Reservoir capacity estimation using SARAL/AltiKa altimetry data coupled with Resourcesat P6-AWiFS and RISAT 1 microwave data

Reservoir water levels extracted from SARAL/AltiKa GDR data for the period 2013–2014 and water spread areas delineated from Resourcesat P6-AWiFS sensor and RISAT 1 microwave data corresponding to SARAL/AltiKa cycles were used for assessment of reservoir capacity in the Mayurakshi reservoir, Jharkhand state, India. It was found that the reservoir capacity based on the SARAL is around 474.62 Mm³ in comparison to in situ based estimate i.e. around 486.6 Mm³, indicating variation of <3%. Further, comparison of these estimates computed using SARAL and in situ with original reservoir capacity (547.59 Mm³) indicated loss of reservoir capacity is around 13.33 and 11.14%, respectively, within a span of 59 years. The hydrographic survey in the year 1999–2000 also proved that the storage capacity has reduced from 547.6 Mm³ in 1955 to 474.8 Mm³ indicating loss of nearly 13.3 % of total live capacity over period of 45 years.     

IRS-P6多光谱正射影像制作

利用GPS控制点或大比例尺地形图控制资料和DEM对IRS-P6进行纠正研究,制定利用IRS-P6制作1∶50 000正射影像图生产工艺流程,以及通过不同纠正方法和调色效果的比较,设计合理的基于IRS-P6正射影像图生产的技术方案。     

Earthquake-induced built-up damage identification using IRS-P6 data: a comparative study using fuzzy-based classifiers

The 8 October 2005 earthquake caused widespread destruction in both the state of Jammu and Kashmir of India and Northern Pakistan. Due to poor accessibility in the hazardous and difficult mountainous terrain, a proper and comprehensive ground-based survey was not possible. However, with the help of remote sensing data and its analysis techniques, it is feasible to undertake both earthquake-related damage identification and assessment. This study attempts to document and identify built-up damaged (BD) areas using spectral indices taking temporal multispectral images from IRS-P6 LISS-IV. Five spectral indices have been used to identify BD areas using supervised possibilistic c-means (PCM) and noise cluster (NC) classifiers, to analyse the satellite data. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices provide the best results for identifying BD areas, while Simple Ratio (SR) index gives the best results for built-up undamaged area identification. Further, it observed that PCM classifier performed better in comparison to NC classifier.     

Pre-processing considerations of IRS-P6 LISS-4 imagery

Wide image swath with a high geometric resolution is required for photogrammetric applications. Both demands can be satisfied using staggered line arrays. Different bands of IRS-P6 LISS-4 sensor use staggered arrays for imaging. This paper describes a method for computing the offset for geometric alignment of odd and even lines of the staggered array of IRS-P6 LISS-4 imagery. The odd and even pixel rows are separated by 35 μm (equal to 5 pixels) in the focal plane in the along-track direction. Slightly different viewing angles of both lines of a staggered array can result in a variable sampling pattern on the ground because of the attitude fluctuations, satellite movement, terrain topography, PSM steering and small variations in the angular placement of the CCD lines (from the pre-launch values) in the focal plane. Non-accounting of this variable sampling value during the video data alignment will introduce deterioration of image quality and geometric discontinuity of features. The stagger parameters can be computed by the reconstruction of the viewing geometry with a calibrated camera geometry model and a public domain DEM. The impact of the line separation in the focal plane during imaging for different viewing configurations and terrain heights are studied and reported in this paper. Computed values from the model are in good agreement with what is observed in the raw image for different view angles. The results verify the model and are representative of the stability of the platform.     

利用IRS-P6卫星数据制作遥感影像地图

本文介绍了以IRS-P6卫星数据作为信息源完成大兴安岭林区的遥感影像地图制作的方法, 通过试验初步认识到:利用IRS-P6卫星数据制作遥感影像地图,可以满足1:50000、 1:25000比例尺的平面制图精度要求,在一些专业和专题应用中可以放大到1:15000比例尺使用。     

Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach

An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy.     

Groundwater potential in the hard rock terrain of western ghats: A case study from Kottayam district,Kerala using resourcesat (IRS-P6) data and GIS techniques

The present study was aimed to identify and delineate the groundwater potential areas in parts of Western Ghats, Kottayam, covering the upper catchment of Meenachil river. The study area is composed rocks of Archaean age and Charnockite dominated over others. The information on lithology, geomorphology, lineaments, slope and land use/land cover was generated using the Resourcesat (IRS P6 LISS III) data and Survey of India (Sol) toposheets of scale 1:50,000 (surveyed in 1969) and integrated them with raster based Geographical Information System (GIS) to identify the groundwater potential of the study area. Thus, a GIS-based model which takes account of local condition/variations has been developed specifically for mapping groundwater potential. On the basis of hydrogeomorphology, three categories of groundwater potential zones namely good, moderate and poor were identified, and delineated. The high potential zones correspond to the fracture valleys, valley fills, pediments and denudational slope, which coincide with the low slope and high lineaments density areas. The low zone mainly comprise structural hills and escarpments and these act as run-off zones. The derived panchayath-wise groundwater potentiality information could be used for effective identification of suitable locations for extraction of potable water for rural populations.     

Comparison of ocean color chlorophyll algorithms for IRS-P4 OCM sensor usingin-situ data

In-situ chlorophyll concentration data and remote sensing reflectance (R_rs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m^-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of R_rs490 nm and R_rs555 nm (where, R_rs is remote sensing reflectance), performed well with r²=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r²=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm.     

Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: A study over the semi-arid region

In this study, we have implemented a fast atmospheric correction algorithm to IRS-P6 advanced wide field sensor (AWiFS) satellite data for retrieving surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code. The algorithm requires information on aerosol optical depth (AOD) for correcting the satellite dataset. The atmospheric correction algorithm has been tested for IRS-P6 AWiFS False colour composites covering the International Crops Research Institute for the Semi-Arid Tropics Farm, Patancheru, Hyderabad, India, under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e. red soil, chick pea, groundnut and pigeon pea crops were conducted to validate the algorithm. Terra MODIS AOD₅₅₀ validated with Microtops-II sun photometer–derived AOD₅₀₀ over the urban region of Hyderabad exhibited very good correlation of ～0.92, suggesting possible use of satellite-derived AOD for atmospheric correction.     

基于IRS-P6卫星影像的高原地貌分类与信息提取

利用遥感影像进行地形地貌制图,因其成图周期短、制图精度高、修改方便和更新快捷等优势,现已成为中小比例尺地貌制图的主要方法。利用高分辨率的IRS-P6卫星影像对青海省天峻县的高原地貌进行分类与信息提取,并制作大比例尺地貌图。采用遥感(remote sensing,RS)和GIS技术对遥感数据和不同来源的辅助数据进行处理,将其统一到同一GIS平台,在此基础上详细阐述了地貌分类、信息提取的技术流程和地貌图制作的方法。研究表明,基于高分辨率遥感影像数据,采用RS和GIS相结合的技术,可以大大降低地貌分类的难度,提高地貌信息提取与制图的速度和效率。     

IRS-P6与SPOT-5卫星影像在土地利用动态遥感监测中应用的比较

IRS-P6卫星影像的最高空间分辨率是5.8 m,在土地利用动态遥感监测领域中有较大的应用潜力,本研究通过与2.5 m分辨率的SPOT-5卫星影像在成图比例尺、响应图斑大小、地类可解度和信息提取精度等方面进行比较试验,结果表明,IRS-P6卫星影像可以制作1︰2.5万和1︰5万中等比例尺的图件,更新或辅助更新1︰2.5万与1︰5万的土地利用现状图.在土地利用动态遥感监测中,可以作为另外一种数据源与单一的SPOT数据源进行互补,使之更快捷地服务于国土资源管理工作.     

Quantitative study on chlorophyll using IRS-P4 OCM data of Tuticorin coastal waters

Quantification of Chlorophyll using IRS-P4 OCM data was attempted. OCM-DAS software developed by Space Applications Centre, Ahmedabad was used in data analysis. The results showed considerable variations in nearshore (case 2) waters due to interference from yellow substance and suspended sediments. However, in offshore areas (case 1) the measurements showed good correlations. The r values are 0.663 for nearshore waters and 0.892 for offshore waters.     

