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Saito</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20R.%20Campos"> José R. Campos</a>, <a href="https://publications.waset.org/abstracts/search?q=L%C3%BAcio%20A.%20de%20C.%20Jorge"> Lúcio A. de C. Jorge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=band%20selection" title="band selection">band selection</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means" title=" fuzzy c-means"> fuzzy c-means</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20image" title=" hyperspectral image"> hyperspectral image</a> </p> <a href="https://publications.waset.org/abstracts/50614/approach-based-on-fuzzy-c-means-for-band-selection-in-hyperspectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3892</span> A West Coast Estuarine Case Study: A Predictive Approach to Monitor Estuarine Eutrophication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vedant%20Janapaty">Vedant Janapaty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estuary" title="estuary">estuary</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20transform" title=" Fourier transform"> Fourier transform</a> </p> <a href="https://publications.waset.org/abstracts/150075/a-west-coast-estuarine-case-study-a-predictive-approach-to-monitor-estuarine-eutrophication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3891</span> Close-Range Remote Sensing Techniques for Analyzing Rock Discontinuity Properties</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sina%20Fatolahzadeh">Sina Fatolahzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20A.%20Sep%C3%BAlveda"> Sergio A. Sepúlveda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents advanced developments in close-range, terrestrial remote sensing techniques to enhance the characterization of rock masses. The study integrates two state-of-the-art laser-scanning technologies, the HandySCAN and GeoSLAM laser scanners, to extract high-resolution geospatial data for rock mass analysis. These instruments offer high accuracy, precision, low acquisition time, and high efficiency in capturing intricate geological features in small to medium size outcrops and slope cuts. Using the HandySCAN and GeoSLAM laser scanners facilitates real-time, three-dimensional mapping of rock surfaces, enabling comprehensive assessments of rock mass characteristics. The collected data provide valuable insights into structural complexities, surface roughness, and discontinuity patterns, which are essential for geological and geotechnical analyses. The synergy of these advanced remote sensing technologies contributes to a more precise and straightforward understanding of rock mass behavior. In this case, the main parameters of RQD, joint spacing, persistence, aperture, roughness, infill, weathering, water condition, and joint orientation in a slope cut along the Sea-to-Sky Highway, BC, were remotely analyzed to calculate and evaluate the Rock Mass Rating (RMR) and Geological Strength Index (GSI) classification systems. Automatic and manual analyses of the acquired data are then compared with field measurements. The results show the usefulness of the proposed remote sensing methods and their appropriate conformity with the actual field data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=rock%20mechanics" title=" rock mechanics"> rock mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=rock%20engineering" title=" rock engineering"> rock engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=slope%20stability" title=" slope stability"> slope stability</a>, <a href="https://publications.waset.org/abstracts/search?q=discontinuity%20properties" title=" discontinuity properties"> discontinuity properties</a> </p> <a href="https://publications.waset.org/abstracts/183400/close-range-remote-sensing-techniques-for-analyzing-rock-discontinuity-properties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183400.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3890</span> Wildfires Assessed By Remote Sensed Images And Burned Land Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20da%20Concei%C3%A7%C3%A3o%20Proen%C3%A7a">Maria da Conceição Proença</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This case study implements the evaluation of burned areas that suffered successive wildfires in Portugal mainland during the summer of 2017, killing more than 60 people. It’s intended to show that this evaluation can be done with remote sensing data free of charges in a simple laptop, with open-source software, describing the not-so-simple methodology step by step, to make it available for county workers in city halls of the areas attained, where the availability of information is essential for the immediate planning of mitigation measures, such as restoring road access, allocate funds for the recovery of human dwellings and assess further restoration of the ecological system. Wildfires also devastate forest ecosystems having a direct impact on vegetation cover and killing or driving away from the animal population. The economic interest is also attained, as the pinewood burned becomes useless for the noblest applications, so its value decreases, and resin extraction ends for several years. The tools described in this paper enable the location of the areas where took place the annihilation of natural habitats and establish a baseline for major changes in forest ecosystems recovery. Moreover, the result allows the follow up of the surface fuel loading, enabling the targeting and evaluation of restoration measures in a time basis planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfires" title=" wildfires"> wildfires</a>, <a href="https://publications.waset.org/abstracts/search?q=burned%20areas%20evaluation" title=" burned areas evaluation"> burned areas evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel-2" title=" sentinel-2"> sentinel-2</a> </p> <a href="https://publications.waset.org/abstracts/144422/wildfires-assessed-by-remote-sensed-images-and-burned-land-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144422.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3889</span> Monitoring of Cannabis Cultivation with High-Resolution Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Levent%20Basayigit">Levent Basayigit</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinan%20Demir"> Sinan Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Burhan%20Kara"> Burhan Kara</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusuf%20Ucar">Yusuf Ucar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cannabis" title="Cannabis">Cannabis</a>, <a href="https://publications.waset.org/abstracts/search?q=drug" title=" drug"> drug</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=object-based%20classification" title=" object-based classification"> object-based classification</a> </p> <a href="https://publications.waset.org/abstracts/74202/monitoring-of-cannabis-cultivation-with-high-resolution-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74202.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">276</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3888</span> Research Analysis of Urban Area Expansion Based on Remote Sensing </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheheryar%20Khan">Sheheryar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Weidong%20Li"> Weidong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Fanqian%20Meng"> Fanqian Meng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Urban Heat Island (UHI) effect is one of the foremost problems out of other ecological and socioeconomic issues in urbanization. Due to this phenomenon that human-made urban areas have replaced the rural landscape with the surface that increases thermal conductivity and urban warmth; as a result, the temperature in the city is higher than in the surrounding rural areas. To affect the evidence of this phenomenon in the Zhengzhou city area, an observation of the temperature variations in the urban area is done through a scientific method that has been followed. Landsat 8 satellite images were taken from 2013 to 2015 to calculate the effect of Urban Heat Island (UHI) along with the NPP-VRRIS night-time remote sensing data to analyze the result for a better understanding of the center of the built-up area. To further support the evidence, the correlation between land surface temperatures and the normalized difference vegetation index (NDVI) was calculated using the Red band 4 and Near-infrared band 5 of the Landsat 8 data. Mono-window algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data using Band 10 and 11 accordingly to convert the top-of-atmosphere radiance (TOA) and to convert the satellite brightness temperature. Along with Landsat 8 data, NPP-VIIRS night-light data is preprocessed to get the research area data. The analysis between Landsat 8 data and NPP night-light data was taken to compare the output center of the Built-up area of Zhengzhou city. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=built-up%20area" title="built-up area">built-up area</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20surface%20temperature" title=" land surface temperature"> land surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=mono-window%20algorithm" title=" mono-window algorithm"> mono-window algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20method" title=" threshold method"> threshold method</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhengzhou" title=" Zhengzhou"> Zhengzhou</a> </p> <a href="https://publications.waset.org/abstracts/133463/research-analysis-of-urban-area-expansion-based-on-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133463.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3887</span> Use of Remote Sensing for Seasonal and Temporal Monitoring in Wetlands: A Case Study of Akyatan Lagoon</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Cilek">A. Cilek</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Berberoglu"> S. Berberoglu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Akin%20Tanriover"> A. Akin Tanriover</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Donmez"> C. Donmez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wetlands are the areas which have important effects and functions on protecting human life, adjust to nature, and biological variety, besides being potential exploitation sources. Observing the changes in these sensitive areas is important to study for data collecting and correct planning for the future. Remote sensing and Geographic Information System are being increasingly used for environmental studies such as biotope mapping and habitat monitoring. Akyatan Lagoon, one of the most important wetlands in Turkey, has been facing serious threats from agricultural applications in recent years. In this study, seasonal and temporal monitoring in wetlands system are determined by using remotely sensed data and Geographic Information Systems (GIS) between 1985 and 2015. The research method is based on classifying and mapping biotopes in the study area. The natural biotope types were determined as coastal sand dunes, salt marshes, river beds, coastal woods, lakes, lagoons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biotope%20mapping" title="biotope mapping">biotope mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=wetlands" title=" wetlands"> wetlands</a> </p> <a href="https://publications.waset.org/abstracts/61888/use-of-remote-sensing-for-seasonal-and-temporal-monitoring-in-wetlands-a-case-study-of-akyatan-lagoon" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61888.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">398</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3886</span> Using Satellite Images Datasets for Road Intersection Detection in Route Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20El-Zahraa%20El-Taher">Fatma El-Zahraa El-Taher</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Taha"> Ayman Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Jane%20Courtney"> Jane Courtney</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Mckeever"> Susan Mckeever</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title="satellite images">satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing%20images" title=" remote sensing images"> remote sensing images</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title=" autonomous vehicles"> autonomous vehicles</a> </p> <a href="https://publications.waset.org/abstracts/145141/using-satellite-images-datasets-for-road-intersection-detection-in-route-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145141.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3885</span> Assessment of Soil Salinity through Remote Sensing Technique in the Coastal Region of Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Hossen">B. Hossen</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Helmut"> Y. Helmut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Soil salinity is a major problem for the coastal region of Bangladesh, which has been increasing for the last four decades. Determination of soil salinity is essential for proper land use planning for agricultural crop production. The aim of the research is to estimate and monitor the soil salinity in the study area. Remote sensing can be an effective tool for detecting soil salinity in data-scarce conditions. In the research, Landsat 8 is used, which required atmospheric and radiometric correction, and nine soil salinity indices are applied to develop a soil salinity map. Ground soil salinity data, i.e., EC value, is collected as a printed map which is then scanned and digitized to develop a point shapefile. Linear regression is made between satellite-based generated map and ground soil salinity data, i.e., EC value. The results show that maximum R² value is found for salinity index SI 7 = G*R/B representing 0.022. This minimal R² value refers that there is a negligible relationship between ground EC value and salinity index generated value. Hence, these indices are not appropriate to assess soil salinity though many studies used those soil salinity indices successfully. Therefore, further research is necessary to formulate a model for determining the soil salinity in the coastal of Bangladesh. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil%20salinity" title="soil salinity">soil salinity</a>, <a href="https://publications.waset.org/abstracts/search?q=EC" title=" EC"> EC</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%208" title=" Landsat 8"> Landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=salinity%20indices" title=" salinity indices"> salinity indices</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing "> remote sensing </a> </p> <a href="https://publications.waset.org/abstracts/139666/assessment-of-soil-salinity-through-remote-sensing-technique-in-the-coastal-region-of-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3884</span> The Inequality Effects of Natural Disasters: Evidence from Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Annop%20Jaewisorn">Annop Jaewisorn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the relationship between natural disasters and inequalities -both income and expenditure inequality- at a micro-level of Thailand as the first study of this nature for this country. The analysis uses a unique panel and remote-sensing dataset constructed for the purpose of this research. It contains provincial inequality measures and other economic and social indicators based on the Thailand Household Survey during the period between 1992 and 2019. Meanwhile, the data on natural disasters, which are remote-sensing data, are received from several official geophysical or meteorological databases. Employing a panel fixed effects, the results show that natural disasters significantly reduce household income and expenditure inequality as measured by the Gini index, implying that rich people in Thailand bear a higher cost of natural disasters when compared to poor people. The effect on income inequality is mainly driven by droughts, while the effect on expenditure inequality is mainly driven by flood events. The results are robust across heterogeneity of the samples, lagged effects, outliers, and an alternative inequality measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inequality" title="inequality">inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20disasters" title=" natural disasters"> natural disasters</a>, <a href="https://publications.waset.org/abstracts/search?q=remote-sensing%20data" title=" remote-sensing data"> remote-sensing data</a>, <a href="https://publications.waset.org/abstracts/search?q=Thailand" title=" Thailand"> Thailand</a> </p> <a href="https://publications.waset.org/abstracts/138576/the-inequality-effects-of-natural-disasters-evidence-from-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138576.