Advanced Land Observing Satellite(ALOS) was successfuly launched on January 24th 2006 at Tanegashima Space Center by H-IIA rocket. The Satellite mass is about 4 tons of weight, Sun synchronous sub-recurrent orbit, repetation cycle is 46 days (sub cycle 2days) and 5 years mission operation. ALOS has four missions such as Cartography, Regional Observation, Resource Survey and Diasater monitoring. After the satelllite launch, there were several opportunities to observe natural disasters in the world.. ALOS will be distribute disaster information through international framework such as Sentinel Asia and International Charter on Disaster Monitoring. The 'Sentinel Asia' initiative was established by space agencies and disaster authorities in the Asia and Oceania, to use Remote Sensing information and Web-GIS data-delivery technologies in support of disaster management in the Asia-Pacific region. Sentinel Asia is 'voluntary and best-efforts-basis initiatives' led by the Asia-Pacific Regional Space Agency Forum (APRSAF) to share the disaster information in the region using the 'Digital Asia' (Web-GIS) platform. International Charter is the membership framework to operate the satellite in case of disaster occurs. and distribute the data and information free of charge. ALOS is nominated both activities to contribute disaster monitoring and mitigation. This paper describes the introduction of ALOS and acquired disaster images to indicate its potential use for disaster monitoring. The design of follow on mission is indispensable to promise continuous monitoring of natural disaster. This paper also describes the initial idea of ALOS follow on mission.

The 'Sentinel Asia' initiative was established by regional space agencies, to use Remote Sensing information and Web-GIS data-delivery technologies in support of disaster management in the Asia-Pacific region. Sentinel Asia is 'voluntary and best-efforts-basis initiatives' led by the Asia-Pacific Regional Space Agency Forum (APRSAF) to share the disaster information in the region using the 'Digital Asia' (Web-GIS) platform and to make the best use of earth observation satellites data for disaster management in the Asia-Pacific region. The system is to initially be an internet-based, node-distributed, information distribution backbone, eventually distributing relevant satellite and in-situ spatial information on multiple hazards in the Asia-Pacific region. The system will also be used by member countries of Sentinel Asia in the Asia-Pacific region to 'trigger' dedicated satellite-data acquisitions by regionally operated satellites through their participating and cooperating space agencies during major and minor disasters in their countries. Operations of Sentinel Asia are to be commenced in October 2006 through a dedicated web site.

Synthetic aperture radar (SAR) has the remarkable ability to examine the Earth's surface, regardless of weather or sunlight conditions. A SAR-based remote sensing system can assess the damage to areas affected by large-scale disasters at an early stage. This can aid in recovery planning. On May 27, 2006 an earthquake struck Yogyakarta, Central Java, Indonesia, causing human suffering and severe building damage. PALSAR (Phased Array Type L-band Synthetic Aperture Radar) onboard the Japanese ALOS (Advanced Land Observing Satellite) imaged the affected areas on the morning following the earthquake. The European satellite, Envisat, also imaged a wider area of central Java two days after the event. This paper applies a damage detection technique based on three time-series images from the SAR dataset covering the Mid Java earthquake. From a macroscopic point of view, the estimated damage distribution closely matched damage assessment derived from high-resolution satellite images and field surveys.

Tropical cyclones are one of the most destructive natural disasters occurring frequently in coastal India. The socio economic impacts of these tropical cyclones are high as they result in enormous loss of life and property every year. In the present study, pre event visible-near IR images and post event Radarsat images were procured and used to identify completely submerged landcovers temporally. The methodology is developed considering a case study on the Kendrapara district of Orissa state, which was hit by a cyclone on 29-30^th October 1999. The pre event IRS 1D LISS III (resolution = 22m) image of Kendrapara district was procured geometrically corrected and classified into several landuse and landcover classes. For landuse/landcover classification, supervised classification technique was used. This georeferenced landuse/landcover map provided the baseline information for the district. Next step involved procurement of immediate temporal post-event SAR images of the cyclone-affected district. These images were geometrically corrected and cleaned for speckle noise. Deterministic approach was used to set up threshold for classifying pixel as completely submerged under water or non submerged for Radarsat SAR images i.e. Radarsat SAR images exactly delineated areas completely submerged under water due to cyclonic floods. This type of analysis will help policy makers in determining the extent of submergence and damage. This methodology would be used as a rapid tool to assess damage. Further, this will help in expediting the release of relief funds as well as aid proper allocation of funds to the affected areas/people.

