scholarly journals Comparison between satellite image analysis and site data for monitoring Trail Road landfill site

2021 ◽  
Author(s):  
Ramona Mirtorabi
Keyword(s):  
Image Analysis ◽  
Surface Temperature ◽  
Satellite Images ◽  
Satellite Image ◽  
Monitoring Program ◽  
Landfill Site ◽  
Site Monitoring ◽  
Landfill Sites ◽  
Satellite Image Analysis ◽  
The City

Human life affects the environment in different ways; therefore monitoring human's actions is very important to safeguarding the environment. Studying the human impact on nature is essential to protecting our environment from contaminations. Landfill sites are one of the most influential structures upon nature. Landfills pose a potential danger to the surrounding environment. Therefore they must be supervised for long periods of time to determine their impact. Monitoring the effects of the landfill sites on the surrounding area over a period of time is a useful tool to analyze and understand its effect on the environment. This research work presents a study which uses data analyzed from satellite images for the monitoring of landfill sites. The data collected from satellite images is compared with the data collected from ground measurements. The main goal of this research is to verify the usefulness of remote sensing as a tool for landfill site monitoring. The ground measurement data used in this study is from yearly reports of a monitoring program by the City of Ottawa that are collect by Dillon Limited. The satellite images used are Landsat satellite images downloaded from the U.S. Geological Survey and Earth Resources, and analyzed by ERDAS IMAGINE and ArcMap software. The images are taken from four years: May 1992, August 1998, October 2000, and September 2001. The images are analyzed in terms of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Results from the LST and NDVI value of different years are compared with the results of monitoring program [sic] that has been conducted for the City of Ottawa. Preliminary data analysis of the satellite images reveals that the surface temperature of the landfill site is always higher than the immediate surrounding areas. Any significant changes in LST and NDVI value, especially in the surrounding vegetation areas, are regarded as suspect sites which may be influenced by the development of the landfill site. The result of the comparison between testing and sampling at monitoring wells with satellite image analysis confirms the areas that are more contaminated. The polluted areas show the same locations from both analyses. However, changes at LST and NDVI value analysis could imply the pollution movement earlier than the traditional site sampling monitoring method. These results show the possibility of combining the ground sampling system and satellite images analysis to improve landfill site monitoring.

scholarly journals Comparison between satellite image analysis and site data for monitoring Trail Road landfill site

2021 ◽  
Author(s):  
Ramona Mirtorabi
Keyword(s):  
Image Analysis ◽  
Surface Temperature ◽  
Satellite Images ◽  
Satellite Image ◽  
Monitoring Program ◽  
Landfill Site ◽  
Site Monitoring ◽  
Landfill Sites ◽  
Satellite Image Analysis ◽  
The City

Human life affects the environment in different ways; therefore monitoring human's actions is very important to safeguarding the environment. Studying the human impact on nature is essential to protecting our environment from contaminations. Landfill sites are one of the most influential structures upon nature. Landfills pose a potential danger to the surrounding environment. Therefore they must be supervised for long periods of time to determine their impact. Monitoring the effects of the landfill sites on the surrounding area over a period of time is a useful tool to analyze and understand its effect on the environment. This research work presents a study which uses data analyzed from satellite images for the monitoring of landfill sites. The data collected from satellite images is compared with the data collected from ground measurements. The main goal of this research is to verify the usefulness of remote sensing as a tool for landfill site monitoring. The ground measurement data used in this study is from yearly reports of a monitoring program by the City of Ottawa that are collect by Dillon Limited. The satellite images used are Landsat satellite images downloaded from the U.S. Geological Survey and Earth Resources, and analyzed by ERDAS IMAGINE and ArcMap software. The images are taken from four years: May 1992, August 1998, October 2000, and September 2001. The images are analyzed in terms of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Results from the LST and NDVI value of different years are compared with the results of monitoring program [sic] that has been conducted for the City of Ottawa. Preliminary data analysis of the satellite images reveals that the surface temperature of the landfill site is always higher than the immediate surrounding areas. Any significant changes in LST and NDVI value, especially in the surrounding vegetation areas, are regarded as suspect sites which may be influenced by the development of the landfill site. The result of the comparison between testing and sampling at monitoring wells with satellite image analysis confirms the areas that are more contaminated. The polluted areas show the same locations from both analyses. However, changes at LST and NDVI value analysis could imply the pollution movement earlier than the traditional site sampling monitoring method. These results show the possibility of combining the ground sampling system and satellite images analysis to improve landfill site monitoring.

scholarly journals Satellite image analysis to evaluate the urban growth and land use changes in the city of Samara from 1975 to 2015

2015 ◽  
Vol 39 (5) ◽  
pp. 818-822 ◽  
Author(s):  
M.S. Boori ◽  
◽  
A.V. Kuznetsov ◽  
K.К. Choudhary ◽  
A.V. Kupriyanov ◽  
...  
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
Urban Growth ◽  
Land Use Changes ◽  
Satellite Image ◽  
Satellite Image Analysis ◽  
The City

scholarly journals Comparing workflow application designs for high resolution satellite image analysis

2021 ◽  
Author(s):  
Aymen Al-Saadi ◽  
Ioannis Paraskevakos ◽  
Bento Collares Gonçalves ◽  
Heather J. Lynch ◽  
Shantenu Jha ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Satellite Image ◽  
High Resolution Satellite Image ◽  
Satellite Image Analysis ◽  
Workflow Application

scholarly journals Metagenomic and satellite analyses of red snow in the Russian Arctic

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1491 ◽  
Author(s):  
Nao Hisakawa ◽  
Steven D. Quistad ◽  
Eric R. Hester ◽  
Daria Martynova ◽  
Heather Maughan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Dna Analysis ◽  
Satellite Image ◽  
Polar Regions ◽  
Russian Arctic ◽  
Metagenomic Dna ◽  
Satellite Imaging ◽  
Franz Josef Land ◽  
Satellite Image Analysis ◽  
Snow And Ice

Cryophilic algae thrive in liquid water within snow and ice in alpine and polar regions worldwide. Blooms of these algae lower albedo (reflection of sunlight), thereby altering melting patterns (Kohshima, Seko & Yoshimura, 1993; Lutz et al., 2014; Thomas & Duval, 1995). Here metagenomic DNA analysis and satellite imaging were used to investigate red snow in Franz Josef Land in the Russian Arctic. Franz Josef Land red snow metagenomes confirmed that the communities are composed of the autotrophChlamydomonas nivalisthat is supporting a complex viral and heterotrophic bacterial community. Comparisons with white snow communities from other sites suggest that white snow and ice are initially colonized by fungal-dominated communities and then succeeded by the more complexC. nivalis-heterotroph red snow. Satellite image analysis showed that red snow covers up to 80% of the surface of snow and ice fields in Franz Josef Land and globally. Together these results show thatC. nivalissupports a local food web that is on the rise as temperatures warm, with potential widespread impacts on alpine and polar environments worldwide.

scholarly journals Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

2020 ◽  
Vol 12 (2) ◽  
pp. 38
Author(s):  
Rani Yudarwati ◽  
Chiharu Hongo ◽  
Gunardi Sigit ◽  
Baba Barus ◽  
Budi Utoyo
Keyword(s):  
Image Analysis ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Leaf Blight ◽  
Rice Crop ◽  
Bacterial Leaf Blight ◽  
First Derivative ◽  
Red Edge ◽  
Multi Temporal ◽  
Satellite Image Analysis

This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively

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