# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 1 Introduction  
### Background and Problem  
Tree species information is being given an increasing amount of attention as a core indicator in forest resource surveys, as the accurate assessment of the composition of tree species in the forest environment will be an asset for forest ecologists, land managers, and commercial harvesters. It can also be used to study biodiversity patterns, estimate timber stocks, or improve the understanding of forest fire risk [1]. Accurate tree species distribution information is crucial for effective forest resource protection efforts. However, the wide range and large area distribution of forests make it excessively time-consuming and labor-intensive to manually identify the tree species they contain. As such, it is difficult to satisfy the requirements for real-time forest monitoring and protection. There is a demand for an effective classification framework to recognize the distribution information of large-area tree species [2]. Remote sensing provides detailed, spectrally rich, continuous spatial, and multi-temporal information, allowing tree species to be identified on the basis of their spectral and structural characteristics. Although satellite remote sensing sensortechnology is rapidly changing, the high-resolution information of forests requires new classification algorithms to bridge the gap between their needs and the wealth of data information. As such, the present study aimed to improve existing automatic tree species classification techniques [3].  
In oversized and hard-to-access areas, remote sensing has been utilized to resolve the deficiencies of traditional field investigation [4]. Within the past four decades, advances in remote sensing technology have made it possible to classify tree species using several image types [5]. Certain remote sensing images limit the ability to recognize tree species due to their relatively low spatial resolution (LR), even when they contain large amounts of information, such as for Sentinel or Landsat data [6]. LiDAR data can provide many structural features of trees [7]. A hyperspectral image (his) is composed of many continuous narrow bands that reveal the spectral details of different tree species [8]. However, as most trees are mixed and marshy plants, it is hard to recognize species in HSIs due to their LR [9]. Conversely, multispectral images (MSIs) provide abundant spatial and contextual information because of their high spatial resolution (HR) [10]. Multispectral images are the most common images used in forest areas [11; 12]. Remote sensing images such as LANDSAT MSS, Landsat TM, Spot, and Sentinel-2 have been used for wetland monitoring, land cover mapping, and tree species identification [13; 14; 15]. The fusion of MSI ahisHSI information can integrate a variety of relevant features to improve the accuracy of tree species classification. A wide variety of features are reflected in the differences hisng HSI and MSI spectral characteristics [16]. The high spectral resolution and subtle divergence of HSIs are advantageous in tree species identification [17; 18]. A single source of spectral information has certain disadvantages that may prevent the accurate classification of tree species in complex areas. Hyperspectral and MS data fusion technology can improve recognition rates. Classification accuracy can also be further improved by the deep learning (DL) of fused spectral and spatial information [19].