Selection of IRS-P6 LISS-4 MO mode band for producing band-sharpened multispectral imagery

The recently launched IRS-P6 satellite has a unique capability of acquiring simultaneously multispectral data at three different spatial resolutions from three independent optical sensors (LISS-4, LISS-3 and AWIFS). Of these, the LISS-4 sensor can be operated in two modes: (i) multispectral (MX) mode covering a swath of 23 km and (ii) monochromatic (MO) mode covering a 70-km swath, both at a spatial resolution of 5 m. One of the important uses of the LISS-4 MO data is in realizing a 5 m band-sharpened multispectral image by merging it with the low-resolution LISS-3 MS image. Operationally anyone of the three LISS-4 bands can be chosen for the MO mode data acquisition. The performance of each band for producing band-sharpened MS images is evaluated, and the choice of the band based on the spatial and spectral characteristics of the merged data is suggested. The LISS-4 Red-band is found to be optimal. It provides band-sharpened imagery with spatial and spectral qualities very similar to the LISS-4 MX data products.     

Understanding heterogeneity in metropolitan India: The added value of remote sensing data for analyzing sub-standard residential areas

Analyzing the heterogeneity in metropolitan areas of India utilizing remote sensing data can help to identify more precise patterns of sub-standard residential areas. Earlier work analyzing inequalities in Indian cities employed a constructed index of multiple deprivations (IMDs) utilizing data from the Census of India 2001 (http://censusindia.gov.in). While that index, described in an earlier paper, provided a first approach to identify heterogeneity at the citywide scale, it neither provided information on spatial variations within the geographical boundaries of the Census database, nor about physical characteristics, such as green spaces and the variation in housing density and quality. In this article, we analyze whether different types of sub-standard residential areas can be identified through remote sensing data, combined, where relevant, with ground-truthing and local knowledge. The specific questions address: (1) the extent to which types of residential sub-standard areas can be drawn from remote sensing data, based on patterns of green space, structure of layout, density of built-up areas, size of buildings and other site characteristics; (2) the spatial diversity of these residential types for selected electoral wards; and (3) the correlation between different types of sub-standard residential areas and the results of the index of multiple deprivations utilized at electoral ward level found previously.     

Study on the utility of IRS-P6 AWiFS SWIR band for crop discrimination and classification

This present study was conducted to find out the usefulness of SWIR (Short Wave Infra Red) band data in AWiFS (Advanced Wide Field Sensor) sensor of Resourcesat 1, for the discrimination of different Rabi season crops (rabi rice, groundnut and vegetables) and other vegetations of the undivided Cuttack district of Orissa state. Four dates multi-spectral AWiFS data during the period from 10 December 2003 to 2 May 2004 were used. The analysis was carried out using various multivariate statistics and classification approaches. Principal Component Analysis (PCA) and separability measures were used for selection of best bands for crop discrimination. The analysis showed that, for discrimination of the crops in the study area, NIR was found to be the best band, followed by SWIR and Red. The results of the supervised MXL classification showed that inclusion of SWIR band increased the overall accuracy and kappa coefficient. The ‘Three Band Ratio’ index, which incorporated Red, NIR and SWIR bands, showed improved discrimination in the multi-date dataset classification, compared to other SWIR based indices.     

Optimum tracking of ship routes in 3g-WAM simulated rough weather using IRS-P4 (MSMR) analysed wind fields

Rough weather ship routing is studied using model hindcast wave climate. With the launch of IRS-P4 (OCEANSAT-I), it became possible to carry out routine wave forecasting over the Indian Ocean. The MSMR channel of the satellite gives scalar wind, which is analysed at National center for Medium Range Weather Forecasting (NCMRWF), India for converting to vector winds. The same is used as input to third generation wave model for the rough weather month of July 2000. Simulations are carried out using Cycle-4 of third generation spectral wave model WAM for regional grid system. This simulated wave climate formed the basis for computing effective ship velocity in the irregular seaway. This study gives a quantitative estimation of change in ship velocity in the open Indian Ocean for a Liberty type ship. The optimal route is charted using Dijkstra’s algorithm for minimal time path between Calcutta and Sumatra. The optimum track information has broad scope for obtaining a safer route, least time route by avoiding delay in schedule with minimum fuel consumption.