Search results for: landsat OLI imagery

Authors: Manta Nowbuth, Marie Anais Kimberley Therese

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The vulnerability of coastal areas to storm surges stands as a critical global concern. The increasing frequency and intensity of extreme weather events have increased the risks faced by communities living along the coastlines Worldwide. Small Island developing states (SIDS) stands out as being exceptionally vulnerable, coastal regions, ecosystems of human habitation and natural forces, bear witness to the frontlines of climate-induced challenges, and the intensification of storm surges underscores the urgent need for a comprehensive understanding of coastal vulnerability. With limited landmass, low-lying terrains, and resilience on coastal resources, SIDS face an amplified vulnerability to the consequences of storm surges, the delicate balance between human activities and environmental dynamics in these island nations increases the urgency of tailored strategies for assessing and mitigating coastal vulnerability. This research uses an approach to evaluate the vulnerability of coastal communities in Mauritius. The Satellite imagery analysis makes use of sentinel satellite imageries, modified normalised difference water index, classification techniques and the DSAS add on to quantify the extent of shoreline erosion or accumulation, providing a spatial perspective on coastal vulnerability. The coastal Vulnerability Index (CVI) is applied by Gonitz et al Formula, this index considers factors such as coastal slope, sea level rise, mean significant wave height, and tidal range. Weighted assessments identify regions with varying levels of vulnerability, ranging from low to high. The study was carried out in a Village Located in the south of Mauritius, namely Rivière des Galets, with a population of about 500 people over an area of 60,000m². The Village of Rivière des Galets being located in the south, and the southern coast of Mauritius being exposed to the open Indian ocean, is vulnerable to swells, The swells generated by the South east trade winds can lead to large waves and rough sea conditions along the Southern Coastline which has an impact on the coastal activities, including fishing, tourism and coastal Infrastructures, hence, On the one hand, the results highlighted that from a stretch of 123km of coastline the linear rate regression for the 5 –year span varies from-24.1m/yr. to 8.2m/yr., the maximum rate of change in terms of eroded land is -24m/yr. and the maximum rate of accretion is 8.2m/yr. On the other hand, the coastal vulnerability index varies from 9.1 to 45.6 and it was categorised into low, moderate, high and very high risks zones. It has been observed that region which lacks protective barriers and are made of sandy beaches are categorised as high risks zone and hence it is imperative to high risk regions for immediate attention and intervention, as they will most likely be exposed to coastal hazards and impacts from climate change, which demands proactive measures for enhanced resilience and sustainable adaptation strategies.

Keywords: climate change, coastal vulnerability, disaster management, remote sensing, satellite imagery, storm surge

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Authors: Basit Aftab, Feng Zhongke

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The land use/land cover change (LULCC) is a significant indicator sensitive to an area's environmental changes. As a rapidly developing capital city near the Himalayas Mountains, the city area of Islamabad, Pakistan, has expanded dramatically over the past 20 years. In order to precisely measure the impact of urbanization on the forest and agricultural lands, the Spatio-temporal analysis of LULCC was utilized, which helped us to know the impacts of urbanization, especially on ecosystem processes, biological cycles, and biodiversity. The Islamabad region's Multispectral Satellite Images (MSI) for 2000, 2010, and 2020 were employed as the remote sensing data source. Local documents of city planning, forest inventory and archives in the agriculture management departments were included to verify the image-derived result. The results showed that from 2000 to 2020, the built-up area increased to 48.3% (505.02 Km2). Meanwhile, the forest, agricultural, and barre land decreased to 28.9% (305.64 Km2), 10.04% (104.87 Km2), and 11.61% (121.30 Km2). The overall percentage change in land area between 2000 – 2020 was recorded maximum for the built-up (227.04%). Results revealed that the increase in the built-up area decreased forestland, barren, and agricultural lands (-0.36, -1.00 & -0.34). The association of built-up with respective years was positively linear (R2 = 0.96), whereas forestland, agricultural, and barren lands association with years were recorded as negatively linear (R2 = -0.29, R2 = -0.02, and R2 = -0.96). Large-scale deforestation leads to multiple negative impacts on the local environment, e.g., water degradation and climate change. It would finally affect the environment of the greater Himalayan region in some way. We further analyzed the driving forces of urbanization. It was determined by economic expansion, climate change, and population growth. We hope our study could be utilized to develop efforts to mitigate the consequences of deforestation and agricultural land damage, reducing greenhouse gas emissions while preserving the area's biodiversity.