In this paper, 30 years conventional data of China are processed, the anomaly of precipitation, land surface temperature and air temperature are calculated and their relations are analyzed by using regressive statistics analysis and Singular Value Decomposition (SVD). The result shows that precipitation anomaly has a good negative correlation to both surface temperature anomaly and air temperature anomaly. Moreover, 20 years satellite brightness temperature anomaly and the same period precipitation anomaly are also calculated and analyzed; the similar result is obtained. It indicates that brightness temperature anomaly is an important factor for drought monitoring by using remote sensing data. Moreover, compared with historic data, the change of Normalized Difference Vegetation Index (NDVI) is another factor for drought monitoring. Drought index is formed by these two factors normalization and mean in weight. This remote sensing method on drought was used to some experiments and the results show that the drought distribution on space is very similar, compared with conventional drought index. Now this method is being used in operational system on drought monitoring in National Satellite Meteorological Center (NSMC), China meteorology administration (CMA).

AQUA AMSR-E L3 soil moisture data of 5 years (2002-06) were analyzed over Gujarat, India for flood analysis. We have used a threshold value (Gujarat 40% and Rajasthan desert 20%) for soil moisture to estimate flood affected area. Time series daily soil moisture data during flood period in Gujarat shows that soil moisture above threshold has been observed during 2004-06. The estimated flood area in Gujarat was 8418, 22618, 6313 sq. km in 2004, 05 and 06 respectively. Recent 2006 flood in Barmer, Rajasthan shows 7366 sq. km flood affected area with 20% as threshold soil moisture. The flood-area correlates well with rainfall data and surface soil moisture distribution. As the resolution of AMSR-E (60 km) is very poor, the area estimation is not so accurate. But the soil moisture trend clearly shows variation in flood affected area with days. The results from this method are to be compared with methods available using other data sets or techniques. Fixing a threshold value for soil moisture of a particular test site is also very important in the estimation of flood affected area.

With nearly 30 years of the accumulated AVHRR data which were collected from NOAA operational polar-orbiting environmental satellites, the area of their applications expanded in the direction of agricultural production modeling, understanding of climate and global change, resource management, and early and more efficient monitoring of the environmental impacts (especially droughts) on economy and society. This becomes possible due to development of Vegetation Health indices (VHI). This paper discusses utility of AVHRR-based VHI for modeling crop and pasture yield with specific emphasis on early drought warning and estimation of losses in agricultural production.

Forest fires cause major losses to forest cover and disturb the ecological balance in our region. Rise in temperature during summer season causing increased dryness, increased activity of human beings in the forest areas, and the type of forest cover in the Garhwal Himalayas are some of the reasons that lead to forest fires. Therefore, generation of forest fire risk maps becomes necessary so that preventive measures can be taken at appropriate time. These risk maps shall indicate the zonation of the areas which are in very high, high, medium and low risk zones with regard to forest fire in the region. In this paper, an attempt has been made to generate the forest fire risk maps based on remote sensing data and other geographical variables responsible for the occurrence of fire. These include altitude, temperature and soil variations. Key thematic data layers pertaining to these variables have been generated using various techniques. A rule-based approach has been used and implemented in GIS environment to estimate fuel load and fuel index leading to the derivation of fire risk zonation index and subsequently to fire risk zonation maps. The fire risk maps thus generated have been validated on the ground for forest types as well as for forest fire risk areas. These maps would help the state forest departments in prioritizing their strategy for combating forest fires particularly during the fire seasons.

The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information Service (NESDIS) as an interactive tool to identify fires and the smoke emissions they produce over North America in an operational environment. The system utilizes 2 geostationary and 5 polar orbiting environmental satellites. Automated fire detection algorithms are employed for each of the sensors. Analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a thorough examination of the satellite imagery. Areas of smoke are outlined by the analyst using animated visible channel imagery. A quantitative assessment of the smoke concentration is not performed at this time. However, integration of automated aerosol and smoke products into the HMS, such as the Geostationary Operational Environmental Satellite (GOES) Aerosol and Smoke Product (GASP) and the MODIS aerosol product in early 2006 and the aerosol product from the Ozone Monitoring Instrument (OMI) in late 2006 are expected to aid in providing smoke concentrations and identifying areas of smoke. HMS analysts also denote fires that are producing smoke emissions detected in satellite imagery as well as the start and end times of the emissions. These fire locations are used as input to the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. HYSPLIT utilizes a dynamic emissions rate for these fires as specified by the Blue Skies framework.

