scholarly journals ADAPTATION OF THE METHOD OF STRUCTURAL-MORPHOMETRIC ANALYSIS TO THE GIS ENVIRONMENT FOR PALEOGEOMORPHOLOGICAL STUDIES OF THE KANIV DNIEPER

2020 ◽  
pp. 6-11
Author(s):  
O. Ivanik ◽  
L. Tustanovska ◽  
D. Kravchenko ◽  
K. Hadiatska
Keyword(s):  
Remote Sensing ◽  
Morphometric Analysis ◽  
Tectonic Evolution ◽  
Remote Sensing Data ◽  
Detailed Comparison ◽  
Time Intervals ◽  
Unique Region ◽  
Quaternary Phase ◽  
Local Erosion ◽  
Gis Environment

Kaniv Dnieper area is a unique region that has evolved through the Neogene-Quaternary phase. The application of geological and geomorphological methods, remote sensing data and GIS made it possible to identify the genetic relationship between the processes of geomorphogenesis and tectogenesis within the Kaniv Dnieper region, to build a number of geological and geomorphological models. The methodology of structural-morphometric analysis is adapted to the GIS and the process of cartometric constructions is automated. An algorithm for creating order maps of valleys and basal surfaces has been developed. Basal surfaces are complex surfaces that combine local erosion bases and express the total movements of the earth's crust over various time intervals. A study of the morphogenesis and tectogenesis of the territory of the Kaniv Dnieper region showed that the neotectonic movements within this region had a differentiated character. Five stages of tectonic evolution were determined during the Neogene-Quaternary stage of its development. Hypsometry of basic surfaces of five orders was investigated, and a detailed comparison of the morphology of single-order surfaces has been made. On the basis of the obtained data on the nature of the surfaces, absolute and relative elevations, the nature of the isobasite pattern, the geomorphological structure of each stage has been analyzed in detail. The conducted studies revealed a regular relationship between the relief and tectonics.

Facilities management using remote sensing data in a GIS environment

1998 ◽  
Vol 43 (9-10) ◽  
pp. 487-491 ◽  
Author(s):  
M. Sebastian ◽  
V. Jayaraman ◽  
M.G. Chandrasekhar
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Facilities Management ◽  
Sensing Data ◽  
Gis Environment

scholarly journals Flash-Flood Potential Assessment by Integrating the Remote Sensing Data and GIS with Reference to Adam Area, Western Saudi Arabia

2020 ◽  
Vol 9 (11) ◽  
pp. 78-84
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Statistical Data ◽  
Flash Flood ◽  
Remote Sensing Data ◽  
Curve Number ◽  
Development Planning ◽  
Return Periods ◽  
Development Plans ◽  
Gis Environment

Heavy rainstorms are common occurrences in the Western mountainous region of Saudi Arabia that results in hazardous floods damaging the infrastructure and development plans. Severe rainstorms and heavy showers cause instant flash floods that result in major damage of properties and loss of human lives. Therefore, it becomes crucial during the development planning that floods are accurately analyzed. For the calculation and spatial mapping of flood features, an integrated remote sensing and GIS methodology has been formed. This new methodology makes use of various landscape, metrological, geological, and land use datasets in a GIS environment by employing the technique of Curve Number (CN) of flood modeling for unrestricted dry catchments. The prediction of rainfall depths for 50 and 100-years are 73.6 and 82.3 mm respectively. 4.3679 and 8.0605 million cubic meters are the flood volumes for 50- and 100-year return periods. Moreover, the flood’s statistical data like the depth and volume of runoff is added in GIS layers’ attribute tables so that all results are collected in the same environment. The application of advanced methodology aids in providing exact estimations and digital results. Moreover, it is economical and can be re-operated in different circumstances as well.

Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach

2018 ◽  
Vol 2 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ajaykumar Kadam ◽  
B. N. Umrikar ◽  
R. N. Sankhua
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Remote Sensing And Gis ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Gis Environment ◽  
Soil Loss Equation

A comprehensive methodology that combines Revised Universal Soil Loss Equation (RUSLE), Remote Sensing data and Geographic Information System (GIS) techniques was used to determine the soil loss vulnerability of an agriculture mountainous watershed in Maharashtra, India. The spatial variation in rate of annual soil loss was obtained by integrating raster derived parameter in GIS environment. The thematic layers such as TRMM [Tropical Rainfall Measuring Mission] derived rainfall erosivity (R), soil erodibility (K), GDEM based slope length and steepness (LS), land cover management (C) and factors of conservation practices (P) were calculated to identify their effects on average annual soil loss. The highest potential of estimated soil loss was 688.397 t/ha/yr. The mean annual soil loss is 1.26 t/ha/yr and highest soil loss occurs on the main watercourse, since high slope length and steepness. The spatial soil loss maps prepared with RUSLE method using remote sensing and GIS can be helpful as a lead idea in arising plans for land use development and administration in the ecologically sensitive hilly areas.

