A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach

Given the alarming global rates of mangrove forest loss it is important that resource managers have access to updated information regarding both the extent and condition of their mangrove forests. Mexican mangroves in particular have been identified as experiencing an exceptional high annual rate of loss. However, conflicting studies, using remote sensing techniques, of the current state of many of these forests may be hindering all efforts to conserve and manage what remains. Focusing on one such system, the Teacapán–Agua Brava–Las Haciendas estuarine–mangrove complex of the Mexican Pacific, an attempt was made to develop a rapid method of mapping the current condition of the mangroves based on estimated LAI. Specifically, using an AccuPAR LP-80 Ceptometer, 300 indirect in situ LAI measurements were taken at various sites within the black mangrove (Avicennia germinans) dominated forests of the northern section of this system. From this sample, 225 measurements were then used to develop linear regression models based on their relationship with corresponding values derived from QuickBird very high resolution optical satellite data. Specifically, regression analyses of the in situ LAI with both the normalized difference vegetation index (NDVI) and the simple ration (SR) vegetation index revealed significant positive relationships [LAI versus NDVI (R ² = 0.63); LAI versus SR (R ² = 0.68)]. Moreover, using the remaining sample, further examination of standard errors and of an F test of the residual variances indicated little difference between the two models. Based on the NDVI model, a map of estimated mangrove LAI was then created. Excluding the dead mangrove areas (i.e. LAI = 0), which represented 40% of the total 30.4 km² of mangrove area identified in the scene, a mean estimated LAI value of 2.71 was recorded. By grouping the healthy fringe mangrove with the healthy riverine mangrove and by grouping the dwarf mangrove together with the poor condition mangrove, mean estimated LAI values of 4.66 and 2.39 were calculated, respectively. Given that the former healthy group only represents 8% of the total mangrove area examined, it is concluded that this mangrove system, considered one of the most important of the Pacific coast of the Americas, is currently experiencing a considerable state of degradation. Furthermore, based on the results of this investigation it is suggested that this approach could provide resource managers and scientists alike with a very rapid and effective method for monitoring the state of remaining mangrove forests of the Mexican Pacific and, possibly, other areas of the tropics.     

A glimpse at short-term controls of evapotranspiration along the southern slopes of Kilimanjaro

Future climate characteristics of the southern Kilimanjaro region, Tanzania, are mainly determined by local land-use and global climate change. Reinforcing increasing dryness throughout the twentieth century, ongoing land transformation processes emphasize the need for a proper understanding of the regional-scale water budget and possible implications on related ecosystem functioning and services. Here, we present an analysis of scintillometer-based evapotranspiration (ET) covering seven distinct habitat types across a massive climate gradient from the colline savanna woodlands to the upper-mountain Helichrysum zone (940 to 3960 m.a.s.l.). Random forest-based mean variable importance indicates an outstanding significance of net radiation (R _net) on the observed ET across all elevation levels. Accordingly, topography and frequent cloud/fog events have a dampening effect at high elevations, whereas no such constraints affect the energy and moisture-rich submontane coffee/grassland level. By contrast, long-term moisture availability is likely to impose restrictions upon evapotranspirative net water loss in savanna, which particularly applies to the pronounced dry season. At plot scale, ET can thereby be approximated reasonably using R _net, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R ² 0.59 to 1.00). While multivariate regression based on pooled meteorological data from all plots proves itself useful for predicting hourly ET rates across a broader range of ecosystems (R ² = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R ² = 0.87). To sum up, our results indicate that valuable insights into land cover-specific ET dynamics, including underlying drivers, may be derived even from explicitly short-term measurements in an ecologically highly diverse landscape.     

