ALOS PALSAR and Hyperion Data Fusion for Land Use Land Cover Feature Extraction

Detailed and enhanced land use land cover (LULC) feature extraction is possible by merging the information extracted from two different sensors of different capability. In this study different pixel level image fusion algorithms (PCA, Brovey, Multiplicative, Wavelet and combination of PCA & IHS) are used for integrating the derived information like texture, roughness, polarization from microwave data and high spectral information from hyperspectral data. Span image which is total intensity image generated from Advanced Land observing Satellite-Phase array L-band SAR (ALOS-PALSAR) quad polarization data and EO-1 Hyperion data (242 spectral bands) were used for fusion. Overall PCA fused images had shown better result than other fusion techniques used in this study. However, Brovey fusion method was found good for differentiating urban features. Classification using support vector machines was conducted for classifying Hyperion, ALOS PALSAR and fused images. It was observed that overall classification accuracy and kappa coefficient with PCA fused images was relatively better than other fusion techniques as it was able to discriminate various LULC features more clearly.     

Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

This study aims to illustrate how remotely sensed oceanic variables and fishing operations data can be used to predict suitable habitat of fishery resources in Geographic Information System. We used sea surface height anomaly (SSHa), sea surface temperature (SST), chlorophyll concentration (CC), photosynthetically active radiation (PAR) and fishing depth as predictor variables. Fishery data of Indian squid (Loligo spp.) and catfish (Tachysurus spp.) for study period (1998–2004) were segregated randomly to create training and validation. Catch was normalized into Catch per unit Effort (kg h^?1). Generalized additive modelling was performed on training data and then tested on validation data. Suitable ranges of SST, CC, SSHa and PAR for different species distributions were derived and integrated to predict their spatial distributions. Results indicated good match between predicted and actual catch. Monthly probability maps of predicted habitat areas coincide with high catch of the particular month for the study period.     

Ecosystem Services in a Transitional Forest Landscape: Shifting Trajectories in Southeast Michigan,USA

North American forests provide multiple ecosystem services, such as carbon storage, biodiversity, and recreation. These services are often coordinated through multifunctional management, whereby various users and owners contribute to collective agendas. Forests in exurban “transition” zones are crucial components in the sustainability of broader metropolitan landscapes, but represent a particularly understudied confluence of ecosystem services and multifunctional management. In this paper, we develop a place-based approach to assess ecosystem services in transitional forests (those between rural and urban). We demonstrate how trajectories of forest composition are linked with shifting ecosystem services that both shape and are shaped by management activities. Sited in Stinchfield Woods, a forest in southeast Michigan, this study draws on a household survey, interviews, ecological data, and archival information. Given variations in priorities over time and among different users, we suggest that coordinated, adaptive management may improve provisioning of ecosystem services in ways that benefit multiple users.     

A Technique of Rainfall Assimilation for Dynamic Extended Range Monsoon Prediction

Summary ?A methodology has been developed to assimilate satellite-measured rainfall during the initial phase of model integration for extended range monsoon prediction. The vertical profiles of latent heating corresponding to different rain rates have been derived from the model statistics. These heating rates have been assimilated through nudging in the thermodynamics equation of the model. This procedure of assimilating observed heating has corrected the simulation of heating location in the model and consequently removed the anomalous sinking motion over Indian landmass. With the correction of vertical circulation, both mean July rainfall over India and the distribution have improved. Interannual variability has been brought out for the years 1987 and 1988. In view of the availability of rainfall profile from Tropical Rain Measuring Mission (TRMM) there is a scope of adopting this method of assimilating observed rainfall, for extended range monsoon prediction. Received February 12, 1999/Revised May 4, 1999     

Compensation of time‐delay for control of civil engineering structures

Time‐delay is an important issue in structural control. Applications of unsynchronized control forces due to time‐delay may result in a degradation of the control performance and it may even render the controlled structures to be unstable. In this paper, a state‐of‐the‐art review for available methods of time‐delay compensation is presented. Then, five methods for the compensation of fixed time‐delay are presented and investigated for active control of civil engineering structures. These include the recursive response method, state‐augmented compensation method, controllability based stabilization method, the Smith predictor method and the Pade approximation method, all are applicable to any control algorithm to be used for controlled design. Numerical simulations have been conducted for MDOF building models equipped with an active control system to demonstrate the stability and control performance of these time‐delay compensation methods. Finally, the stability and performance of the phase shift method, that is well‐known in civil engineering applications, have also been critically evaluated through numerical simulations. Copyright © 2000 John Wiley & Sons, Ltd.     

