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.     

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.     

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.     

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.     

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.     

Satellite- versus temperature-derived green wave indices for predicting the timing of spring migration of avian herbivores

According to the green wave hypothesis, herbivores follow the flush of spring growth of forage plants during their spring migration to northern breeding grounds. In this study we compared two green wave indices for predicting the timing of the spring migration of avian herbivores: the satellite-derived green wave index (GWI), and an index of the rate of acceleration in temperature (GDDjerk). The GWI was calculated from MODIS normalized difference vegetation index (NDVI) satellite imagery and GDDjerk from gridded temperature data using products from the global land data assimilation system (GLDAS). To predict the timing of arrival at stopover and breeding sites, we used four years (2008–2011) of tracking data from 12 GPS-tagged barnacle geese, a long-distance herbivorous migrant, wintering in the Netherlands, breeding in the Russian Arctic. The stopover and breeding sites for these birds were identified and the relations between date of arrival with the date of 50% GWI and date of peak GDDjerk at each site were analyzed using mixed effect linear regression. A cross-validation method was used to compare the predictive accuracy of the GWI and GDDjerk indices. Significant relationships were found between the arrival dates at the stopover and breeding sites for the dates of 50% GWI as well as the peak GDDjerk (p < 0.01). The goose arrival dates at both stopover and breeding sites were predicted more accurately using GWI (R²_cv = 0.68, RMSD_cv = 5.9 and  R²_cv= 0.71, RMSD_cv = 3.9 for stopover and breeding sites, respectively) than GDDjerk. The GDDjerk returned a lower accuracy for prediction of goose arrival dates at stopover ( R²_cv = 0.45, RMSD_cv = 7.79) and breeding sites (R²_cv = 0.55, RMSD_cv = 4.93). The positive correlation between the absolute residual values of the GDDjerk model and distance to the breeding sites showed that this index is highly sensitive to latitude. This study demonstrates that the satellite-derived green wave index (GWI) can accurately predict the timing of goose migration, irrespective of latitude and therefore is suggested as a reliable green wave index for predicting the timing of avian herbivores spring migration.     

Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

Canopy height (H_canopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate H_canopy and AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize H_canopy and AGB were predicted by a multiple stepwise linear (SWL) regression model (R² = 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R² = 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for H_canopy, and 0.05 kg/m² for AGB. (3) The SfM point metrics, especially the mean point height (H_mean) greatly contributed to the estimation model of maize H_canopy and AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management.     

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.     

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.     

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.     

Remote sensing data predict indicators of soil functioning in semi-arid steppes,central Spain

A substantial part of current research efforts on desertification are devoted to establish monitoring systems to evaluate the status of natural resources and the onset of desertification processes. Methodologies based on ground-collected soil and plant indicators are being increasingly used for this aim because they are affordable yet do not compromise accuracy. Despite their inherent value, these methods have limitations regarding the extent of the area that can be monitored using them. Such limitations can be overcome combining field-based approaches with remote sensing data, which allow the establishment of monitoring programs over large areas. In this article we tested the relationship between a field methodology based on indicators of ecosystem functioning, the landscape function analysis (LFA), and a vegetation index (NDVI) obtained from satellite images of the ASTER sensor using data gathered in Stipa tenacissima steppes from central Spain. LFA uses soil surface indicators to assess the condition of a given ecosystem by producing three numerical indices (stability, infiltration and nutrient cycling) reflecting the status of basic soil functions. We found a significant positive linear relationship between the NDVI, the three LFA indices and some key structural attributes of vegetation related to the cover of perennial plants. Our results indicate that NDVI can be used as a surrogate of ecosystem functioning in semi-arid Mediterranean steppes, and thus can be a helpful index to monitor the functional status of large areas in these ecosystems, and the possible onset of desertification processes.     

