Indian Remote Sensing Research Articles

High Spatial and Spectral Details Retention Fusion and Evaluation

To retain spatial and spectral details simultaneously from source images is a trade-off in image sharpening. Fourier and wavelet transform based image fusion methods retain better spectral quality but represent less spatial details as in source images. Wavelets and Laplacian pyramid perform well only at linear discontinuities because they do not consider the geometric properties of structures and do not exploit the regularity of edges. In this paper we proposed a novel image fusion method to preserve high spatial and spectral details based on the curvelet transforms. Curvelet transforms overcome the difficulty to identify the critical transient features. To retain high spatial details, finer and detailed scale coefficients of High Resolution (HR) PAN image are substituted in Low Resolution (LR) multispectral (MS) bands in the Fast Discrete Curvelet Transforms (FDCT) domain. Spectral details in the fused image are preserved by following the shape of the spectral reflectance curve of each pixel in the resample MS image. Spectral profile of the each pixel in spatially fused image is parallel to the spectral profile of the corresponding pixel in the resampled MS image. For experimental study of this method, Indian Remote Sensing (IRS) Resourcesat-1 LISS IV images are used as LR MS image and Cartosat-1 images are used as HR PAN Image. Proposed fusion method is evaluated against state of the art fusion techniques and quality measures.

Snowmelt runoff modelling in data scarce Nuranang catchment of eastern Himalayan region

Snowmelt runoff modelling is very important tool to estimate the ice and snow induced runoff volume in the mountain basins. However, unavailability of data is a key challenge for snowmelt runoff modelling especially in the eastern Himalayan region. In this paper, an attempt has been made to apply the Windows-based Snowmelt Runoff Model (WinSRM) to estimate the snowmelt runoff from a small representative seasonally snow-covered eastern Himalayan catchment with high data scarcity located in Arunachal Pradesh. The 52km2 catchment, having very steep average slope with an elevation difference of more than 1800m from lowest to highest point, has been divided into three elevation zones. The satellite images of the catchment are acquired from Indian Remote Sensing Satellite IRS-P6 (LISS-III/AWiFS). Snow cover mapping is done using normalised difference snow index (NDSI) method. Snow depletion curves of each zone are generated from daily snow covered area percentage (SCA%) obtained by interpolating periodic SCA% determined from cloud free images, which is also a rarity in this region during the snow depletion season. A logarithmic relationship is established between SCA% and average mean air temperature (AMAT) to estimate the SCA% for validation period of 2004 for which cloud free IRS-P6 Satellite images were not available. The WinSRM model is calibrated using three years data of 2006, 2007, and 2009 for the depletion period and is validated for depletion period of 2004. It is shown that the SRM model can be used satisfactorily in such data scarce catchments of eastern Himalaya. The procedures adopted in this study to handle different limited data conditions and estimate some of the required model parameters are also explained in this paper.

Influence of land use/land cover on the water chemistry of Wular Lake in Kashmir Himalaya (India)

Abstract Introduction The impacts of land use/land cover (LULC) pattern on surface components of water are less well recognized, particularly in the Kashmir Himalaya. The paper highlights the relationship between LULC and water chemistry for Wular Lake in Kashmir Himalaya. Methods We used Indian Remote Sensing satellite (IRS) P6 using LISS III sensor with a spatial resolution of 23.5 m for various LULC categories. ERDAS IMAGINE 9.0 and ArcGIS 9.3 were used to generate the LULC map. Water parameters like pH and temperature were measured in the field, while parameters like dissolved oxygen, free carbon dioxide, chloride, alkalinity nitrogen and phosphorus values were determined by APHA (Standard Methods for Examination of Water and Wastewater, 1998). Results The present study provides us the information about the proportion of different land cover categories and their correlation with some limnological parameters of Wular Lake. Among the various physicochemical parameters, low dissolved oxygen (DO) was observed in the LULC class which has the highest percentage of agricultural land (47.04%; r = 0.688 P < 0.05) followed by horticultural land (19.10%; r = 0.554; P < 0.01) in its catchment. The catchment area with greater percentage of agricultural fields drains maximum fertilizers in the lake, resulting in growth of microorganisms that deplete the dissolved oxygen content in the water body. Conclusions Pearson matrix predicts that agricultural land, built up, horticultural land and wasteland seem to be the foremost factors responsible for the deterioration of the water quality of the lake. Careful planning of land use is needed to bring significant reductions in nutrient exports from agriculture through various tributaries, in order to restore and maintain water quality of the lake. If this trend of agriculturization manifestation continues unabated, it will be difficult to restore the lake water quality and to subsequently correct ecological manifestation of eutrophication through which the lake is undergoing at present.

