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1.
S. S. Ray Anil Sood Sushma Panigrahy J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2005,33(4):475-481
This paper presents the work done in Bathinda District of Punjab state of India for evaluating the cropping system efficiency
using multi-date, multi-year and multi-sensor satellite based remote sensing data along with various spatial and non-spatial
collateral data. Three efficiency indices, such as Multiple Cropping Index (MCI), Area Diversity Index (DI), Cultivated Land
Utilization Index (CLUI), have been worked out to characterize the cropping systems. The salient findings point out that,
the MCI has, increased remarkably. A further increase is possible by only taking a third crop. The ADI has increased in kharif
(rainy) season, due to introduction of rice in the cotton belt, however in rabi (winter) season the ADI has reduced nearly
to one, showing it to be a mono-cropped situation. The CLUI is low (> 0.5) in many blocks, showing there is a great scope
to improve it. Since in summer the land is remaining unutilized, a summer crop can very well be taken up to improve it. 相似文献
2.
Satellite remote sensing technique can be effectively utilised in mapping and monitoring the river course changes and associated geomorphological features. Ravi river, flowing along the Indo-Pakistan border, has been in the limelight for its repeated flood havoc during monsoon and abrupt encroachment at some places in the Indian territory, where it was not flowing earlier. This river, meandering in zig-zag fashion along the International boundary in Amritsar and Gurdaspur districts of Punjab, poses perennial threats to the nations’s economy due to extensive destruction happening every year. An attempt has been made to map the shift of this river and the associated geomorphological features along its course using the Indian Remote Sensing Satellite data (IRS-IA and IB LISS-IIFCC) of the period 1991–1993 and the Survey of India topographic sheets of the period 1972–1973. The study shows that there has been drastic changes in the course of Ravi during a span of 20 years due to human activities along its course. The river has shifted its course considerably towards India since its topography is against it. River training structures/bundhs, built by the neighbouring country, across and very near to the earlier river course has been the main reason for this drastic shifting. It is estimated that such massive structures could turn the river course towards India by atleast 1 to 5 km in the border districts of Punjab. This shifting of Ravi along international border poses a serious threat to the Nation’s defence system. 相似文献
3.
M.?R.?PandyaEmail author D.?B.?Shah H.?J.?Trivedi M.?M.?Lunagaria V.?Pandey S.?Panigrahy J.?S.?Parihar 《Journal of the Indian Society of Remote Sensing》2013,41(4):787-796
Spectral features of plant species in the visible to SWIR (0.4–2.5 μm) region have been studied extensively, but scanty attention has been given to plant thermal infrared (TIR: 4–14 μm) properties. This paper presents preliminary results of a study that was conducted first time in India to measure radiance and emissivity properties of eight plant species in TIR spectral region in the field conditions using a FTIR (Fourier Transform Infrared) field spectroradiometer working in 4–14 μm at an agriculture experimental farm. Several spectral features in the emissivity spectra of plant species were observed that are probably related to the leaf chemical constituents, such as cellulose and xylan (hemicellulose) and structural aspects of leaf surface like abundance of trichomes and texture. Observations and results from the field measurements were supported by the laboratory measurements like biochemical analysis. These preliminary field emissivity measurements of leaves in TIR show that there is useful spectral information that may be detectable by field-based instrument. More detailed field and laboratory measurements are underway to explore this research theme. 相似文献
4.
Rimjhim Bhatnagar Singh S. S. Ray S. K. Bal B. S. Sekhon G. S. Gill Sushma Panigrahy 《Journal of the Indian Society of Remote Sensing》2013,41(2):301-308
Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best (F?>?115) in discriminating above classes, while LCA and SINDRI were best (F?>?100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100–2300 m region would be sufficient to discriminate crop residue from other co-existing classes. 相似文献
5.
Sheshakumar K. Goroshi R. P. Singh S. Panigrahy J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):315-321
This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH4) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer
for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to
analyze the atmospheric CH4 concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index
(NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH4 concentration. Analysis showed mean monthly CH4 concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value
in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH4 concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region
and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation.
This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH4 concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth
period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different
satellite based data in understanding the variability of atmospheric CH4 concentration in relation to vegetation. 相似文献
6.
K. R. Manjunath Shibendu Shankar Ray Sushma Panigrahy 《Journal of the Indian Society of Remote Sensing》2011,39(4):599-602
Hyperspectral remote sensing, because of its large number of narrow bands, has shown possibility of discriminating the crops.
Current study was carried out to select the optimum bands for discrimination among pulses, cole crops and ornamental plants
using the ground-based Hyperspectral data in Patha village, Lalitpur district, Uttar Pradesh state and Kolkata, West Bengal
state. The field observations of reflectance were taken using a 512-channel spectroradiometer with a range of 325–1075 nm.