Ground level environmental protein concentrations in various ecuadorian environments: Potential uses of aerosolized protein for ecological research

Large quantities of free protein in the environment and other bioaerosols are ubiquitous throughout terrestrial ground level environments and may be integrative indicators of ecosystem status. Samples of ground level bioaerosols were collected from various ecosystems throughout Ecuador, including pristine humid tropical forest (pristine), highly altered secondary humid tropical forest (highly altered), secondary transitional very humid forest (regrowth transitional), and suburban dry montane deforested (suburban deforested). The results explored the sensitivity of localized aerosol protein concentrations to spatial and temporal variations within ecosystems, and their value for assessing environmental change. Ecosystem specific variations in environmental protein concentrations were observed: pristine 0.32 ± 0.09 μg/m³, highly altered 0.07 ± 0.05 μg/m³, regrowth transitional 0.17 ± 0.06 μg/m³, and suburban deforested 0.09 ± 0.04 μg/m³. Additionally, comparisons of intra-environmental differences in seasonal/daily weather (dry season 0.08 ± 0.03 μg/m³ and wet season 0.10 ± 0.04 μg/m³), environmental fragmentation (buffered 0.19 ± 0.06 μg/m³ and edge 0.15 ± 0.06 μg/m³), and sampling height (ground level 0.32 ± 0.09 μg/m³ and 10 m 0.24 ± 0.04 μg/m³) demonstrated the sensitivity of protein concentrations to environmental conditions. Local protein concentrations in altered environments correlated well with satellite-based spectral indices describing vegetation productivity: normalized difference vegetation index (NDVI) (r² = 0.801), net primary production (NPP) (r² = 0.827), leaf area index (LAI) (r² = 0.410). Moreover, protein concentrations distinguished the pristine site, which was not differentiated in spectral indices, potentially due to spectral saturation typical of highly vegetated environments. Bioaerosol concentrations represent an inexpensive method to increase understanding of environmental changes, especially in densely vegetated ecosystems with high canopies or in areas needing high spatial and temporal resolution. Further research to expand understanding of the applicability of bioaerosol concentrations for environmental monitoring is supported by this pilot study.     

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.     

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.     

Soil bacterial community structure and physicochemical properties in mitigation wetlands created in the Piedmont region of Virginia (USA)

Wetland creation is a common practice for compensatory mitigation in the United States. Vegetation attributes have been used as a quick measure of mitigation success in most post-creation monitoring, while little attention has been paid to soils that provide the substrate for flora and fauna to establish and develop. Created wetland soils are often found not indicative of ‘hydric soil’ with a lack of development of physicochemical properties (i.e., bulk density, moisture content, and carbon and nitrogen contents) comparable to those in natural wetlands. Moreover, soil bacterial communities are rarely examined though they are integrally involved in biogeochemical functions that are critical for ecosystem development in created wetlands. We analyzed soil physicochemistry and profiled soil bacterial community structure using amplicon length heterogeneity polymerase chain reaction (LH-PCR) of 16S ribosomal DNA in three relatively young wetlands (<10 years old) created in the Piedmont region of Virginia. We examined the data by site and by specific conditions of each site (i.e., induced microtopography and hydrologic regime). Multidimensional scaling (MDS) and analysis of similarity (ANOSIM) showed clear clustering and significant differences both in soil physicochemistry (Global R = 0.70, p = 0.001) and in soil bacterial community profiles (Global R = 0. 77, p = 0.001) between sites. Soil physicochemistry (Global R = 1, p = 0.005) and bacterial community structure (Global R = 0.79, p = 0.005) of soils significantly differed by hydrologic regime within a wetland, but not by microtopography treatment. A significant association was found between physicochemistry and bacterial community structure in wetland soils, revealing a close link between two attributes (ρ = 0.39, p = 0.002). C/N (carbon to nitrogen) ratio was the best predictor of soil bacterial community patterns (ρ = 0.56, p = 0.001). The diversity of soil bacterial community (Shannon's H′) differed between sites with a slightly higher diversity observed in a relatively older created wetland, and seemed also fairly determined by hydrologic regime of a site, with a relatively dry site being more diverse.     

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.     

NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin,USA

In this study, we developed a new approach that adjusted normalized difference vegetation index (NDVI) pixel values that were near saturation to better characterize the cropland performance (CP) in the Greater Platte River Basin (GPRB), USA. The relationship between NDVI and the ratio vegetation index (RVI) at high NDVI values was investigated, and an empirical equation for estimating saturation-adjusted NDVI (NDVI_sat__adjust) based on RVI was developed. A 10-year (2000–2009) NDVI_sat__adjust data set was developed using 250-m 7-day composite historical eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. The growing season averaged NDVI (GSN), which is a proxy for ecosystem performance, was estimated and long-term NDVI non-saturation- and saturation-adjusted cropland performance (CP_non__sat__adjust, CP_sat__adjust) maps were produced over the GPRB. The final CP maps were validated using National Agricultural Statistics Service (NASS) crop yield data. The relationship between CP_sat__adjust and the NASS average corn yield data (r = 0.78, 113 samples) is stronger than the relationship between CP_non__sat__adjust and the NASS average corn yield data (r = 0.67, 113 samples), indicating that the new CP_sat__adjust map reduces the NDVI saturation effects and is in good agreement with the corn yield ground observations. Results demonstrate that the NDVI saturation adjustment approach improves the quality of the original GSN map and better depicts the actual vegetation conditions of the GPRB cropland systems.     

Estimating biomass and carbon mitigation of temperate coniferous forests using spectral modeling and field inventory data

Realizing the importance of forest carbon monitoring and reporting in climate change, the present study was conducted to derive spectrally modeled aboveground biomass and mitigation using Landsat data in combination with sampled field inventory data in the coniferous forests of Western Himalaya. After conducting preliminary survey in 2009, 90 quadrats (45 each for calibration and validation) of 0.1 ha were laid in six forest types for recording field inventory data viz. diameter at breast height, height, slope and aspect. Biomass carbon (Mg ha^− 1) was worked out for different forest types and crown density classes (open with 10–40% crown density and closed with > 40% crown density) using recommended volume equations, ratios and factors. Biomass carbon map (aboveground + belowground) was generated for the entire region using geospatial techniques. Normalized difference vegetation index (NDVI) was generated and spectral values were extracted to establish relation (R² = 0.72, p < 0.01) with the field inventory data. The model developed was validated (R² = 0.73, p < 0.01) with 45 sample observations not used earlier for predicting and generating biomass carbon map (2009) for the entire region. The data from field based inventory indicates highest total biomass carbon (171.40, σ ± 23.19) Mg ha^− 1 for Fir–Spruce (closed) which has relatively more mature girth classes and low tree density. This value was found to be significantly higher than other forest types. Lowest biomass carbon was observed for Blue Pine (open) (37.15, σ ± 11.82) Mg ha^− 1. The NDVI values for the entire region ranged from 0 to 0.62 and consequently the spectrally derived aboveground biomass carbon varied from 0 to 600 Mg ha^− 1. The study demonstrates the application of mapping, spectral responses and sampled field inventory for type wise assessment of carbon mitigation in temperate coniferous forests of Himalayas.     

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.     

Comparing the sensitivity of diverse macroinvertebrate metrics to a multiple stressor gradient in Mediterranean streams and its influence on the assessment of ecological status

Biological indicators are increasingly being used as integrative measures of the ecosystem health in streams, particularly those using macroinvertebrate assemblage composition. Monitoring biological quality of rivers has not a long tradition in some Mediterranean European countries like Spain. Several macroinvertebrate metrics have been recently proposed to assess ecological status in Mediterranean streams, so it is necessary to compare the use of proposed biological quality metrics to select the most appropriate ones.In the present work, two classic richness metrics (total number of families and number of the Ephemeroptera, Plecoptera and Trichoptera families), three indices (IBMWP, IASPT and t-BMWQ) and two multimetric indices, recently proposed to be used in Mediterranean streams (ICM-9 and ICM-11a or IMMi-L), were compared by the analysis of the sensitivity of these metrics to a multiple stressor gradient which reflected the main pressures present in the study area. For this purpose, data from 193 sites sampled in spring (95 reference sites and 98 disturbed sites) belonging to five different Mediterranean stream types present in 35 basins were studied.The results showed that the adjusted regression coefficients (r²) for all seven metrics in the exponential regression models were higher than linear ones, thus indicating an exponential relationship between metrics and the environmental alteration. The two studied multimetric indices presented higher regression coefficients (r² = 0.590–0.669) than the three indices (r² = 0.524–0.574) and the two metrics (r² = 0.471–0.525), therefore showing a better response to a stressor gradient in Mediterranean streams. Within the multimetric indices group, ICM-11a showed the highest regression coefficients. Based on the results obtained, we suggest using the ICM-11a, apart from the IBMWP, to assess ecological status in Mediterranean streams.