Since last few years, ISRO has embarked upon the development of two complex Synthetic Aperture Radar (SAR) missions, viz. Spaceborne Radar Imaging Satellite (RISAT) and Airborne SAR for Disaster Mangement (DMSAR), as a capacity building measure under country's Disaster Management Support (DMS) Program, for estimating the extent of damage over large areas (~75 Km) and also assess the effectiveness of the relief measures undertaken during natural disasters such as cyclones, epidemics, earthquakes, floods and landslides, forest fires, crop diseases etc. Synthetic Aperture Radar (SAR) has an unique role to play in mapping and monitoring of large areas affected by natural disasters especially floods, owing to its unique capability to see through clouds as well as all-weather imaging capability. The generation of SAR images with quick turn around time is very essential to meet the above DMS objectives. Thus the development of SAR Processors, for these two SAR systems poses considerable challenges and design efforts. Considering the growing user demand and inevitable necessity for a full-fledged high throughput processor, to process SAR data and generate image in real or near-real time, the design and development of a generic SAR Processor has been taken up and evolved, which will meet the SAR processing requirements for both Airborne and Spaceborne SAR systems. This hardware SAR processor is being built, to the extent possible, using only Commercial-Off-The-Shelf (COTS) DSP and other hardware plug-in modules on a Compact PCI (cPCI) platform. Thus, the major thrust has been on working out Multi-processor Digital Signal Processor (DSP) architecture and algorithm development and optimization rather than hardware design and fabrication. For DMSAR, this generic SAR Processor operates as a Quick Look SAR Processor (QLP) on-board the aircraft to produce real time full swath DMSAR images and as a ground based Near-Real Time high precision full swath Processor (NRTP). It will generate full-swath (6 to 75 Kms) DMSAR images in 1m / 3m / 5m / 10m / 30m resolution SAR operating modes. For RISAT mission, this generic Quick Look SAR Processor will be mainly used for browse product generation at NRSA-Shadnagar (SAN) ground receive station. RISAT QLP/NRTP is also proposed to provide an alternative emergency SAR product generation chain. For this, the S/C aux data appended in Onboard SAR Frame Format (x, y, z, x', y', z', roll, pitch, yaw) and predicted orbit from previous days Orbit Determination data will be used. The QLP / NRTP will produce ground range images in real / near real time. For emergency data product generation, additional Off-line tasks like geo-tagging, masking, QC etc needs to be performed on the processed image. The QLP / NRTP would generate geo-tagged images from the annotation data available from the SAR P/L data itself. Since the orbit & attitude information are taken as it is, the location accuracy will be poorer compared to the product generated using ADIF, where smoothened attitude and orbit are made available. Additional tasks like masking, output formatting and Quality checking of the data product will be carried out at Balanagar, NRSA after the image annotated data from QLP / NRTP is sent to Balanagar. The necessary interfaces to the QLP/NRTP for Emergency product generation are also being worked out. As is widely acknowledged, QLP/NRTP for RISAT and DMSAR is an ambitious effort and the technology of future. It is expected that by the middle of next decade, the next generation SAR missions worldwide will have onboard SAR Processors of varying capabilities and generate SAR Data products and Information products onboard instead of SAR raw data. Thus, it is also envisaged that these activities related to QLP/NRTP implementation for RISAT ground segment and DMSAR will be a significant step which will directly feed into the development of onboard real time processing systems for ISRO's future space borne SAR missions. This paper describes the design requirements, configuration details and salient features, apart from highlighting the utility of these Quick Look SAR processors for RISAT and DMSAR, for generation of emergency products for Disaster management.

A large-scale earthquake with a magnitude of 7.6 occurred on October 8, 2005 in the northern part of Pakistan. By the analysis using high-resolution images from IKONOS and SPOT-5 satellites, we clarified that the slope failures caused by the earthquake were concentrated on the northeastern side of the earthquake faults. In addition, GSI detected the existence of surface rupture of the earthquake faults. In this paper, the authors explain the difference of interpretation characteristics of high-resolution satellite imageries, between 2.5-meter class resolution and 1-meter class, and between single imagery and stereo pair imageries. The targeted features for interpretation are such as landslides, surface earthquake faults, damaged buildings and land liquefaction. In addition, the interpretation characteristics of ALOS PRISM for disaster monitoring are reported, in case of the Middle Java earthquake.