Weed mapping in cotton using ground-based sensors and GIS

Site-specific weed management presupposes the careful monitoring and mapping of weed infestation areas. Cut-edge sensor technologies coupled with geographical information systems (GIS) provide the means for reliable decision-making concerning weed management even in sub-field level. In present research, two different spectral sensing systems were engaged in order to digitally map weed patches as grown in four different cotton fields in Central Greece. The systems used were a set of two Crop Circle multispectral sensors ACS-430 and a digital camera Nikon D300S. The spaces between cotton rows were scanned and photographed with the two systems accordingly. Raw recorded data were stored and analyzed in GIS environment producing spatially interpolated maps of red-edge normalized difference vegetation index (NDVI) and weed cover percentage values. Both mapping approaches were satisfactorily related to weed distribution as occurred in the fields; however, the photographic method tended to underestimate weed populations. Correlation of red-edge NDVI and weed cover values, at the points where photographs were taken, as revealed by Pearson’s correlation coefficient was high (r?>?0.83) and statistically significant at the 0.01 level. A first-degree linear equation adequately modeled (R²?>?0.7) the between value pair relations, strengthening the validity of the two methodologies in spatially monitoring weed patches. The methodologies and the technologies used in the study can be used for yearly mapping weed flora in cotton cultivation and potentially constitute a means of rationalizing herbicide application in terms of doses and spatio-temporal decision-making.     

Estimation of underwater visibility in coastal and inland waters using remote sensing data

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z _sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z _sd , namely, turbidity and length attenuation coefficient (1/(c + K _d ), c = beam attenuation coefficient; K _d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R _rs (443) + R _rs (490))/(R _rs (555) + R _rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K _d ) at 531 nm, K _d (490) is estimated as a function of reflectance ratio (R _rs (670)/R _rs (490)), which provides the bio-optical link between chlorophyll concentration and K _d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z _sd and in situ Z _sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z _sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z _sd and in situ Z _sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z _sd and in situ Z _sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z _sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z _sd products. The modeled and satellite-derived Z _sd values were compared with measurement data which yielded RMSE = 0.079, MRE = ?0.016, and R ²  = 0.95 for the modeled Z _sd and RMSE = 0.075, MRE = 0.020, and R ²  = 0.95 for the satellite-derived Z _sd products.     

A multi-year study following BACI design reveals no short-term impact of <Emphasis Type="Italic">Bti</Emphasis> on chironomids (Diptera) in a floodplain in Eastern Austria

Short-term impacts of aerial application of Bacillus thuringiensis israelensis (Bti) on Culicidae and Chironomidae were investigated over several years in temporary waters of the Dyje and Morava floodplains in Eastern Austria. The sampling followed a Before-After-Control-Impact (BACI) approach with sampling dates immediately before and shortly after the application and was repeated for 3 years. To test for effects of the Bti treatment on the two Diptera families, linear mixed-effects models were used. Data analysis included the factors Before-After and Control-Impact as fixed effects, while general temporal and spatial variables were random effects. One hundred sixteen taxa of chironomids were identified. Abundance varied between 2 and 1125 larvae per m², while culicid densities reached values of several 100 ind. per liter. Total culicid abundance significantly decreased after the Bti treatment, whereas no significant effects were found on the abundance of total chironomids and dominant chironomid subfamilies, tribes, and genera, on relative proportions of chironomid feeding guilds, diversity, and species composition. Further studies from this area are needed to extend the investigation over a period of several weeks in order to reveal possible delayed effects of the larvicide application.     

A regional algorithm to model mesozooplankton biomass along the southwestern Bay of Bengal

A three-dimensional regression analysis attempted to model mesozooplankton (MSP) biomass using sea surface temperature (SST) and chlorophyll-a (Chl-a). The study was carried out from January 2014 to July 2015 in the southwestern Bay of Bengal (BoB) and sampling was carried out on board Sagar Manjusha and Sagar Purvi. SST ranged from 26.2 to 33.1 °C while Chl-a varied from 0.04 to 6.09 μg L^?1. During the course of the study period, there was a weak correlation (r?= 0.32) between SST and Chl-a statistically. MSP biomass varied from 0.42 to 9.63 mg C m^?3 and inversely related with SST. Two kinds of approaches were adopted to develop the model by grouping seasonal datasets (four seasonal algorithms) and comprising all datasets (one annual algorithm). Among the four functions used (linear, paraboloid, the Lorentzian and the Gaussian functions), paraboloid model was best suited. The best seasonal and annual algorithms were applied in the synchronous MODIS-derived SST and Chl-a data to estimate the MSP biomass in the southwestern BoB. The modelled MSP biomass was validated with field MSP biomass and the result was statistically significant, showing maximum regression coefficient for the seasonal algorithms (R²?=?0.60; p?=?0.627; α?= 0.05), than the annual algorithm (R²?=?0.52; p?=?0.015, α?=?0.05).     