Development of a scheme for atmospheric correction of Resourcesat-2 AWiFS data

This paper presents a method called SACRS2, a scheme for atmospheric correction of RS2-AWiFS (Resourcesat2-Advanced Wide Field Sensor) data. The SACRS2 is a computationally fast scheme developed from a physics-based detailed radiative transfer model 6SV for correcting large amount of data from the high-repetivity AWiFS sensor. The method is based on deriving a set of equations with coefficients which depend on the spectral bands of the RS2-AWiFS sensor through forward signal simulations by 6SV. Semi-empirical formulations provided in the SMAC method with a few improvements have been used to describe various atmospheric interactions. A total of 112 coefficients for different equations are determined using the best fit equations against the computations of the 6SV. After the specific coefficients for the RS2-AWiFS spectral bands are determined, the major inputs of the scheme are raw digital numbers recorded by RS2-AWiFS sensor, atmospheric columnar water vapour content, ozone content, aerosol optical thickness at 550nm and viewing-illumination conditions. Results showed a good performance of the SACRS2 with a maximum relative error in the SACRS2 simulations ranged between 1% for a reflectance of 0.5 and 8.6% for reflectance of 0.05 with respect to 6SV computations. Validation of retrieved surface reflectance using the SACRS2 scheme with respect to in-situ measurements at two sites indicated a capability of this scheme to determine the surface reflectance within 10%. This is a first of its kind scheme developed for the atmospheric correction of any Indian Remote Sensing satellite data. A package containing the SACRS2 software is available on the MOSDAC website for the researchers.

Extent of sewage pollution in coastal environment of Mumbai, India: an object‐based image analysis

AbstractThe coastal water quality of Mumbai is deteriorating by receiving partially treated effluent from wastewater treatment facilities, sewage discharges from ocean outfalls and discharges from point and non‐point sources in the creek and coast. A novel approach of object‐based image analysis has been used in this research study to assess the extent of sewage pollution in the coastal environment of Mumbai. For this, Indian Remote Sensing P6 Linear Imaging Self Scanning IV image was used for multiresolution segmentation and rule‐based image classification as per normalised difference water index and normalised difference turbidity index. Water quality regions as per classification were strongly correlated with observed water quality parameters. Based on classified regions and water quality parameters, extent of sewage pollution in the coast was ranked from high to least polluted. The approach developed in this methodology should be tested in similarly polluted waters to ascertain its adaptability for assessing the spatial extent of sewage pollution.

Comparison of spectral characteristics between EO-1 ALI and IRS-P6 LISS-III imagery

Data from the Indian Remote Sensing Satellite (IRS) P6 have been widely used for integrated land and water resources management. To complement and substitute data measured from other similar satellites and obtain constant measurements of the Earth's surface features, we evaluated the spectral characteristics between IRS-P6 LISS-III, a sensor of IRS P6, and EO- 1 ALI by comparing their top-of-atmosphere (TOA) reflectance and normalized difference vegetation index (NDVI) of three nearly simultaneous image pairs. In particular, due to the difference in NIR band design between LISS-III and ALI, the spectral characteristics of NIR band of LISS-III were compared with the two NIR bands of ALI. The results demonstrate that a distinct linear correlation exists between the spectral characteristics of LISS-III and ALI, with R 2 values ranging from 0.976 to 0.995 for TOA reflectance and from 0.992 to 0.997 for NDVI. Therefore, a mutual complementation and substitution of the TOA reflectance and NDVI between LISS-III and ALI images is feasible. Moreover, both TOA reflectance and NDVI of LISS-III are more similar to those of ALI at band 4 than to those of ALI at band 4P due to the difference in the two NIR bands of ALI.

