Flood risk analysis in the Kosi river basin, north Bihar using multi-parametric approach of Analytical Hierarchy Process (AHP)

The Kosi river in north Bihar plains, eastern India presents a challenge in terms of long and recurring flood hazard. Despite a long history of flood control management in the basin for more than 5 decades, the river continues to bring a lot of misery through extensive flooding. This paper revisits the flooding problem in the Kosi river basin and presents an in-depth analysis of flood hydrology. We integrate the hydrological analysis with a GIS-based flood risk mapping in parts of the basin. Typical hydrological characteristics of the Kosi river include very high discharge variability, and high sediment flux from an uplifting hinterland. Annual peak discharges often exceed the mean annual flood and the low-lying tracts of the alluvial plains are extensively inundated year after year. Our flood risk analysis follows a multi-parametric approach using Analytical Hierarchy Process (AHP) and integrates geomorphological, land cover, topographic and social (population density) parameters to propose a Flood Risk Index (FRI). The flood risk map is validated with long-term inundation maps and offers a cost-effective solution for planning mitigation measures in flood-prone areas.     

Multi-sensor Space-Borne Earth Observation Data for Characterizing Fluvial-Geomorphic Provinces: A Case Study from Kosi River,India

The dynamism of geomorphic provinces in fluvial systems present considerable ambiguities in mapping by remote sensing. This necessitates use of multiple satellite data to characterize such depositional provinces. We use, an integrated dataset to characterize the geomorphic provinces (e.g. active flood plain, older food plain, fan etc.) of the Kosi River (Bihar), India. This is done using contrast in spectral signatures derived from multispectral bands (of IRS-P6 LISS III), radiant temperature (from ETM+) and radar-roughness (from radar brightness image RISAT-1). ASTER DEM has been used in deriving topographic profiles. The optical imagery, enables regional characterization through direct tonal changes (e.g. active flood plain is brighter than older flood plain). The radiant temperatures show variations across provinces. Geomorphic transitions are represented by topographic breaks. Radar backscatter imagery, show differences in radar-return from different sub-provinces. Observations made using specific sensor characterize each provinces and is supplementary/complimentary to the parameter(s) from other sensors.     

Mumbai 2005, Bihar 2008 Flood Reflected in Mass Changes Seen by GRACE Satellites

Gravity Recovery And Climate Experiment (GRACE) satellites were launched on March 17 2002 to derive with unprecedented accuracy, estimates of the global high resolution model of the earth’s gravity field. Local gravity changes with change in mass or mass redistribution. The mass changes can be due to hydrological events, seismic events or postglacial rebound, majorly. GRACE is sensitive to changes at large spatial scale since the resolution of GRACE is 400 km. Hydrological activities over basins provide sufficient mass changes to be detected by GRACE. In this research paper the discussion would be about two major flooding events in India, one being the 2005 monsoon flooding in Mumbai and nearby states and other being flood experienced by Bihar in 2008. The GRACE data is in the form of matrix consisting spherical harmonic coefficients. These coefficients are processed to obtain mass changes in terms of equivalent water height at a spatial scale of 400 km. The strategy of analysis is also discussed which need to be followed depending upon limitations of GRACE observation and requirement of application, here in this case application is flood induced mass change detection. Time-series and residual plots are generated and they show the flooding events for the concerned area as outliers. Better visualisation is obtained by residual plot, if there is a trend or systematic behaviour in time-series. This work points towards the qualitative capability of GRACE to detect flooding events at large spatial scale. Quantitative analysis requires in-situ data over the period of GRACE which is not possible for the cases discussed here.     

Study of drainage pattern of Jharia Coalfield (Bihar), India, through Remote Sensing technology

Study of drainage pattern of Jharia Coalfield as observed on IRS-IA LISS II image shows that the region is drained by 11 streams with general flow direction to north east to south east and ultimately joining to trunk river Damodar. The perennial river Damodar flows from west to east and approximately marks the southern limit of the famous Jharia Coalfied. The average stream length in the region varies from 15 km to 110 km with average hasin area from 10 km² to 150 km². The general pattern is essentially a coarse dendritc with very gentle (0 to 1%) to sluggish stream flow condition over a gentle sloping (1 to 3%) topographic surface characteristic of old age streams in matured erosion terrain (Paleo-pediplain).     

基于GIS和RS的灾害管理系统实践

通过建立灾害管理系统来预防和处理灾害事件已成为一个有效的手段,并在实际中取得了一些成绩。本文在基于G IS和RS的技术环境下,针对灾害管理系统进行实践,提出了一些原则和方法。     

Run-off potential indices of watersheds in Tilaiya Catchment,Bihar (India) through use of remote sensing and implementation of GIS

The present study was aimed at assessing the potential of remote sensing data in providing input to the SCS model developed by USDA and exploring the possibilities of improving the model through implementation of a GIS package. Subsequently, prioritization of watersheds in Tilaiya Catchment area (Bihar, India) based on their run-off potential indices was envisaged. The results demonstrated the capability of Landsat data in providing multithematic maps which could be used to provide input to the run-off model. Implementation of the GIS package enabled computation of run-off potential indices on pixed-by-pixel basis imparting physically distributed approach to the model.     

