, whilst proportion of head was positively related (P<0.05 to <0.001). Proportion of head was best related to the ME content (R² = 0.72; y = 3.26 + 0.009X), MP content (R² = 0.72; y = 12.31 + 0.079X), and DM yield (R² = 0.41; y = 9.02 + 0.009X), ME yield (R² = 0.76, y = 19494 + 165.5X), and MP yield (R² = 0.75, y = 34.37 + 1.32X) of WCR. Results suggest that to optimize yield and nutritive value, WCR should be ensiled within 40 DAF and the proportion of head should be equal to or more than 500 g per kg DM of WCR silage. Stage of maturity and proportion of head of WCR predict yields of DM, ME and MP of WCR, and their contents, in WCR silage with acceptable accuracy. However, these relationships need to be validated using large data sets and in vivo studies.     

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.     

Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R² = 0.99 for streambed width, R² = 0.82 for riparian zone width, R² = 0.89 for PPC, R² = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia.     

A combined passive and active musculoskeletal model study to estimate L4-L5 load sharing

A number of geometrically-detailed passive finite element (FE) models of the lumbar spine have been developed and validated under in vitro loading conditions. These models are devoid of muscles and thus cannot be directly used to simulate in vivo loading conditions acting on the lumbar joint structures or spinal implants. Gravity loads and muscle forces estimated by a trunk musculoskeletal (MS) model under twelve static activities were applied to a passive FE model of the L4-L5 segment to estimate load sharing among the joint structures (disc, ligaments, and facets) under simulated in vivo loading conditions. An equivalent follower (FL), that generates IDP equal to that generated by muscle forces, was computed in each task. Results indicated that under in vivo loading conditions, the passive FE model predicted intradiscal pressures (IDPs) that closely matched those measured under the simulated tasks (R² = 0.98 and root-mean-squared-error, RMSE = 0.18 MPa). The calculated equivalent FL compared well with the resultant force of all muscle forces and gravity loads acting on the L4-L5 segment (R² = 0.99 and RMSE = 58 N). Therefore, as an alternative approach to represent in vivo loading conditions in passive FE model studies, this FL can be estimated by available in-house or commercial MS models. In clinical applications and design of implants, commonly considered in vitro loading conditions on the passive FE models do not adequately represent the in vivo loading conditions under muscle exertions. Therefore, more realistic in vivo loading conditions should instead be used.     

Improved retrieval of Secchi depth for optically-complex waters using remote sensing data

Water transparency is one of the ecological indicators for describing water quality and the underwater light field which determines its productivity. In the European Water Framework Directive (WFD) as well as in the European Marine Strategy Framework Directive (MSFD) water transparency is used for ecological status classification of inland, coastal and open sea waters and it is regarded as an indicator for eutrophication in Baltic Sea management (HELCOM, 2007). We developed and compared different empirical and semi-analytical algorithms for lakes and coastal Nordic waters to retrieve Secchi depth (Z_SD) from remote sensing data (MERIS, 300 m resolution). The algorithms were developed in water bodies with high coloured dissolved organic matter absorption (a_CDOM(442) ranging 1.7–4.0 m⁻¹), Chl a concentration (0.5–73 mg m⁻³) and total suspended matter (0.7–37.5 g m⁻³) and validated against an independent data set over inland and coastal waters (0.6 m < Z_SD < 14.8 m). The results indicate that for empirical algorithms, using longer wavelengths in the visible spectrum as a reference band decreases the RMSE and increases the coefficient of determination (R²). The accuracy increased (R² = 0.75, RMSE = 1.33 m, n = 134) when Z_SD was retrieved via an empirical relationship between Z_SD and K_d(490). The best agreement with in situ data was attained when Z_SD was calculated via both the diffuse and the beam attenuation coefficient (R² = 0.89, RMSE = 0.77 m, n = 89). The results demonstrate that transparency can be retrieved with high accuracy over various optical water types by the means of ocean color remote sensing, improving both the spatial and temporal coverage. The satellite derived Z_SD product could be therefore used as an additional source of information for WFD and MSFD reporting purposes.     