Keywords: urbanization, Himalaya mountains, landuse landcover change (LULCC), remote sensing., multi-spectral satellite imagery

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Authors: Alisa Salimova, Jiane Zuo, Christopher Homer

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The objective of this study is to analyse the North Canal riparian zone land use with the help of remote sensing analysis in ArcGis using 30 cloudless Landsat8 open-source satellite images from May to August of 2013 and 2017. Land cover, urban construction, heat island effect, vegetation cover, and water system change were chosen as the main parameters and further analysed to evaluate its impact on the North Canal water quality. The methodology involved the following steps: firstly, 30 cloudless satellite images were collected from the Landsat TM image open-source database. The visual interpretation method was used to determine different land types in a catchment area. After primary and secondary classification, 28 land cover types in total were classified. Visual interpretation method was used with the help ArcGIS for the grassland monitoring, US Landsat TM remote sensing image processing with a resolution of 30 meters was used to analyse the vegetation cover. The water system was analysed using the visual interpretation method on the GIS software platform to decode the target area, water use and coverage. Monthly measurements of water temperature, pH, BOD, COD, ammonia nitrogen, total nitrogen and total phosphorus in 2013 and 2017 were taken from three locations of the North Canal in Tongzhou district. These parameters were used for water quality index calculation and compared to land-use changes. The results of this research were promising. The vegetation coverage of North Canal riparian zone in 2017 was higher than the vegetation coverage in 2013. The surface brightness temperature value was positively correlated with the vegetation coverage density and the distance from the surface of the water bodies. This indicates that the vegetation coverage and water system have a great effect on temperature regulation and urban heat island effect. Surface temperature in 2017 was higher than in 2013, indicating a global warming effect. The water volume in the river area has been partially reduced, indicating the potential water scarcity risk in North Canal watershed. Between 2013 and 2017, urban residential, industrial and mining storage land areas significantly increased compared to other land use types; however, water quality has significantly improved in 2017 compared to 2013. This observation indicates that the Tongzhou Water Restoration Plan showed positive results and water management of Tongzhou district had been improved.

Keywords: North Canal, land use, riparian vegetation, river ecology, remote sensing

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Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Sujata S. Kulkarni

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Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. This paper gives a review on effective texture classification method. The objective of the problem of texture representation is to reduce the amount of raw data presented by the image, while preserving the information needed for the task. Texture analysis is important in many applications of computer image analysis for classification include industrial and biomedical surface inspection, for example for defects and disease, ground classification of satellite or aerial imagery and content-based access to image databases.

Keywords: compressed sensing, feature extraction, image classification, texture analysis

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Authors: A. Saghatelyan, Sh. Asmaryan, G. Tepanosyan, V. Muradyan