Free software: A review,in the context of disaster management

This article examines the nature of freely available geospatial software and information systems in the context of disaster management. The use of geospatial data is crucial to effective disaster management, from preparedness to response and recovery. However, to make efficient use of available data and information – before, during and after a disaster – reliable software is required. The software applications examined in this paper range from Geographical Information Systems, to the processing of remotely sensed images, crowd-source mapping, web applications and content management systems. Trends and challenges are considered, and guidelines are given, to foster and encourage the provision of information by Freeware and Open Source Software. Free geoinformatics can help to optimize the limited financial, technological and manpower resources that many organisations face, providing a sustainable input to analytical activities.     

基于短信的时空地震灾情收集与管理

地震现场各种信自获取与传输是应急指挥工作中的重要内容,有关灾情的资料是政府部门防震减灾决策的重要依据.本文研究了震后灾情信息的分类与编码,探讨了灾情信息的时空特性,阐述了T-GIS在地震灾情信息获取与发布工作中的应用,在基态修正时空数据模型的基础上提出了基于短信的时空地震灾情收集与管理模型,采用ArcGIS二次开发技术...     

An evaluation of integrating multisourced sensors for disaster management

Disaster management and in particular disaster response phase are highly time-sensitive and dynamic processes, demanding that real-time information reaches disaster responders prior making critical decisions. During the last decade, disaster management has been widely enabled through utilizing spatial data sourcing and related technologies in the whole process of collection, access, and usage of disaster information. Currently, there are unique challenges that cannot be met without incorporating in situ sensing as an emerging technology for sourcing and managing disaster information. These include (1) high temporal and spatial resolution of information, (2) broad range of disaster data, and (3) automated operations. Incorporating in situ sensing into the disaster management process can potentially address such challenges by providing data that support all of these requirements. Following an examination of current concepts and methods for integrating multisourced sensors, a framework of the requirements for integrating in situ sensors for disaster management, is suggested. Based on this framework and its components, an evaluation of the methods is developed and applied. The results highlight that information integration of multisourced sensors is a major challenge and has not yet adequately addressed for sensor data enablement of disaster management.     

Soil,landuse and land evaluation studies of Patna area Bihar

The soil and landuse surveys have been conducted in Patna area, Bihar, using aerial photos of 1:25,000 scale. Three major systems, Ganges Gandak and interfluvial plain, have been identified in the area. These were further sub-divided into levees plains and channels etc. The soils were classified according to Soil axonomy. The major land use of the area is cultivation (62.2%) (Upland, lowland and wet land crops) plantation (1.8%) habitation (16.0%), water bodies (8.8%), barren lands (9.4%) and miscellaneous (1.8%). The soils of the area have been evaluated for different land utilization types-upland crops, lowland crops, and habitation. For paddy 75.:%, upland crops 8.2% and for habitation 57%, area was found suitable.     

Estimation of leaf area index using PROSAIL based LUT inversion,MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation,North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R² = 0.9, RMSE = 0.14), peak (R² = 0.87, RMSE = 0.21) and senescence (R² = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.     

Utilization of high-resolution EGM2008 gravity data for geological exploration over the Singhbhum-Orissa Craton,India

High-resolution EIGEN6C4 and EGM2008 Bouguer gravity data of 2190 degree spherical harmonic over the Singhbhum-Orissa Craton, India, have been generated from the International Centre for Global Earth Models. The Bouguer gravity anomaly difference maps of (i) in situ and EIGEN6C4, (ii) in situ and EGM2008 and iii) EIGEN6C4 and EGM2008 of the study area are compared. It reveals that EIGEN6C4 has lesser systematic error than EGM2008. However, from different profile plots of Bouguer gravity, east–west horizontal derivative and north–south horizontal derivative anomalies of the in situ, EIGEN6C4 and EGM2008, it is observed that most of the signatures of lithounits and geological structural elements are delineated very well by EGM2008 and match 94–98% with those of EIGEN6C4. Further, the Bouguer gravity, east–west horizontal derivative and north–south horizontal derivative anomalies of EGM2008 data over the study area have been used effectively for identifying various lithounits and geological structural elements.     

