Using Data Analytics and Machine Learning in Sustainable Forest Management from Remote Sensing Data

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dc.contributor.advisorKhaiter, Peter A.
dc.contributor.authorSysoeva, Polina
dc.date.accessioned2023-08-04T15:16:59Z
dc.date.available2023-08-04T15:16:59Z
dc.date.issued2023-08-04
dc.date.updated2023-08-04T15:16:59Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractNowadays, remote sensing has become a widely used technique to acquire data for ecosystem service assessment (ESA) and other sustainable management practices. Remotely Sensed Data (RSD) is particularly crucial in locations where in situ observations are either limited or completely impossible due to their inaccessibility, such as mountainous areas. However, due to the unique features of the RSD, obtaining substantial insights requires specific preprocessing steps and strong computational algorithms, such as machine learning (ML). In the research, we present a methodology integrating RSD with data analytic and machine learning techniques for the needs of ESA. A pipeline for preprocessing EOS data, transforming into features, and experimenting with tuning of the ML algorithms is developed. A practical application of the proposed approach is demonstrated through assessing the impact of extreme weather events on forest ecosystems and their carbon sequestration abilities in two areas of the Kashmir Valley, Jammu & Kashmir, India.
dc.identifier.urihttps://hdl.handle.net/10315/41367
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subjectGeographic information science
dc.subjectSustainability
dc.subject.keywordsEcosystem service assessment
dc.subject.keywordsEarth Observing System
dc.subject.keywordsGeographic Information Systems
dc.subject.keywordsMachine learning
dc.subject.keywordsMountainous forests
dc.subject.keywordsIndia
dc.titleUsing Data Analytics and Machine Learning in Sustainable Forest Management from Remote Sensing Data
dc.typeElectronic Thesis or Dissertation

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