## 2 Related Works  
The feasibility of machine learning approaches has been demonstrated in applications, such as earth observation [20], detecting changes on the earth's surface [21], and fire management [22]. Several studies have used machine learning for forest management and deforestation [23]. Studies have focused on various applications, including estimation of forest biophysical properties based on decoders and encoders [24; 25], plant pattern identification and classification [26; 27; 28; 29; 30; 31; 32], semantic segmentation [33], and assessment of the sustainability of forest management [9; 10; 34]. Additionally, machine learning studies have been carried out on the interpretation and extraction of forest features [35; 36; 37; 38]. With regard to deforestation, which is the general scope of this paper, machine learning approaches can be classified into two categories: approaches detecting the location of areas at risk of deforestation and approaches analyzing the variables that drive deforestation [39]. [PERSON] et al. [40] proposed a machine learning model to enhance the estimates of forest land cover type and forest structural metrics. It is a multitask model that performs both classification and regression concurrently, thereby consolidating several independent tasks and models into one stream. [PERSON] et al. [41] applied a machine learning model to detect land use changes in the Amazon. Based on change interpretation, they could identify areas with high risk of being burned and improve current fire scar mapping by enabling the distinction between fires in primary forests and fires in previously burned areas. [PERSON] et al. [42] proposed a study to detect daily deforestation in the Amazon rainforest. They developed an approach to train machine learning models on satellite images, and conducted a spectrum temporal analysis of the deforestation area. The approach aided in understanding the dynamics of the deforestation in the Amazon rainforest. [PERSON] et al. proposed a spatially explicit model of deforestation in order to predict the potential magnitude and spatial pattern of Amazon deforestation. The model was validated and identified spatial areas of deforestation that accumulates over time [43]. [PERSON] et al. developed a spatially explicit model for detecting Australia's forest cover change using long short-term memory (LSTM) deep learning neural networks. The model was applied to a multidimensional spatiotemporal dataset. The results showed that the model is reliable for projecting forest cover and agricultural production [44]. With regard to palm oil, some studies have focused on the detection of oil palm plantations based on satellite imagery. [PERSON] et al. used the texture of trees to train machine learning models in order to distinguish oil palm from high-resolution remote sensing images of Malaysian forests. The implementation results showed that most of the oil palm trees were correctly detected [45; 46]. [PERSON] et al. used a support vector machine (SVM) classifier and a Mahalanobis distance (MD) classifier to undertake supervised classifications of oil palm plantations. They used Landsat, PALSAR, and combined Landsat and PALSAR data for two locations in peninsular Malaysia. Results indicate that accuracies from combined Landsat and PALSAR are better than accuracies from Landsat or PALSAR alone for both study areas using both classifiers [47]. [PERSON] et al. [48] proposed a method to improve the accuracy of detecting and classifying oil palm types (e.g., mature or young). They used the random forest algorithm based on improved grid search optimization (IGSO-RF) in order to establish a classification model and detect oil palm plantations. The results showed that the proposed method improved the detection accuracy of oil palm plantation.