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3883</span> Estimation of Maize Yield by Using a Process-Based Model and Remote Sensing Data in the Northeast China Plain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia%20Zhang">Jia Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fengmei%20Yao"> Fengmei Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanjing%20Tan"> Yanjing Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (P < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002-2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process-based%20model" title="process-based model">process-based model</a>, <a href="https://publications.waset.org/abstracts/search?q=C4%20crop" title=" C4 crop"> C4 crop</a>, <a href="https://publications.waset.org/abstracts/search?q=maize%20yield" title=" maize yield"> maize yield</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=Northeast%20China%20Plain" title=" Northeast China Plain"> Northeast China Plain</a> </p> <a href="https://publications.waset.org/abstracts/28997/estimation-of-maize-yield-by-using-a-process-based-model-and-remote-sensing-data-in-the-northeast-china-plain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28997.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3882</span> Optimized Weight Selection of Control Data Based on Quotient Space of Multi-Geometric Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wang">Bo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-source%20image%20geometric%20process" title="multi-source image geometric process">multi-source image geometric process</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20precision%20geometric%20positioning" title=" high precision geometric positioning"> high precision geometric positioning</a>, <a href="https://publications.waset.org/abstracts/search?q=quotient%20space%20of%20multi-geometric%20features" title=" quotient space of multi-geometric features"> quotient space of multi-geometric features</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20weight%20selection" title=" optimized weight selection"> optimized weight selection</a> </p> <a href="https://publications.waset.org/abstracts/76115/optimized-weight-selection-of-control-data-based-on-quotient-space-of-multi-geometric-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">290</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3881</span> Remote Sensing Application in Environmental Researches: Case Study of Iran Mangrove Forests Quantitative Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neda%20Orak">Neda Orak</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Zarei"> Mostafa Zarei </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Environmental assessment is an important session in environment management. Since various methods and techniques have been produces and implemented. Remote sensing (RS) is widely used in many scientific and research fields such as geology, cartography, geography, agriculture, forestry, land use planning, environment, etc. It can show earth surface objects cyclical changes. Also, it can show earth phenomena limits on basis of electromagnetic reflectance changes and deviations records. The research has been done on mangrove forests assessment by RS techniques. Mangrove forests quantitative analysis in Basatin and Bidkhoon estuaries was the aim of this research. It has been done by Landsat satellite images from 1975- 2013 and match to ground control points. This part of mangroves are the last distribution in northern hemisphere. It can provide a good background to improve better management on this important ecosystem. Landsat has provided valuable images to earth changes detection to researchers. This research has used MSS, TM, +ETM, OLI sensors from 1975, 1990, 2000, 2003-2013. Changes had been studied after essential corrections such as fix errors, bands combination, georeferencing on 2012 images as basic image, by maximum likelihood and IPVI Index. It was done by supervised classification. 2004 google earth image and ground points by GPS (2010-2012) was used to compare satellite images obtained changes. Results showed mangrove area in bidkhoon was 1119072 m2 by GPS and 1231200 m2 by maximum likelihood supervised classification and 1317600 m2 by IPVI in 2012. Basatin areas is respectively: 466644 m2, 88200 m2, 63000 m2. Final results show forests have been declined naturally. It is due to human activities in Basatin. The defect was offset by planting in many years. Although the trend has been declining in recent years again. So, it mentioned satellite images have high ability to estimation all environmental processes. This research showed high correlation between images and indexes such as IPVI and NDVI with ground control points. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IPVI%20index" title="IPVI index">IPVI index</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%20sensor" title=" Landsat sensor"> Landsat sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20supervised%20classification" title=" maximum likelihood supervised classification"> maximum likelihood supervised classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Nayband%20National%20Park" title=" Nayband National Park"> Nayband National Park</a> </p> <a href="https://publications.waset.org/abstracts/41430/remote-sensing-application-in-environmental-researches-case-study-of-iran-mangrove-forests-quantitative-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3880</span> Efficacy of Remote Sensing Application in Monitoring the Effectiveness of Afforestation Project in Northern Nigeria </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Garba">T. Garba</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Y.%20Babanyara"> Y. Y. Babanyara</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20G.%20Ilellah"> K. G. Ilellah</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Modibbo"> M. A. Modibbo</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20O.%20Quddus"> T. O. Quddus</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20J.%20Sani"> M. J. Sani </a> </p> <p class="card-text"><strong>Abstract:</strong></p> After the United Nation Convention on Desertification (UNCD) in 1977 which was preceded by extensive, regional, and local studies, and consultations with numerous scientists, decision-makers, and relevant institutions. Global Plan of Action to Combat Desertification (PACD) was formulated, endorsed by member Countries. The role of implementing PACD was vested with Governments of countries affected by desertification. The Federal Government of Nigeria as a signatory and World Bank funded and implement afforestation project aimed at combating desertification between 1988 and 1999. This research, therefore, applied remote sensing techniques to assess the effectiveness of the project. To achieve that a small portion of about 143,609 hectares was curved out from the project area. Normalized Difference of the Vegetative Index (NDVI) and Land Use Land Cover were derived from Landsat TM 1986, Landsat ETM 1999 and Nigeria Sat 1, 2007 of the project area. The findings show that there was an increase in cultivated area due to the project from 1986 through 1999 and 2007. This is further buttressed by the three NDVI imageries due to their high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007 These signifies the gradual physical development of Afforestation project in the area. In addition, it was also verified by histograms of changes in vegetation which indicated an increased vegetative cover from 60,192 in 1986, to 102,476 in 1999 and then to 88,343 in 2007. The study concluded that Remote Sensing approach has actually confirmed that the project was indeed successful and effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=afforestation" title="afforestation">afforestation</a>, <a href="https://publications.waset.org/abstracts/search?q=desertification" title=" desertification"> desertification</a>, <a href="https://publications.waset.org/abstracts/search?q=landsat" title=" landsat"> landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetative%20index" title=" vegetative index"> vegetative index</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/8290/efficacy-of-remote-sensing-application-in-monitoring-the-effectiveness-of-afforestation-project-in-northern-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8290.