Example of Journal of the Indian Society of Remote Sensing format
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Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format
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Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format Example of Journal of the Indian Society of Remote Sensing format
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Journal of the Indian Society of Remote Sensing — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Geography, Planning and Development #250 of 704 up up by 3 ranks
Earth and Planetary Sciences (miscellaneous) #58 of 106 down down by 2 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 610 Published Papers | 1353 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/04/2023
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

0.997

15% from 2018

Impact factor for Journal of the Indian Society of Remote Sensing from 2016 - 2019
Year Value
2019 0.997
2018 0.869
2017 0.81
2016 0.725
graph view Graph view
table view Table view

2.2

38% from 2019

CiteRatio for Journal of the Indian Society of Remote Sensing from 2016 - 2020
Year Value
2020 2.2
2019 1.6
2018 1.4
2017 1.6
2016 1.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 15% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 38% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.371

9% from 2019

SJR for Journal of the Indian Society of Remote Sensing from 2016 - 2020
Year Value
2020 0.371
2019 0.339
2018 0.311
2017 0.337
2016 0.33
graph view Graph view
table view Table view

0.713

9% from 2019

SNIP for Journal of the Indian Society of Remote Sensing from 2016 - 2020
Year Value
2020 0.713
2019 0.652
2018 0.77
2017 0.754
2016 0.718
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of the Indian Society of Remote Sensing

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Springer

Journal of the Indian Society of Remote Sensing

The aims and scope of the Journal of the Indian Society of Remote Sensing areto help towards advancement, dissemination and application of the knowledge of Remote Sensing technology, which is deemed to include photo interpretation, photogrammetry, aerial photography, image pro...... Read More

Geography, Planning and Development

Earth and Planetary Sciences (miscellaneous)

Social Sciences

i
Last updated on
17 Apr 2023
i
ISSN
0255-660X
i
Impact Factor
Medium - 0.574
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/S12524-010-0020-Z
Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches
Biswajeet Pradhan1

Abstract:

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regr... Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%). read more read less

Topics:

Landslide (58%)58% related to the paper, Fuzzy logic (53%)53% related to the paper, Multivariate statistics (52%)52% related to the paper
304 Citations
Journal Article DOI: 10.1007/BF03007341
Morphometric analysis using remote sensing techniques in the chaka sub-basin, purulia district, West Bengal
S. K. Nag1

Abstract:

Remote sensing techniques using satellite imagery have proved to be an indispensible tool in morphometric analysis and groundwater studies. In the present area, morphometric analysis using temote sensing techniques have been carried out in Chaka river sub-basin of Manbazar Block, Purulia district, West-Bengal, which is one of... Remote sensing techniques using satellite imagery have proved to be an indispensible tool in morphometric analysis and groundwater studies. In the present area, morphometric analysis using temote sensing techniques have been carried out in Chaka river sub-basin of Manbazar Block, Purulia district, West-Bengal, which is one of the most drought affected area in West-Bengal. The parameters worked out include Bifurcation ratio (Rb), Stream length (Lu). Form factor (Rf), Circulatory ratio (Rc), and Drainage density. The morphometric analysis suggests that the area is covered by fractured, resistant, permeable rocks, the drainage network not so affected by tectonic disturbances. read more read less

Topics:

Drainage density (55%)55% related to the paper
299 Citations
Journal Article DOI: 10.1007/BF02989988
Check dam positioning by prioritization of micro-watersheds using SYI model and morphometric analysis — Remote sensing and GIS perspective
K. Nooka Ratnam1, Y. K. Srivastava2, V. Venkateswara Rao1, E. Amminedu1, K. S. R. Murthy1

Abstract:

Sediment Yield Index (SYI) model and results of morphometric analysis have been used to prioritize watersheds and to locate sites for checkdam positioning in Tarafeni watershed in Midnapur district. West Bengal. Various thematic maps such as land use/land cover, slope, drainage, soil etc. were prepared from 1RS ID LISS III di... Sediment Yield Index (SYI) model and results of morphometric analysis have been used to prioritize watersheds and to locate sites for checkdam positioning in Tarafeni watershed in Midnapur district. West Bengal. Various thematic maps such as land use/land cover, slope, drainage, soil etc. were prepared from 1RS ID LISS III digital data, SOI toposheets of 1:50,000 scale and other reference maps. Morphometric parameters such as bifurcation ratio (Rb). drainage density (Dd), texture ratio (T), length of overland flow (Lo), stream frequency (Fu), compactness coefficient (Cc), circularity ratio (Rc), elongation ratio (Er), shape factor (Bs) and form factor (Rf) were computed. Automated demarcation of prioritization of micro-watersheds was done by using GIS overlaying technique by assigning weight factors to all the identified features in each thematic map and ranks were assigned to the morphometric parameters. Five categories of priority viz., very high, high, medium, low and very low, were given to all the watersheds in both the methods. Sixty-two micro-watersheds using SYI method and twenty-three micro-watersheds using morphometric have been prioritized as very high priority. Final priority map was prepared by considering the commonly occurred very high-prioritized micro-watersheds in both SYI model and morphometric analysis. Twenty-four suitable sites were identified for check dam construction in 21 highly prioritized watersheds. It is proved that integrated study of SYI model and morphometric analysis yield good result in prioritization of watersheds. read more read less