scholarly journals ESTABLISHING THE SCHEME OF THE FAULTS IN SOUTH CENTRAL COASTAL REGION ON THE BASIS OF INTERPRETING THE GEOLOGICAL-GEOPHYSICAL DATA

2018 ◽  
Vol 17 (4) ◽  
pp. 393-405
Author(s):  
Bui Nhi Thanh ◽  
Duong Quoc Hung ◽  
Nguyen Van Luong ◽  
Nguyen Van Diep ◽  
Mai Duc Dong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Seismic Data ◽  
Tectonic Evolution ◽  
Coastal Region ◽  
Remote Sensing Data ◽  
Geological Hazards ◽  
Sensing Data ◽  
South Central ◽  
Shallow Seismic

The scheme of the faults in South Central coastal region was established on the basis of interpreting the high resolution shallow seismic data and the deep-seismic data, in combination with the previous studies on geodynamics, tectonic evolution, geological hazards of the South Central coastal region. The fault systems were formed based on updated geophysical, geomorphological, tectonophysic and remote sensing data, including 19 faults in 3 directions: Sub-longitudinal (8 faults), NE-SW (7 faults) and NW-SE (4 faults).

scholarly journals Hydrologic behaviour of Tapi river catchment using morphometric analysis

2014 ◽  
Vol 6 (2) ◽  
pp. 442-450 ◽  
Author(s):  
Vishal K. Ingle ◽  
A. K. Mishra ◽  
A. Sarangi ◽  
D. K. Singh ◽  
V. K. Seghal
Keyword(s):  
Remote Sensing ◽  
Morphometric Analysis ◽  
Remote Sensing Data ◽  
Drainage Density ◽  
Drainage Network ◽  
Elongation Ratio ◽  
Drainage Pattern ◽  
River Catchment ◽  
Seventh Order ◽  
Drainage Texture

The study area Tapi River catchment covers 63,922.91 Sq.Km comprising of 5 five Sub-catchments: Purna river catchment (18,473.6 sq.km) Upper Tapi catchment (10,530.3 sq. km), Middle Tapi catchment (4,997.3 sq km), Girna river catchment (10,176.9 sq.km) and lower Tapi catchment (19,282.5 sq.km.). The drainage network of 5 Sub-catchments was delineated using remote sensing data. The morphometric analysis of 5 Sub-catchments has been carried out using GIS softwares – ArcMap. The drainage network showed that the terrain exhibits dendritic to sub-dendritic drainage pattern. Stream orders ranged from sixth to seventh order. Drainage density varied between 0.39 and 0.43km/ km2and had very coarse to coarse drainage texture. The relief ratio ranged from 0.003 to 0.007. The mean bifurcation ratio varied from 4.24 to 6.10 and falls under normal basin category. The elongation ratio showed that all catchment elongated pattern. Thus, the remote sensing techniques proved to be a competent tool in morphometric analysis.

scholarly journals Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

2021 ◽  
Vol 14 (1-2) ◽  
pp. 38-46
Author(s):  
Balázs Jakab ◽  
Boudewijn van Leeuwen ◽  
Zalán Tobak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Remote Sensing Data ◽  
Graphical Interface ◽  
Training Time ◽  
Average Accuracy ◽  
Gis Environment

Abstract Agricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like spatial planning, environmental protection, agricultural statistics and taxation. Therefore, with this study we aim to develop a methodology to detect plastic greenhouses in remote sensing data using machine learning algorithms. This research presents the results of the use of a convolutional neural network for automatic object detection of plastic greenhouses in high resolution remotely sensed data within a GIS environment with a graphical interface to advanced algorithms. The convolutional neural network is trained with manually digitized greenhouses and RGB images downloaded from Google Earth. The ArcGIS Pro geographic information system provides access to many of the most advanced python-based machine learning environments like Keras – TensorFlow, PyTorch, fastai and Scikit-learn. These libraries can be accessed via a graphical interface within the GIS environment. Our research evaluated the results of training and inference of three different convolutional neural networks. Experiments were executed with many settings for the backbone models and hyperparameters. The performance of the three models in terms of detection accuracy and time required for training was compared. The model based on the VGG_11 backbone model (with dropout) resulted in an average accuracy of 79.2% with a relatively short training time of 90 minutes, the much more complex DenseNet121 model was trained in 16.5 hours and showed a result of 79.1%, while the ResNet18 based model showed an average accuracy of 83.1% with a training time of 3.5 hours.

Implication of Remote Sensing Data under GIS Environment for Appraisal of Irrigation System Performance

2021 ◽  
Author(s):  
Sultan Ahmad Rizvi ◽  
Afeef Ahmad ◽  
Muhammad Latif ◽  
Abdul Sattar Shakir ◽  
Aftab Ahmad Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
System Performance ◽  
Irrigation System ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Gis Environment
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