A synoptic survey of select wastewater-tracer compounds and the pesticide imidacloprid in Florida’s ambient freshwaters

Current wastewater treatment technologies do not remove many unregulated hydrophilic compounds, and there is growing interest that low levels of these compounds, referred to as emerging contaminants, may impact human health and the environment. A probabilistic-designed monitoring network was employed to infer the extent of Florida’s ambient freshwaters containing the wastewater (Includes reuse water, septic systems leachate, and wastewater treatment effluent.) indicators sucralose, acetaminophen, carbamazepine, and primidone and those containing the widely used pesticide imidacloprid. Extent estimates with 95% confidence bounds are provided for canals, rivers, streams, small and large lakes, and unconfined aquifers containing ultra-trace concentrations of these compounds as based on analyses of 2015 sample surveys utilizing 528 sites. Sucralose is estimated to occur in greater than 50% of the canal, river, stream, and large lake resource extents. The pharmaceuticals acetaminophen, carbamazepine, and primidone are most prevalent in rivers, with approximately 30% of river kilometers estimated to contain at least one of these compounds. Imidacloprid is estimated to occur in 50% or greater of the canal and river resource extents, and it is the only compound found to exceed published toxicity or environmental effects standards. Geospatial analyses show sucralose detection frequencies within Florida’s drainage basins to be significantly related to the percentage of urban land use (R²?=?0.36, p?<?0.001), and imidacloprid detection frequencies to be significantly related to the percentage of urban and agricultural land use (R²?=?0.47, p?<?0.001). The extent of the presence of these compounds highlights the need for additional emerging contaminant studies especially those examining effects on aquatic biota.     

Agricultural practices in grasslands detected by spatial remote sensing

The major decrease in grassland surfaces associated with changes in their management that has been observed in many regions of the earth during the last half century has major impacts on environmental and socio-economic systems. This study focuses on the identification of grassland management practices in an intensive agricultural watershed located in Brittany, France, by analyzing the intra-annual dynamics of the surface condition of vegetation using remotely sensed and field data. We studied the relationship between one vegetation index (NDVI) and two biophysical variables (LAI and fCOVER) derived from a series of three SPOT images on one hand and measurements collected during field campaigns achieved on 120 grasslands on the other. The results show that the LAI appears as the best predictor for monitoring grassland mowing and grazing. Indeed, because of its ability to characterize vegetation status, LAI estimated from remote sensing data is a relevant variable to identify these practices. LAI values derived from the SPOT images were then classified based on the K-Nearest Neighbor (KNN) supervised algorithm. The results points out that the distribution of grassland management practices such as grazing and mowing can be mapped very accurately (Kappa index?=?0.82) at a field scale over large agricultural areas using a series of satellite images.     

Integrating multiple vegetation indices via an artificial neural network model for estimating the leaf chlorophyll content of <Emphasis Type="Italic">Spartina alterniflora</Emphasis> under interspecies competition

