phd thesis in gis and remote sensing

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Ph.D. in Geospatial Analytics

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• Mapping a Dynamic Planet
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Our innovative Ph.D. program brings together researchers from across NC State University to train a new generation of interdisciplinary data scientists skilled in developing novel understanding of spatial phenomena and in applying new knowledge to grand challenges.

This one-of-a-kind degree focuses on integrative thinking and experiential learning:

• Collaborative, cross-disciplinary teamwork  unites students and faculty from many research fields
• Guaranteed funding  for four years includes a competitive minimum stipend of $30,000, health insurance, and tuition
• Professional seminar  supports student success through training in science communication, proposal writing and geospatial data ethics
• Travel funding is available for students to attend scientific conferences
• Program values include prioritizing student mental health and work/life balance, open data, environmental and social justice, and a commitment to collaboration, community and equity

If your research goals intersect geospatial problem-solving from any number of fields, you will find your fit here.  Our  Faculty Fellows  advise students interested in a range of disciplines––from design, to social and behavioral sciences, natural resources and the environment, computer science, engineering and more––and approach their work in a range of  geospatial research areas . Students with strong backgrounds in quantitative methods in geography, data science, remote sensing and earth sciences are strongly encouraged to apply. We are especially committed to increasing the representation of students that have been historically excluded from participation in U.S. higher education.

Find recent publications by our students and faculty through NC State’s  Libraries Citation Index and learn more about the achievements of our students and alumni.

Program news

May 09, 2024

Exploring Principles of Open Data and Data Sovereignty in Landscape Ecology

As part of a collaborative project co-produced with the Eastern Band of Cherokee Indians, Geospatial Analytics Ph.D. student Christina Perella is exploring how the way data defines communities can be at odds with how communities define themselves.

Components to Consider in Choosing Plant Disease Forecast Models, a Presentation at IALE

Geospatial Analytics Ph.D. student John Polo studies how different data collection methods and model structures affect forecasts of plant disease spread in agricultural fields and forests.

May 06, 2024

Pack Reflections: Laura Tomkins ’24

Tomkins graduated in May 2024 with a Ph.D. in Geospatial Analytics. She is currently employed as a senior atmospheric scientist by Karen Clark and Company in Boston, Massachusetts.

Apply for a Ph.D. in Geospatial Analytics

Ten fully funded Ph.D.  graduate assistantships  with $30,000 salary, benefits, and tuition waiver are available for Fall 2024 through the Center for Geospatial Analytics.

Applications for Fall 2024 admissions are now open. The application deadline is February 1, 2024 – all recommendations and test scores must be received by this date.

There are several opportunities for students to receive a stipend above the base rate of $30,000. These fellowships do not require an additional application:

• Goodnight Doctoral Fellowship. One to two incoming students with a track record of exceptional achievement in the sciences will earn an additional $10,000 per year + all student fees waived for four years
• University Graduate Fellowship. Five incoming students will receive an additional $4,000 in their first year
• Diversity Enhancement Fellowship. Two incoming students will receive an additional $2,000 in their first year
• Mansour Doctoral Fellowship. One incoming international student will be nominated to receive an additional $10,000 in their first year

Admission Requirements

Our most competitive applicants will have

• Significant quantitative research experience outside of the classroom, beyond basic data collection or data entry
• Computational/quantitative background, including a combination of the following coursework or demonstrated skills: statistics, advanced mathematics, quantitative research methods, R, Python
• Prior coursework, background and/or research interests in the area of geospatial analytics
• For international applicants: IBT TOEFL score ≥ 80 overall (18 in each section), IELTS score ≥ 6.5 on each section, Duolingo English ≥ 110. Scores are not required for citizens of  these countries  or who have completed at least one year of full time study at U.S. college or university

Supporting Documents

• Official NC State Graduate School  application.
• Unofficial transcripts  from all colleges/universities attended (official transcripts are only required if admitted to the program).
• Your academic and career goals as well as your motivation in pursuing a Ph.D.
• Research experiences and background/skills that would make you a successful Ph.D. student in geospatial analytics
• Relevant research interests
• Your specific interest in the Ph.D. in Geospatial Analytics at NC State
• 3 letters of recommendation.  Submit the names and contact information for your recommenders through the online application, and they will receive an email with instructions for submitting their letters online. Please select recommenders who can speak to your academic and/or research potential.
• Curriculum vitae/resume.
• Optional GRE scores. Taking the GRE is strongly recommended for international students who have not previously studied in the U.S.

If you have questions about the application process, please contact  Rachel Kasten , Graduate Services Coordinator ([email protected], 919-515-2800). Please note that there is a required application fee of $75 for domestic applicants and $85 for international applicants. McNair Scholars will have the application fee waived. This fee cannot be waived or reduced for international students.

More information for prospective international students can be  found here .

Degree Requirements

The Ph.D. program consists of

• 72 credit hours beyond the Bachelor’s degree .  The core required courses comprise 18 credit hours. The remaining 54 credit hours are devoted to an individually tailored selection of electives and research.
• an off-campus professional experience.  By the beginning of their third year in the program, students participate in an experiential learning activity within government (local, state, federal), industry, private and academic research institutions, or other organizations in the geospatial arena. Students consult with their advisors to identify specific opportunities that will enhance their doctoral program.
• a comprehensive exam.  Students will complete both written and oral exams by the end of their fifth semester in order to be admitted to candidacy.
• a   written dissertation  and  final dissertation oral defense  required to complete the degree.

Core Curriculum

The core curriculum includes the following courses; click course names to view descriptions. Students are required to take GIS 710 and any three additional core courses, as well as six elective credits:

GIS 710: Geospatial Analytics for Grand Challenges

Students examine why sustainable solutions to grand societal challenges need geospatial analytics. Emphasis is placed on the roles that location, spatial interaction and multi-scale processes play in scientific discovery and communication. Discussion of seminal and leading-edge approaches to problem-solving is motivated by grand challenges such as controlling the spread of emerging infectious disease, providing access to clean water and creating smart and connected cities. Students also engage in several written and oral presentation activities focused on data science communication skills and professionalization.

GIS 711: Geospatial Data Management

Applied experience in the architecture of geospatial data management, including open source options. The course introduces students to: (i) spatial and temporal data types (OGC specification, GPS and accelerometer matching), (ii) spatial predicates, (iii) spatial indices and (iv) spatial query processing. In addition, students will be exposed to modern spatial data management systems like NoSQL and graph databases, and data integration principles including protected health information (PHI/HIPAA).

GIS 712: Environmental Earth Observation and Remote Sensing

Advanced understanding of physical principles of remote sensing, image processing and applications from earth observations. Awareness of tradeoffs between earth observing sensors, platforms and analysis techniques will help prepare the students to critically assess remote sensing products and devise solutions to environmental problems. Students will be able to communicate the complexities of image analysis and will be better prepared to integrate earth observations into their areas of expertise. Topics include electromagnetic energy and radiative transfer; US and international orbital and suborbital data acquisition platforms; passive and active imaging and scanning sensors; spatial, spectral, radiometric, and temporal resolutions; geometric corrections and radiometric calibrations; preprocessing of digital remotely sensed data; advanced image analysis including enhancement, enhancement, classification, geophysical variable retrieval, error and sensitivity analysis; data fusion; data assimilation; and integration of remotely  sensed data with other data types in a geospatial modeling context.

GIS 713: Geospatial Data Mining and Analysis

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data (~70% of all digital data), and the emergence of geosocial media and location sensing technologies has transformed the field in recent years. This course reviews the current state of the art in spatial, temporal and spatiotemporal data mining and looks at real-world applications ranging from geosocial networks to climate change impacts. Course introduces various spatial and temporal pattern families and teaches how to incorporate spatial relationships and constraints into data mining approaches like clustering, classification, anomalies and colocations.

GIS 714: Geospatial Computation and Simulation

Methods, algorithms and tools for geospatial modeling and predicting spatio-temporal dimensions of environmental systems. The course covers the physical, biological, and social processes that drive dynamics of landscape change. Deterministic, stochastic, and multi-agent simulations are explained, with emphasis on coupling empirical and process based models, techniques for model calibration and validation and sensitivity analysis. Applications to real-world problems are explored, such as modeling multi-scale flow and mass transport, spread of wildfire, biological invasions and urbanization.

GIS 715: Geovisualization

Principles of visualization design and scripting for geospatial visualization. This course provides a systematic framework of visualization design principles based on the human visual system and explores open-source geospatial data visualization tools. Topics include challenges and techniques for visualizing large multivariate dataset, spatio-temporal data and landscape changes over time. Students have the opportunity to work with broad range of visualization technologies, including frontiers in immersive visualization, tangible interaction with geospatial data and eye tracking.

Frequently Asked Questions

Below are some of the most frequently asked questions we have received about the Ph.D. program in Geospatial Analytics. If your questions are still not answered here, please feel free to contact us through the form below.

Can the program be completed online or part-time?

No, the Ph.D. in Geospatial Analytics is a traditional full-time on-campus program.

