scholarly journals Glacier changes monitoring in Bhutan High Himalaya using remote sensing technology

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
Mithun Kumar ◽  
Ayad M. Fadhil Al-Quraishi ◽  
Ismail Mondal
Keyword(s):  
Remote Sensing ◽  
Mountain Range ◽  
Landsat Images ◽  
Maximum Likelihood Classification ◽  
Sensing Technology ◽  
Outburst Floods ◽  
Glacier Changes ◽  
Study Results ◽  
Himalayan Mountain ◽  
Downstream Area

The primary objectives of this study are to identify glacier cover, detect changes of glacier from 1978 to 2017 in Bhutan, and identification of vulnerable zone based on glacier changes. Landsat images and remote sensing (RS) techniques, Maximum Likelihood Classification approach of Supervised Learning of image processing has been utilized to detect and monitor the glacier cover changes of Bhutan over the years (1978-2017). The study results revealed that total area of the glacier changes in 1978 and 2017 has decreased by 2.54%. Most of the glacier zones are found in the northern part of the country, basically near the Greater Himalayan Mountain Range. Gasa, Bamthung and Lhuentse districts of Bhutan were found to have more glacier changes area than the other districts, that the areas experienced more changes of glacier cover over 40 years. Where more changes in the glacier are detected, the area should be more vulnerable in terms of both landslide, unreliability in growing useful land covers and Glacier Lakes Outburst Floods (GLOFS) in downstream area.

scholarly journals EXTRACTING THE WATERLINE FROM LANDSAT IMAGES

2009 ◽  
Vol 12 (12) ◽  
pp. 52-58
Author(s):  
Thao Thi Phuong Pham ◽  
Duan Dinh Ho ◽  
To Van Dang
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Numerical Models ◽  
Scientific Research ◽  
Remote Sensing And Gis ◽  
Landsat Images ◽  
Shoreline Changes ◽  
Remote Sensing Technology ◽  
Spectral Bands ◽  
Sensing Technology

Remote sensing technology nowadays is one of the most useful tools for scientific research in general and for oceanography in particular. From satellite images, the useful information such as waterline images can be extracte for a large region simultaneously. After tidal adjustments, the waterlines can be used as the observed shorelines which are important inputs for estimating shoreline changes by either using the integration of remote sensing and GIS or using numerical models. Based on the spectral bands of various Landsat images, the paper presents the methods to detect the waterlines in Phan Thiet region in the 40 years period using the images of 1973, 1976, 1990, and 2002 respectively. The extracted results relatively agree with the information of waterline from the images.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

scholarly journals Remote Sensing Data Classification Technique: A Review

2021 ◽  
pp. 67-75
Author(s):  
Vaibhav A. Didore ◽  
Dhananjay B. Nalawade ◽  
Renuka B. Vaidya
Keyword(s):  
Remote Sensing ◽  
Minimum Distance ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
Image Data ◽  
Soil Science ◽  
K Nearest Neighbor ◽  
Maximum Likelihood Classification ◽  
Sensing Technology ◽  
Cover Class

Remote sensing is the prominent technology to study the ecology of the earth. Classification is a commonly used technique for quantitative analysis of remote sensing image data. It is based on the concept of segmentation of spectral regions into regions that can be associated with a soil cover class of interest for a particular application. As an advanced remote sensing tool, Hyperspectral remote sensing technology has been studied in many applications such as geology, topography, biology, soil science, hydrology, plants and ecosystems, atmospheric science. In this paper, Supervised Decision tree; Minimum distance; Maximum likelihood classification; Parallelepiped; K-nearest neighbor; and Unsupervised K-mean; & ISODATA algorithm are reviewed. This review is helpful to the researchers who are studying this emerging field i.e. HRS.

scholarly journals POLA PERKEMBANGAN RUANG DI KABUPATEN SEMARANG DENGAN MEMANFAATKAN DATA CITRA LANDSAT

2017 ◽  
Vol 5 (1) ◽  
pp. 58
Author(s):  
Pangi Pangi ◽  
Muharar Ramadhan ◽  
Khristiana Dwi Astuti ◽  
Intan Muning Harjanti ◽  
Reny Yesiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Growth ◽  
Landsat Images ◽  
Toll Road ◽  
Remote Sensing Technology ◽  
Central Java ◽  
Sensing Technology ◽  
Controlling Mechanism ◽  
The City

The development of the city is influenced by the growth on the physical, economic, and social activities in the city. In the case of Semarang Regency, this development is included in the category fairly fast, particularly after the opening of toll road Semarang-Bawen. By utilizing remote sensing technology and satellite imagery, the pattern of these developments can be mapped and analyzed. The purpose of this study was to identify the pattern of development of space in Semarang Regency by using multitemporal Landsat images. The application of these images can be a mean of monitoring and controlling mechanism of the urban growth in the fast growing city in Central Java, such as in Semarang Regency. Hence, the focus of this analysis is on data of built up area. During the period of 43 years (1972 - 2015) land use change that occurred in the district of Semarang had increased for about 13,000 Ha or an average of 300 ha/year. The biggest changes are located in the Tengaran, Susukan and Bergas sub district that grow more than 20 ha / year. 

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology

Application of remote sensing technology to the relation of submarine pipelines and coastal morphology

1995 ◽  
Vol 32 (2) ◽  
pp. 77-83
Author(s):  
Y. Yüksel ◽  
D. Maktav ◽  
S. Kapdasli
Keyword(s):  
Remote Sensing ◽  
Sediment Transport ◽  
Surf Zone ◽  
Hydrodynamic Forces ◽  
Construction Site ◽  
Coastal Morphology ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Sea Bed

Submarine pipelines must be designed to resist wave and current induced hydrodynamic forces especially in and near the surf zone. They are buried as protection against forces in the surf zone, however this procedure is not always feasible particularly on a movable sea bed. For this reason the characteristics of the sediment transport on the construction site of beaches should be investigated. In this investigation, the application of the remote sensing method is introduced in order to determine and observe the coastal morphology, so that submarine pipelines may be protected against undesirable seabed movement.

scholarly journals Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

2021 ◽  
Vol 11 (15) ◽  
pp. 6923
Author(s):  
Rui Zhang ◽  
Zhanzhong Tang ◽  
Dong Luo ◽  
Hongxia Luo ◽  
Shucheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Identification Accuracy ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Single Time ◽  
Growing Period ◽  
Sensing Technology ◽  
Sar Imagery ◽  
Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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