The building damage detection technique which we have developed has been successfully applied to past events such as the earthquakes in Kobe in 1995, India in 2001, and Bam in 2003 by using the compound index, z-value, a value derived from the correlation and difference in intensities between pre- and post-event SAR images. This technique was applied to the areas affected in the Niigata-ken Chuetsu earthquake of October 23, 2004 by using one pair of Radarsat images taken before and after the earthquake. However, it was not possible to identify any significant distribution of damaged buildings. In our study, we examined the reasons for that and proposed a new technique that uses two pairs (pre-seismic and co-seismic) of SAR images to identify smaller building damage ratios in less densely built-up areas as compared to the previous technique. The main idea is to minimize the effects of signal noise and temporal changes of the earth's surface on building damage estimation by calculating the difference values of the two pre-event images and one postevent image. From a macroscopic point of view, the distributions of both difference values of the z-value and the correlation coefficient in built-up areas in Niigata-ken Chuetsu region were in good agreement with damage reported in survey reports. In former Yamakoshi village, located in the highlands, we could also identify large-scale landslides with accuracy as good as that of interpretation from aerial photos.

The differential SAR interferometry (DInSAR) has been increasing used to monitor ground surface displacements, which may be caused by various natural disasters such as earthquakes, landslides, mining activities, avalanches etc. Conventionally, these displacements were being estimated through field measurements, which are time consuming, hazardous and with data collected over few point locations. Since all the development and rehabilitation works after a natural disaster strikes is carried out on regional basis, any information at spatial level is advantageous in planning, management and monitoring activities. In recent years, the application of Differential SAR interferometry is gaining momentum to estimate the surface displacements at millimeter level accuracy. The displacement maps produced via this technique provide information at spatial level in the region thereby assisting in judicious developmental and planning works in an efficient and cost-effective manner. The aim of this paper is provide an overview of the use of Differential SAR Interferometry (DinSAR) technology for the study of surface displacements. As a case study, land subsidence occurred due to coal mining in Jharia coal fields, Jharkhand, have been estimated through this technique. All the procedural steps in implementing the approach based on DinSAR have been explained in a simplified manner.

This paper presents a newly developed multi-level detection methodology using high-resolution optical satellite images. It aims to balance the quick response requirement and the details of detected results and hence, to satisfy various user demands. Damage extent is firstly detected from only post-disaster image on the first level, texture-based processing. This level quickly maps the damage extent and damage distribution but not in details. In some focused areas, the second level with object-based processing will derive further details of the damage using both pre- and post- data. The methodology is demonstrated on QuickBird images acquired over the damage areas of Bam, Iran, which was extensively devastated by the December 2003 earthquake. The detected results show a good agreement with the ones by visual detection and field survey.

It is well known that natural disasters such as earthquakes, landslides, floods, etc. cause enormous damage to lives and property. The assessment of risk as a potential for adverse consequences, loss, harm to human population due to the occurrence of natural disasters, particularly the landslides in Himalayan region therefore becomes imperative. Landslide risk assessment (LRA) techniques can be applied at different stages in the decision-making process, starting from developmental planning on a regional scale to a particular site evaluation at local scale. The LRA depends on the probability of landslide hazard and the vulnerability of risk elements. The landslide probability depends on both the preparatory (i.e., inherent ground characteristics) and triggering (i.e., earthquake and rainfall) factors. Vulnerability may be defined as the level of potential damage, or degree of loss, of risk elements subjected to landslide occurrences. The assessment of vulnerability is somewhat subjective and on a regional scale it is largely based on the importance of risk elements in human society. Hence, the appropriate vulnerability factor may be assessed systematically by expert judgment. In the present study, a linguistic rule based fuzzy approach is developed and implemented to prepare the landslide risk assessment (LRA) of Darjeeling Himalayas. The LRA has been considered as a function of landslide potential (LP) and resource damage potential (RDP), which have been characterized by the landslide susceptibility zonation (LSZ) map and the resource map (i.e., land use land cover map including the road network) of the area respectively. Fuzzy membership values representing LP and RDP have been assigned to different categories of LSZ and resource maps based on the criteria developed on a linguistic scale. Landslide risk assessment matrix (LRAM) has been generated as a function of the fuzzy membership values, which reflects the relative risk values for different combinations of landslide potential and resource damage potential. These landslide risk values have been classified into six different zones namely, no risk, very low risk (VLR), low risk (LR), moderate risk (MR), high risk (HR) and very high risk (VHR) to ultimately prepare LRA map of the area. Based on this map, a risk management action plan may be suggested to avoid the possible risk to the resources available in the area.