I am currently in a master’s degree program and will complete my degree in the spring. Can I still apply now to start the Ph.D. program in the fall?

Yes. We accept unofficial transcripts with your application. Official transcripts will be requested if you are admitted to the program.

Do I need to have been a geography major to apply?

No, we welcome applications from students with strong computational skills from diverse backgrounds, including computer science, data science, environmental science, ecology, engineering, and more.

Do I need a master’s degree to apply?

No, students may enroll without a master’s degree. Successful applicants, however, will have had previous academic research experience.

Do you offer application fee waivers?

Application fee waivers are offered only for domestic students who have participated in specific research programs (i.e. McNair Scholars).

Is financial assistance available?

Incoming doctoral students receive a tuition waiver, health insurance benefits, and a $30,000 stipend.

Do I need to secure an advisor before applying?

While you are encouraged to connect with faculty who share your interests prior to applying (the application will ask you to name a preferred advisor), students can be admitted on program funding without a specific advisor/position.

What kinds of projects might I work on?

Students in the Geospatial Analytics doctoral program work on a diverse range of data science frontiers intersecting multiple disciplines, with funding available from the Ph.D. program as well as from external grants secured by faculty. Assistantships are each fully funded for four years. Below are a sample of the opportunities that were available in previous years. For more details about each opportunity, and to learn about past projects, visit our Graduate Assistantships page .

• Landscape Connectivity Dynamics in Surface Water Networks — Join the Geospatial Analysis for Environmental Change Lab to investigate climate and land-use change effects on landscape connectivity dynamics.
• Seasonality from Space — Join the Spatial Ecosystem Analytics Lab on a NASA-funded project investigating satellite data fusion and time series analysis.
• Winter Weather — Join the Environment Analytics group to study the complex interactions within snow storms and wintery mix storms.
• Modeling Forest and Water Resources under Changing Conditions — Join the Watershed Ecology lab group and combine various data sources to create projections of future landscape conditions.
• Modeling Agricultural and Water Resource Dynamics — Join the Biosystems Analytics Lab to study the effects of global and local change on fresh and estuarine water quality, land-sea connectivity and agroecosystem productivity.
• Surface Water Dynamics from Space — Join the Geospatial Analysis for Environmental Change Lab to investigate hydroclimatic drivers of surface water extent dynamics and advance quantification of water extent and volume.
• Remote Sensing Forest Gap Dynamics — Join the Applied Remote Sensing and Analysis lab group to examine the role and influence of forest gaps in relation to localized large-scale disturbances.

Funding is available for additional projects, and in all cases students are encouraged to develop research questions and methods that suit their interests and career goals.

We’re here to help! Contact us for more information about the Ph.D. in Geospatial Analytics.

Explore Opportunities

Our graduate assistantships are fully funded with a yearly stipend, tuition support, and benefits. Learn more about opportunities at NC State and the Research Triangle to enrich your graduate experience.

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Geographic Information Science, PHD

On this page:, at a glance: program details.

• Location: Tempe campus
• Second Language Requirement: No

Program Description

Degree Awarded: PHD Geographic Information Science

The PhD program in geographic information science fosters training of next generation scientists and engineers who will excel at theoretical, computational, analytical and technical knowledge in transdisciplinary geospatial sciences.

Students in this doctoral program have the opportunity to conduct research at the Spatial Analysis Research Center alongside world-renowned faculty specializing in remote sensing and earth observation, GIS, geoinformatics, spatial statistics and spatial-temporal analysis. Researchers are investigating a variety of issues, including voting habits, health crises, altering landscapes and more --- all in an attempt to better understand this complex and changing world.

The program has sufficient flexibility to allow for individual needs and interests, allowing students to create a plan of study that fits their personal, academic and professional goals.

Additional Details

Frequently Asked Questions

Degree Requirements

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core (12 credit hours) GCU 585 Geographic Research Design and Proposal Writing (3) GIS 520 GIScience Issues and Debates (3) GIS 521 Geographic Information Science Programming (3) GIS 571 Spatial Statistics for Geography and Planning (3)

Electives or Research (55 credit hours)

Remote Sensing (3 credit hours)

Other Requirements (2 credit hours) GCU 591 or GPH 591 Seminar: Geography Colloquium (2)

Culminating Experience (12 credit hours) GIS 799 Dissertation (12)

Additional Curriculum Information Students select electives, remote sensing and other requirements seminar coursework in consultation with their academic advisor.

Admission Requirements

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in geography, geology, earth science, computer programming, GIS, environmental science, geomatics or a related field from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program, or applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts
• three letters of recommendation
• written statement
• professional resume
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

Letters of recommendation should be from academic professionals or professional colleagues capable of evaluating the applicant's abilities, accomplishments and professional potential.

The written statement must address the following questions:

• What area of specialization within GIS do you wish to pursue, and why?
• Describe the aspects of your education that will enable you to pursue this area of specialization.
• What additional training do you feel you can obtain at Arizona State University to realize your education and career goals?

Also in the written statement, applicants should provide any other information (for example: research experience or information which might be drawn from a resume) that they feel the committee should consider in the application for admission. The written statement should be no longer than two pages. A generic statement, often sent to multiple universities, does not substitute for a statement that addresses the applicant's reasons for applying to this doctoral program in geographic information science.

Next Steps to attend ASU

Learn about our programs, apply to a program, visit our campus, application deadlines, career opportunities.

Professionals with expertise in geospatial information science research, theory and practice are in high demand across sectors and industries, including in institutions of higher education, consulting firms and government agencies. Skills in geographical data science, mapping and data analysis are valuable to businesses and institutions that rely on data-driven approaches to solve complex real-world problems.

Career examples include:

• computer scientist
• conservation scientist
• geoscientist
• geospatial information scientist or technologist
• geospatial intelligence analyst
• remote sensing scientist or technologist

Program Contact Information

If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.

• [email protected]
• 480/965-8733

Image for Geospatial Science and Engineering (Ph.D.) - Remote Sensing Specialization

Geospatial Science and Engineering (Ph.D.) - Remote Sensing Specialization

Secondary navigation, earth observation.

The Geospatial science and engineering (GSE) Ph.D. is an interdisciplinary program that combines advanced coursework with cutting-edge research to advance the field of geospatial sciences. The focus is on transforming geospatial data into relevant information through acquisition, processing, characterization, analysis and modeling in order to understand geographic patterns, processes and relationships at scales ranging from landscapes to the globe.

To achieve these aims, the geospatial sciences integrate the geographic disciplines of cartography, geodesy, geographic information systems and remote sensing with elements of mathematics, statistics, the natural sciences, the social sciences and engineering. The resulting array of geospatial concepts, methods, technologies and datasets are used to address a wide range of pertinent questions about the functioning of the biosphere and its implications for sustainability of natural resources, agricultural productivity, biodiversity, environmental quality and human welfare in a rapidly-changing world.

Current faculty research interests include quantitative remote sensing, land cover and land use change, geography, climate change, and fire science as well as applications of geospatial technologies in agriculture, meteorology, natural resource management and other fields. The program seeks highly motivated students with strong backgrounds in the geospatial sciences or a closely-related field to complement these efforts.

Is it for you?

This program will be a good fit if you:.

• Have an undergraduate or master's degree in geography or related field.
• Have a genuine curiosity about the world.
• Enjoy both the hard sciences and social sciences.
• Like working with technology.
• Are a problem solver and analytical thinker.

Career Opportunities

• Professor/teacher
• Remote sensing specialist
• GIS specialist
• Federal employee

IIIT Hyderabad has announced suspension of the course work with immediate effect on 14th-March-2020 and has made it mandatory for all undergraduate students (first year to fourth year batches) and postgraduate students (MTech first and second year batches) doing only course work to return home by 18th-March-2020; research students (MS, PhD and Dual Degree students registered for thesis credits) can choose to stay and continue their research work. Institute will announce plans for finishing the Spring semester's coursework by 20th-March-2020.

PhD in Spatial Informatics

Spatial informatics research programme.

Spatial Informatics, also referred to as Geospatial sciences and technologies, Geo-Informatics and such deals with the study, research and technology advancement in the fields of Remote Sensing, Geospatial Science and Systems, and its various applications across multiple disciplines including Spatial Modelling and Simulations.

The research group at Lab for Spatial Informatics, IIIT Hyderabad works on a range of research areas in these related fields ranging from Spatial, Spatio-temporal data handling and data processing to GeoVisualization; from Spatial Data bases to Spatio-temporal analytics and Spatial BigData; from Satellite/UAV image analysis to GeoAI; from applications in Land use, Environment, Forests, Hydrology, Climate to Urban and traffic simulation models; Web and Mobile GIS to Geospatial standards. A brief glimspe of on-going research topics can be seen in the following figure. The group has also worked and contributed to Open Source software development/FOSS4G activities.

For more information and current research works, you may visit LSI Center’s website [ 1 ] [ 2 ] [ 3 ]; and current years RnD Showcase posters.

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About the program.