Topics:

Drainage density (50%)50% related to the paper
279 Citations
Journal Article DOI: 10.1007/BF02991569
Prioritisation of subwatersheds based on morphometric analysis of drainage basin: a remote sensing and gis approach
Sujata Biswas1, S. Sudhakar1, V. R. Desai1

Abstract:

Remote Sensing and Geographic Information System (GIS) techniques are being effectively used in recent times as tools in determining the quantitative description of basin geometry i.e., morphometric analysis. In the present study, each of the nine subwatersheds of the major part of Nayagram Block in the Midnapore District, We... Remote Sensing and Geographic Information System (GIS) techniques are being effectively used in recent times as tools in determining the quantitative description of basin geometry i.e., morphometric analysis. In the present study, each of the nine subwatersheds of the major part of Nayagram Block in the Midnapore District, West Bengal have been studied in terms of the morphometric parameters - stream length, bifurcation ratio, drainage density stream frequency, texture ratio, form factor, circularity ratio and elongation ratio and prioritised all the subwatersheds under study. The results suggest that the ratio between cumulative stream length and stream order is constant throughout the successive orders of a basin. The morphometric parametersbifurcation ratio and drainage density, confirm that the area is under dense vegetation cover and virtually the drainage has not been affected by structural disturbances. The form factor values indicate that the basin has moderately high and short duration peak flows. It was observed from the prioritisation of subwatersheds, the subwatershed 8 has got the highest priority because of high erosion intensity considering the above mentioned morphometric parameters. The subwatersheds have been analysed through Sediment Yield Index (SYI) model also and the results tally in most of the cases with the present approach. The subwatershed 8 is coming under priority—I by SYI model also, which indicates that the morphometric analysis could be used for prioritisation of subwatersheds even without the availability of reliable soil maps of the area under study. read more read less

Topics:

Drainage density (60%)60% related to the paper, Drainage basin (51%)51% related to the paper
264 Citations
Journal Article DOI: 10.1007/BF03030860
Morphometric analysis of sub-watersheds in the pavagada area of Tumkur district, South India using remote sensing and gis techniques
S. Srinivasa Vittala, S. Govindaiah1, H. Honne Gowda

Abstract:

The study area covers 570 km2 comprising of 9 sub-watersheds (Dalavayihalli, Maddalenahalli, Talamaradahalli, Puluvalli tank, Nagalamadike, Gowdatimmanahalli, Naliganahalli, Devadabetta and Byadanur) range from 49 to 75 km2 forming part of Pennar river basin around Pavagada. The drainage network of 9 sub-watersheds was deline... The study area covers 570 km2 comprising of 9 sub-watersheds (Dalavayihalli, Maddalenahalli, Talamaradahalli, Puluvalli tank, Nagalamadike, Gowdatimmanahalli, Naliganahalli, Devadabetta and Byadanur) range from 49 to 75 km2 forming part of Pennar river basin around Pavagada. The drainage network of 9 sub-watersheds was delineated using remote sensing data - Geocoded FCC of bands - 2 3 4 of IRS 1 C and 1 D (LISS III+PAN merged) on 1:50,000 scale and SOI topomaps were used as reference. The morphometric analysis of 9 sub-watersheds has been carried out using GIS softwares - Arclnfo and Are View. The drainage network shows that the terrain exhibits dendritic to sub-dendritic drainage pattern. Stream orders ranges from fourth to fifth order. Drainage density varies between 1.55 and 2.16 km/ km2 and has very coarse to coarse drainage texture. The relief ratio range from 0.006 to 0.021. The mean bifurcation ratio varies from 3.21 to 4.88 and falls under normal basin category. The elongation ratio shows that Devedabetta sub-watershed possesses circular shape while remaining sub-watersheds mark elongated pattern. Hence from the study it can be concluded that remote sensing techniques proved to be a competent tool in morphometric analysis. read more read less

Topics:

Drainage density (63%)63% related to the paper, Drainage basin (53%)53% related to the paper, Relief ratio (51%)51% related to the paper, Drainage (51%)51% related to the paper
229 Citations
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13. What is Sherpa RoMEO Archiving Policy for Journal of the Indian Society of Remote Sensing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of the Indian Society of Remote Sensing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of the Indian Society of Remote Sensing?

The 5 most common citation types in order of usage for Journal of the Indian Society of Remote Sensing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of the Indian Society of Remote Sensing Endnote style according to Elsevier guidelines.

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