The stratigraphically important Tawaghat - Jipti Route corridor along Kali River Valley in Pithoragarh district of Uttaranchal State is characterized by formidable physical features. The lofty hill ranges, steep valleys, cliffs, gorges and huge accumulation of scree and debris mass owe their origin to complex physical, geologic and tectonic processes. Being a part of the active Himalayan orogenic belt, the natural hazards viz. landslides and earthquakes forms an integral part of the study area. In the investigated area, landslides are by far the most significant natural hazard in terms of damage caused to lives and properties. Landslides in the study area are triggered both due to natural phenomena (high rainfall, seismicity) and anthropogenic activities (road development and deforestation). Commonly observed slope failures include block slide, debris slide and earth creep. The presented study aims to develop a methodology that could produce a hazard map over a large area with higher degree of accuracy in a GIS environment; incorporating utility of information theory in landslide hazard zonation. In all, 37 variables are identified as conditioning and triggering factors and accordingly probabilistic prediction map is prepared by this method. On the basis of histogram distribution, the polygon elements are classified into five hazard classes viz. very low (Ij <= -0.02), Low (-0.02 < Ij < 0.103) moderate (0.10 0.40) landslide hazard prone zones. Further, this probabilistic prediction map is compared with the actual landslide map generated from recent satellite data (IRS ID LISS-III+PAN, December 2002) for the accuracy of prediction. The generated hazard maps agree with the observed landslide incidences. Thus, this adopted approach effectively proves its efficacy in deriving a reliable landslide hazard zonation.

Flooding continues to exact a significant economic and humanitarian toll worldwide. Rainfall estimates from satellite data represent an important source of information for monitoring and predicting these events, particularly in regions where radar data are unavailable and the rain gauge network is unsuitable for smaller-scale applications. This paper will present several real-time satellite-based rainfall estimation and forecasting techniques that are in use at NOAA/NESDIS that take advantage of the global coverage offered by both the geostationary and polar-orbiting satellite constellations. One is the Hydro-Estimator rainfall algorithm, which produces 4- to 5-km resolution estimates of rainfall at sub-hourly time scales from geostationary infrared data. Another is the Tropical Rainfall Potential (TRaP) algorithm, which produces 24-hour forecasts of rainfall from landfalling tropical cyclones based on extrapolation of current microwaveestimated rain rates along the predicted storm track. Examples of these and other techniques will be presented, along with future advances that are anticipated as new instruments become available on upcoming satellite missions.

Drought is the major disaster, which occurs in some part of India every year due to monsoon variability. India has established satellite based National Agricultural Drought Assessment and Monitoring System (NADAMS), at National Remote Sensing Agency, Department of Space since 1987. NADAMS provides near real time monitoring and early warning of drought conditions at National level using NOAA AVHRR and at regional level using IRS WiFS and AWiFS data. ISRO-NASA-NOAA science cooperation project has been initiated during 2005 for development of satellite based decision support drought monitor system in India. Initially, the evaluation of GVI based indices for drought early warning in India was taken up. The study was carried out over five small regions each covering part of a district and over five large regions each covering few districts in each state of Gujarat, Maharashtra and Rajasthan states and the result of the study is presented in this paper. The weekly GVI based indices such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) for the period from 1991-2004 over 5 small regions covering part of districts namely Banaskantha district of Gujarat state to represent Bajra crop, Surendra nagar district of Gujarat state to represent Cotton crop, Nasik district of Maharashtra to represent Bajra crop, Bhandara district to represent Rice crop and Akola district of Maharastra to represent Jowar crop was selected. The weekly GVI based indices over 5 large regions with larger database from 1981 to 2004 covering few districts of Rajasthan state to represent winter wheat and few districts of Maharashtra state to represent Jowar, Rice and Cotton crops were selected. The comparison of seasonal average VCI, TCI and VHI with the corresponding crops yield over 5 small regions indicate better regression coefficient for VHI than VCI or TCI. The comparison over 5 large regions covering larger data base from 1982-2004 indicate better regression coefficient for VCI than VHI or TCI. Results of the study suggests over smaller region, the VCI and TCI combined VHI indices relates better with crop yields, whereas over larger region, the VCI itself relates better with crop yields than with TCI or the VCI and TCI combined VHI.