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3879</span> Landscape Classification in North of Jordan by Integrated Approach of Remote Sensing and Geographic Information Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taleb%20Odeh">Taleb Odeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nizar%20Abu-Jaber"> Nizar Abu-Jaber</a>, <a href="https://publications.waset.org/abstracts/search?q=Nour%20Khries"> Nour Khries</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The southern part of Wadi Al Yarmouk catchment area covers north of Jordan. It locates within latitudes 32° 20’ to 32° 45’N and longitudes 35° 42’ to 36° 23’ E and has an area of about 1426 km2. However, it has high relief topography where the elevation varies between 50 to 1100 meter above sea level. The variations in the topography causes different units of landforms, climatic zones, land covers and plant species. As a results of these different landscapes units exists in that region. Spatial planning is a major challenge in such a vital area for Jordan which could not be achieved without determining landscape units. However, an integrated approach of remote sensing and geographic information Systems (GIS) is an optimized tool to investigate and map landscape units of such a complicated area. Remote sensing has the capability to collect different land surface data, of large landscape areas, accurately and in different time periods. GIS has the ability of storage these land surface data, analyzing them spatially and present them in form of professional maps. We generated a geo-land surface data that include land cover, rock units, soil units, plant species and digital elevation model using ASTER image and Google Earth while analyzing geo-data spatially were done by ArcGIS 10.2 software. We found that there are twenty two different landscape units in the study area which they have to be considered for any spatial planning in order to avoid and environmental problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landscape" title="landscape">landscape</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20planning" title=" spatial planning"> spatial planning</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing "> remote sensing </a> </p> <a href="https://publications.waset.org/abstracts/23106/landscape-classification-in-north-of-jordan-by-integrated-approach-of-remote-sensing-and-geographic-information-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">534</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3878</span> Assessing the Effects of Land Use Spatial Structure on Urban Heat Island Using New Launched Remote Sensing in Shenzhen, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Liua">Kai Liua</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongbo%20Sua"> Hongbo Sua</a>, <a href="https://publications.waset.org/abstracts/search?q=Weimin%20Wangb"> Weimin Wangb</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Liangb"> Hong Liangb </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urban heat island (UHI) has attracted attention around the world since they profoundly affect human life and climatological. Better understanding the effects of landscape pattern on UHI is crucial for improving the ecological security and sustainability of cities. This study aims to investigate how landscape composition and configuration would affect UHI in Shenzhen, China, based on the analysis of land surface temperature (LST) in relation landscape metrics, mainly with the aid of three new satellite sensors launched by China. HJ-1B satellite system was utilized to estimate surface temperature and comprehensively explore the urban thermal spatial pattern. The landscape metrics of the high spatial resolution remote sensing satellites (GF-1 and ZY-3) were compared and analyzed to validate the performance of the new launched satellite sensors. Results show that the mean LST is correlated with main landscape metrics involving class-based metrics and landscape-based metrics, suggesting that the landscape composition and the spatial configuration both influence UHI. These relationships also reveal that urban green has a significant effect in mitigating UHI in Shenzhen due to its homogeneous spatial distribution and large spatial extent. Overall, our study not only confirm the applicability and effectiveness of the HJ-1B, GF-1 and ZY-3 satellite system for studying UHI but also reveal the impacts of the urban spatial structure on UHI, which is meaningful for the planning and management of the urban environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urban%20heat%20island" title="urban heat island">urban heat island</a>, <a href="https://publications.waset.org/abstracts/search?q=Shenzhen" title=" Shenzhen"> Shenzhen</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20remote%20sensing%20sensor" title=" new remote sensing sensor"> new remote sensing sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing%20satellites" title=" remote sensing satellites"> remote sensing satellites</a> </p> <a href="https://publications.waset.org/abstracts/15982/assessing-the-effects-of-land-use-spatial-structure-on-urban-heat-island-using-new-launched-remote-sensing-in-shenzhen-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15982.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3877</span> Monitoring Large-Coverage Forest Canopy Height by Integrating LiDAR and Sentinel-2 Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaobo%20Liu">Xiaobo Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rakesh%20Mishra"> Rakesh Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun%20Zhang"> Yun Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20canopy%20height" title=" forest canopy height"> forest canopy height</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR"> LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=Sentinel-2" title=" Sentinel-2"> Sentinel-2</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20regression" title=" random forest regression"> random forest regression</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a> </p> <a href="https://publications.waset.org/abstracts/161534/monitoring-large-coverage-forest-canopy-height-by-integrating-lidar-and-sentinel-2-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3876</span> Estimation of Reservoir Capacity and Sediment Deposition Using Remote Sensing Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Odai%20Ibrahim%20Mohammed%20Al%20Balasmeh">Odai Ibrahim Mohammed Al Balasmeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Tapas%20Karmaker"> Tapas Karmaker</a>, <a href="https://publications.waset.org/abstracts/search?q=Richa%20Babbar"> Richa Babbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the reservoir capacity and sediment deposition were estimated using remote sensing data. The satellite images were synchronized with water level and storage capacity to find out the change in sediment deposition due to soil erosion and transport by streamflow. The water bodies spread area was estimated using vegetation indices, e.g., normalize differences vegetation index (NDVI) and normalize differences water index (NDWI). The 