Temporal Analysis of Spatial Distribution of Built-Up Area in Peri-Urban Areas of Cochin, Kerala: Case-Study of Sub-Watershed in Periyar River

An increase in the built-up area in the watershed is a preliminary measurable indicator to understand the effect of urban development on the hydrologic response of the watershed. In this paper, the land use/ land cover map of sub watershed of Periyar River processed from Indian Remote Sensing(IRS) satellite is created to understand the spatial distribution of built –up area and to detect the expansion of built up area in the peri-urban areas of Cochin, Kerala, India. The Satellite imagery of IRS –1C LISS – III dated 7th February 1995 and IRS –P6 LISS – III dated 13th February 2005 were imported in ERDAS Imagine to create LU/LC map using Maximum Likelihood Method of supervised classification. A total of ten classes broadly including forest, plantation, grassland, barren land and built-up were classified. The forest cover factor was computed in ArcGIS which is used as a parameter for runoff calculation to assess the contribution of land cover on runoff modelling. A 6% increase in built –up is detected comparing the LU/ LC map indicating that it is undergoing rapid development. The forest cover factor has a slight difference from 0.57 to 0.62indicating that barren and grassland are been primarily converted to built-up area. The LU/LC map can be used as a preliminary indicator of man-made induced impervious surface. The areal extent of satellite image provides a means for effective planning in future.

Monitoring land use changes associated with urbanization: An object based image analysis approach

Land use/land cover (LULC) change occurs due to natural and anthropogenic causes. In developing countries, rapid industrialization and urbanization imposes a major threat to natural environment. Present study was carried out to monitor the LULC changes due to urbanization in a rapidly changing river basin, India. The purpose of choosing the river basin was to analyze past changes and predict possible consequences within a defined natural boundary. Multi-temporal images acquired from Landsat and Indian Remote Sensing (IRS) satellites between 1992–2009 as well as a digital elevation model was used to generate historical and current LULC pattern in the basin. An object based image analysis technique was employed for precise classification of multi-temporal images followed by GIS-based change detection studies. The study reveals that the built up area has increased significantly and added 288 km2 between 1992–2009. Increase in built up area is attributed to decrease in wastelands and agricultural land. The expansion of built up area along major transportation networks, specifically after the year 2000 shows the rapid rate of urbanization in the basin.

Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques

The shoreline change extraction and change detection analysis is an important task that has application in different fields such as development of setback planning, hazard zoning, erosion-accretion studies, regional sediment budgets and conceptual or predictive modeling of coastal morphodynamics. Shoreline delineation is difficult, time consuming, and sometimes impossible for entire coastal system when using traditional ground survey techniques. Recent advances in remote sensing and geographical information system (GIS) techniques are overcoming the difficulties in extraction of shoreline position and detection of shoreline changes. In the present paper, an automatic shoreline detection method using histogram equalization and adaptive thresholding techniques is developed. The shoreline of Netravati-Gurpur rivermouth area along Mangalore coast, West Coast of India have been extracted from Indian Remote Sensing Satellite (IRS P6) LISS-III (2005, 2007 and 2010) and IRS R2 LISS-III (2013) satellite images using developed automatic shoreline detection method. The delineated shorelines have been analyzed using Digital Shoreline Analysis System (DSAS), a GIS Software tool for estimation of shoreline change rates through two statistical techniques such as, End Point Rate (EPR) and Linear Regression Rate (LRR). The Bengre spit, Northern sector of Netravati-Gurpur river mouth is under accretion an average of 2.95 m/yr (EPR) and 3.07 m/yr (LRR) and maximum accretion obtained is 8.51 m/yr (EPR) and 8.69 m/yr (LRR). Southern sector, the Ullal spit is under erosion an average of -0.56 m/yr (EPR) and -0.59 m/yr (LRR).