China is one of the main soybean production countries in the world and soybean is of great importance in agricultural industry, domestic consumption and international trade. In recent years, however, China has become the largest soybean importer in the world. Therefore timely credible information about soybean planting area and production is essential for government decision making and agricultural management on domestic consumption and international trade. Moreover, information on soybean planting and continuous planting location is critical for distributing farmer subsidies and production management. In this paper, an operational system based on multi-resolution remotely sensed data was developed for the soybean area inventory and continuous cropping area monitoring. A stratified sampling method is employed to extract and locate major soybean-planting regions, which are later surveyed using remote sensing data. At the same time, sub regions are constructed based on cropping systems in which remotely sensed data of different resolutions are applied for the soybean area estimation and replanting area location assessment.

An evaluation of C- (developed at SAC) and X- band (data of opportunity) Airborne SAR (ASAR) data was done for flood signatures. The data was collected over parts of a perennially flood affected area of Darbhanga district located in the Bihar State. This activity is carried out as an R&D effort under the Disaster Management Support Programme (DMSP) at SAC. The ASAR data was critically analyzed for identifying flood features and with an aim to develop it finally as a system dedicated for DMS. The need for such a system is important as many a times there are gaps in satellite coverage over tropical regions for flood damage assessment besides it was observed that the varying flight directions provided additional information as regards the flood signatures. Both the airborne data were available in near synchronous time frame and with VV polarization. A comparison of C- and X- band ASAR showed that the inundated area was estimated better in X- band (62.8 %) as compared to C- (50.2 %) and that the combined data set gave further improvement (71.0 %).

Since June, 2004, studies on triggering factors of the red tide have been carried out in Awara Space Radio Observatory (ASRO), Fukui University of Technology utilizing directly received data of MODIS on the Terra and Aqua satellites which have been acquired in ASRO. Preliminary results of the data analyses for the period from July, 2001 to April, 2005 indicate conditions, for the appearance of the red tide bloom in Wakasa bay as follows: (1) the threshold amount of chlorophyll-a is close to 1.5mg/m³, (2) the range of sea surface temperature (SST) is limited in a range from 12 to 20^oC and (3) the period of sunlit time in spring is also a significantly sensitive factor. We propose here to utilize MODIS band1 images corresponding to a red band with spatial resolution of 250m together with NDVI (Normalized Difference Vegetation Index) images which has also spatial resolution of 250m, for the confirmation of the red tide. The problem of coincidence between colored region due to SS (Suspended Sediment) and red tide region using only band1 of MODIS, has been solved by using NDVI images in addition to band1 images together as two dimensional diagram.

The drought affects agricultural crops by diminishing amount of water necessary for vegetation. Deficit of soil moisture in specific vegetation growth stage causes the reduction in crop yield. The research, which has been carried out in Poland, gives consistent information on soil and vegetation growth over agricultural regions using various satellite-derived soil - vegetation indices. A wide range of ground-based measurements such as soil moisture, leaf area, and biomass were collected on nearly same dates of satellite overpass. The different soil moisture indices have been calculated on the basis of evapotranspiration derived from the surface temperature obtained from NOAA/AVHRR and meteorological data. The temperature condition index (TCI) characterising the status of crop development has been obtained from Global Area Coverage (GAC) data derived from NOAA images. Furthermore, latent heat fluxes and NDVI values have been calculated and implemented as the input to the models. This paper describes the analysis and results of a study to improve the detection and monitoring of drought conditions.