PhD Programme in Spatial Informatics is aimed at promoting research in the areas of GeoSpatial technologies (Remote Sensing and GIS) and its related areas. The program provides opportunities for students to work in different areas of GI science and system related research, new methodologies and algorithm development for both RS and GIS and in all related application domain areas.

The PhD student is expected to carry out the academic and research requirements in pursuance of his/her PhD program. This will include, but not limited to, publishing and presenting the findings in top rated related journals and conferences. Also, s/he may need to contribute to projects in related areas and actively participate in the activities of the research group.

 Eligibility

The candidate should have completed or completing a Master’s program (Master of Science/M.E/M.Tech/MSc/MCA/MA).

While a student with a Masters Degree in Geospatial/Spatial sciences or Geoinformatics is eligible to the PhD program, students with Master’s in other related areas like Geogrpahy, Forestry, Urban Planning, Computer science, etc are also eligible if they have done or have sufficient experience in working or handling Geospatial data and a good understanding of its concepts. Applicants will have to write and clear the common General Aptitude paper.  The Spatial science subjective paper may be tested/evaluated during or just before the interviews.

* Note Students with background in Computer Sceince can also apply for the regular PhD program in CSE offered here at IIIT-H and do research in Spatial Informatics area. It be noted that such students will be governed by the PhD in CSE program guidelines.

Modes of Admission to PhD in SI

The admissions selection processes is mainly through the Post Graduate Entrance Exam (PGEE). See https://pgadmissions.iiit.ac.in/ for details regarding this process.  Applicants will have to write and clear the common General Aptitude paper.  The Spatial science subjective paper may be tested/evaluated during or just before the interviews.

Alternatively, exceptional students and students pursuing research at IIIT already can also apply through the PG Standing Committee. An Interview will be held for all such candidates. For details Click here

Program Requirements - Institute Regulations

A student of the PhD program in Spatial Informatics is expected to successfully complete the following 4 stages (I to IV) for the award of the degree.

I. Course requirements (Depth)

A minimum of three courses, each at or above 4000 level course, will need to be done by the student with a satisfactory grade of competence. Of these courses, one or more should be from the Compulsory course group and one from Group 1.

II. Breadth Qualifiers

Students of Geospatial/Spatial and other non-CSE background are required to do 3 or more courses from Groups 2 to 4 (in consultation with the Advisor). While Students of CSE background are required to do the foundations and systems part of the breadth courses/qualifiers as specified in PhD in CSE program guidelines.  An average grade of B or above is required for the Breadth Courses. Courses may preferably be done within the first 4 semesters.

After finishing the Breadth Courses, there will be a Comprehensive Viva, clearing of which clears the student of the Breadth Qualifier requirements.

Course Groups are as follows. Always consult your advisor for a better understanding of the courses required as per your research area/plan.

Compulsory SI related courses like Remote Sensing, GIS, and other main program courses.

Group 1: Related Domain courses in Environmental and Natural Sciences, Hydrology, Agriculture, Social Sciences and others

Group 3: Computer science courses that are used or fundamental in geospatial technologies and the research problem you will work on. It may be courses like Digital Image processing, Pattern recognition, Algorithms, Databases, Computer vision, and others that are offered at the Institute.

Group 4: Other courses of relevance to the student’s research area/plan. They may be course in AI, Cognitive Science, and others.

III. Dissertation proposal defense

The student is expected to defend the dissertation proposal in front of an appropriate committee appointed by the Dean (Research). A student is declared a PhD candidate after this requirement is satisfied.

IV. Dissertation Final defense

On submission of the full thesis and clearing the final defense, the student will be awarded the PhD.

Regular Review The progress of the student will be reviewed once every semester, till the thesis proposal is submitted. If in the first 2 years, there has been no satisfactory progress, the candidate may be asked to leave the PhD program.

** For any updates/changes to these regulations, please see   http://lsi.iiit.ac.in

For Lab-overview, please  click here

Click here  for Detailed Curriculum Document

Page last updated on April, 2023

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Dissertations / Theses on the topic 'GIS and Remote Sensing'

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Dambe, Natalia. "Riverine flooding using GIS and remote sensing." Master's thesis, Faculty of Engineering and the Built Environment, 2020. https://hdl.handle.net/11427/31738.

Gustavsson, Andreas, and Selberg Martin. "Delineation of Ditches in Wetlandsby Remote Sensing." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354612.

Al, Sghair Fathi Goma. "Remote sensing and GIS for wetland vegetation study." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4581/.

Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.

Ahmadzadeh, M. R. "Reasoning with uncertainty in remote sensing." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/804/.

Almond, Simon John. "Remote sensing within GIS for woodland inventory and monitoring." Thesis, University of Portsmouth, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386832.

Firoozi, Nejad Behnam. "Population mapping using census data, GIS and remote sensing." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.705917.

Mason, Philippa Jane. "Landslide hazard assessment using remote sensing and GIS techniques." Thesis, Imperial College London, 1999. http://hdl.handle.net/10044/1/8899.

Blackburn, George Alan. "Remote sensing of deciduous woodlands : a tool for ecological investigations." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239872.

Berberoglu, Suha. "Optimising the remote sensing of Mediterranean land cover." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285646.

McNulty, Wendy Lynn. "THE CREATION OF A GIS DATABASE AND THE DETERMINATION OF SLUDGE'S SPECTRAL SIGNATURE IN AN AGRICULTURAL SETTING." Bowling Green State University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1120596906.

Sahar, Liora. "Using remote-sensing and gis technology for automated building extraction." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37231.

Ratnayake, Ranitha. "Remote sensing and GIS application for monitoring forest management operations." Thesis, University of Nottingham, 2004. http://eprints.nottingham.ac.uk/11309/.

Murnion, Shane D. "Neural and genetic algorithm applications in GIS and remote sensing." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337024.

Saini, Aditya. "Mapping snow cover in Siberia using GIS and remote sensing." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/94.

Huang, Junyi. "Investigation on landslide susceptibility using remote sensing and GIS methods." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/33.

Jennings, Laura. "A Storm Water Runoff Investigation Using Gis and Remote Sensing." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149613/.

Faraklioti, M. "Classification of sets of mixed pixels in remote sensing." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844613/.

Villeneuve, Julie. "Delineating wetlands using geographic information system and remote sensing technologies." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3135.

Solomon, Semere. "Remote Sensing and GIS : Applications for Groundwater Potential Assessment in Eritrea." Doctoral thesis, KTH, Civil and Architectural Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3491.

An integrated approach with remote sensing, GeographicInformation Systems (GIS) and more traditional fieldworktechniques was adopted to assess the groundwater potential inthe central highlands of Eritrea. Digitally enhanced colorcomposites and panchromatic images of Landsat TM and Spot wereinterpreted to produce thematic maps such as lithology andlineaments. The potential of the Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) data forlithological and lineament mapping was evaluated. Topographicparameters such as surface curvature, slope and drainagesystems were derived from digital elevation models and used tomap landforms. Digital elevation models (DEM) derived fromcontours and acquired in the Shuttle Radar Topographic Mission(SRTM) were compared in relation to location, drainage networksand lineament extraction. Fracture patterns and spacing weremeasured in the field in different rock types and compared withlineaments. Selected springs and wells were visited to studytheir topographic and hydrogeological setting. Well logs,pumping tests, water table depth in dry and wet season as wellas location of wells were collected. All thematic layersincluding hydrogeological data were integrated and analysed ina geographic information system. A groundwater potential mapwas generated and compared with yield data. Groundwaterrecharge was estimated based on water level fluctuations inlarge dug wells and chloride mass-balance method.

Principal component analysis for rock type mapping providedbetter results with ASTER than with Landsat TM data. DEM datapermitted to create detailed landform maps useful torgroundwater potential assessment. DEM derived from SRTM dataare better for detection of drainage systems and linearfeatures than those derived from contours. Most of the fracturesystems corresponding to lineaments are either extensionalrelated to normal faults and dykes, or shear fractures relatedto strike-slip faults. N-S, NW-SE, WNW-ESE, NE-SW and ENE-WSWare dominant fracture orientations with often very densespacing. High yielding wells and springs are often related tolarge lineaments and corresponding structural features such asdykes. Typically wells and springs in basaltic areas havehigher yields mainly due to primary joints. Young alluvialsediments with high permeability and deeply weathered rocklayers are important for water supply especially in hydraulicconnection with fracture systems in crystalline bedrock.Groundwater potential zones demarcated through the model are inagreement with bore well yield data. The spatial distributionof groundwater potential zones shows regional patterns relatedto lithologies, lineaments, drainage systems and landforms.Recharge rates of 10 - 50 mm were estimated in this region. Theresults demonstrate that the integration of remote sensing,GIS, traditional fieldwork and models provide a powerful toolin the assessment and management of water resources anddevelopment of groundwater exploration plans.

Key words: Remote sensing, Geographic InformationSystems, groundwater, geomorphology, Digital elevation model,lithology, hard rock, lineament, structures, hydrogeology,Eritrea

Zhang, Bo. "Data Mining, Gis And Remote Sensing: Application In Wetland Hydrological Investigation." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1220021657.

