A. K. Saraf

Integrated remote sensing and GIS for groundwater exploration and identification of artificial recharge sites

IRS-LISS-II data along with other data sets have been utilized to extract information on the hydrogeomorphic features of a hard rock terrain in the Sironj area of Vidisha district of Madhya Pradesh, India. The study exhibits reservoir induced artificial groundwater recharge downstream of surface water reservoirs. IRS-LISS-II data have been supported by information derived from DEM, drainage and groundwater data analysed in a GIS framework. The present study attempts to select suitable sites for groundwater recharge in a hard rock area through recharge basins or reservoirs, using an integrated approach of remote sensing and GIS. Criteria for GIS analysis have been defined on the basis of groundwater conditions in the area and appropriate weightage has been assigned to each information layer according to its relative contribution towards the desired output. The integrated study helps in designing a suitable groundwater management plan for a hard rock terrain.

Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

The main purpose of this study is to compare the following six GIS-based models for susceptibility mapping of earthquake triggered landslides: bivariate statistics (BS), logistic regression (LR), artificial neural networks (ANN), and three types of support vector machine (SVM) models that use the three different kernel functions linear, polynomial, and radial basis. The models are applied in a tributary watershed of the Fu River, a tributary of the Jialing River, which is part of the area of China affected by the May 12, 2008 Wenchuan earthquake. For this purpose, eleven thematic data layers are used: landslide inventory, slope angle, aspect, elevation, curvature, distance from drainages, topographic wetness index (TWI), distance from main roads, distance from surface rupture, peak ground acceleration (PGA), and lithology. The data layers were specifically constructed for analysis in this study. In the subsequent stage of the study, susceptibility maps were produced using the six models and the same input for each one. The validations of the resulting susceptibility maps were performed and compared by means of two values of area under curve (AUC) that represent the respective success rates and prediction rates. The AUC values obtained from all six results showed that the LR model provides the highest success rate (AUC=80.34) and the highest prediction rate (AUC=80.27). The SVM (radial basis function) model generates the second-highest success rate (AUC=80.302) and the second-highest prediction rate (AUC=80.151), which are close to the value from the LR model. The results using the SVM (linear) model show the lowest AUC values. The AUC values from the SVM (linear) model are only 72.52 (success rates) and 72.533 (prediction rates). Furthermore, the results also show that the radial basis function is the most appropriate kernel function of the three kernel functions applied using the SVM model for susceptibility mapping of earthquake triggered landslides in the study area. The paper also provides a counter-example for the widely held notion that validation performances of the results from application of the models obtained from soft computing techniques (such as ANN and SVM) are higher than those from applications of LR and BA models.

A Landsat TM based comparative study of surface and subsurface fires in the Jharia coalfield, India

Landsat TM data were used to detect surface and subsurface fires in the Jharia coalfield (JCF). TM-6 was useful for mapping subsurface fires and TM-5 and TM-7 were useful for mapping surface fires. The distribution pattern of fires was studied and their temperature and areal extents were estimated. A comparison of the distribution of surface and subsurface coal fires indicated that at some sites fires occur only on the surface or only in the subsurface, while at other sites both surface and subsurface fires occur.

Cover: Satellite detects surface thermal anomalies associated with the Algerian earthquakes of May 2003

On 21 May 2003, Algeria was hit by a powerful shallow focus earthquake of magnitude Mw56.8 (http://neic.usgs.gov/neis/bulletin/03_EVENTS/eq_030521/) at 18:44 (UTC) which led to the death of 2276 people, injured more than 11 000 people and left 200 000 people homeless (http://www.reliefweb.int/w/ rwb.nsf/6686f45896f15dbc852567ae00530132/b49cf0730dc7884149256d480021ee90? OpenDocument). The geographical location of the epicentre was 36.90uN latitude and 3.71uE longitude (figure 1), just offshore from the province of Boumerdes, and about 60 km ENE of the capital city of Algiers. The province of Boumerdes, including the coastal city of Boumerdes, Thenia, Rouiba and the eastern district of Algiers are among the heavily damaged regions by this ‘Boumerdes earthquake’ (www.Reliefweb.int/w/ rwb.Nsf/6686f45896f15dbc852567ae00530132/b342fd7d3ea5bb285256d4000671dce? OpenDocument). The earthquake has been named after the worst hit region, Boumerdes in Algeria. Notable large-scale concrete structure damage was witnessed in this earthquake. Since Algeria’s independence from France in 1962, the country has seen a rapid rise in urbanization. There has been a growth in the building of concrete structures in the cities. The heavily damaged or collapsed buildings were mainly built within the last decade, some just completed or in the process of completion. Widespread liquefaction, rock falls, landslides and ground cracking were reported in the earthquake-affected region. However, no clear case of fault rupturing was reported (www-megacities.physik.uni-karlsruhe.de/wwwmega/downloads/QuakeReport1_2June03.pdf). A tsunami generated with an estimated wave height of 2 m caused damage to boats and underwater telephonic cables off the Balearic Islands, Spain (http://neic.usgs.gov/neis/bulletin/03_EVENTS/eq_030521/). This major earthquake was followed by a number of low intensity earthquakes (table 1), which continued till 29 May 2003. Minutes after the 6.8 magnitude earthquake played havoc in northern Algeria, a 5.7 Mw earthquake occurred at 18:51 (UTC), with an epicentre at 36.97uN latitude and 3.85uE longitude (http:// neic.usgs.gov/neis/bulletin/03_EVENTS/eq_030527/neic_uhbj_m.html). Other aftershocks of magnitude greater than 5 (Mw) occurred on 22, 27 and 28 May 2003. More than 21 aftershocks (table 1) were reported within nine days beginning from 21 May 2003, ranging in magnitude from 2.4 to 5.8 (Mw). Land Surface Temperature (LST) maps generated from thermal images of NOAA-AVHRR datasets can be used to monitor the Earth’s thermal regime for any

