Study of Proposed Methodology for Updating Topographical Maps Using Satellite Image Orthorectified by DEM

2014 ◽  
Vol 8 (1) ◽  
Author(s):  
A Kamel ◽  
B Alnefar
Keyword(s):  
Satellite Image ◽  
Topographical Maps

GEOMETRIC PROPERTIES ANALYSIS OF GERAN VALLEY BASIN IN ALJAZEERA DISTRICT-ANBAR PROVINCE

2020 ◽  
pp. 43-66
Author(s):  
Mushtaq Gharbi
Keyword(s):  
Data Base ◽  
Cross Sections ◽  
Satellite Image ◽  
Dry Valleys ◽  
Natural Factors ◽  
Basin Topography ◽  
Quantitative Properties ◽  
Area Unit ◽  
Oval Shape ◽  
Topographical Maps

The basin of Gran valley is considered as one of dry valleys in Aljazeera district that belongs to Hit municipality 180 km west Baghdad. Area unit was conformed from some measured quantitative properties. Geomorphological and morphometric properties was used to construct data base relied on topographical maps, satellite image and DEM. Basin topography was studied. These landforms were parted into topological, erosional, sedimentary and carstic origins forms. Furthermore, morphometric properties represented the relationships among factors, corrosion processes and terrestrial phenomena such as area, forms, topography and aquatic drain net. Moreover, longitudinal, cross- sections and natural factors were associated. Results revealed that area of basin was 91.41 km2 which its water flew in Euphrates. The basin was very meandering semi-oval shape. Its river levels were 4 with 121 courses that possessed overall length of 149.7 km.

scholarly journals Identification And Characterization Of Geoheritage Resources At Hulu Langat, Selangor

2021 ◽  
Vol 72 ◽  
pp. 191-204
Author(s):  
Tanot Unjah ◽  
◽  
Muzaffar Yusry ◽  
Norbert Simon ◽  
◽  
...  
Keyword(s):  
Hot Spring ◽  
Satellite Image ◽  
Landscape Diversity ◽  
Contact Metamorphism ◽  
Natural Landscape ◽  
Scientific Value ◽  
Identification And Characterization ◽  
Geomorphological Features ◽  
Topographical Maps

Identification and characterization of geodiversity for sites need more systematic approach for the purpose of conserving site with high geoheritage value. The present of igneous and metamorphic rock at Hulu Langat creates diversity in rock, features and geomorphological features includes hot spring, waterfall, hill and mountain that hold potential as a protected geosite of heritage value. Besides, the previous tin mining activities created by the contact metamorphism, left series of lakes and ponds while the nearby mountain is susceptible area as watershed that turn into dams around the area. Analysis on topographical maps, satellite image and aerial photograph interpretation aided in identification potential geosite based on geomorphological diversity. A total of 31 potential geosites with geological-scientific value have been identified in this study compare to 10 from the previous study. The potential geosite have been classified into rock diversity, natural landscape diversity and anthropogenic landscape diversity. Characterisation recognise 6 of these potential geosite are best example or tip location to illustrate the geological element, 4 of these geosites are the only occurrence of the geological element, 27 of the potential geosites are well preserved and 4 hold the scientific knowledge written in the international journal. The identification and characterisation of geoheritage resources are crucial steps before evaluation or assessment, ranking and conservation or development being propose, while strengthen the conservation geology approach.

Improvement of Satellite Image Resolution Using Discrete Wavelet Transform

2012 ◽  
Vol 3 (2) ◽  
pp. 170-173
Author(s):  
R.Srithulasiraman R.Srithulasiraman ◽  
◽  
M.Gobinath M.Gobinath
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Satellite Image ◽  
Image Resolution ◽  
Discrete Wavelet

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.

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