Mangrove Restoration and Regeneration Monitoring in Gulf of Kachchh, Gujarat State, India, Using Remote Sensing and Geo-Informatics

Indian coast harbors richly diverse and critical coastal habitats like coral reefs and mangroves. Mangroves form one of the most important ecosystems of coastal and marine areas. It safeguards the ecology of the coastal areas and provides livelihood opportunities to the fishermen and pastoral families living in these areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. The restoration and plantation of mangroves have received a lot of attentions worldwide. To assess the impact of mangrove plantation activities and to monitor the mangrove regeneration and restoration in various villages, a joint study under the Integrated Coastal Zone Management Project (ICZMP) was taken up by Gujarat Ecology Commission (GEC) and Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG) in the Gulf of Kachchh, Gujarat State. The major objective of this study was to monitor the increase in mangrove cover in coastal areas of Gulf of Kachchh using the Indian Remote Sensing Satellite data of 2005, 2011 and 2014. The mangrove regeneration was monitored using multi-temporal Indian Remote Sensing Satellite (IRS) LISS-III and LISS-IV digital data covering Gulf of Kachchh region. The multi-temporal IRS LISS-III data covering Gulf of Kachchh of October-2005, November-2011 and LISS-IV data of April-2014 was analyzed. The mangrove density and mangrove area in different talukas was estimated based on the analysis of IRS LISS-III digital data. The mangroves have been delineated based on the pink colour observed on satellite images and the area was estimated in the Geographic Information System (GIS) environment. The taluka or block-level mangrove areas were estimated and changes in the areas were monitored during the period of six years from 2005 to 2011. It was observed that the areas where mangrove regeneration activities were carried out with active participation of Community Based Organizations (CBOs), mangrove density as well as mangrove area have substantially increased in the Gulf of Kachchh region.

Assessment of Natural Resources in Part of Lucknow, District Uttar Pradesh using Unsupervised Classification

Abstract The application of digital image processing enables us to extract information about land use and land cover pattern, water quality, forest cover in an area with remarkable accuracy which can be established by ground truthing and Survey of India topographic maps. The main objectives of this project were to gain knowledge of remote sensing and digital image processing and its potential applications, acquire information about Indian Remote Sensing Satellites, seek to understand the difference between human interpretation and digital image processing, to prepare a satellite image of Lucknow using geospatial authoring software, ERDAS imagine. For this purpose a satellite image of Lucknow was procured and segregated it into different bands inorder to prepare spectral, spatial and surface profile of water, land, vegetation and wasteland. This image was then radiometrically enhanced by haze and noise reduction techniques. The various natural resources in the area were then broadly classified into water body, vegetation, settlement and waste land using unsupervised classification.

A Unified Software Framework for Automatic Precise Georeferencing of Large Remote Sensing Image Archives

Though georeferencing satellite images is a solved industry problem long back, every satellite sensor data is unique to process due to its proprietary information and expected geometric accuracies usually demands use of reference data. Industry is moving towards unifying frameworks, sensor models where the users have to just plug-in details. The growing archive is inevitable and therefore (re) processing requirements have to handle huge image archives with state of art georeferencing standards automatically. Use of reference data is necessary to georeference the data sets to better accuracies beyond systematic corrections. This paper presents an approach for such tedious task of automatically reliably georeference huge image archives involving reference image/control points use in georeferencing Indian Remote Sensing satellite images.

Real-time validation of Indian Remote Sensing Satellite data down link chain performance and evaluation of online data analysis with BER measurement

Indian Remote Sensing Satellites transmit video data in X-band and telemetry data in S-band. Currently, there is no available mechanism to validate the data link chain performance from onboard satellite to ground station in real-time. In order to achieve the objective, a novel method of Real-time online Bit Error Rate (BER) measurement is formulated using Real-time BER validating system (RTBVS) for all IRS missions. The mechanism is designed, realized and implemented at Satellite ground station to evaluate total satellite data receive chain performance in real-time itself. The Real Time BER Validating System (RTBVS) is tested successfully and is made operational at Satellite Data Reception Station for IRS Satellite Series. The RTBVS design module consists of advanced high speed parallel frame synchronizer for high data rate satellites, flywheel logic for reliability and stability of good frame sync detection and synchronization, and display of BER on the fly in a suitable window logic of one million bits or selectable window greater than 106 bits. RTBVS aids in the routine offline checks viz., local loop checks conducted daily, far field bore sight radiation tests carried out at regular intervals at ground station, before acquisition of satellite for Ground station performance validation. Further, the required test PN data passes from satellite can be avoided by using RTBVS mechanism and that particular orbit can be released for real-time data reception. The routine off line checks validate the satellite data receive chain at ground station only, but the total data receive chain from onboard satellite to ground station can be established using the RTBVS system which displays the online BER reading and helps in evaluating the station performance.

