It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.

Chinese pine has tremendous applications in many fields. Mapping the distribution of Chinese pine is of great importance for government decision-making and forest management. In order to extract Chinese pine on a large scale, efficient algorithms and open remote-sensing datasets are needed. It is widely believed that machine learning algorithms and medium-resolution remote-sensing datasets can work well for this purpose. Unfortunately, their performance for Chinese pine extraction has remained unclear until now. Therefore, this study aims to explore the ability of the different machine learning algorithms and open remote-sensing datasets for Chinese pine extraction over large areas on Google Earth Engine (GEE). So, based on the combination of three typical machine learning algorithms, namely deep neural network (DNN), support vector machine (SVM), random forest (RF), and three open medium-resolution remote-sensing datasets, namely Sentinel-2, Gaofen-1, and Landsat-8 OLI, 27 models are constructed and GEE, with its powerful computing ability, is used. The main findings are as follows: (1) DNN has the highest accuracy for Chinese pine extraction, followed by SVM and RF; DNN is more sensitive to spatial geometric information, while SVM and RF algorithms are more sensitive to spectral information. (2) Spectral indexes are helpful for improving the extraction accuracy of Chinese pine. The extraction accuracy by using Gaofen-1 dataset increases 7.6% after adding spectral indexes, while the accuracies by using Sentinel-2 and Landsat-8 datasets increase 1.8% and 1.9% after adding spectral indexes, respectively. (3) The extraction accuracy by using DNN and Sentinel-2 dataset with spectral indexes is the highest, with an overall accuracy of 94.4%. (4) The area of Chinese pine is 153.73 km², accounting for 5.06% of the administrative area of Karaqin Banner, and it is convenient to extract Chinese pine on a large scale by using GEE.

It is crucial to accurately identify precious tree pests in a real, complex natural environment in order to monitor the growth of precious trees and provide growers with the information they need to make effective decisions. However, pest identification in real complex natural environments is confronted with several obstacles, including a lack of contrast between the pests and the background, the overlapping and occlusion of leaves, numerous variations in pest size and complexity, and a great deal of image noise. The purpose of the study was to construct a segmentation method for identifying precious tree pests in a complex natural environment. The backbone of an existing Mask region-based convolutional neural network was replaced with a Swin Transformer to improve its feature extraction capability. The experimental findings demonstrated that the suggested method successfully segmented pests in a variety of situations, including shaded, overlapped, and foliage- and branch-obscured pests. The proposed method outperformed the two competing methods, indicating that it is capable of accurately segmenting pests in a complex natural environment and provides a solution for achieving accurate segmentation of precious tree pests and long-term automatic growth monitoring.

Mapping the distribution of forest resources at tree species levels is important due to their strong association with many quantitative and qualitative indicators. With the ongoing development of artificial intelligence technologies, the effectiveness of deep-learning classification models for high spatial resolution (HSR) remote sensing images has been proved. However, due to the poor statistical separability and complex scenarios, it is still challenging to realize fully automated and highly accurate forest types at tree species level mapping. To solve the problem, a novel end-to-end deep learning fusion method for HSR remote sensing images was developed by combining the advantageous properties of multi-modality representations and the powerful features of post-processing step to optimize the forest classification performance refined to the dominant tree species level in an automated way. The structure of the proposed model consisted of a two-branch fully convolutional network (dual-FCN8s) and a conditional random field as recurrent neural network (CRFasRNN), which named dual-FCN8s-CRFasRNN in the paper. By constructing a dual-FCN8s network, the dual-FCN8s-CRFasRNN extracted and fused multi-modality features to recover a high-resolution and strong semantic feature representation. By imbedding the CRFasRNN module into the network as post-processing step, the dual-FCN8s-CRFasRNN optimized the classification result in an automatic manner and generated the result with explicit category information. Quantitative evaluations on China’s Gaofen-2 (GF-2) HSR satellite data showed that the dual-FCN8s-CRFasRNN provided a competitive performance with an overall classification accuracy (OA) of 90.10%, a Kappa coefficient of 0.8872 in the Wangyedian forest farm, and an OA of 74.39%, a Kappa coefficient of 0.6973 in the GaoFeng forest farm, respectively. Experiment results also showed that the proposed model got higher OA and Kappa coefficient metrics than other four recently developed deep learning methods and achieved a better trade-off between automaticity and accuracy, which further confirmed the applicability and superiority of the dual-FCN8s-CRFasRNN in forest types at tree species level mapping tasks.

Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U²-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km², accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment.