The composition of time-series images and using the technique SMOTE ENN for balancing datasets in land use/cover mapping

challenge for sustainable spatial planning, particularly in response to
transformation and degenerative landscape processes. These
disturbances lead to the vulnerability of inhabitants and habitat and
climate changes and socio-economic development in the region.
Several studies have proposed different methods and techniques to
monitor the spatial and temporal changes of LULC. Machine learning
is a more popular method. However, the problem of data imbalance
is a significant challenge, and the classification results tend to bias
the majority classes for unbalanced data. Therefore, this study's
objective is to develop a state-of-the-art technique to reduce the
problem of data imbalance in LULC classification in Vietnam based
on machine learning and SMOTE (Synthesizing Minority
Oversampling Technology) with Edited Nearest Neighbor (ENN).
Various statistical indices, including Kappa and Accuracy, have been
used to assess the performance for the classification of Landuse/cover. The results indicate that integrating oversampling and
under-sampling with SMOTE ENN gave better overall accuracy and
generalization. We also find that the expected proportion of chance
agreement after oversampling is higher than before (Kappa score
before and after oversampling is 0.905244 and 0.974379,
respectively). This study provides an effective method to monitor
spatial and temporal land cover change in Vietnam; it plays a role as
a framework for other relevant research related to land cover change,
which can support planning and sustainable management of the
territory.