The stepwise discriminant analysis was carried out and separability measures, such as Wilks’ lambda and F-Value were used as criteria for identifying the narrow bands. The analysis showed that, the best four bands for pulse crop
discrimination lie mostly in NIR and early MIR regions i.e. 750, 800, 940 and 960 nm. Within cole crops discrimination is
primarily determined by the green, red and NIR bands of 550, 690, 740, 770 and 980 nm. The separability study showed the bands
420,470,480,570,730,740, 940, 950, 970, 1030 nm are useful for discriminating flowers. 相似文献
7.
Sushma Panigrahy Shibendu Shankar Ray K. R. Manjunath P. S. Pandey S. K. Sharma Anil Sood Manoj Yadav P. C. Gupta N. Kundu Jai Singh Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):355-364
Cropping system study is not only useful to understand the overall sustainability of agricultural system, but also it helps
in generating many important parameters which are useful in climate change impact assessment. Considering its importance,
Space Applications Centre, took up a project for mapping and characterizing major cropping systems of Indo-Gangetic Plains
of India. The study area included the five states of Indo-Gangetic Plains (IGP) of India, i.e. Punjab, Haryana, Uttar Pradesh,
Bihar and West Bengal. There were two aspects of the study. The first aspect included state and district level cropping system
mapping using multi-date remote sensing (IRS-AWiFS and Radarsat ScanSAR) data. The second part was to characterize the cropping
system using moderate spatial resolution multi-date remote sensing data (SPOT VGT NDVI) and ground survey. The remote sensing
data was used to compute three cropping system performance indices (Multiple Cropping Index, Area Diversity Index and Cultivated
Land Utilization Index). Ground survey was conducted using questionnaires filled up by 1,000 farmers selected from 103 villages
based on the cropping systems map. Apart from ground survey, soil and water sampling and quality analysis were carried out
to understand the effect of different cropping systems and their management practices. The results showed that, rice-wheat
was the major cropping system of the IGP, followed by Rice-Fallow-Fallow and Maize-Wheat. Other major cropping systems of
IGP included Sugarcane based, Pearl millet-Wheat, Rice-Fallow-Rice, Cotton-Wheat. The ground survey could identify 77 cropping
systems, out of which 38 are rice-based systems. Out of these 77 cropping systems, there were 5 single crop systems, occupying
6.5% coverage (of all cropping system area), 56 double crop systems with 72.7% coverage, and 16 triple crop systems with 20.8%
coverage. The cropping system performance analysis showed that the crop diversity was found to be highest in Haryana, while
the cropping intensity was highest in Punjab state. 相似文献
8.
Dhaval Vyas N.S.R. Krishnayya K.R. Manjunath S.S. Ray Sushma Panigrahy 《International Journal of Applied Earth Observation and Geoinformation》2011
There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination. 相似文献
9.
S. Panigrahy M. Chakraborty K. R. Manjunath N. Kundu J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2000,28(1):59-65
Radarsat ScanSAR Narrow (SN2) data acquired on July 24 and August 17, 1997 were used to analyse the signature of rice crop in West Bengal, India. The analysis showed that the lowland practice of cultivation gives a distinct signature to rice due to the initial water background. The relatively stable backscatter from water bodies in temporal data enhanced the separability of rice fields from water using two date data. Around 94 per cent classification accuracy was achieved for rice crop using two date data. It was feasible to discriminate rice sub-classes based on their planting period like early and late crop. The analysis indicates the suitability of ScanSAR data for large area rice crop monitoring as it has a wide swath of 300 km. 相似文献
10.
S. Panigrahy J. S. Parihar N. K. Patel V. K. Dadhwal T. T. Medhavy B. K. Ghose N. Ravi K. C. Pani B. K. Panigrahy V. N. Sridhar R. R. Mohanty S. K. Nanda D. P. Tripathy P. K. Mishra H. P. Bhatt S. R. Oza S. Sudhakar K. S. Sudha P. Kumar N. K. Das 《Journal of the Indian Society of Remote Sensing》1991,19(1):17-26
Rice is one of the most important foodgrains grown in India. Attempts have been made to estimate kharif rice acreage of Orissa state since 1986 using digital remote sensing data from Landsat MSS/TM and/or IRS-1A. Accuracies of the estimates obtained have been evaluated against BES (Bureau of Economics and Statistics) estimate. This paper describes the methodology adopted for rice acreage estimation of Orissa state, the results obtained for three years, i.e. 1986–87, 1988–89 and 1989–90, and their accuracy. 相似文献