3D reservoir bathymetry was modeled by integrated water level, storage capacity, and area. From the models of different time span, the change in reservoir storage capacity was estimated. Another reservoir with known water level, storage capacity, area, and sediment deposition was used to validate the estimation technique. The t-test was used to assess the results between observed and estimated reservoir capacity and sediment deposition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20data" title="satellite data">satellite data</a>, <a href="https://publications.waset.org/abstracts/search?q=normalize%20differences%20vegetation%20index" title=" normalize differences vegetation index"> normalize differences vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=normalize%20differences%20water%20index" title=" normalize differences water index"> normalize differences water index</a>, <a href="https://publications.waset.org/abstracts/search?q=NDWI" title=" NDWI"> NDWI</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20capacity" title=" reservoir capacity"> reservoir capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=sedimentation" title=" sedimentation"> sedimentation</a>, <a href="https://publications.waset.org/abstracts/search?q=t-test%20hypothesis" title=" t-test hypothesis"> t-test hypothesis</a> </p> <a href="https://publications.waset.org/abstracts/125321/estimation-of-reservoir-capacity-and-sediment-deposition-using-remote-sensing-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3875</span> Monitoring the Change of Padma River Bank at Faridpur, Bangladesh Using Remote Sensing Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilme%20Faridatul">Ilme Faridatul</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wu"> Bo Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bangladesh is often called as a motherland of rivers. It contains about 700 rivers among all these the Padma River is one of the largest rivers of Bangladesh. The change of river bank and erosion has become a common environmental natural hazard in Bangladesh. The river banks are under intense pressure from natural processes such as erosion and accretion as well as anthropogenic processes such as urban growth and pollution. The Padma River is flowing along ten districts of Bangladesh among all these Faridpur district is most vulnerable to river bank erosion. The severity of the river erosion is so high that each year a thousand of populations become homeless and lose their agricultural lands. Though the Faridpur district is most vulnerable to river bank erosion no specific research has been conducted to identify the changing pattern of river bank along this district. The outcome of the research may serve as guidance to prepare river bank monitoring program and management. This research has utilized integrated techniques of remote sensing and geographic information system to monitor the changes from 1995 to 2015 at Faridpur district. To discriminate the land water interface Modified Normalized Difference Water Index (MNDWI) algorithm is applied and on screen digitization approach is used over MNDWI images of 1995, 2002 and 2015 for river bank line extraction. The extent of changes in the river bank along Faridpur district is estimated through overlaying the digitized maps of all three years. The river bank lines are highlighted to infer the erosion and accretion and the changes are calculated. The result shows that the middle of the river is gaining land through sedimentation and the both side river bank is shifting causing severe erosion that consequently resulting the loss of farmland and homestead. Over the study period from 1995 to 2015 it witnessed huge erosion and accretion that played an active role in the changes of the river bank. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20bank" title="river bank">river bank</a>, <a href="https://publications.waset.org/abstracts/search?q=erosion%20and%20accretion" title=" erosion and accretion"> erosion and accretion</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20monitoring" title=" change monitoring"> change monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/57859/monitoring-the-change-of-padma-river-bank-at-faridpur-bangladesh-using-remote-sensing-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3874</span> Investigating Seasonal Changes of Urban Land Cover with High Spatio-Temporal Resolution Satellite Data via Image Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hantian%20Wu">Hantian Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Huang"> Bo Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuan%20Zeng"> Yuan Zeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Divisions between wealthy and poor, private and public landscapes are propagated by the increasing economic inequality of cities. While these are the spatial reflections of larger social issues and problems, urban design can at least employ spatial techniques that promote more inclusive rather than exclusive, overlapping rather than segregated, interlinked rather than disconnected landscapes. Indeed, the type of edge or border between urban landscapes plays a critical role in the way the environment is perceived. China experiences rapid urbanization, which poses unpredictable environmental challenges. The urban green cover and water body are under changes, which highly relevant to resident wealth and happiness. However, very limited knowledge and data on their rapid changes are available. In this regard, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understating the driving forces of urban landscape changes can be a significant contribution for urban planning and studying. High-resolution remote sensing data has been widely applied to urban management in China. The map of urban land use map for the entire China of 2018 with 10 meters resolution has been published. However, this research focuses on the large-scale and high-resolution remote sensing land use but does not precisely focus on the seasonal change of urban covers. High-resolution remote sensing data has a long-operation cycle (e.g., Landsat 8 required 16 days for the same location), which is unable to satisfy the requirement of monitoring urban-landscape changes. On the other hand, aerial-remote or unmanned aerial vehicle (UAV) sensing are limited by the aviation-regulation and cost was hardly widely applied in the mega-cities. Moreover, those data are limited by the climate and weather conditions (e.g., cloud, fog), and those problems make capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Particularly, during the rainy season, no data are available even for Sentinel Satellite data with 5 days interval. Many natural events and/or human activities drive the changes of urban covers. In this case, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understanding the mechanism of urban landscape changes can be a significant contribution for urban planning and studying. This project aims to use the high spatiotemporal fusion of remote sensing data to create short-cycle, high-resolution remote sensing data sets for exploring the high-frequently urban cover changes. This research will enhance the long-term monitoring applicability of high spatiotemporal fusion of remote sensing data for the urban landscape for optimizing the urban management of landscape border to promoting the inclusive of the urban landscape to all communities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urban%20land%20cover%20changes" title="urban land cover changes">urban land cover changes</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20spatiotemporal%20fusion" title=" high spatiotemporal fusion"> high spatiotemporal fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20management" title=" urban management"> urban management</a> </p> <a href="https://publications.waset.org/abstracts/129767/investigating-seasonal-changes-of-urban-land-cover-with-high-spatio-temporal-resolution-satellite-data-via-image-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129767.