## 1 Introduction  
Tree species identification is important for the effective management of forests as a natural resource, which influences the accuracy of timber volume estimation [1]. The number and type of tree species in a forest stand are also related to ecosystem parameters like biodiversity and habitat quality and are, therefore, important indicators for describing the ecological value of a forest [2]. An effective environmental management of ecosystems requires accurate and spatially detailed assessments of tree species numbers and distributions [3]. Remote sensing is complementary to traditional field surveys for obtaining species information, particularly within large and inaccessible areas [4]. Over the last four decades, advances in remote sensing technology have enabled the classification of tree species using several image types [5]. [PERSON] et al. [1] concluded that most previous studies were concentrated in temperate regionsand applied images with different resolutions and different sensors to continuously improve the classification accuracy of tree species. The type of imagery is a major factor in tree species classification, as the spatial and spectral resolution can influence the accuracy of a classification [6]. High spatial resolution satellite images, such as those acquired by Systeme Probatoire d'Observation de la Terre (SPOT), QuickBird [7], IKONOS, and WorldView-3 [8] satellites, can distinguish detailed color and texture features. Due to the limited spectral information of high spatial resolution images, it is difficult to distinguish tree species with the same texture features such as white birch and aspen [4,6]. Although medium spatial resolution images, such as Landsat 8 and Sentinel-2 imagery [9], can provide more band information, lower spatial resolution limits tree classification accuracy. Light detection and ranging (LiDAR) data can provide a range of features related mainly to the structure of trees [5]. However, LiDAR sensors at the moment are mounted mainly on airborne platforms, which makes it difficult to obtain a large collection region [1]. Although satellite LiDAR sensors exist, it is limited by the distance of the pulse footprint so that full detailed coverage data cannot be obtained [10]. Hyperspectral images contain multiple continuous narrow bands, providing significant levels of detail, which allow for the distinction of fine spectral variations among tree species [2,11]. Where multispectral classification fails to capture the slight spectral differences that occur between tree species, data-rich hyperspectral imagery can improve classifications by providing sufficient information to discriminate between spectrally similar targets [12]. This has resulted in the extensive use of hyperspectral imagery for tree species classifications [6,13,14]. Compared with the previous hyperspectral data, such as Hyperion and HJ-1 images, the Orbita Hyperspectral Satellite images (OHS-1) used in this study have a higher spatial resolution (10 m), which has greater potential to classify tree species. In terms of classification methods, the commonly used methods include traditional machine learning methods such as support vector machine (SVM), random forest (RF), and fuzzy mathematics, as well as object-oriented methods [8,9,15]. Although many adopted methods of tree species classification have achieved high accuracy results, there is still a lack of highly automated classification algorithms that automatically extract a given classification task without pre-defined feature crafting algorithms due to various environmental conditions such as temperature, precipitation, and terrain. Thus, advanced data-driven approaches to learn forest species classification automatically through high-level feature representations are highly desirable [16]. Recently, deep learning has been widely used in land use classification [17, 18, 19], cloud detection [20], building extraction [21,22], and crop classification [23,24].