An indirect method of estimating leaf area index in a tropical deciduous forest of India

Rapid, reliable and meaningful estimates of leaf area index (LAI) are essential to functional characterization of forest ecosystems including biomass and primary productivity studies. Accurate LAI estimates of tropical deciduous forest are required in studies of regional and global change modeling. Tropical deciduous forest due to higher species richness, multiple species association, varied phenophases, irregular stem densities and basal cover, multistoried canopy architecture and different micro-climatic conditions offers dynamism to the understanding of the LAI dynamics of different PFTs in an ecosystem. This investigation reports a new indirect method for measurement of leaf area index (LAI) in a topical moist deciduous forest in Himalayan foothills using LAI-2000 Plant Canopy Analyzer. We measured the LAI in two seasons (summer; leaf senescence stage and post-monsoon; full green stage) in three (dry miscellaneous, sal mixed and teak plantations) plant functional types (PFT) in Katerniaghat Wildlife Sanctuary, India. Ground LAI values ranged between 2.41 and 6.89, 1.17 and 7.71, and 1.92 and 5.19 during post-monsoon season and 1.36–4.49, 0.67–3.1 and 0.37–1.83 during summer season in dry miscellaneous, sal mixed and teak plantation, respectively. We observed strong correlation between LAI and community structural parameters (tree density, basal cover and species richness), with maximum with annual litter fall (R² > 0.8) and aboveground biomass (AGB) (R² > 0.75). We provided equations relating LAI with AGB, which can be utilized in future studies for this region and can be reasonably extrapolated to other regions with suitable statistical extrapolations. However, the relations between LAI and other parameters can be further improved with incorporation of data from optimized and seasonal sampling. Our indirect method of LAI estimation using litter fall as a proxy, offers repetitive potential for LAI estimate in other PFTs with relatively time and cost-effective way, thereby generating quicker and reliable data for model run for regional and global change studies.     

Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake,China

Knowledge of the composition and areal distribution of aquatic vegetation types, as well as their seasonal and interannual variations, is crucial for managing and maintaining the balance of lake ecosystems. In this study, a series of remotely sensed images with a resolution of 30 m (HJ-CCD and Landsat TM) were collected and used to map the distribution of aquatic vegetation types in Taihu Lake, China. Seasonal and interannual dynamics of aquatic vegetation types were explored and analyzed. The distribution areas of Type I (emergent, floating-leaved and floating vegetation) and Type II (submerged vegetation) were used to model their growing season phenology by double logistic functions. The resulting double logistic models showed, the area of Type I reached its peak in mid-August, and the maximum area for Type II occurred in mid-September. From 1984 to 2013, Type I area increased continuously from 59.75 km² to 148.00 km² (R² = 0.84), whereas the area covered by Type II first increased and then decreased, with a trend conforming to a significant quadratic curve (R² = 0.83). The eutrophication and stable state of Taihu Lake was assessed using a simple indicator which was expressed as a ratio of Type II area to Type I area. The results showed that the eutrophication in the lake might have been increasing in the area studied since 2000. Additionally, the results showed that air temperature had likely a direct effect on the growth of Type I (R² = 0.66) and a significant, but delayed, effect on the growth of Type II.     

Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity

Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m²/m² and biases of 0.42–0.55 m²/m² for all three simulated datasets and an RMSE of 1.22 m²/m² with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties.     

Electromyographic and mechanomyographic responses across repeated maximal isometric and concentric muscle actions of the leg extensors

The purpose of the present study was to examine the patterns of responses for torque, electromyographic (EMG) amplitude, EMG mean power frequency (MPF), mechanomyographic (MMG) amplitude, and MMG MPF across 30 repeated maximal isometric (ISO) and concentric (CON) muscle actions of the leg extensors. Twelve female subjects (21.1 ± 1.4 yrs; 63.3 ± 7.4 kg) performed ISO and CON fatigue protocols with EMG and MMG signals recorded from the vastus lateralis. The relationships for torque, EMG amplitude, EMG MPF, MMG amplitude, and MMG MPF versus repetition number were examined using polynomial regression. The results indicated there were decreases (p < 0.05) across the ISO muscle actions for torque (r² = 0.95), EMG amplitude (R² = 0.44), EMG MPF (r² = 0.62), and MMG MPF (r² = 0.48), but no change in MMG amplitude (r² = 0.07). In addition, there were decreases across the CON muscle actions for torque (R² = 0.97), EMG amplitude (R² = 0.46), EMG MPF (R² = 0.86), MMG amplitude (R² = 0.44), and MMG MPF (R² = 0.80). Thus, the current findings suggested that the mechanisms of fatigue and motor control strategies used to modulate torque production were similar between maximal ISO and CON muscle actions.     