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3873</span> Assessing the Theoretical Suitability of Sentinel-2 and Worldview-3 Data for Hydrocarbon Mapping of Spill Events, Using Hydrocarbon Spectral Slope Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Tunde%20Olagunju">K. Tunde Olagunju</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Scott%20Allen"> C. Scott Allen</a>, <a href="https://publications.waset.org/abstracts/search?q=Freek%20Van%20Der%20Meer"> Freek Van Der Meer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of hydrocarbon oil in remote sensing images is often the first step in monitoring oil during spill events. Most remote sensing methods adopt techniques for hydrocarbon identification to achieve detection in order to model an appropriate cleanup program. Identification on optical sensors does not only allow for detection but also for characterization and quantification. Until recently, in optical remote sensing, quantification and characterization are only potentially possible using high-resolution laboratory and airborne imaging spectrometers (hyperspectral data). Unlike multispectral, hyperspectral data are not freely available, as this data category is mainly obtained via airborne survey at present. In this research, two (2) operational high-resolution multispectral satellites (WorldView-3 and Sentinel-2) are theoretically assessed for their suitability for hydrocarbon characterization, using the hydrocarbon spectral slope model (HYSS). This method utilized the two most persistent hydrocarbon diagnostic/absorption features at 1.73 µm and 2.30 µm for hydrocarbon mapping on multispectral data. In this research, spectra measurement of seven (7) different hydrocarbon oils (crude and refined oil) taken on ten (10) different substrates with the use of laboratory ASD Fieldspec were convolved to Sentinel-2 and WorldView-3 resolution, using their full width half maximum (FWHM) parameter. The resulting hydrocarbon slope values obtained from the studied samples enable clear qualitative discrimination of most hydrocarbons, despite the presence of different background substrates, particularly on WorldView-3. Due to close conformity of central wavelengths and narrow bandwidths to key hydrocarbon bands used in HYSS, the statistical significance for qualitative analysis on WorldView-3 sensors for all studied hydrocarbon oil returned with 95% confidence level (P-value ˂ 0.01), except for Diesel. Using multifactor analysis of variance (MANOVA), the discriminating power of HYSS is statistically significant for most hydrocarbon-substrate combinations on Sentinel-2 and WorldView-3 FWHM, revealing the potential of these two operational multispectral sensors as rapid response tools for hydrocarbon mapping. One notable exception is highly transmissive hydrocarbons on Sentinel-2 data due to the non-conformity of spectral bands with key hydrocarbon absorptions and the relatively coarse bandwidth (> 100 nm). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrocarbon" title="hydrocarbon">hydrocarbon</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20spill" title=" oil spill"> oil spill</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrocarbon-substrate%20combination" title=" hydrocarbon-substrate combination"> hydrocarbon-substrate combination</a>, <a href="https://publications.waset.org/abstracts/search?q=Sentinel-2" title=" Sentinel-2"> Sentinel-2</a>, <a href="https://publications.waset.org/abstracts/search?q=WorldView-3" title=" WorldView-3"> WorldView-3</a> </p> <a href="https://publications.waset.org/abstracts/139188/assessing-the-theoretical-suitability-of-sentinel-2-and-worldview-3-data-for-hydrocarbon-mapping-of-spill-events-using-hydrocarbon-spectral-slope-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">221</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3872</span> Modeling Breathable Particulate Matter Concentrations over Mexico City Retrieved from Landsat 8 Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20T.%20Sepulveda-Hirose">Rodrigo T. Sepulveda-Hirose</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20B.%20Carrera-Aguilar"> Ana B. Carrera-Aguilar</a>, <a href="https://publications.waset.org/abstracts/search?q=Magnolia%20G.%20Martinez-Rivera"> Magnolia G. Martinez-Rivera</a>, <a href="https://publications.waset.org/abstracts/search?q=Pablo%20de%20J.%20Angeles-Salto"> Pablo de J. Angeles-Salto</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Herrera-Ventosa"> Carlos Herrera-Ventosa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to diminish health risks, it is of major importance to monitor air quality. However, this process is accompanied by the high costs of physical and human resources. In this context, this research is carried out with the main objective of developing a predictive model for concentrations of inhalable particles (PM10-2.5) using remote sensing. To develop the model, satellite images, mainly from Landsat 8, of the Mexico City’s Metropolitan Area were used. Using historical PM10 and PM2.5 measurements of the RAMA (Automatic Environmental Monitoring Network of Mexico City) and through the processing of the available satellite images, a preliminary model was generated in which it was possible to observe critical opportunity areas that will allow the generation of a robust model. Through the preliminary model applied to the scenes of Mexico City, three areas were identified that cause great interest due to the presumed high concentration of PM; the zones are those that present high plant density, bodies of water and soil without constructions or vegetation. To date, work continues on this line to improve the preliminary model that has been proposed. In addition, a brief analysis was made of six models, presented in articles developed in different parts of the world, this in order to visualize the optimal bands for the generation of a suitable model for Mexico City. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20quality" title="air quality">air quality</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling%20pollution" title=" modeling pollution"> modeling pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=particulate%20matter" title=" particulate matter"> particulate matter</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/106322/modeling-breathable-particulate-matter-concentrations-over-mexico-city-retrieved-from-landsat-8-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3871</span> Coordinative Remote Sensing Observation Technology for a High Altitude Barrier Lake</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Xin">Zhang Xin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Barrier lakes are lakes formed by storing water in valleys, river valleys or riverbeds after being blocked by landslide, earthquake, debris flow, and other factors. They have great potential safety hazards. When the water is stored to a certain extent, it may burst in case of strong earthquake or rainstorm, and the lake water overflows, resulting in large-scale flood disasters. In order to ensure the safety of people's lives and property in the downstream, it is very necessary to monitor the barrier lake. However, it is very difficult and time-consuming to manually monitor the barrier lake in high altitude areas due to the harsh climate and steep terrain. With the development of earth observation technology, remote sensing monitoring has become one of the main ways to obtain observation data. Compared with a single satellite, multi-satellite remote sensing cooperative observation has more advantages; its spatial coverage is extensive, observation time is continuous, imaging types and bands are abundant, it can monitor and respond quickly to emergencies, and complete complex monitoring tasks. Monitoring with multi-temporal and multi-platform remote sensing satellites can obtain a variety of observation data in time, acquire key information such as water level and water storage capacity of the barrier lake, scientifically judge the situation of the barrier lake and reasonably predict its future development trend. In this study, The Sarez Lake, which formed on February 18, 1911, in the central part of