Khaiter, Peter A.Sysoeva, Polina2023-08-042023-08-042023-08-04https://hdl.handle.net/10315/41367Nowadays, 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.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyGeographic information scienceSustainabilityElectronic Thesis or Dissertation2023-08-04Ecosystem service assessmentEarth Observing SystemGeographic Information SystemsMachine learningMountainous forestsIndia