Using conjunctively electro-optical and radar systems has been a part of India's Earth Observation (EO) strategy for disaster management. To address the gaps in the operational systems of disaster management, increasingly improved quality of information in terms of spatial scale, temporal scale and all weather capability mapping are called for and the EO satellites have accordingly been configured. For example, CCD camera (1 km spatial resolution) in GEO orbiting INSAT satellites, which work in conjunction with polar orbiting IRS WiFS (188 m spatial resolution) for real time coarse observations of the events such as forest fire, floods etc is in operation. To address the subtle features associated with agricultural drought, Resourcesat has been configured with Advanced WiFS having 55 m spatial, 5 days repetativitity, 740 km swath and 10 bits radiometry. It is a unique mission with variety of payloads viz., AWiFS, LISS 4 (5.8 m multi-spectral; 22 days repetativitity) and PAN from the same platform. The Digital Elevation Models (DEM) emanating from Cartosat are providing valuable inputs to characterize geo-physical terrain vulnerability. Radar Imaging Satellite (RISAT), with all weather capability mission, is yet another mission configured for disaster management. Taking into account the flood dynamics as well as the river basin parameters, RISAST is being configured with multiparametric C-band SAR with 5 imaging modes; 1-2 m spatial resolution; 224 km swath; 7 days repetitivity and 8 bits quantizations. Integrating these capabilities, space based Disaster Management Support (DMS) systems, in India, has been built upon committing EO enabled products and services for disaster reduction on operational basis.

On 18 July 2004, a localized heavy rainfall occurred in the Fukui area, which is located on the Japan-Sea side of the Japan Islands, in conjunction with the intensified Baiu-front. The Fukui heavy rainfall resulted in many disasters, e.g., landslide, debris flow, dyke-break and flood, especially in the Reihoku district of Fukui Prefecture. One of the most damage was caused by water from a broken dyke of Asuwa-river in Fukui city. Moreover, a lot of damage of the field and the house by the flood and the debris flow occurred in the upstream region in the Asuwa-river. This paper discusses the Fukui heavy rainfall from a view point of remote-sensing. Remote-sensing data of Goes 9, MODIS, ASTER, IKONOS and Aerial orthophotograph are used. The movement of the cloud by Goes 9 images is corresponding at the place and the time of the downpour. The height of the cloud at the downpour is examined by using MODIS thermal band data. The false-color images and the NDVI images of ASTER and IKONOS before and after the downpour are compared, and the damage part is detected. The false-color images and the NDVI images of ASTER and IKONOS are useful for the disaster detection of the downpour, because the influence of the plant and muddy water can be used.

In many countries like India, risk analysis is limited to hazard mapping, showing areas where different levels of hazard can be expected. The available risk information is usually at too limited in spatial and temporal resolution to provide useful information on increasingly complex and dynamic risk patterns. Risk maps, based on coarse resolution Earth Observation (EO) data, give the impression of uniform hazard and vulnerability patterns over wide areas. As such risk is quite complex and dynamic. Risk analysis strategies have normally been restricted to the physical aspects. In most countries it is extremely rare to find risk analysis to take account of the social, economic, institutional and cultural aspects of vulnerability. The absence of conceptual and spatial models capable of representing the social, economic and cultural dimensions of vulnerability is another problem. Many aspects of vulnerability are difficult to quantify. The development of advanced models is still at the frontier of geo-informatics research, with the result that there are still no tried and tested procedures available for building social vulnerability aspects into risk information systems. The present paper suggests couple of approaches wherein multi-date EO data have strategically been used for risk assessment due to floods and drought.

It is important for efficient post disaster management to develop a methodology to identify affected areas from remote sensing data. Inundation of the tsunami generated by the 2004 Sumatra earthquake caused severe damage in coastal areas in Sri Lanka, located 1,500km away from the epicenter. As the preliminary stage for extraction of the tsunami inundation areas from high-resolution satellite images, the characteristics of the pre- and post-event images in Batticaloa city, the eastern part of Sri Lanka, are examined. Normalized difference vegetation index (NDVI) calculated from the images is utilized since the densities of the vegetations in the affected area are decreased due to the tsunami inundation. The distribution of the difference of NDVI between pre- and post-event images is compared with the actual inundation area observed in the field survey. The result shows that the difference in the inundation areas shows larger than that in the non-inundation areas. The areas that NDVI is remarkably decreased after the event are extracted as the inundated areas. The distribution of the extracted areas correlates with the actual inundation area.