Yang, Ming-Der. "Adaptive short-term water quality forecasts using remote sensing and GIS /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu148794273980509.

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Archer, David John. "Monitoring geological processes on the Chott el Djerid playa using the ERS-1 SAR." Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296630.

Harris, Andrew John Lang. "Thermal monitoring of volcanoes from space at low spatial resolution." Thesis, Open University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309863.

Krzys, Bethaney L. "Remote identification of wetlands in Mahoning and Trumbull County, Ohio." [Kent, Ohio] : Kent State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1227650462.

Harwood, Joseph Walter IV. "Delineation and GIS Mapping of Urban Heat Islands Using Landsat TM Imagery." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1208562366.

Kelgenbaeva, Kamilya. "Agronomic Suitability Studies in the Russian Altai Using Remote Sensing and GIS." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1212669959876-32328.

Belden, Deborah Jeanne. "Geomorphological mapping of the K2 area, Pakistan using GIS and remote sensing." Diss., [Missoula, Mont.] : The University of Montana, 2008. http://etd.lib.umt.edu/theses/available/etd-06112008-121208/.

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Zhang, Xiaoyang. "Soil-erosion modelling at the global scale using remote sensing and GIS." Thesis, King's College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321948.

Koon, Michael. "A spatial and temporal analysis of conifers using remote sensing and GIS." Huntington, WV : [Marshall University Libraries], 2004. http://www.marshall.edu/etd/descript.asp?ref=401.

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Roy, David Paul. "The geometric correction of airborne remotely sensed scanner imagery." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318207.

Cheesman, Joanne E. "Modelling long-term runoff from upland catchments." Thesis, Manchester Metropolitan University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389290.

Chopping, M. J. "Linear semi-empirical kernel-driven bidirectional reflectance distribution function models in monitoring semi-arid grasslands from space." Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262949.

Reunanen, P. (Pasi). "Landscape responses of the Siberian flying squirrel ( Pteromys volans ) in northern Finland:the effect of scale on habitat patterns and species incidence." Doctoral thesis, University of Oulu, 2001. http://urn.fi/urn:isbn:9514264967.

Ranatunga, Thushara D. "Development of a GIS and Remote Sensing Based Study Tool for Tree Identification." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1227241623.

Crosta, Alvaro Penteado. "Mapping of residual soils by remote sensing for mineral exploration in SW Minas Gerais State, Brazil." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/47830.

Shen, Lin. "GIS-based Multi-criteria Analysis for Aquaculture Site Selection." Thesis, University of Gävle, Department of Industrial Development, IT and Land Management, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-7532.

The pearl oyster Pinctada martensii or Pinctada fucata is the oyster for produce the South China Sea Pearl, and the production of pearl oyster Pinctada martensii plays a key role for the economic and social welfare of the coastal areas. To guarantee both rich and sustainability of providing pearl oyster productions, addressing the suitable areas for aquaculture is a very important consideration in any aquaculture activities. Relatively rarely, in the case of site selection research, the researchers use GIS analysis to identify suitable sites in fishery industry in China. Therefore, I decided to help the local government to search suitable sites form the view of GIS context. This study was conducted to find the optimal sites for suspended culture of pearl oyster Pinctada martensii using GIS-based multi-criteria analysis. The original idea came from the research of Radiarta and his colleagues in 2008 in Japan. Most of the parameters in the GIS model were extracted from remote sensing data (Moderate Resolution Imaging Spectroradiometer and Landsat 7). Eleven thematic layers were arranged into three sub-models, namely: biophysical model, social-economic model and constraint model. The biophysical model includes sea surface temperature, chlorophyll-α concentration, suspended sediment concentration and bathymetry. The criteria in the social-economic model are distance to cities and towns and distance to piers. The constraint model was used to exclude the places from the research area where the natural conditions cannot be fulfilled for the development of pearl oyster aquaculture; it contains river mouth, tourism area, harbor, salt fields / shrimp ponds, and non-related water area. Finally those GIS sub-models were used to address the optimal sites for pearl oyster Pinctada martensii culture by using weighted linear combination evaluation. In the final result, suitability levels were arranged from 1 (least suitable) to 8 (most suitable), and about 2.4% of the total potential area had the higher levels (level 6 and 7). These areas were considered to be the places that have the most suitable conditions for pearl oyster Pinctada martensii for costal water of Yingpan.

Sumaryono, Sumaryono. "Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-123909.

Haq, Mohammed Rajibul. "Development of a remote sensing and GIS-based landslide susceptibility model for scotland." Thesis, University of Dundee, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510683.

Yang, Lisa S. M. Massachusetts Institute of Technology. "Application of high resolution remote sensing and GIS techniques for evaluating urban infrastructure." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120199.

Suzuoki, Yukihiro. "Human Impacts Study on Cuyahoga Valley National Park using GIS and Remote Sensing." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1216649639.

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Martinez-Rodriguez, Juan Guillermo 1958. "Sensitivity analysis across scales and watershed discretization schemes using ARDBSN hydrological model and GIS." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/282879.

Akbar, A. Ali Mohd Sadiq. "Application of remote sensing methods for discrimination of surficial sand types in Qatar Peninsula, the Arabian Gulf." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295012.

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Remote sensing and GIS for wetland vegetation study

Al Sghair, Fathi Goma (2013) Remote sensing and GIS for wetland vegetation study. PhD thesis, University of Glasgow.

Remote Sensing (RS) and Geographic Information System (GIS) approaches, combined with ground truthing, are providing new tools for advanced ecosystem management, by providing the ability to monitor change over time at local, regional, and global scales.

In this study, remote sensing (Landsat TM and aerial photographs) and GIS, combined with ground truthing work, were used to assess wetland vegetation change over time at two contrasting wetland sites in the UK: freshwater wetland at Wicken Fen between 1984 and 2009, and saltmarsh between 1988 and 2009 in Caerlaverock Reserve. Ground truthing studies were carried out in Wicken Fen (UK National Grid Reference TL 5570) during 14th - 18th June 2010: forty 1 m2 quadrats were taken in total, placed randomly along six transects in different vegetation types. The survey in the second Study Area Caerlaverock Reserve (UK National Grid Reference NY0464) was conducted on 5th - 9th July 2011, with a total of forty-eight 1 m2 quadrats placed randomly along seven transects in different vegetation types within the study area. Two-way indicator species (TWINSPAN) was used for classification the ground truth samples, taking separation on eigenvalues with high value (>0.500), to define end-groups of samples. The samples were classified into four sample-groups based on data from 40 quadrats in Wicken Fen, while the data were from 48 quadrats divided into five sample-groups in Caerlaverock Reserve.

The primary analysis was conducted by interpreting vegetation cover from aerial photographs, using GIS combined with ground truth data. Unsupervised and supervised classifications with the same technique for aerial photography interpretation were used to interpret the vegetation cover in the Landsat TM images. In Wicken Fen, Landsat TM images were used from 18th August 1984 and 23rd August 2009; for Caerlaverock Reserve Landsat TM imagery used was taken from 14th May 1988 and 11th July 2009. Aerial photograph imagery for Wicken Fen was from 1985 and 2009; and for Caerlaverock Reserve, from 1988 and 2009.

Both the results from analysis of aerial photographs and Landsat TM imagery showed a substantial temporal change in vegetation during the period of study at Wicken Fen, most likely primarily produced by the management programme, rather than being due to natural change. In Cearlaverock Reserve, results from aerial photography interpretation indicated a slight change in the cover of shrubs during the period 1988 to 2009, but little other change over the study period.

The results show that the classification accuracy using aerial photography was higher than that of Landsat TM data. The difference of classification accuracy between aerial photography and Landsat TM, especially in Caerlaverock Reserve, was due to the low resolution of Landsat TM images, and the fact that some vegetation classes occupied an area less than that of the pixel size of the TM image. Based on the mapping exercise, the aerial photographs produced better vegetation classes (when compared with ground truthing data) than Landsat TM images, because aerial photos have a higher spatial resolution than the Landsat TM images.

Perhaps the most important conclusion of this study is that it provides evidence that the RS/GIS approach can provide useful baseline data about wetland vegetation change over time, and across quite expansive areas, which can therefore provide valuable information to aid the management and conservation of wetland habitats.

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Remote sensing and GIS applications in earth and environmental systems sciences

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• Published: 15 November 2021
• Volume 3 , article number  870 , ( 2021 )

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• Helder I. Chaminé   ORCID: orcid.org/0000-0002-9740-935X 1 , 2 ,
• Alcides J. S. C. Pereira 3 ,
• Ana C. Teodoro 4 &
• José Teixeira 5  

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Remote sensing provides essential data about objects at or near the earth’s surface and the atmosphere based on radiation reflected or emitted from objects or areas in multiscale and multitemporal approaches. Remote sensing techniques use satellite and or airborne sensors to collect information about a given object or area. Thus, they depend on their physical, chemical, biological and or geological properties. The measurement and recording of the electromagnetic radiation are made by sensors mounted on a platform (namely, satellite, aerial, unmanned airborne systems) above the earth’s surface. The sensors can be mounted from a few hundred meters above the earth’s surface (e.g., high-resolution multispectral and hyperspectral imagers, light detection and ranging (LiDAR), and radar systems) to hundreds (or even thousands) of kilometres (e.g., orbital satellites). Remote sensing data collection methods can be passive or active. Passive sensors (e.g., spectral imaging) detect natural radiation emitted or reflected by the observed object or area. Active sensors have their own energy source, which is emitted in the direction of the object (e.g., radar), and the resultant signal reflected back is measured (e.g., [ 5 , 6 ]).