Time-Series analysis of MODIS NDVI data along with ancillary data for Land use/Land cover mapping of Uttarakhand

Abstract. Land use and land cover plays an important role in biogeochemical cycles, global climate and seasonal changes. Mapping land use and land cover at various spatial and temporal scales is thus required. Reliable and up to date land use/land cover data is of prime importance for Uttarakhand, which houses twelve national parks and wildlife sanctuaries and also has a vast potential in tourism sector. The research is aimed at mapping the land use/land cover for Uttarakhand state of India using Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The study also incorporated smoothening of time-series plots using filtering techniques, which helped in identifying phenological characteristics of various land cover types. Multi temporal Normalized Difference Vegetation Index (NDVI) data for the year 2010 was used for mapping the Land use/land cover at 250m coarse resolution. A total of 23 images covering a single year were layer stacked and 150 clusters were generated using unsupervised classification (ISODATA) on the yearly composite. To identify different types of land cover classes, the temporal pattern (or) phenological information observed from the MODIS (MOD13Q1) NDVI, elevation data from Shuttle Radar Topography Mission (SRTM), MODIS water mask (MOD44W), Nighttime Lights Time Series data from Defense Meteorological Satellite Program (DMSP) and Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data were used. Final map product is generated by adopting hybrid classification approach, which resulted in detailed and accurate land use and land cover map.

Sunkoshi landslide in Nepal and its possible impact in India: A remote sensing based appraisal

Abstract. A devastating landslide that killed at least 156 people occurred on the right bank of Sunkoshi river at village Jure, 70 km northeast of Kathmandu in Nepal. It not only affected hydroelectric projects in the near vicinity but also had potential to flood large tracts of downstream area in Nepal and north Bihar. Timely action by Nepal Army and Indian authorities have averted a major disaster but satellite data analysis (using IRS Resourcesat-2 LISS IV, WorldView-2, RISAT-1, Radarsat-2 etc.) reveal that the site was easily identifiable on satellite image as a potential landslide zone that has been affected by landslide related mass wasting in the past. It was reactivated and intermittently slided during 2000–2012 and finally when the whole mass came down on 2nd August 2014 pushing materials up to the opposite slope, a landslide dammed lake was formed on Sunkoshi river which extended up to a distance of 2.5–3 km as observed on 5–6th August on satellite images and the volume was estimated to be around 8.26–10.5 MCM, which needs to be released safely to avert any further disaster. Attempt is made to simulate the event to assess the run out and debris thickness using process based model. Satellite observation (using IRS Resourcesat-2 LISS IV, WorldView-2, RISAT-1, Radarsat-2 etc.), aided by hydrological data obtained through telemetry immensely helped to assess the landslide dimension, lake extent, volume, discharge, water level and travel time of water in Sunkoshi river. But the question remains how to manage the large amount of debris that Sunkoshi brings to its downstream areas causing river aggradations that eventually increases the flood risk. In such situations, the most immediate landslide remedial measures are four fold: monitoring of landslide, dam and reservoir using remote sensing and hydrological observations, controlled release of water, water discharge management at Kosi barrage and mechanism/preparedness for evacuation. The Sunkoshi landslide has brought to forefront the core issue of analysing mass wasting in all major river valleys outside Indian territory that can pose risk to Indian population. In this regard, it is needless to say that remotely sensed images through Indian Remote Sensing satellites and many other satellites can provide most crucial information in short time.