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One of major concern to Armenia has been soil degradation emerged as a result of unsustainable management and use of grasslands, this in turn largely impacting environment, agriculture and finally human health. Hence, assessment of soil degradation is an essential and urgent objective set out to measure its possible consequences and develop a potential management strategy. Since recently, an essential tool for assessing pasture degradation has been remote sensing (RS) technologies. This research was done with an intention to measure preciseness of Linear spectral unmixing (LSU) and NDVI-SMA methods to estimate soil surface components related to degradation (fractional vegetation cover-FVC, bare soils fractions, surface rock cover) and determine appropriateness of these methods for mapping man-induced soil degradation in high mountain pastures. Taking into consideration a spatially complex and heterogeneous biogeophysical structure of the studied site, we used high resolution multispectral QuickBird imagery of a pasture site in one of Armenia’s rural communities - Nerkin Sasoonashen. The accuracy assessment was done by comparing between the land cover abundance data derived through RS methods and the ground truth land cover abundance data. A significant regression was established between ground truth FVC estimate and both NDVI-LSU and LSU - produced vegetation abundance data (R2=0.636, R2=0.625, respectively). For bare soil fractions linear regression produced a general coefficient of determination R2=0.708. Because of poor spectral resolution of the QuickBird imagery LSU failed with assessment of surface rock abundance (R2=0.015). It has been well documented by this particular research, that reduction in vegetation cover runs in parallel with increase in man-induced soil degradation, whereas in the absence of man-induced soil degradation a bare soil fraction does not exceed a certain level. The outcomes show that the proposed method of man-induced soil degradation assessment through FVC, bare soil fractions and field data adequately reflects the current status of soil degradation throughout the studied pasture site and may be employed as an alternate of more complicated models for soil degradation assessment.

Keywords: Armenia, linear spectral unmixing, remote sensing, soil degradation

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Authors: Aayushi Pandey, Manoj Kumar Pandey, Ashutosh Tiwari, Kireet Kumar

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Glaciers play an important role in climate change and are sensitive phenomena of global climate change scenario. Glaciers in Himalayas are unique as they are predominantly valley type and are located in tropical, high altitude regions. These glaciers are often covered with debris which greatly affects ablation rate of glaciers and work as a sensitive indicator of glacier health. The aim of this study is to map high altitude Glacier surface with a focus on glacial lake and debris estimation using different techniques in Nagling glacier of dhauliganga basin in Himalayan region. Different Image Classification techniques i.e. thresholding on different band ratios and supervised classification using maximum likelihood classifier (MLC) have been used on high resolution sentinel 2A level 1c satellite imagery of 14 October 2017.Here Near Infrared (NIR)/Shortwave Infrared (SWIR) ratio image was used to extract the glaciated classes (Snow, Ice, Ice Mixed Debris) from other non-glaciated terrain classes. SWIR/BLUE Ratio Image was used to map valley rock and Debris while Green/NIR ratio image was found most suitable for mapping Glacial Lake. Accuracy assessment was performed using high resolution (3 meters) Planetscope Imagery using 60 stratified random points. The overall accuracy of MLC was 85 % while the accuracy of Band Ratios was 96.66 %. According to Band Ratio technique total areal extent of glaciated classes (Snow, Ice ,IMD) in Nagling glacier was 10.70 km2 nearly 38.07% of study area comprising of 30.87 % Snow covered area, 3.93% Ice and 3.27 % IMD covered area. Non-glaciated classes (vegetation, glacial lake, debris and valley rock) covered 61.93 % of the total area out of which valley rock is dominant with 33.83% coverage followed by debris covering 27.7 % of the area in nagling glacier. Glacial lake and Debris were accurately mapped using Band ratio technique Hence, Band Ratio approach appears to be useful for the mapping of debris covered glacier in Himalayan Region.

Keywords: band ratio, Dhauliganga basin, glacier mapping, Himalayan region, maximum likelihood classifier (MLC), Sentinel-2 satellite image

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Authors: Ahmed Elmarhomy, K. Terada

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An automated method for counting passerby has been proposed using virtual-vertical measurement lines. Space-time image is representing the human regions which are treated using the segmentation process. Different color space has been used to perform the template matching. A proper template matching has been achieved to determine direction and speed of passing people. Distinguish one or two passersby has been investigated using a correlation between passerby speed and the human-pixel area. Finally, the effectiveness of the presented method has been experimentally verified.

Keywords: counting people, measurement line, space-time image, segmentation, template matching

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Authors: Natalia Fontes De Oliveira

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Nature and women have a long age association that has persisted throughout history, cultures, literature, and arts. Women’s physiological functions of reproduction and childbearing are viewed as closer to nature as a binary opposition to men, who have metaphorically and historically been associated with culture. To liberate from strictures of phallogocentric rhetoric, a radical critique of the categories of nature and culture must be undertaken. This paper proposes that nature writing in Margaret Atwood’s The Testaments is used subversively as a form of rebellion to disrupt the metaphorical relationship between women and nature. In tune with ecofeminist concerns, the imagery rewrites patriarchal paradigms of binary oppositions as the protagonists narrate a complex and plural relationship between nature and women.

