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  Subjects -> GEOGRAPHY (Total: 493 journals)
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Journal of Remote Sensing & GIS
Number of Followers: 37  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 2230-7990 - ISSN (Online) 2321-421X
Published by STM Journals Homepage  [66 journals]
  • Quantitative Analysis of Morphometry using G.I.S. of Pattankodoli Nala,
           Kolhapur, M.S., India.

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      Authors: Yogita A. Patil, Abhijit J. Patil, Siddhesh Jathar
      Pages: 1 - 8
      Abstract: Drainage basin characteristics depends on various factors like geology of the area, soil type, hydrological setting of the area etc. Study of morphometric characters is helpful to know more about lithological structures, geomorphological features and conditions, lineaments and hydrological characteristics of the area which in turn throws light on ground water conditions and movement of the area. Study area is Pattankodoli Nala Basin, bounded by latitude 16°36'31'' N to 16°40'44'' N and longitude 74°18'37'' E to 74°22'39'' E in Survey of India (SIO) Toposheet numbers 47L/6 on the scale 1:50000. Morphometric analysis has been carried out and various morphometric aspects have been studied. On the basis of quantitative analysis of morphometric parameters, it found that the Pattankadoli Nala is 4th ordered and the whole river contains 50 streams. The low drainage density and low stream frequency indicates that the drainage nala has less runoff in the channel. The basin is having elongated shape and gentle slope. Both relief and drainage density are low to moderate. It is found that the South Western part of the basin has moderate to good ground water potential and is favorable for artificial recharge site construction.
      PubDate: 2022-05-13
      Issue No: Vol. 13, No. 1 (2022)
       
  • Hyperspectral Image Compression & Classification: A Survey

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      Authors: Tahereen Rizvi, B. Sucharitha, Nida Zia, Syed Diraar Ahmed
      Pages: 9 - 23
      Abstract: The applications of Hyper spectral images (HSI) are many, which include agriculture, food quality, remote sensing, medical diagnostics and safety assessment. Hyper spectral image analysis has been used for detecting contaminants and identifying defects in food. It also utilizes advanced software and hardware tools hence allowing users to diagnose and detect pathologies. In this paper an avant-garde investigation about hyper spectral image compression and classification techniques which can be used in various applications like broadcasting of television, remote sensing via satellite, storage and classification of medical images, pictures and documents has been made. Significant increase in multimedia products has created a need to enhance, extract, store and interpret the information received in the most effective manner. The size of a hyper spectral image comprises approximately 138.81 megabytes and hence requires large space for storage. Hence, hyper spectral image compression is of great importance as it reduces the data redundancy and also the hardware space required for storage. Hyper spectral image classification has gained great research attention due to the increasing demand of feature information extraction. This survey focuses on describing the recent advances in spectral–spatial classification of hyperspectral images and various recent advancements in compression techniques for input HSIs.   
      PubDate: 2022-06-06
      Issue No: Vol. 13, No. 1 (2022)
       
  • Land use and Land cover mapping for Yellanuru Mandal of Anantapur, AP,
           India.

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      Authors: B. N. Anusha, B. Pradeep Kumar, K. Raghu Babu, P. Padma Sree, B. Narayana Swamy, P. Ravi Kumar
      Pages: 24 - 30
      Abstract: Land use and landcover (LULC) change has become a critical component of current methods for not just urban planning but also managing a region's natural resources. The focus of this research is to evaluate the LULC scenario that occurred in Yellanuru Mandal of Anantapur district, AP, India between 2010 and 2020 using Remote Sensing (RS) and Geographical Information Systems (GIS). This study was conducted by gathering Landsat data over a decade, from 2010 to 2020, using ERDAS Imagine 2014 and Arc GIS software, and generating a LULC map using a supervised classification approach. The research region's LULC is divided into five types: vegetation, fallow land, agricultural land, developed land, and water bodies/river. Our findings suggest that satellite data are well adapted to classifying land use/land cover at the subpixel level, where land use categories are linked with homogenous land cover.
      PubDate: 2022-06-15
      Issue No: Vol. 13, No. 1 (2022)
       
 
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