Impact of Indian ocean dipole on the coastal upwelling features off the southwest coast of India

A three-dimensional regional ocean model is used to examine the impact of positive Indian ocean dipole (pIOD) events on the coastal upwelling features at the southwest coast of India (SWCI). Two model experiments are carried out with different surface boundary conditions that prevailed in the normal and pIOD years from 1982 to 2010. Model experiments demonstrate the weakening of coastal upwelling at the SWCI in the pIOD years. The reduced southward meridional wind stress off the SWCI leads to comparatively lower offshore Ekman transport during August–October in the pIOD years to that in normal years. The suppressed coastal upwelling results in warmer sea surface temperature and deeper thermocline in the pIOD years during June–September. The offshore spatial extent of upwelled colder (<?22 °C) water was up to 75.5° E in August–September in normal years that was limited up to 76.2° E in pIOD years. The heat budget analysis reveals the decreased contribution of vertical entrainment process to the mixed layer cooling in pIOD years which is almost half of that of normal years in October. The net heat flux term shows warming tendency during May–November with a higher magnitude (+?0.4 °C day^?1) in normal years than pIOD years (+?0.28 °C day^?1). The biological productivity is found to reduce during the pIOD years as the concentration of phytoplankton and zooplankton decreases over the region of coastal upwelling at SWCI. Nitrate concentration in the pIOD years dropped by half during August–September and dropped by an order of magnitude in October as compared to its ambient concentration of 13 μmol L^?1 in normal years.     

Atmospheric Correction Over Coastal Turbid Waters of Bay of Bengal Using OCEANSAT-I Ocean Colour Monitor (OCM) Data

Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules, ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived at 0.945, which is an improvement over the standard atmospheric correction procedure.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Establishing a predictive performance indicator for real‐time hybrid simulation

Real‐time hybrid simulation (RTHS) is increasingly being recognized as a powerful cyber‐physical technique that offers the opportunity for system evaluation of civil structures subject to extreme dynamic loading. Advances in this field are enabling researchers to evaluate new structural components/systems in cost‐effective and efficient ways, under more realistic conditions. For RTHS, performance metric clearly needs to be developed to predict and evaluate the accuracy of various partitioning choices while incorporating the dynamics of the transfer system, and computational/communication delays. In addition, because of the dynamics of the transfer system, communication delays, and computation delays, the RTHS equilibrium force at the interface between numerical and physical substructures is subject to phase discrepancy. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In this paper, a new performance indicator, predictive performance indicator (PPI), is proposed to assess the sensitivity of an RTHS configuration to any phase discrepancy resulting from transfer system dynamics and computational/communication delays. The predictive performance indicator provides a structural engineer with two sets of information as follows: (i) in the absence of a reference response, what is the level of fidelity of the RTHS response? and (ii) if needed, what partitioning adjustments can be made to effectively enhance the fidelity of the response? Moreover, along with the RTHS stability switch criterion, this performance metric may be used as an acceptance criteria for conducting single‐degree‐of‐freedom RTHS. Copyright © 2014 John Wiley & Sons, Ltd.     

Comparative analysis of contributing parameters for rainfall-triggered landslides in the Lesser Himalaya of Nepal

In the Himalaya, people live in widely spread settlements and suffer more from landslides than from any other type of natural disaster. The intense summer monsoons are the main factor in triggering landslides. However, the relations between landslides and slope hydrology have not been a focal topic in Himalayan landslide research. This paper deals with the contributing parameters for the rainfall-triggered landslides which occurred during an extreme monsoon rainfall event on 23 July 2002, in the south-western hills of Kathmandu valley, in the Lesser Himalaya, Nepal. Parameters such as bedrock geology, geomorphology, geotechnical properties of soil, and clay mineralogy are described in this paper. Landslide modeling was performed in SEEP/W and SLOPE/W to understand the relationship of pore water pressure variations in soil layers and to determine the spatial variation of landslide occurrence. Soil characteristics, low angle of internal friction of fines in soil, medium range of soil permeability, presence of clay minerals in soil, bedrock hydrogeology, and human intervention were found to be the main contributing parameters for slope failures in the region.