The Analysis of Coastal Erosion and Erosion Impact Assessment in the East Coast

Various development projects occurring on the coast cause an imbalance of surface sediments, causing coastal disasters or irreversible coastal erosion. Coastal erosion caused by the influence of various port structures built through coastal development can be directly identified by evaluating changes in the sediment budget, long-shore sediment, and cross-shore sediment. In other words, it will be possible to evaluate the causality between coastal development and coastal erosion by classifying regions due to single cause and regions due to multiple causes according to the changes in the sediment classified into the three types mentioned above. In this study, the cause of long-term and continuous erosion was analyzed based on the analysis results of the coastal development history and the Coastal Erosion Monitoring targeting the coast of Gangwon-do and Gyeongsangbuk-do on the east coast. In addition, in order to evaluate the degree of erosion caused by the construction of artificial coastal structures, the concept of erosion impact assessment was established, three methods were proposed for the impact assessment. The erosion impact of Hajeo port was assessed using the results of satellite image analysis presented in the Coastal Erosion Monitoring Report, it was assessed that the development of Hajeo port had an impact of 93.4% on erosion, and that of the coastal road construction had an impact of 6.6%.

Importance of Local Studies of Vascular Plant Communities in Conservation and Management: A Case Study in Susticacán, Zacatecas, Mexico

Some parts of the globe have a deficient vegetation coverage survey causing localized plant community qualities generalized from larger scales, hindering their particular configuration. This process is emphasized in megadiverse countries such as Mexico by transformation and loss of land cover. This can be reflected in the municipality of Susticacán, Zacatecas, settled in a mountainous, scarcely explored area, the Sierra de los Cardos. This study aimed to characterize its plant communities, produce a fine-scale map and compare them to other descriptions. Oak forests, pine forests, grasslands, nopaleras, chaparral, and rock outcrop vegetation were detected through satellite image analysis, sampled, statistically evaluated, and their descriptions supported by the literature. The first two presented a high diversity and endemism, despite a small surface. The chaparral occupied the largest area, and its structure and composition suggest its secondary vegetation in expansion. The presence of exotic–invasive species and human activities threaten the native flora. This study is the first to provide detailed information on the plant communities in Susticacán and is a model for the study of local-scale regions. It highlights the importance of describing and mapping them as a contribution to delineate conservation and management efforts.

Deteksi Perubahan Garis Pantai Menggunakan Teknik Geospasial, Studi Kasus Kecamatan Tejakula

The coastal area in Tejakula Subdistrict has many potential benefits for local people and the development of the region. Regarding its economic perspective, marine biodiversity can be utilized for tourism development. In terms of culture and history, this area has many archaeological findings ranging from prehistory until the colonial period. However, the recent study from Balai Arkeologi Bali stated the objects were sunk into underwater at a depth of 1-2 meters due to the abrasion process. Therefore, this research discussed the changes of shoreline in the Tejakula Subdistrict area as a preliminary study to protect and preserve its potential values. This research, both in terms of economy, culture, and history. The calculation of shoreline changes is using the Digital Shoreline Analysis System (DSAS) application with Net Shoreline Movement (NSM) and End Point Rate (EPR) methods. The results showed that the coastal segment in Tejakula District experienced an abrasion change with an abrasion rate of 0.89 m/year based on the SPOT satellite image analysis and 0.17 m/year from Landsat satellite imagery.

Deep Learning in Satellite Imaging: A Survey

In recent time deep learning has been extensively applied in satellite image analysis, the aim of this work was to conduct a thorough review on the application of deep learning in satellite imaging, moreover we have also provide a detail description regarding the principles of satellite image capturing, in addition to the mathematical models of image processing techniques used in satellite images such as image denoising, image filtering, image segmentation and histogram equalization. We have also discuss some of the aspect of deep learning but not in deep. Finally we have pave away for further research directions both in satellite imaging and deep learning.

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

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.

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

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.