Keywords: ecofeminism, Margaret Atwood, nature writing, women's writing

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Authors: Guntur Bagus Pamungkas

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As part of a tropical country that is normally rich in forest land areas, Indonesia has always been in the world's spotlight due to its significantly increasing process of deforestation. In one hand, it is related to the mainstay for maintaining the sustainability of the earth's ecosystem functions. On the other hand, they also cover the various potential sources of the global economy. Therefore, it can always be the target of different scale of investors to excessively exploit them. No wonder the emergence of disasters in various characteristics always comes up. In fact, the deforestation phenomenon does not only occur in various forest land areas in the main islands of Indonesia but also includes Java Island, the most densely populated areas in the world. This island only remains the forest land of about 9.8% of the total forest land in Indonesia due to its long history of it, especially in Central Java Province, the most densely populated area in Java. Again, not surprisingly, this province belongs to the area with the highest frequency of disasters because of it, landslides in particular. One of the areas that often experience it is Banjarnegara District, especially in mountainous areas that lies in the range from 1000 to 3000 meters above sea level, where the remains of land forest area can easyly still be found. Even among them still leaves less untouchable tropical rain forest whose area also covers part of a neighboring district, Pekalongan, which is considered to be the rest of the world's little paradise on Earth. The district's landscape is indeed beautiful, especially in the Dieng area, a major tourist destination in Central Java Province after Borobudur Temple. However, annually hazardous always threatens this district due to this landslide disaster. Even, there was a tragic event that was buried with its inhabitants a few decades ago. This research aims to find part of the concept of effective forest management through monitoring the presence of remaining forest areas in this area. The research implemented monitoring of deforestation rates using the Stochastic Cellular Automata-Markov Chain (SCA-MC) method, which serves to provide a spatial simulation of land use and cover changes (LULCC). This geospatial process uses the Landsat-8 OLI image product with Thermal Infra-Red Sensors (TIRS) Band 10 in 2020 and Landsat 5 TM with TIRS Band 6 in 2010. Then it is also integrated with physical and social geography issues using the QGIS 2.18.11 application with the Mollusce Plugin, which serves to clarify and calculate the area of land use and cover, especially in forest areas—using the LULCC method, which calculates the rate of forest area reduction in 2010-2020 in Banjarnegara District. Since the dependence of this area on the use of forest land is quite high, concepts and preventive actions are needed, such as rehabilitation and reforestation of critical lands through providing proper monitoring and targeted forest management to restore its ecosystem in the future.

Keywords: deforestation, populous area, LULCC method, proper control and effective forest management

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Authors: Ahmed Elaksher

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Over the last few years, significant progresses have been made and new approaches have been proposed for efficient collection of 3D spatial data from Unmanned aerial vehicles (UAVs) with reduced costs compared to imagery from satellite or manned aircraft. In these systems, a low-cost GPS unit provides the position, velocity of the vehicle, a low-quality inertial measurement unit (IMU) determines its orientation, and off-the-shelf cameras capture the images. Structure from Motion (SfM) and photogrammetry are the main tools for 3D surface reconstruction from images collected by these systems. Unlike traditional techniques, SfM allows the computation of calibration parameters using point correspondences across images without performing a rigorous laboratory or field calibration process and it is more flexible in that it does not require consistent image overlap or same rotation angles between successive photos. These benefits make SfM ideal for UAVs aerial mapping. In this paper, a direct comparison between SfM Digital Elevation Models (DEM) and those generated through traditional photogrammetric techniques was performed. Data was collected by a 3DR IRIS+ Quadcopter with a Canon PowerShot S100 digital camera. Twenty ground control points were randomly distributed on the ground and surveyed with a total station in a local coordinate system. Images were collected from an altitude of 30 meters with a ground resolution of nine mm/pixel. Data was processed with PhotoScan, VisualSFM, Imagine Photogrammetry, and a photogrammetric algorithm developed by the author. The algorithm starts with performing a laboratory camera calibration then the acquired imagery undergoes an orientation procedure to determine the cameras’ positions and orientations. After the orientation is attained, correlation based image matching is conducted to automatically generate three-dimensional surface models followed by a refining step using sub-pixel image information for high matching accuracy. Tests with different number and configurations of the control points were conducted. Camera calibration parameters estimated from commercial software and those obtained with laboratory procedures were comparable. Exposure station positions were within less than few centimeters and insignificant differences, within less than three seconds, among orientation angles were found. DEM differencing was performed between generated DEMs and few centimeters vertical shifts were found.