Evaluation of urban growth effects on surface runoff using SCS-CN method and Green-Ampt infiltration model

Evaluation of land cover/use change and its impact on hydrological design aspects plays a vital role in assessing the capacity of existing storm water conveyance systems and its capability towards the anticipated peak flow. This paper attempts to illustrate an application of Soil Conservation Service (SCS-CN) method for runoff estimation using continuous time series rainfall. And also, the present work proposes a Land use factor based on the Curve Number to correct the infiltration rate according to the prevailing land cover/use. Indian remote sensing (IRS) satellite data for the years 1989, 1992, 1995, 1998, 2001, 2004, 2007, and 2010 are used to evaluate the growth of Tiruchirapalli city, India city and to assess its impact on surface runoff. The Geographical information system (GIS) is used to prepare the different layers belonging to various land covers identified from remotely sensed data. The sub-watersheds are created using Digital Elevation Model (DEM) prepared by ARC GIS software. The single storm event and 50-year return period rainfall data are used as a hydrological input to the model to evaluate the increase in runoff over the years due to change in the land cover of the study area. The study reveals that the impact of land cover/use change is more significant for longer duration storm than short duration storm at the same magnitude. Further, it is noticed from the study that the runoff depth and runoff co-efficient is large for short duration rainfall than long duration rainfall at the same magnitude.

Retrieval of Surface Reflectance using SACRS2: a Scheme for Atmospheric Correction of ResourceSat-2 AWiFS data

Abstract. The Indian Remote Sensing (IRS) satellite series has been providing data since 1988 through various Earth observation missions. Before using IRS data for the quantitative analysis and parameter retrieval, it must be corrected for the atmospheric effects because spectral bands of IRS sensors are contaminated by intervening atmosphere. Standard atmospheric correction model tuned for the IRS sensors was not available for deriving surface reflectance. Looking at this gap area, a study was carried out to develop a physicsbased method, called SACRS2- a Scheme for Atmospheric Correction of Resourcesat-2 (RS2) AWiFS data. SACRS2 is a computationally fast scheme developed for correcting large amount of data acquired by RS2-AWiFS sensor using a detailed radiative transfer model 6SV. The method is based on deriving a set of coefficients which depend on spectral bands of the RS2-AWiFS sensor through thousands of forward signal simulations by 6SV. Once precise coefficients of all the physical processes of atmospheric correction are determined for RS2-AWiFS spectral bands then a complete scheme was developed using these coefficients. Major inputs of the SACRS2 scheme are raw digital numbers recorded by RS2-AWiFS sensor, aerosol optical thickness at 550 nm, columnar water vapour content, ozone content and viewing-geometry. Results showed a good performance of SACRS2 with a maximum relative error in the SACRS2 simulations ranged between approximately 2 to 7 percent with respect to reference 6SV computations. A complete software package containing the SACRS2 model along with user guide and test dataset has been released on the website (www.mosdac.gov.in) for the researchers.

An experimental case study to estimate Pre-harvest Wheat Acreage/Production in Hilly and Plain region of Uttarakhand state: Challenges and solutions of problems by using satellite data

Abstract. Wheat is an economically important Rabi crop for the state, which is grown on around 26 % of total available agriculture area in the state. There is a variation in productivity of wheat crop in hilly and tarai region. The agricultural productivity is less in hilly region in comparison of tarai region due to terrace cultivation, traditional system of agriculture, small land holdings, variation in physiography, top soil erosion, lack of proper irrigation system etc. Pre-harvest acreage/yield/production estimation of major crops is being done with the help of conventional crop cutting method, which is biased, inaccurate and time consuming. Remote Sensing data with multi-temporal and multi-spectral capabilities has shown new dimension in crop discrimination analysis and acreage/yield/production estimation in recent years. In view of this, Uttarakhand Space Applications Centre (USAC), Dehradun with the collaboration of Space Applications Centre (SAC), ISRO, Ahmedabad and Uttarakhand State Agriculture Department, have developed different techniques for the discrimination of crops and estimation of pre-harvest wheat acreage/yield/production. In the 1st phase, five districts (Dehradun, Almora, Udham Singh Nagar, Pauri Garhwal and Haridwar) with distinct physiography i.e. hilly and plain regions, have been selected for testing and verification of techniques using IRS (Indian Remote Sensing Satellites), LISS-III, LISS-IV satellite data of Rabi season for the year 2008–09 and whole 13 districts of the Uttarakhand state from 2009–14 along with ground data were used for detailed analysis. Five methods have been developed i.e. NDVI (Normalized Differential Vegetation Index), Supervised classification, Spatial modeling, Masking out method and Programming on visual basics methods using multitemporal satellite data of Rabi season along with the collateral and ground data. These methods were used for wheat discriminations and preharvest acreage estimations and subsequently results were compared with Bureau of Estimation Statistics (BES). Out of these five different methods, wheat area that was estimated by spatial modeling and programming on visual basics has been found quite near to Bureau of Estimation Statistics (BES). But for hilly region, maximum fields were going in shadow region, so it was difficult to estimate accurate result, so frequency distribution curve method has been used and frequency range has been decided to discriminate wheat pixels from other pixels in hilly region, digitized those regions and result shows good result. For yield estimation, an algorithm has been developed by using soil characteristics i.e. texture, depth, drainage, temperature, rainfall and historical yield data. To get the production estimation, estimated yield multiplied by acreage of crop per hectare. Result shows deviation for acreage estimation from BES is around 3.28 %, 2.46 %, 3.45 %, 1.56 %, 1.2 % and 1.6 % (estimation not declared till now by state Agriculture dept. For the year 2013–14) estimation and deviation for production estimation is around 4.98 %, 3.66 % 3.21 % , 3.1 % NA and 2.9 % for the consecutive above mentioned years i.e. 2008–09, 2009–10, 2010–11, 2011–12, 2012–13 and 2013–14. The estimated data has been provided to State Agriculture department for their use. To forecast production before harvest facilitate the formulation of workable marketing strategies leading to better export/import of crop in the state, which will help to lead better economic condition of the state. Yield estimation would help agriculture department in assessment of productivity of land for specific crop. Pre-harvest wheat acreage/production estimation, is useful to facilitate the reliable and timely estimates and enable the administrators and planners to take strategic decisions on import-export policy matters and trade negotiations.

