EVALUATING THE PERFORMANCE OF SENTINEL 2A AND PLANETSCOPE SATELLITE IMAGING SYSTEMS IN FOREST COVER CLASSIFICATION AT TA DUNG NATIONAL PARK, DAK NONG, VIETNAM
Keywords:
SE-2A, PlanetScope, SVM, GIS, forest cover classification, remote sensingAbstract
In this study, we conducted an analysis and comparison of the effectiveness of two types of satellite imagery, Sentinel 2A (SE-2A) and PlanetScope, in classifying forest cover in Ta Dung National Park, Vietnam, based on impact levels. The primary objective was to evaluate the accuracy and efficacy of these two types of imagery in providing data on the status and distribution of forest cover, supporting the management and conservation of forest resources. The Support Vector Machine (SVM) algorithm was utilized for classification of both satellite imagery sources. The results revealed differences in classification accuracy and the estimation of the area of various forest cover types between the two data sources. Both types of imagery yielded high reliability in forest cover classification, with an accuracy rate above 90% for forests and other land types and over 80% for each forest cover type (minimally impacted forest, moderately impacted forest, and heavily impacted forest). The study findings indicate that despite its lower resolution, SE-2A imagery still provides a reliable accuracy of 84.5% compared to 87.94% for PlanetScope imagery. PlanetScope is highly reliable but incurs costs and is limited by the availability of only four bands of imagery. Meanwhile, SE-2A offers a larger number of bands, with 13 bands of imagery, and its data is provided free of charge and updated periodically. Therefore, the selection of satellite data sources for research should consider the budget, research objectives, and evaluate the advantages and disadvantages of each source to make an appropriate decision. Furthermore, integrating data from both types of satellite imagery is being explored as a new experimental direction to improve the quality of input data and enhance classification accuracy, thereby potentially increasing the effectiveness of natural resource conservation management, especially in the context of current climate change.
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