Keywords: UAV, photogrammetry, SfM, DEM

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Authors: Ebrahim Taherzadeh, Helmi Z. M. Shafri, Kaveh Shahi

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One of the most important tasks in urban area remote sensing is detection of impervious surface (IS), such as building roof and roads. However, detection of IS in heterogeneous areas still remains as one of the most challenging works. In this study, detection of concrete roof using an object-oriented approach was proposed. A new rule-based classification was developed to detect concrete roof tile. The proposed rule-based classification was applied to WorldView-2 image. Results showed that the proposed rule has good potential to predict concrete roof material from WorldView-2 images with 85% accuracy.

Keywords: object-based, roof material, concrete tile, WorldView-2

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Authors: Ludmila Vucolova

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The thought is a process that underlies consciousness and cognition and understanding its origin and processes is a longstanding goal of many academic disciplines. By integrating over twenty novel ideas and hypotheses of this theoretical proposal, we can speculate that thought is an emergent property of coded neural events, translating the electro-chemical interactions of the body with its environment—the objects of sensory stimulation, X, and Y. The latter is a self- generated feedback entity, resulting from the arbitrary pattern of the motion of a body’s motor repertory (M). A culmination of these neural events gives rise to a thought: a state of identity between an observed object X and a symbol Y. It manifests as a “state of awareness” or “state of knowing” and forms our perception of the physical world. The values of the variables of a construct—X (object), S1 (sense for the perception of X), Y (object), S2 (sense for perception of Y), and M (motor repertory that produces Y)—will specify the particular conscious percept at any given time. The proposed principle of interaction between the elements of a construct (X, Y, S1, S2, M) is universal and applies for all modes of communication (normal, deaf, blind, deaf and blind people) and for various language systems (Chinese, Italian, English, etc.). The particular arrangement of modalities of each of the three modules S1 (5 of 5), S2 (1 of 3), and M (3 of 3) defines a specific mode of communication. This multifaceted paradigm demonstrates a predetermined pattern of relationships between X, Y, and M that passes from generation to generation. The presented analysis of a cognitive experience encompasses the key elements of embodied cognition theories and unequivocally accords with the scientific interpretation of cognition as the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses, and cognition means thinking and awareness. By assembling the novel ideas presented in twelve sections, we can reveal that in the invisible “chaos”, there is an order, a structure with landmarks and principles of operations and mental processes (thoughts) are physical and have a biological basis. This innovative proposal explains the phenomenon of mental imagery; give the first insight into the relationship between mental states and brain states, and support the notion that mind and body are inseparably connected. The findings of this theoretical proposal are supported by the current scientific data and are substantiated by the records of the evolution of language and human intelligence.

Keywords: agent, awareness, cognitive, element, experience, feedback, first person, imagery, language, mental, motor, object, sensory, symbol, thought

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Authors: Muhardiyan Erawan, Zaenal Mutaqin

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Balingara River is one of the rivers with the type Gravel-Bed in Indonesia. Gravel-Bed Rivers easily deformed in a relatively short time due to several variables, that are climate (rainfall), river discharge, topography, rock types, and land cover. To determine stream channel changes in Balingara River used Landsat 7 and 8 and analyzed planimetric or two dimensions. Parameters to determine changes in the stream channel are sinuosity ratio, Brice Index, the extent of erosion and deposition. Changes in stream channel associated with changes in land cover then analyze with a descriptive analysis of spatial and temporal. The location of a stream channel has a low gradient in the upstream, and middle watershed with the type of rock in the form of gravel is more easily changed than other locations. Changes in the area of erosion and deposition influence the land cover changes.