Risk management support through India Remote Sensing Satellites

Abstract. Remote Sensing along with Geographical Information System (GIS) has been proven as a very important tools for the monitoring of the Earth resources and the detection of its temporal variations. A variety of operational National applications in the fields of Crop yield estimation , flood monitoring, forest fire detection, landslide and land cover variations were shown in the last 25 years using the Remote Sensing data. The technology has proven very useful for risk management like by mapping of flood inundated areas identifying of escape routes and for identifying the locations of temporary housing or a-posteriori evaluation of damaged areas etc. The demand and need for Remote Sensing satellite data for such applications has increased tremendously. This can be attributed to the technology adaptation and also the happening of disasters due to the global climate changes or the urbanization. However, the real-time utilization of remote sensing data for emergency situations is still a difficult task because of the lack of a dedicated system (constellation) of satellites providing a day-to-day revisit of any area on the globe. The need of the day is to provide satellite data with the shortest delay. Tasking the satellite to product dissemination to the user is to be done in few hours. Indian Remote Sensing satellites with a range of resolutions from 1 km to 1 m has been supporting disasters both National & International. In this paper, an attempt has been made to describe the expected performance and limitations of the Indian Remote Sensing Satellites available for risk management applications, as well as an analysis of future systems Cartosat-2D, 2E ,Resourcesat-2R &RISAT-1A. This paper also attempts to describe the criteria of satellite selection for programming for the purpose of risk management with a special emphasis on planning RISAT-1(SAR sensor).

Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level

Accurate information on the extent of waterlogging is required for flood prediction, monitoring, relief and preventive measures. The rule-based classification algorithms were used for differentiating waterlogged areas from other ground features using Resourcesat-2 AWiFS satellite imagery (Indian Remote Sensing Satellite with spatial resolution of 56 m). Two spectral indices normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used for extracting waterlogged areas in Sri Muktsar Sahib district of Punjab, India. These indices extracted the waterlogged areas (cropped areas inundated with water) but the water features were less enhanced in the NDWI-derived image (when compared with MNDWI-derived image) due to negative values of NDWI and, mixing of water with built up features. The water features were more enhanced with MNDWI and the values of MNDWI were positive for water features mixed with vegetation. The overall accuracy of waterlogged areas extracted from the MNDWI image was 96.9% with the Kappa coefficient of 0.89. The digital elevation model (DEM) was extracted from ASTER-GDEM. The relationships among depth to the water table recorded before the incessant rain in the region, DEM and classified MNDWI images explained the differences in the extent of waterlogging in various directions of the study area. These results suggest that MNDWI can be used to better delineate water features mixed with vegetation compared to NDWI.