In this approach, remote sensing provides an impressive amount of geospatial information and data. That offers a cost-effective way in environmental and ground change detection and monitoring. Thus, it is a powerful tool in fundamental and applied sciences, particularly in the environmental, geographical and geoscience fields.

Recent technological advances in Geographic Information System (GIS) techniques and methodologies, combined with the analysis of remotely sensed data, have proven to be powerful tools in fundamental and applied geosciences (e.g., geological mapping, geomorphology, structural geology, hydrogeology, geophysics, geological exploration), applied sciences and engineering (geomatic, geological, geotechnical, mining, civil, environmental), geography and land planning, hydrology and water resources, atmospheric science and meteorology, natural hazards, among others. In addition, GIS methodologies is a forefront approach to support conceptual site models and site investigations mapping encompassing data analysis, visual analytics and support design solutions (e.g., [ 1 , 2 , 7 , 8 , 9 ]).

Geovisualisation is a developing field of computing science with the fundamental approach that displaying visual representations of data assists humans in generating ideas and hypotheses about the data set (e.g., [ 3 , 4 , 9 ]). In the applied sciences, coupling remote sensing and GIS-based mapping are helpful for data visualisation, spatial analysis, and a better understanding of the functioning of the earth, water and environmental systems. Thus, GIS and remote sensing played a crucial role in research and practice, with several applications for spatial data, geovisualisation and modelling in earth and environmental sciences.

The topical collection (TC) on “Remote Sensing and GIS Applications in Earth and Environmental Systems Sciences” includes 25 selected contributions in remote sensing, geospatial analysis, and GIS-based mapping for the earth and environmental systems. The themed issue highlights key emerging research topics in remote sensing for geomorphology, geosciences, engineering, water resources, urban planning, natural hazards. In addition, the TC includes several model regions that shape the spectrum of the theme, mainly in Asia (India, Bangladesh, Pakistan, Mongolia), the Middle East (Turkey, Iraq, Iran), Africa (Nigeria, Ghana), America (USA, Brazil), and Europe (Portugal). Thus, the TC is of interest to all researchers and practitioners in geomatic engineering, applied geosciences, water management, hydrologic engineering, urban planning and natural hazards.

There is a comprehensive array of applications for remote sensing and GIS-based mapping applications in earth and environmental sciences, as shown in this TC. Articles here address several approaches: (i) a set of papers addressing studies in remote sensing-based mapping on biodiversity, forestry and land cover issues; (ii) several papers are related mainly to GIS mapping and remote sensing techniques for delineating potential groundwater recharge zones, remote sensing and GIS-based analysis for urban sprawl, sustainable groundwater resources management for the evaluation of potential recharge zones using geospatial and Multiple-Criteria Decision-Making (MCDA) techniques, and GIS-based modelling for irrigation water suitability; (iii) a valuable set of papers highlighting a novel technique for developing flood hazard map by using AHP (Analytical Hierarchy Process), case studies underlining depletion of surface water bodies and floodplains using geospatial analysis, and land use/cover mapping change derivated from flood issues; (iv) a set of articles stressing the importance and application of spatial variability analysis and GIS-mapping on soil studies; (v) several case studies related to assessment of public open spaces and landscape quality, integrated remote sensing and field-based mapping to delineate glacial landform features, and remote sensing-based evaluation on river morphology evolution; (vi) other papers highlight several applications of remote sensing techniques, such as: coupling a hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification; discussing a geospatial analysis of electricity in an integrated hybrid renewable energy system model; presenting a numerical approach for ionospheric delay estimation of single-frequency NavIC satellite receiver, and showing a study related to the integration of C band SAR (Synthetic-Aperture Radar) and optical temporal data for identification of paddy fields.

Remote sensing and GIS-based mapping for the earth and environmental systems need to advance towards a comprehensive cartographic reasoning concept founded, among others, in geomatic techniques, fieldwork, georeferenced data using high-precision GPS (Geographical Positioning System) for the fieldwork survey and high-resolution digital imagery acquired by an unmanned aerial vehicle (UAV), earth-based systems conceptualisation and numerical modelling. Last but not least, remote sensing and GIS applications for the earth and environmental systems address a conceptual and practical context for a better understanding of the functioning of the natural systems in climate change framework and support design solutions with natural hazards.

Chaminé HI, Teixeira J, Freitas L, Pires A, Silva RS, Pinho T, Monteiro R, Costa AL, Abreu T, Trigo JF, Afonso MJ, Carvalho JM (2016) From engineering geosciences mapping towards sustainable urban planning. Eur Geol J 41:16–25

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Acknowledgements

The guest editors are deeply grateful for the challenge and enthusiastic support of the Managing Editor, Dr. Clifford Chuwah (Earth and Environmental Sciences Section), at all the stages of preparing this topical collection. A word of appreciation also to the full support during the preparation process of topical collection (TC) of the Assistant Editor, Dr. Inna Melnyk, JEO Assistant, Ms. Vidhya Velayudhan, and the Springer production team for their efforts in editing TC. A special thanks to the reviewers for their valuable inputs during the peer-reviewing process to enhance the overall quality of the manuscripts.

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Alcides J. S. C. Pereira

Earth Sciences Institute (ICT) – FCUP Pole, Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Porto, Portugal

Ana C. Teodoro

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Chaminé, H.I., Pereira, A.J.S.C., Teodoro, A.C. et al. Remote sensing and GIS applications in earth and environmental systems sciences. SN Appl. Sci. 3 , 870 (2021). https://doi.org/10.1007/s42452-021-04855-3

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The Washington, D.C. metropolitan area is an exceptional location in which to pursue geographic research. Many national and international agencies and organizations are within a short distance of the campus. Major national research laboratories are close by, including the NASA Goddard Space Flight Center, the Joint Global Change Research Institute, the USDA Beltsville Agricultural Research Center, the National Archives, Bureau of the Census, National Institutes of Health, USGS, National Geospatial-Intelligence Agency (NGA), NOAA and the Offices of the US Global Change Research Program. International and non-governmental agencies are also located within easy reach, including Conservation International, The Nature Conservancy, World Wildlife Fund, the World Bank, the National Geographic Society, and many others. Corporations, businesses, and nonprofit organizations that use geographical applications are also well represented. Libraries on campus and nearby are unrivaled anywhere in the world. The University of Maryland is also located in a region of extraordinary geographic diversity, including two major urban centers (Baltimore and Washington, D.C.), the Appalachian Mountains, Piedmont, Coastal Plain, Chesapeake Bay, and the Atlantic Coast.

The specific geographic research specializations represented by the Faculty include:

• Geospatial-Information Science and Remote Sensing :  Collecting and interpreting geospatial data is central to everything we do as geographers, whether on computers or in the field. From local events to multi-scale processes, our faculty are developing and applying advanced remote sensing capabilities and GI Science that will help us to develop the next generation of GI technologies and understanding of the world’s geography. Our strengths include advanced computer modeling, scientific and geographic visualization, sensor calibration and design, image processing, geocomputing, spatial statistics, and semantic learning.
• Human Dimensions of Global Change – Coupled Human and Natural Systems :  The Department’s ultimate research goal is to advance an integrated understanding of the coupled Earth system including spatially distributed human processes. Our research addresses both fundamental and applied issues in coupled human and natural systems, such as population, socio-economic development, consumption and production, poverty, climate impacts and adaptation, vulnerability and mitigation, as well as the examination of policy options and trade-offs on sustainability. Our scientists investigate both the human socio-economic system and the climate system, and their linkages.
• Land Cover – Land Use Change :  Land cover and land-use change is a key interface between human and natural systems. Our scientists are world leaders in the remote sensing of land-cover changes. This information is actively combined with human socio-economic data to study past land cover and land use change and to inform advanced modeling of spatially-explicit future scenarios. These methods are actively being used to simultaneously address social, economic, carbon, climate, biodiversity and other aspects of land-use changes. We develop agricultural monitoring systems and look at societal impacts, adaptions and vulnerability to fire, droughts, floods, desertification, and other catastrophic events.
• Carbon, Vegetation Dynamics and Landscape-Scale Processes :  The department carries out a broad array of research focused on monitoring vegetation dynamics, with a particular focus on mapping and studying human and natural disturbances and their landscape-scale impacts, as well as changes to the earth surface as a result of climate variability. This research involves integration of field-based research with remotely-sensed observations to address key scientific uncertainties. Alterations to the global carbon cycle are changing atmospheric composition and climate with implications for human well-being and a particular focus of our research is on monitoring and modeling the terrestrial carbon cycle with unprecedented sophistication and resolution.

