Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas

Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.     

Topo-stress based probabilistic model for shallow landslide susceptibility zonation in the Nepal Himalaya

While dealing with slope stability issues, determining the state of stress and the relation between driving force and resisting force are the fundamental deterministic steps. Gravitational stresses affect geologic processes and engineering operations in slopes. Considering this fact, a concept of topo-stress evaluation is developed in this research and used to produce a shallow landslide susceptibility map in a model area. The topo-stress introduced in this research refers to the shear stress induced by the gravitational forces on the planes parallel to the ground surface. Weight of the material on a slope and friction angle of the jointed rock mass are the two fundamental parameters that are considered to govern topo-stress in this study. Considering topo-stress as a main factor for initiating shallow landslides, a GIS-based probabilistic model is developed for shallow landslide susceptibility zonation. An ideal terrain in central Nepal is selected as the study area for this purpose. Two event-based shallow landslide inventories are used to predict accuracy of the model, which is found to be more than 78 % for the first event-landslides and more than 76 % for the second event-landslides. It is evident from these prediction rates that the probabilistic topo-stress model proposed in this work is quite acceptable when regional scale shallow landslide susceptibility mapping is practiced, such as in the Himalayan rocky slopes.     

Spatial Distribution of Shear Wave Velocity for Late Quaternary Alluvial Soil of Kanpur City,Northern India

Empirical correlation between standard penetration resistance (SPT-N) and shear wave velocity measured by seismic downhole techniques are prepared of the alluvial soil of quaternary age for the Kanpur city. The Kanpur city is having seismic threat from Himalaya and it falls in seismic zone III according to seismic zones of India. Standard penetration test as well seismic downhole test has been carried out up to 30 m at twelve different locations of Kanpur city. The measured SPT-N values and shear wave velocity values are used to develop empirical correlation between SPT-N and shear wave velocity. The proposed correlations have been compared with the existing regression equations by various other investigators. It is found that the proposed correlation exhibit good performance (10 % error bar). Also the measured shear wave velocity has been used to prepare spatially distributed contour map of 50, 75 and 100 m/s using ArcGIS-9 software. It is observed that the shear wave velocity values for the northern part of Kanpur city vary from 125 to 825 m/s. In southern part, it is varying from 125 to 500 m/s where as in the central part of the city the shear wave velocity varies from 125 to 375 m/s. The eastern part of the city also shows some variation in shear wave velocity which ranges from 250 to 625 m/s. The western part of the city shows the variation of shear wave velocity from ≤125 to 500 m/s. The soil type of the study area are classified as per NEHRP and new Italian O.P.M.C classification system as B, C and D type soil with having site period of 0.1–0.9 s and Poisson’s ratio varying from 0.1 to 0.4.     

Evaluation of geotechnical properties of overburden dump for better reclamation success in mining areas

Coal-mining activities are precursors to land degradation, a part of which is directly related to the generation of huge quantity of overburden (OB) material. The problem of managing OB is mitigated through reclamation, which is directly associated with the geotechnical properties of these dumps. The main objective of this study was to assess the potential of an alternative management plan of OB by analyzing physical properties of these dumps. This alternative management plan of OB could enhance reclamation success by fostering conditions conducive for vegetation growth. This is especially required in old mining areas, where salvaging of topsoil was not done, and as a result, reclamation was affected. For the present study, 12 representative OB samples were collected from two open-cast coal mines (OCP-A and OCP-B) of Bastacolla area in Jharia Coalfield, Dhanbad, India. The samples were analyzed for different geotechnical properties. The results revealed that optimization of these parameters could help in enhancing the reclamation success of the area. From land-use classification, it was observed that about 242 ha of the study area was OB dump; if these areas are reclaimed successfully, then they can act as a potential source of carbon sequestration, and furthermore, they will improve the esthetics and the overall environmental quality at these degraded areas.     

Electrokinetic retention, migration and remediation of nitrates in silty loam soil under hydraulic gradients

Contamination of groundwater by nitrates leaching from intensive agricultural and livestock operations have become a major concern for surrounding communities that use groundwater as their water supply. High levels of nitrate in drinking water poses a significant risk to human health, i.e., methaemoglobinaemia (“blue baby” syndrome).

The traditional pump-and-treat method is ineffective in medium to fine-textured agricultural soils due to the low hydraulic conductivity. This paper presents the results of a laboratory experiment investigating the feasibility of using electrokinetic treatment in retaining, accumulating, moving and remediating nitrates in a silty loam soil under hydraulic gradients.

A hydraulic gradient of 1.25 was applied to the horizontal soil columns to simulate a groundwater movement system. The study was performed in two stages. During the first stage of the experiment, the anode located at the inflow end of the columns was able to retard the movement of nitrates even under a hydraulic gradient of 1.25. After 15 days of flow, the effluent nitrate concentration in the control column rose to 90 mg l⁻¹ while no nitrates were detected in the effluent from columns subjected to the electrokinetic treatment.

After 15 days, the polarity of the electrodes was switched and this second stage lasted another 20 days. The cathode near the inflow end promoted the conversion of nitrates entering the column to other forms. The anode near the outflow end promoted the migration and accumulation of negatively charged nitrate ions towards the outflow end. By the 12th day, the nitrate concentrations in the electrokinetically treated columns were brought down to <5 mg NO₃-N l⁻¹. Electrokinetic treatment retarded nitrate movement against a hydraulic gradient of 1.25 and effectively restored a medium-textured soil contaminated with NO₃-N.

The NO₂-N level remained below 1 mg l⁻¹ throughout the experiment. The hydraulic conductivity varied between 1.0E–7 and 3.6E–7 m s⁻¹. The current requirement varied between 3 and 6 mA.     

A multi-layered backpropagation artificial neural network algorithm for UV spectral classification

In this paper we present an application of an artificial neural network model based on a multi-layered backpropagation algorithm for spectral classification of UV data from the International Ultraviolet Explorer (IUE) low dispersion spectra reference atlas. The model used is similar to that of von Hippel et al. (1994), and is found to reduce the classification error as compared to the recently reported results on the same data set (Gulati et al. 1994b). The improved version of the network is much simpler in structure and the training time is reduced by a factor of almost 20. Such networks will prove very useful in efficient classification of large databases Subject headings: neural networks, stellar spectra, classification     

A model of producer incentives for livestock disease management

We examine the management of livestock diseases from the producers‘ perspective, incorporating information and incentive asymmetries between producers and regulators. Using a stochastic dynamic model, we examine responses to different policy options including indemnity payments, subsidies to report at-risk animals, monitoring, and regulatory approaches to decreasing infection risks when perverse incentives and multiple policies interact. This conceptual analysis illustrates the importance of designing efficient combinations of regulatory and incentive-based policies.