the Pamir as a result of blockage of the Murgab River valley by a landslide triggered by a strong earthquake with magnitude of 7.4 and intensity of 9, is selected as the research area. Since the formation of Lake Sarez, it has aroused widespread international concern about its safety. At present, the use of mechanical methods in the international analysis of the safety of Lake Sarez is more common, and remote sensing methods are seldom used. This study combines remote sensing data with field observation data, and uses the 'space-air-ground' joint observation technology to study the changes in water level and water storage capacity of Lake Sarez in recent decades, and evaluate its safety. The situation of the collapse is simulated, and the future development trend of Lake Sarez is predicted. The results show that: 1) in recent decades, the water level of Lake Sarez has not changed much and remained at a stable level; 2) unless there is a strong earthquake or heavy rain, it is less likely that the Lake Sarez will be broken under normal conditions, 3) lake Sarez will remain stable in the future, but it is necessary to establish an early warning system in the Lake Sarez area for remote sensing of the area, 4) the coordinative remote sensing observation technology is feasible for the high altitude barrier lake of Sarez. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coordinative%20observation" title="coordinative observation">coordinative observation</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster" title=" disaster"> disaster</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title=" geographic information system"> geographic information system</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a> </p> <a href="https://publications.waset.org/abstracts/111228/coordinative-remote-sensing-observation-technology-for-a-high-altitude-barrier-lake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111228.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3870</span> Evaluation of Environmental Impact Assessment of Dam Using GIS/Remote Sensing-Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ntungamili%20Kenosi">Ntungamili Kenosi</a>, <a href="https://publications.waset.org/abstracts/search?q=Moatlhodi%20W.%20Letshwenyo"> Moatlhodi W. Letshwenyo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Negative environmental impacts due to construction of large projects such as dams have become an important aspect of land degradation. This paper will review the previous literature on the previous researches or study in the same area of study in the other parts of the world. After dam has been constructed, the actual environmental impacts are investigated and compared to the predicted results of the carried out Environmental Impact Assessment. GIS and Remote Sensing, play an important role in generating automated spatial data sets and in establishing spatial relationships. Results from other sources shows that the normalized vegetation index (NDVI) analysis was used to detect the spatial and temporal change of vegetation biomass in the study area. The result indicated that the natural vegetation biomass is declining. This is mainly due to the expansion of agricultural land and escalating human made structures in the area. Urgent environmental conservation is necessary when adjoining projects site. Less study on the evaluation of EIA on dam has been conducted in Botswana hence there is a need for the same study to be conducted and then it will be easy to be compared to other studies around the world. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Botswana" title="Botswana">Botswana</a>, <a href="https://publications.waset.org/abstracts/search?q=dam" title=" dam"> dam</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20impact%20assessment" title=" environmental impact assessment"> environmental impact assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20vegetation%20index%20%28NDVI%29" title=" normalized vegetation index (NDVI)"> normalized vegetation index (NDVI)</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/35626/evaluation-of-environmental-impact-assessment-of-dam-using-gisremote-sensing-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35626.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">411</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3869</span> Estimating PM2.5 Concentrations Based on Landsat 8 Imagery and Historical Field Data over the Metropolitan Area of Mexico City</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20T.%20Sepulveda-Hirose">Rodrigo T. Sepulveda-Hirose</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20B.%20Carrera-Aguilar"> Ana B. Carrera-Aguilar</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20Andree%20Ramirez-Casas"> Francisco Andree Ramirez-Casas</a>, <a href="https://publications.waset.org/abstracts/search?q=Alondra%20Orozco-Gomez"> Alondra Orozco-Gomez</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Angel%20Sanchez-Caro"> Miguel Angel Sanchez-Caro</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Herrera-Ventosa"> Carlos Herrera-Ventosa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High concentrations of particulate matter in the atmosphere pose a threat to human health, especially over areas with high concentrations of population; however, field air pollution monitoring is expensive and time-consuming. In order to achieve reduced costs and global coverage of the whole urban area, remote sensing can be used. This study evaluates PM2.5 concentrations, over the Mexico City´s metropolitan area, are estimated using atmospheric reflectance from LANDSAT 8, satellite imagery and historical PM2.5 measurements of the Automatic Environmental Monitoring Network of Mexico City (RAMA). Through the processing of the available satellite images, a preliminary model was generated to evaluate the optimal bands for the generation of the final model for Mexico City. Work on the final model continues with the results of the preliminary model. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollution%20modeling" title="air pollution modeling">air pollution modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%208" title=" Landsat 8"> Landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=PM2.5" title=" PM2.5"> PM2.5</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/108009/estimating-pm25-concentrations-based-on-landsat-8-imagery-and-historical-field-data-over-the-metropolitan-area-of-mexico-city" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108009.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3868</span> Analysing Trends in Rice Cropping Intensity and Seasonality across the Philippines Using 14 Years of Moderate Resolution Remote Sensing Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhogendra%20Mishra">Bhogendra Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Andy%20Nelson"> Andy Nelson</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirco%20Boschetti"> Mirco Boschetti</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorenzo%20Busetto"> Lorenzo Busetto</a>, <a href="https://publications.waset.org/abstracts/search?q=Alice%20Laborte"> Alice Laborte</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rice is grown on over 100 million hectares in almost every country of Asia. It is the most important staple crop for food security and has high economic and cultural importance in Asian societies. The combination of genetic diversity and management options, coupled with the large geographic extent means that there is a large variation in seasonality (when it is grown) and cropping intensity (how often it is grown per year on the same plot of land), even over relatively small distances. Seasonality and intensity can and do change over time depending on climatic, environmental and economic factors. Detecting where and when these changes happen can provide information to better understand trends in regional and even global rice production. Remote sensing offers a unique opportunity to estimate these trends. We apply the recently published PhenoRice algorithm to 14 years of moderate resolution