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Thesis - Geo-information Science and Remote Sensing

The thesis is a compulsory part of every Master study programme of Wageningen University & Research. A major thesis is between 24 and 39 Ects and is at least 36 Ects for the master programme Geo-information Science (MGI).

Geo-information science thesis topics are narrowly related to the research programme of the Laboratory of Geo-informationt Science and Remote Sensing (GIRS). This research program covers a wide range of subjects. The following main themes have been selected to delineate the laboratory's identity:

• Sensing & measuring
• Modelling & visualization
• Integrated land monitoring
• Human - space interaction
• Empowering & engaging communities

The thesis research is conducted under supervision of a staff member of the GIRS group, but might also take place in another institute or company.

• Students have to follow the GRS thesis procedure. Complete guidelines for doing a thesis Geo-information Science are available via Brightspace.
• Required documents and forms are available via Brightspace. Contact us for access to this Brightspace page.
• For the planning of the thesis research an overview with dates for the midterm presentations and colloquia are scheduled.
• Thesis topics can be selected from the GRS thesis topic list or under conditions be proposed by individual students.
• Past thesis projects can be consulted.

Compulsory course in

• Master Geo-Information Science (MGI)

Restricted Optional in

• Master Biosystems Engineering (MBE)
• Master Urban Environmental Management (MUE)

• Our Promise
• Our Achievements
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• Proposal Writing
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• Paper Writing
• Paper Publish
• Synopsis Writing
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• Survey Paper
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• Journal Support
• Research Topics in GIS and Remote Sensing

Remote Sensing is a practice of collecting information of long-distance objects (like earth) and let to analyze collected data for achieving meaningful information. Similarly, geographical information system (GIS) helps to sense, process, modify and manage different types of geographical-based information . Further, it is also capable to represent the energy pattern in the form of digital data . Overall, it enables measurement, record, and interpreting remote sensing images.

This page is prepared to highlight the important Research Topics in GIS and Remote Sensing with Latest Research Areas, Issues, Techniques, and Datasets!!!

Now, we can see the significant types of remote sensing sensors . These sensors are used to sense and capture environmental information from a remote location . Based on the needs of the application, we need to choose the appropriate sensors . Since, each sensor has unique characteristics, functionalities, and purposes . So, one should know the various sensor types before creating the simulation infrastructure . Our developers help you to select the optimal one for your project based on the application requirements.

Types of Remote Sensing Sensors

• Capture data based on fixed geostationary platforms
• Capture and Generate sensed images
• Capture own source of own energy. For instance: radar
• Capture data on run-time moving around the platform
• Capture natural energy type
• Capture and measure radiation to generate profiles (passive sensor type)

Once you select the appropriate sensors, build the simulation infrastructure by deploying the required sensors and other entities for remote sensing. Then, implement your proposed techniques to fulfil your application needs. The primary operations involved in remote sensing models are data collection, feature identification, feature extraction, processing, and classification . Further, it may vary depending on the handpicked research topic. Here, we have given you the general working process of the remote sensing model .

Basic Outline for Remote Sensing 

• At first, the image archive collects geospatial information
• Then, extract the essential features from collected data
• Next, pass over the extracted image features as input for classification
• At last, perform any satellite image operations (search, classification, and retrieval)
• If you attempting for classification then classify the data based on dry land, pasture, fruit trees, green urban areas, water bodies/course, port areas, etc.

Next, we can see the different ranges of spectral bands used for remote sensing and GIS developments . These ranges are incorporated with sensors used for remote data capturing . Also, it will help in selecting the sensors for application. Here, we have given you some commonly used sensor ranges with their purposes and real-time examples. Similarly, we also assist you in the sensor selection process.   

Spectral Bands for Remote Sensing Sensors 

• Utilized for agricultural assessment
• For instance – plant growth estimation, crop yield prediction, cultural representation
• Utilized for urban zone characterization
• For instance – Identification of buildings, roads, etc.
• Gather data at a high spatial resolution
• Utilized for inspecting features of water
• For instance – water quality analysis, coastal area mapping, water depth identification
• Utilized for examining features of soil
• For instance – soil type detection, cultural feature identification, geology analysis
• Utilized for water quality inspection and chlorophyll absorption band
• For instance – health crop detection, plant type representation, plant state evaluation, outlining geologic and soil boundaries

Furthermore, the resource team has given you the list of remote sensing and GIS research areas. Here, we have classified the areas based on the application fields. And, it not only includes research areas but also important research ideas with the information of algorithm, sensors, GIS, satellite, and model. Further, if you are seeking other important research notions in your interested areas then create a bond with us. We are ready to share our latest collections of Research Topics in GIS and Remote Sensing .

Innovative Research Ideas in GIS and Remote Sensing 

• Modeling of Strom and Flood in Urban Area – SWMM, Huff curve, GIS data preprocessing and imagining
• Measuring of Rainfall – GPM, TRMM, and GIS data analysis and visualization
• Flash Flood Identification – CT, JFI, CDFs, GIS analysis, and TMPA real-time 3B2RT
• Soil Water Content Detection – GIS spatial analysis, GPR, FO, and CMP
• Irrigation Plan – GIS visualization, HTM image categorization, and UAV
• Glacier Mapping – GIS, TIN 3D model, Landsat, and ASTER GDEM
• Groundwater and Subsiding Analysis – GPS and GIS spatial analysis
• Rainfall-Runoff Simulation – CoLM, LSM, CoLM+LF, GIS data preprocess and RCM
• Flood Design – GPM IMERG, GIS visualization, and GSSHA
• Flood Prediction – GIS visualization, MOGA scheme, and ARX regressor

Now, we can see the general steps involved in the GIS and remote sensing model . In the above section, we have already specified the outline of working processes involved in the GIS and remote sensing model . Similar to that, here, we have given you the general 3-step procedure to develop a simple remote sensing model .

Simulation steps for GIS and remote sensing

• Monitor and collect the remote sensing data and store them in server
• Next, get the user requests from the user domain
• Then, provide the requested data from the server to respective users in the form of applications/services

Although GIS and Remote Sensing field has met several research growths, it is also equipped with different scientific challenges in execution . These make scholars choose this field for identifying effective problem-solving solutions . In order to find the recent research challenges, issues, and research gaps, we will analyze several current research papers.

Research Issues in Remote Sensing 

• Automated Machine Learning in Neural Network Design
• Distributed DL architecture for Massive storage, GPUs and VMs
• Training and testing of large-scale Geospatial datasets
• For instance: Data Source – OpenStreetMap
• Efficient Development of New DNN architectures in Remote Sensing

In addition, we can see the latest techniques that are widely used for solving recent problem Research Topics in GIS and remote sensing . Beyond this list of techniques, we also support you in other advanced algorithms. In the case of a complex issue, we design a new algorithm or combine two or more techniques as hybrid techniques . So, we are capable to crack any kind of complex problem. Also, we are smart in selecting appropriate techniques/algorithms based on project needs.   

Algorithms for GIS and Remote Sensing 

• Swarm Intelligence
• Self‐Organizing Map
• Knowledge‐Based Model
• Bayesian Belief Network
• Reinforcement Learning (RL)
• Artificial Neural Network (ANN)
• Support Vector Machine (SVM)
• Auto-encoder and Neurocomputing
• Genetic Programming and Algorithm
• Convolutional Neural Network (CNN)
• Naive Bayes and Bayesian Network
• Feedforward Neural Network (FNN)
• Multi‐Agent and Smart Agent System
• Recurrent Neural Network (RNN)

For any kind of Research Topics in GIS and remote sensing , the platform is more important which holds the remote sensors. Generally, satellites and aircraft are well-known for remote sensing . Then, balloons and helicopters are used in some of the real-time cases. For real-time scenarios, one should consider the orbit, attitude, load, altitude of the platform . But the real-world development and deployment are very expensive. So, everyone is moving towards the simulation for analyzing the real behavior of the system with direct implementation. Here, we have given some extensively preferred platforms for GIS and remote sensing development and data storage. 

What are the platforms used for GIS and remote sensing? 

• SciQL / MonetDB

Additionally, we have given you the developer-friendly programming languages which are supported in the above-specified platforms. Our developers are skillful to import any external modules, libraries and packages depending on project needs. And also, we are unique in handpicking solving techniques. Since we always choose advanced techniques and algorithms to yield the best and accurate results. 

