The species and distribution of trees in a forest are critical to the understanding of forest ecosystem processes and the development of forest management strategies. Subtropical forest landscapes feature a complex canopy structure and high stand density. Studies on the effects of classification algorithms on the remote sensing-based identification of tree species are few. GF-2 is the first satellite in China with sub-meter accuracy which has the high resolution and short replay cycle. Here, we considered three representative tree types (Masson pine, Chinese fir, and broadleaved evergreen trees) in the southern subtropical evergreen broadleaved forest region of China as research objects. We quantitatively compared the effects of five machine learning algorithms, including the backpropagation neural network, k-nearest neighbour, polytomous logistic regression, random forest (RF) and support vector machine (SVM), and four features (vegetation index, band reflectance, textural features, and topographic factors) on tree species identification using Gaofen-2 panchromatic and multispectral remote sensing images and field survey data. All five classification algorithms could effectively identify major tree species in subtropical forest areas (overall accuracy [OA] > 87.40%, kappa coefficient > 81.08%). The SVM model exhibited the best identification ability (OA = 90.27%, kappa coefficient = 85.37%), followed by RF (OA = 88.90%, Kappa coefficient = 83.30%). The combination of band reflectance, vegetation index, and the topographic factor performed exhibited the best, followed by the combination of band reflectance, vegetation index, textural feature, and topographic factor. In addition, we find that the classifier constructed by a single feature is not as effective as the combination of multiple feature factors. The addition of topographic factors can significantly improve the ability of tree species identification. According to the results of the five classifiers, the separability of the three tree species was good. The producer’s accuracy and user’s accuracy of Masson pine were more than 90%, and the evergreen broad-leaved tree and Chinese fir were more than 80%. The commission errors and omission errors of the three tree species were evergreen broadleaved tree > Chinese fir > Masson pine. The variable importance assessment results showed that the normalized difference greenness index, altitude, and the modified soil-adjusted vegetation index were the key variables. The results of this study used GF-2 to accurately identify the main tree species of subtropical evergreen forests in China, which can help forest managers to regularly monitor tree species composition and provide theoretical support for forest managers to formulate policies, monitor sustainable plans for wood mining, and forest conservation and management measures.

Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, current fuel management often lacks detailed vertical fuel distribution, limiting accurate fire risk assessment and effective fuel policy implementation. In this study, backpack laser scanning (BLS) is used to estimate several 3D structural parameters, including canopy height, crown base height, canopy volume, stand density, vegetation area index (VAI), and vegetation coverage, to characterize the fuel structure characteristics and vertical density distribution variation in different stands of subtropical forests in China. Through standard measurement using BLS point cloud data, we found that canopy height, crown base height, stand density, and VAI in the lower and middle-height strata differed significantly among stand types. Compared to vegetation coverage, the LiDAR-derived VAI can better show significant stratified changes in fuel density in the vertical direction among stand types. Among stand types, conifer-broadleaf mixed forest and C. lanceolata had a higher VAI in surface strata than other stand types, while P. massoniana and conifer-broadleaf mixed forests were particularly unique in having a higher VAI in the lower and middle-height strata, corresponding to the higher surface fuel and ladder fuel in the stand, respectively. To provide more informative support for forest fuel management, BLS LiDAR data combined with other remote sensing data were advocated to facilitate the visualization of fuel density distribution and the development of fire risk assessment.