Keywords: Brice Index, erosion, deposition, gravel-bed, land cover change, sinuosity ratio, stream channel change

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Authors: Vinai K. Singh

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In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

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Authors: Navya Saira George, Patroba Achola Odera

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This study aims to determine spatio-temporal expansion of Kochi City, situated on the west coast of Kerala State in India. Remote sensing and GIS techniques have been used to determine land use/cover and urban expansion of the City. Classification of Landsat images of the years 1973, 1988, 2002 and 2018 have been used to reproduce a visual story of the growth of the City over a period of 45 years. Accuracy range of 0.79 ~ 0.86 is achieved with kappa coefficient range of 0.69 ~ 0.80. Results show that the areas covered by vegetation and water bodies decreased progressively from 53.0 ~ 30.1% and 34.1 ~ 26.2% respectively, while built-up areas increased steadily from 12.5 to 42.2% over the entire study period (1973 ~ 2018). The shift in land use from agriculture to non-agriculture may be attributed to the land reforms since 1980s.

Keywords: Geographical Information Systems, Kochi City, Land use/cover, Remote Sensing, Urban Sprawl

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Authors: Shirly May Balais

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This study aims to evaluate the effects of vocabulary instruction in improving the students’ learning in science. The teacher-researcher utilized the vocabulary instructional materials in enriching the science vocabulary of grade four learners. The students were also given an achievement test to determine the effects of vocabulary instructional materials. The assessment indicated that students had shown improvement in comprehension and science literacy. This also helps the students to grasp, understand, and communicate appropriate science concepts and the integration of imagery makes learning science fun. In this research, descriptive qualitative methods and observation interviews were used to describe the effects of using vocabulary instructional materials in improving the science vocabulary of grade four learners. The students’ perceptions were studied, analyzed, and interpreted qualitatively.

Keywords: instruction, learning, science, vocabulary

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Authors: Michael Mickson

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The recent development of unmanned aerial vehicles (UAVs) has been increasing the number of technical solutions that can be used to identify, map, and manage the effects of geohazards. UAVs are generally cheaper and more versatile than traditional remote-sensing techniques, and they can be therefore considered as a good alternative for the acquisition of imagery and other remote sensing data before, during and after a natural hazard event. This study aims to use UAV for investigating areas susceptible to high mobility flows such as debris flow in Dominica, especially after the 2017 Hurricane Maria. The use of UAVs in identifying, mapping and managing of natural hazards helps to mitigate the negative effects of natural hazards on livelihood, properties and the built environment.

Keywords: unmanned aerial vehicle (UAV), geohazards, remote sensing, mapping, Dominica

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Authors: Isa Muhammad Zumo, Musa Lawan

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This study analyzes the land use/land cover changes in Damaturu metropolis from 1986 to 2005. LandSat TM Images of 1986, 1999, and 2005 were used. Built-up lands, agric lands, water body and other lands were created as themes within ILWIS 3.4 software. The images were displayed in False Colour Composite (FCC) for a better visualization and identification of the themes created. Training sample sets were collected based on the ground truth data during field the checks. Statistical data were then extracted from the classified sample set. Area in hectares for each theme was calculated for each year and the result for each land use/land cover types for each study year was compared. From the result, it was found out that built-up areas have a considerable increase from 37.71 hectares in 1986 to 1062.72 hectares in 2005. It has an annual increase rate of approximately 0.34%. The results also reveal that there is a decrease of 5829.66 hectares of other lands (vacant lands) from 1986 to 2005.

