A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data

The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions. The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging Spectroradiometer Indian Pines hyperspectral benchmark data set and presented.     

Characteristics of cirrus clouds and its radiative properties based on lidar observation over Chung-Li, Taiwan

In this paper, characterization of cirrus clouds are made by using data from ground based polarization lidar and radiosonde measurements over Chung-Li (24.58°N, 121.10°E), Taiwan for a period of 1999–2006. During this period, the occurrence of cirrus clouds is about 37% of the total measurement nights over the lidar site. Analysis of the measurement gives the statistical characteristics about the macrophysical properties such as occurrence height, ambient temperature, and its geometrical thickness while the microphysical properties are interpreted in terms of extinction coefficient, optical depth, effective lidar ratio and depolarization ratio. The effective lidar ratio has been retrieved by using the simulation technique of backscattered lidar signals. The effect of multiple scattering has been taken into the account by a model calculation. Summer (Jun–Aug) shows the maximum appearances of cirrus due to its formation mechanism. It is shown that tropopause cirrus clouds may occur with a probability of about 24%. These clouds are usually optically thin and having laminar in structure with some cases resembling the characteristics similar to that of polar stratospheric clouds (PSCs). The radiative properties of the cirrus clouds are also discussed in detail by the empirical equations with results show a positive feedback on any climate change.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

Vulnerability and adaptation to climate variability and water stress in Uttarakhand State, India

This paper presents a participatory approach to investigate vulnerability and adaptive capacity to climate variability and water stress in the Lakhwar watershed in Uttarakhand State, India. Highly water stressed microwatersheds were identified by modelling surface runoff, soil moisture development, lateral runoff, and groundwater recharge. The modelling results were shared with communities in two villages, and timeline exercises were carried out to allow them to trace past developments that have impacted their lives and livelihoods, and stimulate discussion about future changes and possible adaptation interventions.     

Electrical Resistivity Imaging technique to delineate coal seam barrier thickness and demarcate water filled voids

Exploration and exploitation of coal seams is one of the major resources for the energy sector in any country but at the same time water filled voids/water logged areas in the old workings of these seams are very critical problems for the coal mining industry. In such situations, disasters like inundation, landslides, collapsing of the old seams may occur. In this regard, it is necessary to find out the water saturated/water filled voids and zones in the mining areas. Since no established technique is available to find such zones, an experimental study using Electrical Resistivity Imaging (ERI) has been carried out in one of the coal mining areas near Dhanbad, to find out the feasibility of finding the barrier thickness and the water logged area in underground coal mines. The area under study forms part of Jharia coalfield in Dhanbad district, Jharkhand state. The coal bearing rocks of Barakar Formation of Lower Permian age (Gondwana period) occur in the area under a thin cover (10 m to15 m) of soil and or alluvium. Coal bearing Barakar Formations consist mainly of sandstone of varying grain size, intercalation of shale and sandstone, grey and carbonaceous-shale and coal seams. Since the water saturation reduces the resistivity of a formation to a large extent, water filled voids and old coal workings are expected to have significant resistivity contrast with the surrounding host rock. Hence, ERI technique was applied in such an environment as this technique uses high-density data acquisition both laterally and vertically by using multiple number of electrodes. Along with ERI, mise-à-la-masse (also called charged body) technique was also employed at one of the promising sites to find out the connectivity of water logged areas and also detection of these old workings from the surface measurements was analyzed. The interpreted 2D resistivity sections have clearly indicated the water bearing zone(s) along the profile which was well confirmed with the existing water level in the nearby borewells. On the other hand, this technique did not identify the size of the coal pillar and gallery (air filled voids), which might be due to the small size of the voids (i.e. about 2 m × 2 m) below a depth of 15m and more but have indicated altogether as a high resistive zone ranging from 600–1000 Ohm-m.     

Light hydrocarbons geochemistry of surface sediment from petroliferous region of the Mehsana block,North Cambay Basin

A study was carried out to test the usefulness of surface geochemical methods as regional evaluation tools in petroliferous region of the Mehsana block, North Cambay Basin. A suite of 135 soil samples collected from the depth of 2.5 m, were analyzed for adsorbed light gaseous hydrocarbons and carbon isotopes (δ¹³C_methane and δ¹³C_ethane). The light gaseous hydrocarbon analysis show that the concentration ranges 402 ppb, 135 ppb, 70 ppb, 9 ppb and 18 ppb of C₁, C₂, C₃, iC₄ and nC₄, respectively. The value of carbon isotopic ranges of methane −29.5 to −43.0‰ (PDB) and ethane −19.1 to −20.9‰ (PDB). This data, when mapped, indicates patterns coinciding with major known oil and/or gas field of Sobhasan/Linch in this study area. The existence of un-altered petroliferous microseeps of catagenetic origin is observed in the study area. A regional study, such as the one described here, can provide important exploration facts concerning the regional hydrocarbon potential in a block. This method has been confirmed and can be applied successfully in frontier basins.     

Major element geochemical variations in a Miocene-Pliocene Siwalik paleosol sequence: Implications to soil forming processes in the Himalayan foreland basin

Elemental mobility based on major element geochemistry from 58 horizons related to six paleosols profiles in a typical Miocene — Pliocene Siwalik fluvial sequence in the NW Himalaya has been reported here. The paleosols developed over felsic parent material of fine to medium grained sandstone indicate notable enrichment of sesquioxides (Al₂O₃ = 29 % and Fe₂O₃ = 54 %) depicting significant leaching and dissolution. The depletion of base cations (mean wt% of Na₂O = 0.24; CaO = 0.51) and SiO₂ (mean wt% = 63.6) in the pedogenic layers and its enrichment in the parental material (mean wt% of Na₂O = 0.44; CaO = 1.3; SiO₂ = 70.1) shows a good gradient of elemental mobility due to pedogenesis. Bivariate plots of the base ratios (Na₂O/K₂O, CaO/K₂O, and MgO/K₂O) vs. Al₂O₃ reveal independent distribution for parent material, pedogenic horizons and the incipient zone indicating the gradual addition/removal of immobile/mobile elements with varying pedogenesis. Discontinuous and segmented pattern of the geochemical parameters enables discrimination of multiple pedogenic episodes and recognition of soil welding processes in the multistorey composite paleosols. We also test the applicability of the geochemical climofunctions: the Mean Annual Precipitation (MAP) and Mean Annual Temperature (MAT); that demands more data for calibration in the Siwalik paleosols.     

High-resolution satellite image segmentation using Hölder exponents

Texture in high-resolution satellite images requires substantial amendment in the conventional segmentation algorithms. A measure is proposed to compute the Hölder exponent (HE) to assess the roughness or smoothness around each pixel of the image. The localized singularity information is incorporated in computing the HE. An optimum window size is evaluated so that HE reacts to localized singularity. A two-step iterative procedure for clustering the transformed HE image is adapted to identify the range of HE, densely occupied in the kernel and to partition Hölder exponents into a cluster that matches with the range. Hölder exponent values (noise or not associated with the other cluster) are clubbed to a nearest possible cluster using the local maximum likelihood analysis.