The invasive species Spartina alterniflora and native species Phragmites australis display a significant co-occurrence zonation pattern and this co-exist region exerts most competitive situations between these two species, competing for the limited space, directly influencing the co-exist distribution in the future. However, these two species have different growth ratios in this area, which increase the difficulty to detect the distribution situation directly by remote sensing. As chlorophyll content is a key indicator of plant growth and physiological status, the objective of this study was to reduce the effect of interspecies competition when estimating Cab content; we evaluated 79 published representative indices to determine the optimal indices for estimating the chlorophyll a and b (Cab) content. After performing a sensitivity analysis for all 79 spectral indices, five spectral indices were selected and integrated using an artificial neural network (ANN) to estimate the Cab content of different competition ratios: the Gitelson ratio green index, the transformed chlorophyll absorption ratio index/optimized soil-adjusted vegetation index, the modified normalized difference vegetation index, the chlorophyll fluorescence index, and the Vogelmann chlorophyll index. The ANN method yielded better results (R ² = 0.7110 and RMSE = 8.3829 μg cm^?2) on average than the best single spectral index (R ² = 0.6319 and RMSE = 9.3535 μg cm^?2), representing an increase of 10.78% in R ² and a decrease of 10.38% in RMSE. Our results indicated that integrating multiple vegetation indices with an ANN can alleviate the impact of interspecies competition and achieve higher estimation accuracy than the traditional approach using a single index.     

Nonlinear Growth Models for Modelling Time Series of Groundwater Nitrate Concentrations

In this paper, a novel approach of modelling time series of nitrate concentrations in drinking water by nonlinear growth functions is proposed. An almost constant increase in the average values of nitrate concentrations occurred at the Vinokov??ak wellfield in the county of Vara?din, Croatia, in the period between 1998 and 2013. Our hypothesis was that the time series would be well adjusted to the nonlinear growth function with asymptotic properties after smoothing performed by the moving average method. We used a general growth model which does not have asymptotic properties and the logistic, Gompertz and Richards functions which do have asymptotic properties. Last three growth functions were modified based on the knowledge of a constant presence of basic nitrate concentrations in the drinking water before growth. The fit of growth curves to experimental data was assessed using R ² and Aikake’s information criterion. The modified growth functions gave the best fit to the smoothed time series of nitrate concentrations with R ² ranging from 0.9960 to 0.9986.     

Assessment of heavy metal pollution in vegetables and relationships with soil heavy metal distribution in Zhejiang province,China

There are increasing concerns on heavy metal contaminant in soils and vegetables. In this study, we investigated heavy metal pollution in vegetables and the corresponding soils in the main vegetable production regions of Zhejiang province, China. A total of 97 vegetable samples and 202 agricultural soil samples were analyzed for the concentrations of Cd, Pb, As, Hg, and Cr. The average levels of Cd, Pb, and Cr in vegetable samples [Chinese cabbage (Brassica campestris spp. Pekinensis), pakchoi (Brassica chinensis L.), celery (Apium graveolens), tomato (Lycopersicon esculentum), cucumber (Colletotrichum lagenarium), cowpea (Vigna unguiculata), pumpkin (Cucurbita pepo L.), and eggplant (Solanum melongena)] were 0.020, 0.048, and 0.043 mg kg^?1, respectively. The Pb and Cr concentrations in all vegetable samples were below the threshold levels of the Food Quality Standard (0.3 and 0.5 mg kg^?1, respectively), except that two eggplant samples exceeded the threshold levels for Cd concentrations (0.05 mg kg^?1). As and Hg contents in vegetables were below the detection level (0.005 and 0.002 mg kg^?1, respectively). Soil pollution conditions were assessed in accordance with the Chinese Soil Quality Criterion (GB15618-1995, Grade II); 50 and 68 soil samples from the investigated area exceeded the maximum allowable contents for Cd and Hg, respectively. Simple correlation analysis revealed that there were significantly positive correlations between the metal concentrations in vegetables and the corresponding soils, especially for the leafy and stem vegetables such as pakchoi, cabbage, and celery. Bio-concentration factor values for Cd are higher than those for Pb and Cr, which indicates that Cd is more readily absorbed by vegetables than Pb and Cr. Therefore, more attention should be paid to the possible pollution of heavy metals in vegetables, especially Cd.     

