scholarly journals MONITORING NATURAL ENVIRONMENTAL INFRINGEMENTS BY TECHNOGENOUS PROCESSES DECLINING COSMOSES

2019 ◽  
Vol 4 (2) ◽  
pp. 64-72
Author(s):  
Aida Kalieva ◽  
Arslanbek Bayshuakov ◽  
Alyona Ermienko
Keyword(s):  
Spectral Analysis ◽  
Software Package ◽  
Satellite Image ◽  
Landsat 8 ◽  
Environmental Disturbances ◽  
Mining Sites ◽  
Industrial Enterprises ◽  
Erdas Imagine ◽  
Spectral Analysis Method

The tasks are given for the interpretation of environmental disturbances by technogenic processes: the selection on a satellite image of areas of natural complexes transformed by various types of economic activity; identification and characterization of sources of anthropogenic environmental impact; construction of the on-board version of the map illustrating the conclusions obtained during the interpretation of the satellite image. To perform the interpretation, images of Landsat 8 satellite images were used in two versions: in natural and false colors. Using the processes of automated decoding, in the ERDAS IMAGINE software package, images with different colors were obtained, allowing to divide objects into classes. The method "Spectral analysis" divided objects into 5 classes. According to the results of the interpretive work in MapInfo Professional, two raster images were superimposed on each other, one of which is a picture in natural colors, the other is an image obtained by the Spectral Analysis method. The imposition of raster images as a substrate allowed us to determine the boundaries of the areas that were subjected to industrial and man-made processes. As a result, a scheme was created illustrating the positions of sites subjected to industrial and man-made processes. Created in the MapInfo Professional software package, the scheme contains mining sites for coal and limestone, as well as industrial enterprises, indicated by numbers in the scheme. As a result, a scheme of environmental disturbances by industrial and man-made processes was obtained.

scholarly journals Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis

2020 ◽  
Vol 12 (23) ◽  
pp. 4001
Author(s):  
Ebrahim Ghaderpour ◽  
Tijana Vujadinovic
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Change Detection ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Stationary Time Series ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Temporal Segmentation ◽  
Jump Detection

Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings.

scholarly journals Habitat Suitability Index Modelling for Bluebull (Boselaphus tragocamelus) in Pench Tiger Reserve, M.P. India

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Abdul Haleem ◽  
Orus Ilyas
Keyword(s):  
Wildlife Conservation ◽  
Indian Subcontinent ◽  
Satellite Image ◽  
Damage Model ◽  
Additive Model ◽  
Crop Damage ◽  
Landsat 8 ◽  
Farming Community ◽  
Tiger Reserve ◽  
Erdas Imagine

The habitats for the wild animals are shrinking due to the clearance of forests for agriculture and industrialization. The idea of wildlife conservation begins with the identification of their acceptable habitat. Since this crucial information helps in the development and maintenance of the protected areas. The requirement of habitat varies with different landscapes.The bluebull (Boselaphus tragocamelus) is Asia’s largest antelope,widespread throughout the northern Indian subcontinent. Peter Simon Pallasin (1766) described it as the only member of the genus Boselaphus.The Wildlife (Protection) Act of 1972 lists it as a Schedule III animal, while the IUCN lists it as Least Concern (LC). Our goal was to design a habitat appropriateness model for blue bull so that it could reduce the conflict with farming community due to crop damage. Model will be develop using RS & GIS technique to protect the species inside the Pench Tiger Reserve (77° 55’ W to 79° 35’ E and 21° 08’ S to 22° 00’ N) the central highlands of India. The satellite data from LANDSAT-8 of 4th April 2015, Path- 144,Row- 45, with a ground resolution of 30 meters, were collected from the USGS website. This satellite image was then transferred in image format to ERDAS IMAGINE 2013 for further analysis. The data from satellites were gathered and analysed. The purpose of the field survey was to gather information about the presence of various ungulates. A ground truthing exercise was also carried out. For data processing and GIS analysis,ERDAS IMAGINE 13 and Arc GIS 10 were used. Analytical Hierarchy Process (AHP) was used Factors were identified who were influencing the spatial distribution of the species for conservation planning. The linear additive model was used for HSI. The results show that 242 km2 (29.48 percent) of Pench Tiger Reserve forest was recognized to be highly suitable for bluebull, while 196 km2 (23.87 percent) was moderately suitable,231 km2 (28.14 percent) was suitable, 109 km2 (13.28 percent) was least suitable, and about 43 km2 (5.249 percent) of PTR was completely avoided by bluebull.

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 Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1593
Author(s):  
Luca Cenci ◽  
Valerio Pampanoni ◽  
Giovanni Laneve ◽  
Carla Santella ◽  
Valentina Boccia
Keyword(s):  
Landsat 8 ◽  
Image Sharpness ◽  
Optical Images ◽  
Data Quality Assessment ◽  
Relevant Role ◽  
Edge Method ◽  
Selection Of ◽  
Statistical Criteria

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

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

Spectral synthesis and residually finite-dimensional algebras

2017 ◽  
Vol 16 (10) ◽  
pp. 1750200 ◽  
Author(s):  
László Székelyhidi ◽  
Bettina Wilkens
Keyword(s):  
Spectral Analysis ◽  
Abelian Group ◽  
Theoretical Approach ◽  
Abelian Groups ◽  
Spectral Synthesis ◽  
Residually Finite ◽  
Finite Dimensional ◽  
Analysis And Synthesis ◽  
Spectral Analysis And Synthesis

In 2004, a counterexample was given for a 1965 result of R. J. Elliott claiming that discrete spectral synthesis holds on every Abelian group. Since then the investigation of discrete spectral analysis and synthesis has gained traction. Characterizations of the Abelian groups that possess spectral analysis and spectral synthesis, respectively, were published in 2005. A characterization of the varieties on discrete Abelian groups enjoying spectral synthesis is still missing. We present a ring theoretical approach to the issue. In particular, we provide a generalization of the Principal Ideal Theorem on discrete Abelian groups.

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

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