Programming Languages for GIS and Remote Sensing 

• GIS heavy-weight Development
• C#, C++/C, and Java
• .NET, Java, C++, and C#
• Mobile Apps Implementation
• iOS, Javascript, and Android
• Libraries for Geospatial Data
• R, Java, C++, JavaScript, C, and Python
• Databases for Geospatial Data
• Structural Query Language (SQL)
• Mapping of Web Services
• Python and JavaScript
• Data Modeling, Handling, and Investigating
• P and Python
• Scripting and Applications for Geographical Information System
• R and Python

Next, we see the top-demanding research notions of the GIS and remote sensing field. Already we have seen the important research areas with ideas in the above section. Here, we have itemized a few significant topics that currently scholars are requesting for their research. From these topics, you can get an idea about the current research direction of GIS and remote sensing . More than these topics, we also give you other interesting topics in the latest and emerging research areas. And also, we assure you that our topics will surely meet the future research scope for further study. 

Latest Research Topics in GIS and Remote Sensing 

• Observation of Dynamic Climate Variation
• Remote-Sensing based Moving Object Detection
• Scene Classification in Remote Sensed Image
• Prediction and Evaluation of Natural Disasters
• Accurate Indication of Ground Object Mobility
• Monitoring of Earth State using DL techniques
• 3D or 4D Data Collection, Analysis, and Classification
• Urban Design and Plan
• Vehicle Mobility in Urban Area Transport
• Monitoring Environmental Status
• Mapping and Semantic Segmentation using Remote Sensing Images
• Urban Area Classification based on Climatic Conditions

Performance Analysis of GIS and Remote sensing  

There are different ways to enhance the efficiency of GSI. For that, one can wisely use geospatial information to improve efficiency in all the phases. Our developers are proficient to elevate the different parameters of the system which is sure to increase the performance. Further, we also suggest you employ best-fitting parameters in the designing phase itself . So, we can obtain effective results after execution.

• At first, we collect the sensed image
• Then, perform pre-processing over sensed data
• Next, extract the useful image features and classify them based on certain conditions
• After that, select the sensitivity factor and apply correlation analysis
• Then, build the model for regression analysis
• At last, assess the performance of the developed model

GIS and Remote Sensing Datasets Description 

Most probably, the remotely sensed data are very precise with the top-quality feature . In some cases, the remotely sensed data may fall under specific faults and inaccuracies due to certain factors . And they are distributed environment, automatic or random failures of sensors ( For instance- stripping based on the uncalibrated detector), and incorrect pre-processing ( For instance – imprecise A-D conversion ).

Here, our developers have given you some key points that you need to follow while selecting and employing geospatial data . We assure you that the following characteristics will help you to attain high-quality of datasets for any kind of application .

• Data (scale, length, and quality)
• Shape (length, area, and width)
• Techniques (collection, sampling, analysis, interpolation, classification)
• Mapping Units (shape and size)
• Position (distance to neighbors, distance to features, spatial location, and border)
• Categorical (majority and proportion)
• Image statistics (maximum, median, minimum, mean, count, sum, standard deviation)

The dataset plays a major role in achieving desired experimental results . So, consider the above-specified tips to choose your dataset. Further, we also assist you in selecting suitable datasets and tools for your project development . Here, we have listed a few globally demanded datasets for GIS and Remote Sensing projects .

List of Datasets on GIS and Remote Sensing 

• Contains sufficient libraries and functionalities
• Allow to design and model harbor detection schemes
• Comprises 3 multiple looks interferometric series via P-SBAS processing chain
• Further, include 230 Sentinel-1 images of Sicily ascending orbits
• Also, addressed as medium-time, long-time, and short-time interferometric dataset
• Encloses guidelines to handle dataset in pdf format
• Along with multi-land cover observations
• Include land information in CORINE that covers 19 classes
• Comprises 590,326 image patches of Sentinel-2
• Urban-based cloud simulated datasets
• Intended for 3D change identification in urban developments
• Most probably, the changes happen on the vertical axis
• Until now, there are no datasets with 3D point clouds (for identifying point-level changes)

Overall, we support you in every step of geographic information system (GIS) and remote sensing research ranges from topic selection to thesis/dissertation submission . In particular, we support you in the research question and answer identification, dataset selection, and development tool/technologies selection . On the whole, we assure you that we satisfy your demands to formulate novel research topics in GIS and Remote Sensing in your stipulated period.

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
• Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

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PhD Fellow in Deep Learning and Satellite Remote Sensing within Maritime Applications

Opportunities > PhD Fellow in Deep Learning and Satellite Remote Sensing within Maritime Applications

Visual Intelligence at The Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø Posted: June 25, 2021

The position is a 3-year PhD fellow in the area of innovating maritime surveillance services, and is affiliated with Innovation Area Earth Observation, with close collaboration to the centre partner Kongsberg Satellite Services (KSAT). The research will be focused, but not limited to, the development of deep learning architectures for object detection in marine environment. Exploring context and dependencies in satellite imagery over a combination of radar and optical satellites is of prime importance, as well as researching new AI solutions to quantify uncertainties in the detections. Advances in utilizing weak and/or noisy labels for learning from limited data will further increase the value of KSAT vast archive of historical data for research.

  Requirements Include:

• Norwegian master degree in physics, mathematics/statistics, computer science, or similar, or a corresponding foreign master degree,
• Background in signal and image processing,
• Background in machine learning and automatic data analysis,
• Experience with remote sensing data analysis (SAR and/or optical),
• Skills in programming,
• Fluency in English

Experience with deep learning (through courses, research projects, or similar), including hands-on experience with software tools such as Pytorch and Tensor Flow, will be considered a strength. Knowledge of pattern recognition and big data processing, plus previous experience in applications related to maritime operations (e.g., ship detection, oil spill), is considered as an asset.

read more…

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Courses are held in English (75%) and German (25%).

31 July for the following winter semester 31 January for the following summer semester

The PhD programme in Geography is based on the research focus "Resources and Sustainability". The research focuses on the interface of human-environment research. The general theme associates process mapping and modelling, creation of material balances (water, air, sediment, carbon, nitrogen and pollutants), the reconstruction of past environmental conditions and the analysis and modelling of the consequences of interventions in biogeochemical cycles. The social science methodology is included in the context of human ecology, political ecology, institutional analysis, and resource-use conflicts.

The Departments of Human Geography and Physical Geography are integrated and associated with the CRC 990 "Ecological and socioeconomic functions of tropical lowland rainforest transformation systems in Sumatra (EFFORTS, phase 3, 2020-2023)". Furthermore, the Department of Human Geography is linked with the Centre for Modern Indian Studies (CeMIS) and integrated in the research unit 2432/1 "Social-ecological systems in the Indian rural-urban interface". In addition, the Department of Cartography, GIS and Remote Sensing is integrated in the Interdisciplinary Center for Sustainable Development (IZNE), and the Institute of Geography is closely linked with the Centre of Biodiversity and Sustainable Land Use (CBL).

The doctoral students learn autonomous scientific work through participation in these international collaborative projects and centres. They also receive knowledge and skills that go beyond a curricular programme of study, namely, through the active participation in the academic affairs of the Faculty and Institute of Geography.

For more information: http://www.uni-goettingen.de/en/430860.html

The focus of the PhD programme lies on the individual research of the PhD students (160 ECTS are gained by dissertation and defence), which is accompanied by a variety of courses: 20 ECTS are gained by completing courses: one compulsory course on theory and methods, three elective courses (e.g., on reflecting research, scientific communication, scientific teaching, key competencies, etc.). Students are free to decide on the specific courses they will complete during each semester.

Fees are around 400 EUR per semester. The fees include a prepaid semester ticket that entitles students to use regional trains (in Lower Saxony and Bremen) and city buses in Göttingen free of charge. Students of the University of Göttingen receive discounts for cultural events. Meals and drinks are also available at reduced prices at all university canteens. Fees: http://www.uni-goettingen.de/fee Semester ticket: http://www.uni-goettingen.de/en/16432.html

The average cost of living in Göttingen is modest compared to other major university cities in Germany. Currently, expenses for accommodation, food, health insurance and books are about 850 EUR per month. Please note that fees for health insurance may vary according to age.

For further information, please see the following link: www.uni-goettingen.de/en/54664.html

Master's degree and at least 150 ECTS credits in one of the following areas: geography, environmental sciences, agricultural sciences, forest sciences, biology, or social or economic sciences The confirmation of one potential supervisor from the Institute of Geography to supervise the doctoral thesis is required upon application. Please see the course website for the entire admission regulations and details about the application procedure: http://www.uni-goettingen.de/en/430860.html .

Applicants must provide proof of their English skills, e.g.:

• Cambridge Certificate in Advanced English, at least grade "B"
• Cambridge Certificate of Proficiency in English, at least grade "C"
• IELTS Academic (International English Language Testing System): at least 6
• TOEFL iBT: at least 80 points
• TOEFL PBT (paper-based): at least 550 points
• CEFR (Common European Framework of Reference): at least level B2 certificate
• UNIcert: at least level III
• successful graduation from an English language study programme

Georg-August-Universität Göttingen Fakultät für Geowissenschaften und Geographie Dekanat Goldschmidtstr. 3 37077 Göttingen Germany

The university supports students in finding part-time jobs in local industries and businesses. A number of student jobs are also available at the university. They are announced on the following website: www.stellenwerk-goettingen.de

Please note that restrictions may apply with your scholarship or visa. Non-EU students are subject to special regulations.