remote sensing (MODIS) data (utilizing 250m resolution 16 day composites from Terra and Aqua) to estimate seasonality and cropping intensity per year and changes over time. We compare the results to the surveyed data collected by International Rice Research Institute (IRRI). The study results in a unique and validated dataset on the extent and change of extent, the seasonality and change in seasonality and the cropping intensity and change in cropping intensity between 2003 and 2016 for the Philippines. Observed trends and their implications for food security and trade policies are also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rice" title="rice">rice</a>, <a href="https://publications.waset.org/abstracts/search?q=cropping%20intensity" title=" cropping intensity"> cropping intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=moderate%20resolution%20remote%20sensing%20%28MODIS%29" title=" moderate resolution remote sensing (MODIS)"> moderate resolution remote sensing (MODIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=phenology" title=" phenology"> phenology</a>, <a href="https://publications.waset.org/abstracts/search?q=seasonality" title=" seasonality"> seasonality</a> </p> <a href="https://publications.waset.org/abstracts/73734/analysing-trends-in-rice-cropping-intensity-and-seasonality-across-the-philippines-using-14-years-of-moderate-resolution-remote-sensing-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3867</span> The Use of Artificial Intelligence to Identify Ore-Prospective Territories in East Kazakhstan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20D.%20Gavrilenko">O. D. Gavrilenko</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20M%20Temirbekov"> N. M Temirbekov</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20A.%20Mustafina"> Z. A. Mustafina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article discusses the possibility of using machine learning algorithms to analyze and synthesize historical geological data with mathematical geophysics and geochemistry data, as well as Earth remote sensing (ERS) results. Creating geoinformation systems for such Big Data with their subsequent processing using artificial intelligence methods provides unique opportunities for more accurate minerogenic zoning of territories, which significantly increases the efficiency of geological exploration. This will improve the accuracy of geological exploration and forecast zones with potential mineral resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=methods%20of%20remote%20sensing%20of%20the%20earth" title="methods of remote sensing of the earth">methods of remote sensing of the earth</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20systems" title=" geographic information systems"> geographic information systems</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=geological%20geophysical%20geochemical%20and%20minerogenic%20data" title=" geological geophysical geochemical and minerogenic data"> geological geophysical geochemical and minerogenic data</a>, <a href="https://publications.waset.org/abstracts/search?q=minerogenic%20model" title=" minerogenic model"> minerogenic model</a> </p> <a href="https://publications.waset.org/abstracts/197862/the-use-of-artificial-intelligence-to-identify-ore-prospective-territories-in-east-kazakhstan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/197862.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">11</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3866</span> Land Cover Change Analysis Using Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahir%20Ali%20Akbar">Tahir Ali Akbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hirra%20Jabbar"> Hirra Jabbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Land cover change analysis plays a significant role in understanding the trends of urban sprawl and land use transformation due to anthropogenic activities. In this study, the spatio-temporal dynamics of major land covers were analyzed in the last twenty years (1988-2016) for District Lahore located in the Punjab Province of Pakistan. The Landsat satellite imageries were downloaded from USGS Global Visualization Viewer of Earth Resources Observation and Science Center located in Sioux Falls, South Dakota USA. The imageries included: (i) Landsat TM-5 for 1988 and 2001; and (ii) Landsat-8 OLI for 2016. The raw digital numbers of Landsat-5 images were converted into spectral radiance and then planetary reflectance. The digital numbers of Landsat-8 image were directly converted into planetary reflectance. The normalized difference vegetation index (NDVI) was used to classify the processed images into six major classes of water, buit-up, barren land, shrub and grassland, sparse vegetation and dense vegetation. The NDVI output results were improved by visual interpretation using high-resolution satellite imageries. The results indicated that the built-up areas were increased to 21% in 2016 from 10% in 1988. The decrease in % areas was found in case of water, barren land and shrub & grassland. There were improvements in percentage of areas for the vegetation. The increasing trend of urban sprawl for Lahore requires implementation of GIS based spatial planning, monitoring and management system for its sustainable development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20cover%20changes" title="land cover changes">land cover changes</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20sprawl" title=" urban sprawl"> urban sprawl</a> </p> <a href="https://publications.waset.org/abstracts/67795/land-cover-change-analysis-using-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3865</span> Approach to Quantify Groundwater Recharge Using GIS Based Water Balance Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20S.%20Rwanga">S. S. Rwanga</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20M.%20Ndambuki"> J. M. Ndambuki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Groundwater quantification needs a method which is not only flexible but also reliable in order to accurately quantify its spatial and temporal variability. As groundwater is dynamic and interdisciplinary in nature, an integrated approach of remote sensing (RS) and GIS technique is very useful in various groundwater management studies. Thus, the GIS water balance model (WetSpass) together with remote sensing (RS) can be used to quantify groundwater recharge. This paper discusses the concept of WetSpass in combination with GIS on the quantification of recharge with a view to managing water resources in an integrated framework. The paper presents the simulation procedures and expected output after simulation. Preliminary data are presented from GIS output only. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=groundwater" title="groundwater">groundwater</a>, <a href="https://publications.waset.org/abstracts/search?q=recharge" title=" recharge"> recharge</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=WetSpass" title=" WetSpass"> WetSpass</a> </p> <a href="https://publications.waset.org/abstracts/33834/approach-to-quantify-groundwater-recharge-using-gis-based-water-balance-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33834.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3864</span> Estimating Algae Concentration Based on Deep Learning from Satellite Observation in Korea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heewon%20Jeong">Heewon Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Seongpyo%20Kim"> Seongpyo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Joon%20Ha%20Kim"> Joon Ha Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=algae%20concentration" title=" algae concentration"> algae concentration</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite" title=" satellite"> satellite</a> </p> <a href="https://publications.waset.org/abstracts/85957/estimating-algae-concentration-based-on-deep-learning-from-satellite-observation-in-korea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85957.pdf" target="_blank" class="btn btn-primary 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