District wise yield models of rice in Bihar based on water requirement and meteorological data

Pre-harvest crop production forecast has been successfully provided by remote sensing technique. However, the probability to get cloud-free optical remote sensing data during kharif season is poor. Microwave data having the capability to penetrate cloud is used in the absence of cloud free optical remote sensing data. Yield models in broad band frequency range are in development stage. Meteorological yield models are developed and predicted yield is combined with area estimated by remote sensing data to provide rice production forecast. This paper describes the methodology adopted for improving the predictability of rice yield before harvest of the crop in Bihar province by taking into consideration meteorological parameters during its growth cycle upto October. Models developed using fortnightly meteorological data have been found to give reasonably fair indications of expected yield of rice in advance of harvest. The yield predictions have been made based on meteorological data and effective rainfall based on water requirement calculations representing a group of districts under similar agro-climatic zones, which could be further improved by incorporating meteorological data of individual districts within each group.     

Automatic search of geospatial features for disaster and emergency management

Although the fast development of OGC (Open Geospatial Consortium) WFS (Web Feature Service) technologies has undoubtedly improved the sharing and synchronization of feature-level geospatial information across diverse resources, literature shows that there are still apparent limitations in the current implementation of OGC WFSs. Currently, the implementation of OGC WFSs only emphasizes syntactic data interoperability via standard interfaces and cannot resolve semantic heterogeneity problems in geospatial data sharing. To help emergency responders and disaster managers find new ways of efficiently searching for needed geospatial information at the feature level, this paper aims to propose a framework for automatic search of geospatial features using Geospatial Semantic Web technologies and natural language interfaces. We focus on two major tasks: (1) intelligent geospatial feature retrieval using Geospatial Semantic Web technologies; (2) a natural language interface to a geospatial knowledge base and web feature services over the Semantic Web. Based on the proposed framework we implemented a prototype. Results show that it is practical to directly discover desirable geospatial features from multiple semantically heterogeneous sources using Geospatial Semantic Web technologies and natural language interfaces.     

志愿者微信地震灾情获取研究

针对现有的志愿者灾情信息获取方式进行了分析,将其分为主动式和被动式两大类。比较发现主动式获取不管是在灾情信息获取的精确度还是在数据处理的难度方面都存在明显的优势。在此基础上研究了基于微信的志愿者地震灾情获取框架与流程。融合地震灾情信息语义分析与公众口语化表达特点将地震灾情信息分为5类,并设计出适用于专业志愿者与普通公众进行灾情上报的灾情语义分类。最后,通过微信公众平台结合ArcGIS Server设计并实现了基于微信的志愿者地震灾情获取系统。     

Geological mapping of Jharia Coalfield,India using GRACE EGM2008 gravity data: a vertical derivative approach

High-resolution satellite gravity data of gravity recovery and climate experiment (GRACE) generated by Earth Gravity Model-2008 (EGM2008) have been utilised for geological mapping of the Jharia coalfield. The generated GRACE EGM2008 classical gravity data have been processed for estimation of gravity anomaly map. The gravity anomaly map has been enhanced using the first and second Vertical Derivatives techniques. Geological and structural maps of the study area have been overlapped over different derivative maps to analyse the correlation with the subsurface geological structures of the study area. Major distinct geological signatures, on different derivative maps, are correlated well with the existing geological map. Moreover, vertical derivative maps of the gravity data generated from GRACE EGM2008 model provide better agreement and understanding for geological setting of the Jharia coalfield.     

基于星载SAR的滑坡灾害形变特征分析

为准确监测重点滑坡的活动状态,预防潜在的地质灾害,本文基于4景ALOS-2数据,利用改进的Pixel-Tracking技术获取金沙江白格滑坡2017—2018年问3个连续时问段内的运动分布,试验结果显示,滑坡体运动存在显著的时空差异性,通过对不同时段的速度对比后显示,该滑坡在灾害发生前期滑动位移存在明显的加速趋势.     

Characterisation of Residue Burning from Agricultural System in India using Space Based Observations

Biomass burning is a global phenomenon with agriculture residue burning having a sizeable share. Biomass burning is a major source of emission of green house gases (GHGs). Thus the space-based observations of global distribution of fire form a key component of climate change studies. This study is a step towards understanding the spatio-temporal occurrence of agricultural residues burning in Indo-Gangetic plains of India using fire products from space borne satellites. The 3 years daily active fire data of MODIS (Aqua/Terra) from August, 2006 to July, 2009 have been used in this study. The data analysis showed that out of total fire events, around 69% contribution comes from agricultural areas and remaining (31%) comes from non-agricultural areas. This is mainly due to the intensive cultivation in this belt. The characterisation analysis revealed that, 84% of agriculture residues burning is from Rice-Wheat system (RWS) alone and remaining 16% in other types of crop rotations. The fire incidents were very high in October–December (55%) compared to that in March–May (36%), further indicating that burning of rice residue is more prevalent than that of wheat.