Foreign applicants should note that it is not easy to find a job to finance their studies, as German students are also searching for jobs. Some proficiency in German may be indispensable to find a job. Non-EU students are permitted to work a maximum of 120 full days (240 half days) per year.

The Accommodation Service of the International Office supports international students who are enrolled at the University of Göttingen in finding accommodation and serves as a point of contact for related queries. The Accommodation Service also publishes suitable offers from private landlords in Göttingen and collaborates with the Student Services ("Studentenwerk"). As the number of available accommodation options in Göttingen is limited, it is highly recommended to contact the Accommodation Service as early as possible.

Further information: https://www.uni-goettingen.de/en/617883.html

Please note: For doctoral students, accommodation services are only available if you have a low income.

The Career Service of the University of Göttingen offers individual support to facilitate your successful transition from the academic to the professional world — whether you want to work in Germany or abroad. Especially for international students aiming for a career entry in Germany, the Career Service provides topic-specific "Career Impulse Sessions", workshops, online learning modules, and a qualification programme in “Building International Careers” as well as digital career tools and a virtual community for international employment opportunities: www.uni-goettingen.de/en/292.html

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• PROJECT ON GIS AND REMOTE SENSING

Remote sensing and GIS are mainly used to capture, operate, analyze, handle, store and demonstrate the geographic data. We offer a formatted procedure to develop a problem statement for a general GIS and remote sensing project. By aiming at urban heat island which is a usual application of these technologies, an example also given for further clarification:

Step-by-Step Direction to Formulating a Problem Statement

• Find the Broad Topic

Initially, begin with detecting the vast domain of passion like agricultural performance, disaster handling, ecological tracking and city development inside the remote sensing and GIS fields.

• Narrow Down to a Unique Issue

Through remote sensing and GIS, locate a particular problem or risk which can be solved inside the wide topic. This challenge must include the possibility for exploration and real-time importance.

• Discuss Current Knowledge and Gaps

Indicate the spaces or issues with previous methods by specifying the aspect which is recently known about the problem. Explain how this issue or related issues can be addressed by remote sensing and GIS in a concise format.

• State the Impact of the Problem

Describe the possible concerns when the problem is not tackled. Detail those who are impacted through this. Discuss the reasons behind the significance of this problem.

• Propose How GIS and Remote Sensing Can Help

To tackle or reduce the issue, overview in what way you hope that remote sensing and GIS can be implemented. For solving the problem, emphasize the specific abilities of these techniques which make them appropriate.

• Create the Problem Statement

Define the issue, the space in the on-going interpretation or technique and how your project will utilize remote sensing and GIS to solve it explicitly by integrating the above components within a brief statement.

Example: Urban Heat Islands  

Urban Environmental Management

Specific Issue

The urban heat island (UHI) is commonly considered as the process of rising temperatures in city regions. This can cause a rise in health hazards and more energy consumption.

Modern Knowledge and Spaces

Recent reduction plans are mostly produced and do not examine the native specifics like the formation of eco-friendly gaps and construction resources, but it is called as urban heat islands solution altered land surroundings and waste heat. GIS and remote sensing are not more frequently utilized in active, applicable development at a municipal level but have been utilized to represent UHI impacts.

Influence of the Problem

Urban populations can enhance sickness and humanity rates that are susceptible to the impacts of heatwaves impaired by UHIs. As a result of enhanced utility of air conditioning which causes more CO2 emissions, energy demands also spike.

GIS and Remote Sensing Application

To observe chronological and dimensional figures of heat dispersion in city regions, remote sensing and GIS can be implemented. Decrease the UHI impact efficiently by this data which can notify intended intrusions like improving the spot of eco-friendly reflective resources, parks and roofs.

Problem Statement

Urban heat islands are impacting the city welfare and power consumption in an unpleasant manner as they support high city temperatures specifically. There is a shortage of intended, applicable policies which examine native city structures and sources in reducing these impacts in spite of previous investigation. To observe the particular dedications of city architectures to the UHI impact and to create restricted and applicable reduction plans, this project focuses on using remote sensing and GIS which can be applied to minimize temperature abnormalities in challenging regions efficiently through urban developers.

 Remote Sensing Can anyone suggest hot research topics in the Remote Sensing?

Sure! We can recommend some most popular research topics in the field of remote sensing. The following is a list of several advanced and trending topics which you can implement for the research in this field:

• Disaster Management and Response
• Topic: For natural calamities such as earthquakes, wildfires, floods and hurricanes, create the present tracking mechanisms with the help of remote sensing data.
• Importance: Save lives and minimizing financial damages possibly by improving disaster readiness and response abilities.
• Climate Change Monitoring
• Topic: To track highlighters of climatic variation like alterations in land cover, glacier retreat and increase of sea-level, utilize remote sensing effectively.
• Importance: By serving in world-wide efforts for reduction and adjustment policies, it offers challenging data to interpret the effects and speed of climate change.
• Agriculture and Food Security
• Topic: Improve farming experiences, forecast productions and track crop health by employing hyperspectral imaging and any remote sensing methods.
• Importance: It is essential for serving an evolving international population and can increase farming renewability and yielding in a specific manner.
• Urban Expansion and the Urban Heat Island Effect
• Topic: Along with the urban heat island impact, observing the extension of city regions and their related ecological effects.
• Importance: To enhance living criteria in fast emerging city regions, it assists reduction ideas and renewable city development.
• Forest and Biodiversity Monitoring
• Topic: Especially in unreachable regions, supervise biodiversity, deforestation rates and forest health using remote sensing applications.
• Importance: For climatic principle and environmental equality, it is essential to preserve biodiversity and control forests sustainably that are considered as necessary.
• Integration of AI and Machine Learning
• Topic: Enhance the understanding, observation and processing of large datasets by collaborating machine learning and AI approaches with remote sensing data.
• Importance: It can cause highly-notified decision making throughout several departments and increases the performance and correctness of data analysis.
• Health and Disease Mapping
• Topic: According to ecological variations, it implements remote sensing data to forecast and represent the distribution of diseases.
• Importance: Particularly for vector-borne diseases such as dengue and malaria fever, this is beneficial in societal welfare development and interruption.
• Water Resources Management
• Topic: For tracking modifications in hydrology, representing water areas and evaluating water standard, remote sensing is useful.
• Importance: Specifically in areas that encounter pollution and water inadequacy problems, this is crucial for efficient water resource handling.
• Soil Moisture and Drought Assessment
• Topic: To serve in farming development and water handling, this has the latest remote sensing techniques for drought evaluation and soil moisture tracking.
• Importance: It assists drought readiness plans and serves to control irrigation in a high-effective way.
• Renewable Energy Siting and Monitoring
• Topic: This uses remote sensing to track previous installations and to detect optimal sites for sustainable energy installations.
• Importance: It is important for combating climatic variation and minimizing reliance on fossil fuels. It also promotes the extension of sustainable power sources.

THESIS ON GIS AND REMOTE SENSING

Picking out a suitable subject for a GIS AND REMOTE SENSING thesis can be quite challenging, particularly for a novice researcher. However, once you’ve landed on the perfect topic, crafting an outstanding thesis becomes a breeze. At phdtopic.com, we specialize in creating flawless, original theses that are guaranteed to impress. We write a perfect thesis right from scratch in a flawless way with nil plagarisim. Start your work today with us to get high grade.

• Comparison of remote sensing evapotranspiration models: Consistency, merits, and pitfalls
• Detection and classification of diseased pine trees with different levels of severity from UAV remote sensing images
• Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest: Exploring the potential of optical and SAR remote sensing
• Diffuse light around cities: New perspectives in satellite remote sensing of nighttime aerosols
• Identifying seasonal differences in migration characteristics of Oriental white stork (Ciconia boyciana) through satellite tracking and remote sensing
• The potential of active and passive remote sensing to detect frequent harvesting of alfalfa
• Investigating the succession process of native desert plants over hydrocarbon-contaminated soils using remote sensing techniques
• Segmentation of waterbodies in remote sensing images using deep stacked ensemble model
• Global–local–global context-aware network for salient object detection in optical remote sensing images
• Simulation of forest carbon fluxes by integrating remote sensing data into biome-BGC model
• Pattern planning and design of tiger hazelnut shrub in urban ecosystem based on remote sensing technology
• Physical informed neural network improving the WRF-CHEM results of air pollution using satellite-based remote sensing data
• An attention-based multiscale transformer network for remote sensing image change detection
• A Self-Training Hierarchical Prototype-based Ensemble Framework for Remote Sensing Scene Classification
• Local patchwise minimal and maximal values prior for single optical remote sensing image dehazing
• MUSTFN: A spatiotemporal fusion method for multi-scale and multi-sensor remote sensing images based on a convolutional neural network
• Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification
• Remote sensing of coastal hydro-environment with portable unmanned aerial vehicles (pUAVs) a state-of-the-art review
• DASFNet: Dense-Attention–Similarity-Fusion Network for scene classification of dual-modal remote-sensing images
• Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images