Abstract: Vegetation density is one type of information collected from vegetation cover. Vegetation density influences evapotranspiration in terrain, which is essential in assessing how vulnerable peatlands are to fire. The Keetch and Byram Drought Index model, which evaluates peatland fire vulnerability, divides vegetation density into heavily grazed, softly grazed, and un-grazed. Manual approaches for analyzing vegetation density in the field, on the other hand, need a significant amount of resources. Image data acquisition, pre-processing, feature extraction, classification, feature selection, classification, and validation are all computer vision approaches used to solve these problems. Artificial intelligence algorithms and machine learning approaches promise outstanding accuracy in modern computer vision research. However, in the classification process, the impact of feature extraction is critical. Pattern identification at Back Propagation Neural Network (BPNN) is problematic because the feature extraction dimension is excessively complicated. The solution to this problem is using the feature engineering technique to choose the characteristics. This research aims to explore how feature engineering influences the accuracy of results. According to the statistics, implementing the recommended strategy can increase accuracy by 1% and increase kappa by 1.5%. This increase in vegetation density classification accuracy might help detect peatland vulnerability sooner. The novel aspect of this paper is that, after feature extraction, a feature engineering strategy is used in the machine learning classification stage to reduce the number of complex dimensions.

Yuslena Sari, Yudi Firmanul Arifin, Novitasari Novitasari and Mohammad Reza Faisal, “Effect of Feature Engineering Technique for Determining Vegetation Density” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130776

@article{Sari2022,
title = {Effect of Feature Engineering Technique for Determining Vegetation Density},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130776},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130776},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Yuslena Sari and Yudi Firmanul Arifin and Novitasari Novitasari and Mohammad Reza Faisal}
}