scholarly journals Lineament automatic extraction analysis for Galal Badra river basin using Landsat 8 satellite image

2019 ◽  
Vol 12 (25) ◽  
pp. 44-55 ◽  
Author(s):  
Safaa Sabah Adhab
Keyword(s):  
Program Analysis ◽  
Satellite Image ◽  
Landsat 8 ◽  
Automatic Extraction ◽  
Lineament Density ◽  
Density Analysis ◽  
Lineament Analysis ◽  
Intersection Density ◽  
Surrounding Areas ◽  
The Relationship

This research including lineament automated extraction by using PCI Geomatica program, depending on satellite image and lineament analysis by using GIS program. Analysis included density analysis, length density analysis and intersection density analysis. When calculate the slope map for the study area, found the relationship between the slope and lineament density.The lineament density increases in the regions that have high values for the slope, show that lineament play an important role in the classification process as it isolates the class for the other were observed in Iranian territory, clearly, also show that one of the lineament hit shoulders of Galal Badra dam and the surrounding areas dam. So should take into consideration the lineaments because its plays an important role in the study area.

scholarly journals A Desktop Study to Determine Mineralization Using Lineament Density Analysis at Kulon Progo Mountains, Yogyakarta and Central Java Province, Indonesia

2019 ◽  
Vol 51 (1) ◽  
pp. 31
Author(s):  
Okki Verdiansyah
Keyword(s):  
Quantitative Methods ◽  
Precious Metals ◽  
Maximum Density ◽  
Volcanic Complex ◽  
Target Area ◽  
Lineament Density ◽  
Central Java ◽  
Data Limitations ◽  
Density Analysis ◽  
Surrounding Areas

A geological study was conducted in Kulon Progo and its surrounding areas (Kulon Progo and Purworejo Regency). It focused on regional geology, tectonic configuration, geodynamics and magmatism, lithology and volcanology, and mineralization. Although there has been considerable research of mineralization in the area—particularly in Kokap (Kulon Progo Regency), Bagelen (Purworejo Regency) and Gupit (Magelang Regency), the potential of precious metals has not been determined due to data limitations. The study combined qualitative and semi-quantitative methods using a desktop geologic analysis, which facilitates lithology interpretation, volcanic boundary system, and lineament density assessment. The geology of the region is composed of an ancient volcanic complex of the Old Andesite Formation formed during the Late Oligocene-Miocene, and the mineralization in Kokap, Bagelen, and Gupit is epithermal. Based on the analysis results, the mineralization occurs in the central to proximal facies of the paleo-volcano, and the system ranges from 2.2 to 3.8 km in diameter. The manual analysis of the lineament density showed that the main direction of the lineaments was SE-NW with a maximum density of 2025.9 m/km2 and an anomaly limit of >1800 m/km2. In the combined semi-automatic analysis, the maximum density was 8.3 km/km2. The target area of mineralization included four anomalous areas, namely Bagelen-Kokap, Salaman, Kaligesing, and Loano, associated with the central and proximal facies of each small paleo-volcano.

scholarly journals DENSITY ANALYSIS OF LINEAMENTS USING REMOTE SENSING DATA

2020 ◽  
pp. 12-18
Author(s):  
Sichugova L.V ◽  
Fazilova D.Sh ◽  
Mukhtorov N.M
Keyword(s):  
Remote Sensing Data ◽  
Radar Data ◽  
Landsat 8 ◽  
Fracture Zones ◽  
Data Density ◽  
Density Analysis ◽  
Density Map ◽  
Radar Satellite ◽  
Lineament Analysis ◽  
Srtm Data

Lineament analysis is an important structural and geological indicator for determining general and local tectonic trends and fracture zones in rocks, especially in mining zones. In this study we used optical and radar satellite data to analyze the density map of lineaments structure data for territory of Kashkadarya region - one of the main gas producing regions in Uzbekistan. The results show that according to Landsat 8 high density of lineaments is located near geology faults. During a detailed exploration, the density map according to SRTM data found that at an azimuth of 1800, 2250, 2700, and 3150 the density is much higher than at an azimuth of 00, 450, 900, and 1350. KEYWORDS: lineaments, optical and radar data, density map, hillshade, rose-diagram

scholarly journals LINEAMENT DENSITY INFORMATION EXTRACTION USING DEM SRTM DATA TO PREDICT THE MINERAL POTENTIAL ZONES

2017 ◽  
Vol 13 (1) ◽  
pp. 67
Author(s):  
Udhi C. Nugroho ◽  
Arum Tjahjaningsih
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Lineament Density ◽  
Resource Potential ◽  
Long Time ◽  
Density Analysis ◽  
Early Exploration ◽  
Shaded Relief ◽  
Fitting Error

