Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Abstract

Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (√k), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables (k), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.