Impacts of ecological water conveyance on groundwater dynamics and vegetation recovery in the lower reaches of the Tarim River in northwest China

The ecological water conveyance project (EWCP) in the lower reaches of the Tarim River provided a valuable opportunity to study hydro-ecological processes of desert riparian vegetation. Ecological effects of the EWCP were assessed at large spatial and temporal scales based on 13 years of monitoring data. This study analyzed the trends in hydrological processes and the ecological effects of the EWCP. The EWCP resulted in increased groundwater storage—expressed as a general rise in the groundwater table—and improved soil moisture conditions. The change of water conditions also directly affected vegetative cover and the phenology of herbs, trees, and shrubs. Vegetative cover of herbs was most closely correlated to groundwater depth at the last year-end (R?=?0.81), and trees and shrubs were most closely correlated to annual average groundwater depth (R?=?0.79 and 0.66, respectively). The Normalized Difference Vegetation Index (NDVI) responded to groundwater depth on a 1-year time lag. Although the EWCP improved the NDVI, the study area is still sparsely vegetated. The main limitation of the EWCP is that it can only preserve the survival of existing vegetation, but it does not effectively promote the reproduction and regeneration of natural vegetation.     

Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers

This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), η _d, of countercurrent spray towers. SDCE values were calculated using MATLAB^® algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit^® scientific software. The predicted model developed from linear–nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C _D) and terminal velocity values (v _T) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.     

Soil tillage erosion estimated by using magnetism of soils—a case study from Bulgaria

A detailed field and laboratory study on small 0.84-ha test site of agricultural land near Sofia (Bulgaria) has been carried out in order to test the applicability of magnetic methods in soil erosion estimation in the particular case of strongly magnetic parent material. Field measurements of magnetic susceptibility were carried out with grid size of 6 m, resulting in 258 data points. Bulk soil material was gathered from 78 grid points. Natural, non-disturbed soil section was sampled near the agricultural field for reference profile of complete undisturbed soil. Surface susceptibility measurements reveal well-defined maxima down slope which, however, cannot be assigned directly to a certain depth interval, corresponding with susceptibilities along the non-disturbed soil profile. This is caused by the high magnetic susceptibility of the lithogenic coarse-grained magnetic fraction. Non-uniqueness is resolved by using magnetic susceptibility of coarse (1 mm >?d?> 63 μm) and fine (d < 63 μm) mechanical fractions and the parameter Δχ = 100*(χ _coarse???χ _fine)/χ _bulk (%). It shows increased values in the C-horizon of undisturbed soil profile, which corresponds to a certain part of the studied area. After the application of an empirical model to predict the values of magnetic parameter after tillage homogenization and removal of soil material from the surface, the amount of soil loss is estimated.     

<Emphasis Type="Italic">Synechococcus</Emphasis> production and grazing loss rates in nearshore tropical waters

Temporal variation of Synechococcus, its production (μ) and grazing loss (g) rates were studied for 2 years at nearshore stations, i.e. Port Dickson and Port Klang along the Straits of Malacca. Synechococcus abundance at Port Dickson (0.3–2.3 × 10⁵ cell ml^?1) was always higher than at Port Klang (0.3–7.1 × 10⁴ cell ml^?1) (p < 0.001). μ ranged up to 0.98 day^?1 (0.51 ± 0.29 day^?1), while g ranged from 0.02 to 0.31 day^?1 (0.15 ± 0.07 day^?1) at Port Klang. At Port Dickson, μ and g averaged 0.47 ± 0.13 day^?1 (0.29–0.82 day^?1) and 0.31 ± 0.14 day^?1 (0.13–0.63 day^?1), respectively. Synechococcus abundance did not correlate with temperature (p > 0.25), but nutrient and light availability were important factors for their distribution. The relationship was modelled as log Synechococcus = 0.37Secchi ? 0.01DIN + 4.52 where light availability (as Secchi disc depth) was a more important determinant. From a two-factorial experiment, nutrients were not significant for Synechococcus growth as in situ nutrient concentrations exceeded the threshold for saturated growth. However, light availability was important and elevated Synechococcus growth rates especially at Port Dickson (F = 5.94, p < 0.05). As for grazing loss rates, they were independent of either nutrients or light intensity (p > 0.30). In nearshore tropical waters, an estimated 69 % of Synechococcus production could be grazed.     

