###### Abstract  
Forests are the most important carbon reservoirs on land, and forest carbon sinks can effectively reduce atmospheric CO\({}_{2}\) concentrations and mitigate climate change. In recent years, various satellites have been launched that provide opportunities for identifying forest types with low cost and high time efficiency. Using multi-temporal remote sensing images and combining them with vegetation indices takes into account the vegetation growth pattern and substantially improves the identification accuracy, but it has high requirements for imaging, such as registration, multiple times, etc. Sometimes, it is difficult to satisfy, the plateau area is severely limited by the influence of clouds and rain, and Gaofen (GF) data require more control points for orthophoto correction. The study area was chosen to be Huize County, situated in Qujing City of Yunnan Province, China. The analysis was using the GF and Landsat images. According to deep learning and remote sensing image feature extraction methods, the semantic segmentation method of F-Pix2 Pix was proposed, and the domain adaptation method according to transfer learning effectively solved the class imbalance in needleleaf/broadleaf forest identification. The results showed that (1) this method had the best performance and a higher accuracy than the existing products, 21.48% in non-forest/forest and 29.44% in needleleaf/broadleaf forest for MIoU improvement. (2) Applying transfer learning domain adaptation to semantic segmentation showed significant benefits, and this approach utilized satellite images of different resolutions to solve the class imbalance problem. (3) It can be used for long-term monitoring of multiple images and has strong generalization. The identification of needleleaf and broadleaf forests combined with the actual geographical characteristics of the forest provides a foundation for the accurate estimation of regional carbon sources/sinks. f 2023  
constellation of satellites for China's high-resolution Earth observation system [9; 10; 11]. Launched on 26 April 2013, the GF-1 satellite offers images with high spatial and temporal resolution, making it suitable for long-term and small-scale studies and providing essential data support for research. Many scholars have investigated remote sensing forest types and developed practical methods, such as single- and multi-temporal techniques [4; 12]. Incorporating vegetation growth patterns, multi-temporal imagery, and vegetation indices significantly enhances forest identification accuracy [13; 14]. Although multi-temporal imagery can significantly increase the accuracy of forest identification, in the plateau region, where clouds and rain can significantly limit this method, single-temporal images tend to be the preferred option in the context [15]. According to the standards of land use/land cover (LULC), forests and non-forests belong to Level-1, while needleleaf and broadleaf forests belong to Level-2 [16]. The identification of forests and non-forests provides a basis for accurately distinguishing the area of needleleaf and broadleaf forests. Conventional and effective type identification methods are not applicable to the complex geographical conditions area, such as the study area we chose. Therefore, we chose deep learning algorithms that can extract deeper level features for semantic segmentation. The current research trend in remote sensing feature identification is the use of deep learning methods, specifically for semantic segmentation (i.e., superpixel), which has recently garnered significant attention [17; 18]. Fully convolutional networks (FCNs) were first proposed by [PERSON] et al. [19] to enable pixel-level segmentation and have since been improved upon by scholars through various applications in different fields. However, FCNs had high requirements for storage overhead, low computing efficiency, and small receptive fields, resulting in limited accuracy.