USING MACHINE LEARNING CLASSIFIERS AND REGRESSION MODELS FOR ESTIMATING THE STAND AGES OF FALCATA PLANTATIONS FROM SENTINEL DATA

Abstract

Falcata is a widely planted Industrial Tree Plantation (ITP) species in the Caraga Region, Mindanao, Philippines, significantly contributing to more than 80% of the country's Falcata log production in recent years. Currently, Falcata plantations face several challenges, especially regarding plantation monitoring and management. The information on stand age is essential for the efficient monitoring and sustainable management of ITPs. With advances in technology and freely available satellite data, primarily from the Sentinel satellites, remote sensing has provided an alternative approach to determining stand age information. In this context, this study used multivariate regression models and machine learning classifiers, namely Support Vector Machine (SVM) and Random Forest (RF) to estimate Falcata stand ages from single-date and multitemporal Sentinel-1 and Sentinel-2 images and vegetation indices (VIs). The eleven multivariate regression models have R2 values ranging from 0.23 and 0.81 and with estimation errors of 1.72 to 3.58 years. The best multivariate regression model developed is an exponential model that relates Falcata stand age with the year 2021 Sentinel-2 surface reflectance bands and year 2021 Sentinel-1 VV and VH polarization bands. The model has training and validation data R2 of 0.61 and 0.55, which are the most consistent among the 11 regression models developed. When used for stand age estimation, this model may underestimate, or overestimate stand age by 1.72 years. When a machine learning approach is to be employed, the RF classifier performed better than SVM, particularly when estimating stand ages using multitemporal Sentinel-1 and Sentinel-2 data and VIs. The classifier has an overall accuracy of 84.69%, the highest among the eight classification results generated by the study.