Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km² (52.5 %), 56,661.1 km² (36.4 %), 51,642.3 km² (33.2 %), 49,773 km² (32 %) and 48,669.4 km² (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 %?year^?1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km²) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.     

Sediment fluxes and the littoral drift along northeast Andhra Pradesh Coast, India: estimation by remote sensing

The littoral drift regime along the northeastern coast of India was investigated by analyzing coastal drift indicators and shoreline changes based on multitemporal satellite images. The study of offshore turbidity patterns and quantitative estimation of suspended sediments was undertaken to understand the magnitude and direction of movement of sediment fluxes. The study revealed that: (1) the character of coastal landforms and sedimentation processes indicate that the sediment transport is bidirectional and monsoon dependent; (2) multidate, multitemporal analysis of satellite images helps to show the nature of sediment transport along the coast. The dominant net sediment transport is in a NE direction along the eastern coast of India. Finally, this assessment demonstrates the potential of remote sensing technology in understanding the coastal morphometric changes, long-term sediment transport, shoreline changes, and offshore turbidity distribution pattern and the implications for the transport of sediment-associated pollutants.     

Delineating shallow saline groundwater zones from Southern India using geophysical indicators

A geophysical survey was conducted over an industrial belt encompassing 80 functional leather factories in Southern India. These factories discharge untreated effluents which pollute shallow groundwater where electrical conductivity (EC) value had a wide range between 545 and 26,600 μS/cm (mean, 3, 901 μS/cm). The ranges of Na⁺ and Cl^? ions were from 46 to 4,850 mg/L (mean, 348 mg/L) and 25 to 10,390 mg/L (mean, 1,079 mg/L), respectively. Geoelectrical layer parameters of 37 vertical electrical soundings were analyzed to demarcate fresh and saline water zones. However, the analysis not did lead to a unique resolution of saline and fresh waters. It was difficult to assign a definitive value to the aquifer resistivity of any area. Thus, geophysical indicators, namely longitudinal unit conductance (S), transverse unit resistance (T), and average longitudinal resistivity (R _s), were calculated for identifying fresh and saline waters. Spatial distributions of S, T, and R _s reflected widely varying ranges for the saline and fresh water zones. Further, the empirical relation of formation factor (F) was established from pore-water resistivity and aquifer resistivity for fresh and saline aquifers, which may be used to estimate local EC values from the aquifer resistivity, where well water is not available.     

Alteration of antioxidant enzymes and impairment of DNA in the SiO2 nanoparticles exposed zebra fish (Danio rerio)

The incorporation of nanoparticles in industrial and biomedical applications has increased significantly in recent years, yet their hazardous and toxic effects have not been studied extensively. While standard toxicological test methods are generally capable of detecting the toxic effects, the choice of relevant methods for nanomaterials is still discussed. Among the various oxide nanomaterials, silica nanoparticles are widely used in biological applications that include nano-medicine. But studies on adverse effects of silica nanoparticle exposure to fish remain unclear. Therefore, the present study was designed to investigate the oxidative toxic effects of silicon dioxide nanoparticles using fish model. The size of the SiO₂ nanoparticles was between 68 and 100 nm which was confirmed by X-ray diffractometer, dynamic light scattering, scanning electron microscope and transmission electron microscope. The zebra fish were exposed to sub-lethal concentrations (5 and 2.5 mg/L) of characterized SiO₂ nanoparticles for a period of 7 days. After 7 days, SiO₂ nanoparticle-treated fishes were sacrificed, and tissues such as liver, muscle and gill were dissected out for the analysis of antioxidant enzymes and DNA fragmentation. The DNA profiles were analysed in the tissues of zebra fish that treated with SiO₂ nanoparticles. Tissues of fish from clean water were used as control, and DNA profiles were analysed. It is found that DNA from control tissues was intact, whereas the tissues treated with SiO₂ were all fragmented. SiO₂ nanoparticle-mediated antioxidant enzymes activities, such as catalase, superoxide dismutase, glutathione (GSH)-S-transferase, glutathione reductase and GSH, in the tissues of zebra fish were measured. The results revealed that alteration of antioxidant enzymes due to SiO₂ nanoparticle can be considered as a biomarker to SiO₂-mediated oxidative stress in biological samples.     

Biogeochemistry of the Penobscot River watershed, Maine, USA: nutrient export patterns for carbon, nitrogen, and phosphorus

Watershed exports of carbon, nitrogen, phosphorus, major solutes, and suspended sediments were examined during five water years in the Penobscot River basin, which forms part of the Gulf of Maine watershed. Mean annual exports of dissolved organic carbon (DOC) in the Penobscot River were 58 kg C ha(-1) year(-1), whereas cumulative yearly watershed flux of DOC during the study period ranged from 8.6 to 16.1 × 10(10) g C year(-1) and averaged 11.7 × 10(10) g C year(-1). Watershed exports of total soluble N (TN) and total soluble P in the Penobscot River averaged 1.9 and 0.02 kg ha(-1) year(-1), respectively. Companion studies in two other major Maine rivers indicated that mean annual exports of DOC and TN in the Androscoggin River were 40 kg C ha(-1) year(-1) and 2.0 kg N ha(-1) year(-1), whereas exports in the Kennebec River were 43 kg C ha(-1) year(-1) and 2.2 kg N ha(-1) year(-1). Extrapolation of results from this investigation and a previous complementary study indicates that estuaries and coastal waters in the Gulf of Maine receive at least 1.0 × 10(10) g N year(-1) and 2.5 × 10(11) g C year(-1) in combined runoff from the four largest Maine river basins. Soluble exports of Ca + Mg + Na minus wet deposition inputs of cations in the Penobscot system were approximately 1,840 mol(c) ha(-1) year(-1), which represents a minimum estimate of cation denudation from the watershed. Based on its low N and P export rates, the Penobscot River watershed represents an example of reference conditions for use as a benchmark in ecological assessments of river water quality restoration or impairment. In addition, the biogeochemical metrics from this study provide an historical baseline for analysis of future trends in nutrient exports from the Penobscot watershed as a function of changing climatic and land use patterns.     