Landsat-TM data for estimating ground temperature and depth of subsurface coal fire in the Jharia coalfield, India

Abstract Coal fires are a ubiquitous problem in coal-mines, the world over. They burn our prime energy resource, lead to atmospheric pollution and render mining of coal hazardous. Processes leading to coal combustion and spread of subsurface fires are briefly examined in this paper and the role of remote sensing in surveillance of coal fires is presented. The present study aims at developing a quick method for estimating the temperature of the ground surface directly above subsurface coal fires. Utility of TM6 and TM7-band data for temperature estimation is briefly reviewed. It is argued that temperature calculations of surface anomalies related to subsurface fires can only be done on the basis of 8–14 μm band data, due to the low temperatures involved. In the Jharia coalfield, it is noted that subsurface fires in various coal-mines are associated with surface thermal anomalies, as has also been confirmed by ground checks. The pattern of TM6 data distribution and ground truth is used to isolate thermal an...

GIS based surface hydrological modelling in identification of groundwater recharge zones

Digital elevation model (DEM) is a storehouse of a variety of hydrological information along with terrain characteristics. In recent years, automatic extraction of drainage network from DEM with the help of Geographical Information System (GIS) has become possible and is now being practised the world over for hydrological studies. In the present study, a comparative analysis of the drainage network derived from DEM and drainage extracted from surveyed topographical maps has been carried out. A comparative analysis based on nearest neighbour analysis on an intersection theme of two drainage networks showed that there is clustering (randomness<1) existing at places which show potential groundwater recharge zones. The suitable groundwater recharge zones identified in the drainage comparative analysis also show good correlation with the suitable recharge maps derived from remote sensing and GIS based procedure. In this study, two different watersheds (a) Dwarkeshwar in Bankura district, West Bengal, India, and (b) Kethan in Vidisha districts of Madhya Pradesh, India have been taken to analyse for identification of suitable groundwater recharge zones. The drainage comparative analysis approach developed and tested successfully in the present study is quick and reliable for the identification of suitable groundwater recharge zones particularly in a hard rock terrain.

Cover: NOAA‐AVHRR detects thermal anomaly associated with the 26 January 2001 Bhuj earthquake, Gujarat, India

The earthquake of 26 January 2001, USGS magnitude Ms57.9 and epicentre at 23u239570 latitude and 70u189510 longitude (figure 1), struck the state of Gujarat at 8:46 a.m. (IST) while India was celebrating her 51st Republic Day. The death toll was estimated at 20 083 according to Gujarat Government figures and was accompanied by wide scale damage to the property and economy of the state of Gujarat. Places like Bhuj, Anjar, Bhachau and Rapar faced near total destruction and Gandhidham, Morvi, Rajkot and Jamnagar faced extensive damage to concrete structures. A total of 7633 villages of 181 talukas in 11 districts were affected (Saraf et al. 2002). Thermal channels (4 and 5) of AVHRR onboard the NOAA series of satellites can be used to monitor the Earth’s thermal regime. Prior to an earthquake, crustal

Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.

Remote sensing observations of pre‐earthquake thermal anomalies in Iran

Stresses acting before an earthquake in tectonically active regions can augment the near ground temperature of the region. Such changes detected through thermal remote sensing can provide important clues about future earthquakes. A post‐earthquake analysis through NOAA‐AVHRR data showed pre‐earthquake thermal anomalies prior to the Bam earthquake on 26 December 2003 and the Dahoeieh‐Zarand earthquake on 21 February 2005 in Iran. It was observed in these earthquakes that there was short‐term temporal increase in land surface temperature (LST) of the regions around the epicenters. The rise in temperature was about 5–10°C. Further, temperature variation curves prepared from air temperature data collected from several meteorological stations around epicentres confirmed the appearance of thermal anomalies prior to several earthquakes between February and March 2005 in Iran. The thermal anomalies went away along with the earthquake events. Release of greenhouse gases from rocks due to the induced pressure before earthquakes can create a localized greenhouse effect. Charge carriers in rocks can be free electrons, which dissociate under high pressure. When they again recombine to attain electron stability they release heat, which can increase the LST of the region.

Delineation of groundwater recharge sites using integrated remote sensing and GIS in Jammu district, India

With the increasing demands for water due to increasing population, urbanization and agricultural expansion, groundwater resources are gaining much attention, particularly in the Kandi region of Jammu district, which faces acute shortages of drinking water throughout the year. Groundwater development programmes need a large volume of data from various sources. This study used integrated remote sensing and geographic information system (GIS) techniques to provide an appropriate platform for convergent analysis of multidisciplinary data and decision making for artificial recharge to groundwater. Thematic maps were constructed using merged Linear Imaging Self Scanner (LISS)‐III and Panchromatic (PAN) remote sensing data and aquifer parameter thematic layers were prepared from conventional field data. The thematic layers of the aquifer parameters were integrated and a map showing the potential zones for artificial recharge to groundwater was generated. By superimposing the drainage network map over this artificial recharge zone map, and also considering the terrain conditions for artificial recharge, suitable sites for replenishing groundwater in the study area were identified.