Empirical distribution functions were applied for improving stability of NDVI data for NOAA environmental satellites. This paper investigates NDVI stability in the NOAA/NESDIS Global Vegetation Index (GVI) data set during 1982- 2003, in the period, which includes five NOAA series satellites. Degradation of NDVI over time and NDVI's shifts between the satellites were estimated for geographical location in China. The method of matching empirical distribution functions improves the time relative stability of NDVI data for all satellites, especially NOAA-9, -11 and -14.

A mathematical model describing urban rainstorm waterlogging distribution was designed by using 2-D unsteady flow equations to simulate urban earth' surface and river flows and combining with 1-D unsteady flow equation to simulate the flow of many secondary rivers and small channels, the dimensions in model of which are smaller than the grid ones. This model was applied to Nanchang city and different kinds of parameters in the model were simplified based on the features of the geography and urban drainage pipe systems. The simulated results are similar to real accumulated water. The errors of the former from the latter are within 20cm (about 83%), but the error greater than 30 cm (about 13%) should not be neglected.

In recent years, the impacts of natural disaster are more and more severe on coastal lowland areas. Aim to the threat of climate change and sea level rise, the natural disaster reduction in coastal lowland areas is paid highly attention. Based on a number of literatures, the paper summarizes the categories and characteristic of natural disasters emerging in coastal lowland areas, such as windstorm and storm surge, hurricanes and hurricane winds, tsunamis and floods, and analyzes the most devastating natural disasters in coastal lowland in the world 2005. The paper also summarizes the effects of typhoons on the coastal lowland areas of China in 2005 and review to analyze the natural disaster mitigation measures and its researches. At last, the paper discusses the vulnerability assessment and response strategies.

With the development of construction of China Cities, there exist a lot of environmental geological problems involved in the geofracture, land subsidence, collapse, landslide, devolution, mudrock flow, floating sand, piping and soft ground deformation. Of big cities whose population is over one million in China, about 30 cities appears the land subsidence region. Other cities locate in the regions of collapse yellow earth or expand soil of strong swell-shrink charasteristic, soft ground and karst. In the paper, the cause and hazard of regionality ground deformation is summed up. The causes of regional land deformation caused by the natural geological effect and activities of human being are analyzed. According to the length of deformation course and endanger of society, economy and life, land deformation involves three types, that is, the delay, rapid and break land deformation. And the concrete countermeasure and method are provided.

The use of remote sensing is becoming indispensable in environmental studies. One of the emerging applications of remote sensing is managing disasters timely and efficiently. The enormous capabilities of remote sensing can be put to use right from the stage of preparedness, planning and mitigation to post disaster relief and recovery. The real time nature of remotely sensed data can be of high value for continuous monitoring and taking necessary actions well in advance. This paper explores the applications of remote sensing and GIS in mapping and assessing multi hazard risks and vulnerabilities. Satellite imageries from various sources are utilized to map the existing landuse/landcover in detail. Geocoded thematic layers are then integrated according to the set parameters to derive multi hazard risks maps. Vulnerability profile is further derived using specific population and properties at risk data. Multispectral and high-resolution satellite images assist in evaluating disastrous events prior to and post occurrence of events.

The landforms always withhold the imprints of characteristic geomorphic processes within it. In the upper reaches of Baspa valley, geomorphic records of glacial origin have been investigated for three glaciers viz., Shaune Garang glacier (North facing), Jorya Garang glacier (North facing) and Saro glacier (South facing). The field investigation was carried out for Shaune Garang glacier and extrapolated for Gor Garang and Saro glacier. This terrain is inaccessible and characterized by highly rugged and adverse climatic conditions. Visual interpretation technique of remote sensing is found to be accurate, economical and time saving, hence, the glacial landforms were mapped from Liss III + Pan merged data of IRS 1D, October 2001. The hanging valleys, terminal moraines and lateral moraines are found to be one of the important geomorphic indicators for reconstruction of paleo-glacial history of Baspa valley. The results show that maximum of three sets of lateral and terminal moraines have been identified in Shaune Garang glacier, Jorya garang shows three sets of terminal and two set of lateral moraine and Saro glacier shows one terminal moraine and two sets of lateral moraine indicating respective stages of deglaciation. The length of surface accumulation observed in above data of Oct, 2001 for Shaune Garang, Jorya Garang and Saro glacier is 3.75km, 11.6km and 1.17km respectively. Thus, it shows that south facing glaciers show minimum surface accumulation as compared to the north facing glacier and maximum three stages of deglaciation have been recorded in present study.