Assessing health impacts in complex eco-epidemiological settings in the humid tropics: Modular baseline health surveys

The quantitative assessment of health impacts has been identified as a crucial feature for realising the full potential of health impact assessment (HIA). In settings where demographic and health data are notoriously scarce, but there is a broad range of ascertainable ecological, environmental, epidemiological and socioeconomic information, a diverse toolkit of data collection strategies becomes relevant for the mainly small-area impacts of interest. We present a modular, cross-sectional baseline health survey study design, which has been developed for HIA of industrial development projects in the humid tropics. The modular nature of our toolkit allows our methodology to be readily adapted to the prevailing eco-epidemiological characteristics of a given project setting. Central to our design is a broad set of key performance indicators, covering a multiplicity of health outcomes and determinants at different levels and scales. We present experience and key findings from our modular baseline health survey methodology employed in 14 selected sentinel sites within an iron ore mining project in the Republic of Guinea. We argue that our methodology is a generic example of rapid evidence assembly in difficult-to-reach localities, where improvement of the predictive validity of the assessment and establishment of a benchmark for longitudinal monitoring of project impacts and mitigation efforts is needed.     

Prediction of the solubility of zinc, copper, nickel, cadmium, and lead in metal-contaminated soils

Risk assessment of metal-contaminated soil depends on how precisely one can predict the solubility of metals in soils. Responses of plants and soil organisms to metal toxicity are explained by the variation in free metal ion activity in soil pore water. This study was undertaken to predict the free ion activity of Zn, Cu, Ni, Cd, and Pb in metal-contaminated soil as a function of pH, soil organic carbon, and extractable metal content. For this purpose, 21 surface soil samples (0–15 cm) were collected from agricultural lands of various locations receiving sewage sludge and industrial effluents for a long period. One soil sample was also collected from agricultural land which has been under intensive cropping and receiving irrigation through tube well water. Soil samples were varied widely in respect of physicochemical properties including metal content. Total Zn, Cu, Ni, Cd, and Pb in experimental soils were 2,015?±?3,373, 236?±?286, 103?±?192, 29.8?±?6.04, and 141?±?270 mg kg^?1, respectively. Free metal ion activity, viz., pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺, as estimated by the Baker soil test was 9.37?±?1.89, 13.1?±?1.96, 12.8?±?1.89, 11.9?±?2.00, and 11.6?±?1.52, respectively. Free metal ion activity was predicted by pH-dependent Freundlich equation (solubility model) as a function of pH, organic carbon, and extractable metal. Results indicate that solubility model as a function of pH, Walkley–Black carbon (WBC), and ethylenediaminetetraacetic acid (EDTA)-extractable metals could explain the variation in pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺ to the extent of 59, 56, 46, 52, and 51 %, respectively. Predictability of the solubility model based on pH, KMnO₄-oxidizable carbon, and diethylenetriaminepentaacetic acid-extractable or CaCl₂-extractable metal was inferior compared to that based on EDTA-extractable metals and WBC.     

Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management

River systems consist of hydrogeomorphic patches (HPs) that emerge at multiple spatiotemporal scales. Functional process zones (FPZs) are HPs that exist at the river valley scale and are important strata for framing whole-watershed research questions and management plans. Hierarchical classification procedures aid in HP identification by grouping sections of river based on their hydrogeomorphic character; however, collecting data required for such procedures with field-based methods is often impractical. We developed a set of GIS-based tools that facilitate rapid, low cost riverine landscape characterization and FPZ classification. Our tools, termed RESonate, consist of a custom toolbox designed for ESRI ArcGIS®. RESonate automatically extracts 13 hydrogeomorphic variables from readily available geospatial datasets and datasets derived from modeling procedures. An advanced 2D flood model, FLDPLN, designed for MATLAB® is used to determine valley morphology by systematically flooding river networks. When used in conjunction with other modeling procedures, RESonate and FLDPLN can assess the character of large river networks quickly and at very low costs. Here we describe tool and model functions in addition to their benefits, limitations, and applications.     

Distribution of radionuclides in surface soils, Singhbhum Shear Zone, India and associated dose

Gamma emitters were estimated in surface soils from a mineralized zone in Eastern India using high purity Germanium detector-based high resolution gamma spectrometry system. Activities of ²³⁸U, ²²⁶Ra, ²³²Th, ²³⁵U, ²²⁷Th, ^234mPa, ²¹⁰Pb, ⁴⁰K, and ¹³⁷Cs were 79?±?50, 81?±?53, 65?±?23, 4?±?2, 5?±?4, 92?±?50, 97?±?45, 517?±?201, and 4?±?2 Bq/kg, respectively. Most radionuclides were observed to follow log-normal distribution. The correlation between physicochemical properties of the samples, like pH, organic matter content, particle size, and moisture content were also studied. Activity ratios of ²²⁶Ra/²³⁸U, ²¹⁰Pb/²²⁶Ra, and ²²⁷Th/²³⁵U indicated deviation from secular equilibrium in some samples. The associated annual effective dose ranged from 0.07 to 0.24 mSv and the mean was calculated to be 0.12?±?0.04 mSv for this region, indicating it to be one of normal natural background radiation.