Keywords: land use, changes, analysis, environmental pollution

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Authors: Sen Tanmoy, Jain Sarika, Panda Jagabandhu

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The studies regarding the impact of urbanization using the WRF-ARW model rely heavily on the static geographical information selected, including domain configuration and land use land cover (LULC) data. Accurate representation of LULC data provides essential information for understanding urban growth and simulating meteorological parameters such as temperature, precipitation etc. Researchers are using different LULC data as per availability and their requirements. As far as India is concerned, we have very limited resources and data availability. So, it is important to understand how we can optimize our results using limited LULC data. In this review article, we explored how a LULC map is generated from different sources in the Indian context and what its significance is in WRF-ARW modeling to study urbanization/Climate change or any other meteorological parameters. Bibliometric analyses were also performed in this review article based on countries of study and indexed keywords. Finally, some key points are marked out for selecting the most suitable LULC map for any urbanization-related study.

Keywords: LULC, LULC mapping, LANDSAT, WRF-ARW, ISRO, bibliometric Analysis.

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Authors: Bello Yusuf Idi

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The Gongola arm of the Upper Banue Trough, Northeastern Nigeria is predominantly covered by the outcrops of Limestone-bearing rocks in form of Sandstone with intercalation of carbonate clay, shale, basaltic, felsphatic and migmatide rocks at subpixel dimension. In this work, subpixel classification algorithm was used to classify the data acquired from landsat 7 Enhance Thematic Mapper (ETM+) satellite system with the aim of producing fractional distribution image for three most economically important solid minerals of the area: Limestone, Basalt and Migmatide. Linear Spectral Unmixing (LSU) algorithm was used to produce fractional distribution image of abundance of the three mineral resources within a 100Km2 portion of the area. The results show that the minerals occur at different proportion all over the area. The fractional map could therefore serve as a guide to the ongoing reconnaissance for the economic potentiality of the formation.

Keywords: linear spectral un-mixing, upper benue trough, gongola arm, geological engineering

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Authors: Naser Ahmadi Sani, Karim Solaimani, Lida Razaghnia, Jalal Zandi

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In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.

Keywords: Haraz basin, change detection, land-use, satellite data

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Authors: Dana R. Thomson, Catherine Linard, Sabine Vanhuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Caiaffa, Megumi Rosenberg, Eleonore Wolff, Tais Grippa, Stefanos Georganos, Helen Elsey

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The Sustainable Development Goals (SDGs) and Urban Health Equity Assessment and Response Tool (Urban HEART) identify dozens of key indicators to help local decision-makers prioritize and track inequalities in health outcomes. However, presentations and discussions at the International Conference on Urban Health (ICUH) 2017 suggested that additional indicators are needed to make decisions and policies. A local decision-maker may realize that malaria or road accidents are a top priority. However, s/he needs additional health determinant indicators, for example about standing water or traffic, to address the priority and reduce inequalities. Health determinants reflect the physical and social environments that influence health outcomes often at community- and societal-levels and include such indicators as access to quality health facilities, access to safe parks, traffic density, location of slum areas, air pollution, social exclusion, and social networks. Indicator identification and disaggregation are necessarily constrained by available datasets – typically collected about households and individuals in surveys, censuses, and administrative records. Continued advancements in earth observation, data storage, computing and mobile technologies mean that new sources of health determinants indicators derived from 'big data' are becoming available at fine geographic scale. Big data includes high-resolution satellite imagery and aggregated, anonymized mobile phone data. While big data are themselves not representative of the population (e.g., satellite images depict the physical environment), they can provide information about population density, wealth, mobility, and social environments with tremendous detail and accuracy when combined with population-representative survey, census, administrative and health system data. The aim of this paper is to (1) flag to data scientists important indicators needed by health decision-makers at the city and sub-city scale - ideally free and publicly available, and (2) summarize for local decision-makers new datasets that can be generated from big data, with layperson descriptions of difficulties in generating them. We include SDGs and Urban HEART indicators, as well as indicators mentioned by decision-makers attending ICUH 2017.