Development of a macrophyte-based index of biotic integrity for Minnesota lakes

Traditional approaches for managing aquatic resources have often failed to account for effects of anthropogenic disturbances on biota that are not directly reflected by chemical and physical proxies of environmental condition. The index of biotic integrity (IBI) is a potentially effective assessment method to integrate ecological, functional, and structural aspects of aquatic systems. A macrophyte-based IBI was developed for Minnesota lakes to assess the ability of aquatic plant communities to indicate environmental condition. The index was developed using quantitative point intercept vegetation surveys for 97 lakes that represent a range of limnological and watershed characteristics. We followed an approach similar to that used in Wisconsin to develop the aquatic macrophyte community index (AMCI). Regional adaptation of the AMCI required the identification of species representative of macrophyte communities in Minnesota. Metrics and scaling methods were also substantially modified to produce a more empirically robust index. Regression analyses indicated that IBI scores reflected statewide differences in lake trophic state (R² = 0.57, F = 130.3, df = 1, 95, p < 0.005), agricultural (R² = 0.51, F = 83.0, df = 1, 79, p < 0.005), urban (R² = 0.22, F = 23.0, df = 1, 79, p < 0.005), and forested land uses (R² = 0.51, F = 84.7, df = 1, 79, p < 0.005), and county population density (R² = 0.14, F = 16.6, df = 1, 95, p < 0.005). Variance partitioning analyses using multiple regression models indicated a unique response of the IBI to human-induced stress separate from a response to natural lake characteristics. The IBI was minimally affected by differences in sample point density as indicated by Monte Carlo analyses of reduced sampling effort. Our analysis indicates that a macrophyte IBI calibrated for Minnesota lakes could be useful for identifying differences in environmental condition attributed to human-induced stress gradients.     

Trispyrazolylmethane piano stool complexes of iron(II) and cobalt(II)

A series of cationic trispyrazolylmethane complexes of the general form [Tm^RM(CH₃CN)₃]²⁺ (Tm = tris(pyrazolyl)methane, 1, R = 3,5-Me₂, M = Fe(II); 2, R = 3-Ph, M = Fe(II); 3, R = 3,5-Me₂, M = Co(II); 4, R = 3-Ph, M = Co(II)) with ‘piano-stool’ structures was prepared by the reaction of the N₃tripodal ligands (Tm^R)with [(CH₃CN)₆M](BF₄)₂ in a 1:1 stoichiometric ratio. Magnetic susceptibility measurements indicate that all four complexes with BF₄⁻ counter anions are paramagnetic, high-spin systems in the solid state with μ_eff at high temperatures of 5.2 (1, S = 2), 5.4 (2, S = 2), 4.9 (3, S = 3/2) and 4.6 (4, S = 3/2) BM, respectively. Comparisons of bond lengths from the metal centre to the Tm^R nitrogen donors, and from the metal centre to the acetonitrile nitrogen donors indicate that the neutral tripodal ligands appear to be more weakly coordinated to the metal centre than are the acetonitrile ligands. Reactions of these tripodal complexes with bidentate phosphine ligands, such as 1,2-diphosphinoethane or 1,2-bis(diallylphosphino)ethane leads to displacement of the tripodal ligand, or to the formation of more thermally stable bis-ligand complexes M(Tm^R)₂ (R = 3,5-dimethyl).     

Identification and evaluation of nutrient limitation on periphyton growth in headwater streams in the Pawnee Nation,Oklahoma

The objective of the study was to identify nutrient impacts, if any, on stream periphyton growth in Black Bear Creek (north central Oklahoma) and its tributaries. Passive diffusion periphytometers were deployed at ten study sites within the Black Bear Creek basin to evaluate periphyton growth in response to nutrient enrichment. These sites were selected to represent a gradient of land uses, from predominantly agricultural to predominantly urban. Periphytometer treatments included phosphorus (P) (1.0 mg/L PO₄-P, n = 10), nitrogen (N) (10.0 mg/L NO₃-N, n = 10), N plus P (n = 10) and control (reverse osmosis-treated water, n = 10). Results indicated that average dissolved inorganic N (DIN, PQL = 0.04 mg/L) concentrations were significantly correlated (R² = 0.63, p < 0.01) with chlorophyll a production on the periphytometer control treatments in the Black Bear Creek basin. Periphytic growth was nutrient-limited (increased chlorophyll a was measured on nutrient-enriched growth media) at four of the ten sites sampled; two sites were limited by N and two sites were co-limited by both N and P. The lotic ecosystem trophic status index (LETSI), the ratio of C to N + P chlorophyll a, was calculated to compare treatment responses across sites. At nutrient-limited sites, LETSI was positively correlated to ambient DIN values (R² = 0.97, p < 0.01). However, some sites that were not nutrient-limited had ambient nutrient concentrations similar to sites with observed nutrient limitation, indicating other factors were limiting periphyton growth at those sites.