Discriminant analysis for the prediction of sand mass distribution in an urban stormwater holding pond using simulated depth average flow velocity data

The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m³ s^?1, representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size?>?0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5 %. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20 %) compared to group 2 (16.90 to 45.55 %). Two Fisher’s linear discriminant functions, F ₁ and F ₂, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F ₁?=??9.405?+?4232.119?×?A???1795.805?×?B?+?281.224?×?C, and F ₂?=??2.842?+?2725.137?×?A???1307.688?×?B?+?231.353?×?C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m³ s^?1, respectively. The model correctly predicts 88.9 and 100.0 % of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8 % are correctly classified. The model predicts that 31.4 % of the model domain areas consist of high-sand mass composition areas and the remaining 68.6 % comprise low-sand mass composition areas.     

Microphytobenthic biomass,species composition and environmental gradients in the mangrove intertidal region of the Andaman Archipelago,India

Monthly sampling in mangrove intertidal sediments of Andaman Archipelago was carried out during a 1-year study (January to December, 2013) in order to analyse the spatial and temporal distribution of microphytobenthos (MPB) and MPB biomass (sediment chlorophyll-a (chl-a)) in the surficial layer 0–1 cm. The MPB community was mainly composed of diatoms. The MPB biomass concentration in surface sediment (0–1 cm) ranged from 0.7 to 16.98 μg cm^?3. Population density of benthic diatoms varied from 78 to 224 ind cm^?3. This study identified 41 diatom taxa (27 pennate diatoms, 14 centric diatoms) in the sediment, and among all the diatom taxa, we distinguished few true planktonic species—Coscinodiscus centralis, Coscinodiscus marginatus, Leptocylindricus danicus, Planktoniella sol, Thalassiosira decipiens, Thalassionema nitzschioides and Thalassiothrix longissima. Overall, a high percentage of diatoms were pennate (81%) as opposed to centric. Based on benthic diatom abundance, species composition and distribution, MPB assemblages of sampling stations were grouped into two distinct clusters: one with St. 1 and St. 3 and another one with St. 2 and St. 4. Canonical correspondence analysis (CCA) revealed seasonality as the most important factor determining variability in diatom species composition among sampling sites. There was a distinct seasonal pattern in MPB biomass distribution and benthic diatom cell density during monsoon and post-monsoon seasons. Our results suggest that among various physical and chemical variables studied, greater levels of overlying water nutrients and sediment textures significantly correlated and were conducive factors for MPB. This is the first detailed study on the MPB from these mangrove sediments, providing benchmark data for future studies about these remote groups of Andaman and Nicobar Islands.     

Bioindicator responses and performance of plant species along a vehicular pollution gradient in western Himalaya

Loss of green cover, and increasing pollution is a prime global concern. The problem calls for screening of pollution-tolerant tree species that can be integrated into plantation drives. Recognizing this, the study analyzed bio-indicator responses and performance of commonly occurring plant species along a pollution gradient in western Himalaya. Based on distance from the road, three sites viz., highly polluted (HP), moderately polluted (MP), and least polluted (LP), were identified. From these sites, leaves of commonly occurring 26 tree species were collected and analyzed for dust accumulation, total chlorophyll, relative water content (RWC), ascorbic acid, and pH using standard protocols. Later, assessment of Air Pollution Tolerance Index (APTI) and Anticipated Performance Indices (API) was carried out. The results revealed variations in biochemical characteristics. The pH, RWC, and total chlorophyll increased with decreasing pollution while ascorbic acid increased with increasing pollution. Dust capturing potential of Ficus carica (1.191 mg/m²) and Toona ciliata (0.820 mg/m²) was relatively higher. Based on the results of APTI, Grevillea robusta was classified as tolerant. It scored significantly higher values (21.06, 21.19, and 19.61 in LP, MP, and HP sites, respectively). Quercus floribunda, G. robusta (68.75% each), Juglans regia (68.7%), and T. ciliata (62.50%) were good performers in HP sites. Acer caesium, Betula utilis, and Morus alba that had low API scores (43.75%) were predicted as poor performers. Thus, G. robusta, Q. floribunda, J. regia, T. ciliata, and F. carica were evaluated as best performers. They could be integrated into plantations drives for environmental management.