Keywords: health determinant, health outcome, mobile phone, remote sensing, satellite imagery, SDG, urban HEART

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Authors: Ahmad Sharif Ahmadi, Yoshitaka Kajita

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Kabul city is experiencing urban land expansion in an unprecedented scale, especially since the last decade. With massive population expansion and fast economic development, urban land has increasingly expanded and encroached upon agriculture land during the urbanization history of the city. This paper evaluates the integrated urban land expansion impact on agriculture land in Kabul city since the formation of the basic structure of the city between 1962-1964. The paper studies the temporal and spatial characteristic of agriculture land and agriculture land loss in Kabul city using geographic information system (GIS) and remote sensing till 2008. Many temporal Landsat Thematic Mapper (TM) imageries were interpreted to detect the temporal and spatial characteristics of agriculture land loss. Different interval study periods, however, had vast difference in the agriculture land loss which is due to the urban land expansion trends in the city. the high number of Agriculture land adjacent to the city center and urban fringe have been converted into urban land during the study period in the city, as the agriculture land is highly correlated with the urban land.

Keywords: agriculture land, agriculture land loss, Kabul city, urban land expansion, urbanization

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Authors: Abebayehu Yohannes, Hsiu-Ling Chen, Chiu-Chen Chang

Abstract:

Mathematics reading and spatial ability are important learning components in mathematics education. However, many students struggle to understand real-world problems and lack the spatial ability to form internal imagery. To cope with this problem, in this study, an interactive e-book was developed. The result indicated that both groups had a significant increase in the mathematics reading ability test, and a significant difference was observed in the overall mathematics reading score in favor of the experimental group. In addition, the interactive e-book learning mode had significant impacts on students’ spatial ability. It was also found that the richness of content with visual and interactive elements provided in the interactive e-book enhanced students’ satisfaction with the teaching material.

Keywords: interactive e-books, spatial ability, mathematics reading, satisfaction, three view

Procedia PDF Downloads 192

Authors: Shahab Khan, Damiya Saghir

Abstract:

With the advent of space technology, a new era began where space, considered the new ‘High ground,’ is used for a variety of commercial (communications, weather and navigational information, Earth resources monitoring and imagery) and military applications (surveillance, tracking, reconnaissance and espionage of adversaries). With the ever-evolving geo-political environment, where now the US foreseeing India as a counterbalance to China’s economic and military rise, significant growth in strategic collaboration between US and India has been witnessed, particularly in the space domain. This is creating a strategic imbalance in South Asia with implications for all regional countries. This research explores the present and future of Indo-US strategic collaboration in the space domain with envisaged effects and challenges for countries in the South Asian region.

Keywords: space, satellites, Indo-US strategic agreements in space domain, balance of power in South Asian region

Procedia PDF Downloads 128

Authors: Rafik Djemili, Hocine Bourouba, M. C. Amara Korba

Abstract:

In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods.

Keywords: brain-computer interface, motor imagery, electroencephalogram, linear discriminant analysis, support vector machine

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Authors: Kapil Pandey, Vishnu Goyal

Abstract:

Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

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Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

Abstract:

Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

Procedia PDF Downloads 546

Authors: Shengjun Yan, Xuan Wang

Abstract:

Water level changes in a shallow lake always introduce dramatic land pattern changes. To achieve sustainable ecosystem service, it is necessary to evaluate habitat quality dynamic and its spatio-temporal variation resulted from water level changes, which can provide a scientific basis for protection of biodiversity and planning of wetland ecological system. Landsat data in the spring was chosen to obtain landscape data at different times based on the high, moderate and low water level of Baiyangdian Shallow Lake. We used the InVEST to evaluate the habitat quality, habitat degradation, and habitat scarcity. The result showed that: 1) the water level of shallow lake changes from high to low lead to an obvious landscape pattern changes and habitat degradation, 2) the most change area occurred in northwestward and southwest of Baiyangdian Shallow Lake, which there was a 21 percent of suitable habitat and 42 percent of moderately suitable habitat lost. Our findings show that the changes of water level in the shallow lake would have a strong relationship with the habitat quality.

Keywords: habitat quality, habitat degradation, water level changes, shallow lake

Procedia PDF Downloads 255