The well-being of the environment is one of the major factors that contributes to sustainability. Sustainable human settlements require local governance to plan, implement, develop, and manage human settlements expansions. This is important as the number anthropogenic activities is directly correlated to the increase in human population within a geographical region. Regional mapping of land cover conversion of natural vegetation to new human settlements is essential. In this paper we explore the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input to a machine learning method. In theory, the performance is increased by improving the estimates on the features by increasing the length of the sliding window. Experiments were conducted in the Limpopo province of South Africa and were found that increasing the length of the sliding window beyond 12 months yield minor improves due to other seasonal and external factors.
History
Publication title
Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium
Pagination
1-4
ISBN
9781509049516
Department/School
School of Engineering
Publisher
IEEE
Place of publication
United States
Event title
2017 IEEE International Geoscience and Remote Sensing Symposium
Event Venue
Texas, United State
Date of Event (Start Date)
2017-07-23
Date of Event (End Date)
2017-07-28
Rights statement
Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.