The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 103–108, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-103-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 103–108, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-103-2021

  28 Jun 2021

28 Jun 2021

EVALUATION OF MACHINE LEARNING CLASSIFIERS FOR MAPPING FALCATA PLANTATIONS IN SENTINEL-2 IMAGE

J. R. Santillan1,2,3 and J. L. E. Gesta1,3 J. R. Santillan and J. L. E. Gesta
  • 1Caraga Center for Geo-Informatics, Caraga State University, Ampayon, Butuan City, 8600, Philippines
  • 2Department of Geodetic Engineering, College of Engineering and Geosciences, Caraga State University, Ampayon, Butuan City, 8600, Philippines
  • 3Industrial Tree Plantation Research and Innovation Center, Caraga State University, Ampayon, Butuan City, 8600, Philippines

Keywords: Machine learning classification, Falcata, Accuracy Assessment, Sentinel-2

Abstract. Efficient and accurate mapping of forest and other industrial tree plantations (ITPs) is essential to ensure better monitoring and sustainable management of these plantations. In Caraga Region, Mindanao, Philippines, ITPs planted with Falcata (Paraserianthes falcataria (L.) Nielsen) are widespread and has contributed to more than 50% of the nationwide log production. At present, there is limited information on the location and extent of existing plantations. This provides an opportunity to evaluate satellite remote sensing approaches for mapping these plantations from images, particularly those provided by the Sentinel-2 mission. The objective of this study is to evaluate machine learning classifiers for mapping Falcata plantations in Sentinel-2 image using a 9 × 9 km2 study area in Caraga Region. It also aims to find the best classifier that can provide acceptable levels of accuracy by utilizing only the four bands of the 10-m spatial resolution and the 9 bands of the 20-m spatial resolution Level 2A Sentinel-2 image, respectively. The following classifiers and their variants were evaluated: Linear Support Vector Machine (SVM), Polynomial SVM, Radial Basis Function (RBF) SVM, Artificial Neural Network (Neural Net), Random Forest (RF), and Maximum Likelihood (ML). One or more of these classifiers have been successfully used in in natural and plantation forest mapping, including tree species classification from remotely sensed images. However, their performance and accuracy in detecting and discriminating Falcata plantations is yet to be evaluated. Results of the evaluation showed that the ML classifier has the highest overall accuracy (OA) of 90.90% and has more consistent values for Producer’s Accuracy (PA), and User’s Accuracy (UA) for Falcata and Non-Falcata classes, and hence, provides better Falcata classification results than the other classifiers when the 10-m spatial resolution Sentinel-2 image was used. The accuracy assessment of the 20-m subset classification provides relative different results from that of the 10-m subset, perhaps due to the inclusion of more bands. The highest OA was obtained by the Linear and RBF SVM classifiers at 92.05% each. The SVM classifiers have consistent performance and produce more accurate classification results than the other classifiers (i.e., more than 90% OA, PA, and UA). From these results, it can be concluded that Maximum Likelihood classifier is best to use for Falcata mapping using the 10-m spatial resolution Sentinel-2 image. For the 20-m resolution image, any of the two SVMs (linear or RBF) is more appropriate to use. However, it should be noted that these results are based on classifications where default parameters were used. Improvement in the classification accuracy may be achieved if these parameters were optimized.