Utilization of remote sensing in geology is based on some identification of main parameters. They were the relief or morphology, flow patterns, and lineament. So it was necessary to study extraction method based on those parameters. This study aimed to obtain lineament density zone in the Geumpang area, Aceh, associated with mineral resource potential. Information of lineament density using remote sensing data was expected to help solve the problems that arised in the activities of early exploration, the difficulty of finding the prospect areas, so that the activities of pre-exploration always required a wide area and required a long time to determine the location of mineral prospect areas, it would have a direct impact on the financial of exploration activities. The used data was Landsat 8 and DEM SRTM of 30 m. The used method was processing of shaded relief on DEM data with the azimuth angle 0o, 45o, 90o, and 135o, then the result of hill shade process was done overlay, so DEM seen from all different azimuth angles. The results of the overlay were processed using the algorithm LINE with parameters such as the radius of the filter in pixels (RADI) 60, the threshold for edge gradient (GTHR) 120, the threshold for the curve length (LTHR) 100, the threshold for line fitting error (FTHR) 3, threshold for angular (ATHR) 30, and the threshold for linking distance (DTHR) 100. Vector lineament data from LINE algorithm process then performed density analysis to obtain lineament density zoning. Results from the study showed that the area has a high density lineament associated with mineral potency, so it was useful for exploration activities to minimize the survey area.

scholarly journals TEMPORAL MONITORING OF GLACIER CHANGE IN DHAULIGANGA BASIN, KUMAUN HIMALAYA USING GEO-SPATIAL TECHNIQUES

2018 ◽  
Vol IV-5 ◽  
pp. 203-207 ◽  
Author(s):  
D. K. Singh ◽  
P. K. Thakur ◽  
B. P. Naithani ◽  
S. Kaushik
Keyword(s):  
Surface Area ◽  
Satellite Image ◽  
Initial Boundary ◽  
Google Earth ◽  
Landsat Image ◽  
Landsat 8 ◽  
Automatic Extraction ◽  
Glacier Inventory ◽  
Aster Gdem ◽  
Downstream Area

<p><strong>Abstract.</strong> Present study is based on the change in surface area of the glaciers in downstream area of Dhauliganga basin, Pithoragarh district, Uttarakhand. For this study, Survey of India toposheets (surveyed in 1963) have been used for preparation of initial boundary of glaciers. Total 11 glaciers have been selected for the study in downstream valley. RGI (Randolph Glacier Inventory) data version 60. (15_RGI 60.SouthAsiaEast) from GLIMS, released July 28 2017, used for measuring temporal change in surface area. Landsat 8 OLI/TIRS image of 06 November 2016 has used to measure present area of glaciers. ASTER GDEM was used for automatic extraction of basin boundary by hydro-processing methods. Glacier boundaries were manually digitised from the Landsat image for better accuracy. The total glacier area was 69.35<span class="thinspace"></span>km<sup>2</sup> (1963), 48.10<span class="thinspace"></span>km<sup>2</sup> (2001) and 34.35 (2016) as determined from SOI toposheet, GLIMS and the Landsat image of 2016 respectively. Total vacated area by the retreat and melting of glaciers from 1963 to 2016 is 35<span class="thinspace"></span>km<sup>2</sup>, which accounts to a total loss of 50<span class="thinspace"></span>% from the total glaciated area. Google Earth imagery was also considered for marking the actual position of glacier outlines. Ice walls in topographic maps are also considered as the part of glacier but in present satellite image they are free from ice.</p>

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan

2021 ◽  
Vol 10 (4) ◽  
pp. 209
Author(s):  
Chih-Ming Tseng ◽  
Yie-Ruey Chen ◽  
Chwen-Ming Chang ◽  
Yung-Sheng Chue ◽  
Shun-Chieh Hsieh
Keyword(s):  
Satellite Image ◽  
Extreme Rainfall ◽  
Land Area ◽  
Rainfall Events ◽  
Land Disturbance ◽  
Bare Land ◽  
Southern Taiwan ◽  
The Impact ◽  
Landslide Distribution ◽  
The Relationship

This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. The genetic adaptive neural network was used in combination with the texture analysis of the geographic information system for satellite image classification and interpretation to analyze land-use change and retrieve disaster records and surface information after five rainfall events from Typhoon Morakot (2009) to Typhoon Nanmadol (2011). The results revealed that except for extreme Morakot rains, the greater the degree of slope disturbance after rain, the larger the exposed slope. Extreme rainfall similar to Morakot strikes may have a greater impact on the bare land area than on slope disturbance. Moreover, the relationship between the bare land area and the index of land disturbance condition (ILDC) is positive, and the ratio of the bare land area to the quantity of bare land after each rainfall increases with the ILDC. With higher effective accumulative rainfall on the slope in the study area or greater slope disturbance, the landslide area at the second landslide point tended to increase.

Soil Salinity Detection Using Salinity Indices from Landsat 8 Satellite Image at Rampal, Bangladesh

2021 ◽  
Author(s):  
Rubaid Hassan ◽  
Zia Ahmed ◽  
Md. Tariqul Islam ◽  
Rafiul Alam ◽  
Zhixiao Xie
Keyword(s):  
Soil Salinity ◽  
Satellite Image ◽  
Landsat 8
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