Forest tree species information plays an important role in ecology and forest management, and deep learning has been used widely for remote sensing image classification in recent years. However, forest tree species classification using remote sensing images is still a difficult task. Since there is no benchmark dataset for forest tree species, a forest tree species dataset (FTSD) was built in this paper to fill the gap based on the Sentinel-2 images. The FTSD contained nine kinds of forest tree species in Qingyuan County with 8,815 images, each with a resolution of 64 × 64 pixels. The images were produced by combining forest management inventory data and Sentinel-2 images, which were acquired with less than 20% clouds from 1 April to 31 October, including the years 2017, 2018, 2019, 2020, and 2021. Then, the images were preprocessed and downloaded from Google Earth Engine (GEE). Four different band combinations were compared in the paper. Moreover, a Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) were also calculated using the GEE. Deep learning algorithms including DenseNet, EfficientNet, MobileNet, ResNet, and ShuffleNet were trained and validated in the FTSD. RGB images with red, green, and blue (PC1, PC2, and NDVI) obtained the highest validation accuracy in four band combinations. ResNet obtained the highest validation accuracy in all algorithms after 500 epochs were trained in the FTSD, which reached 84.91%. As a famous and widely used remote sensing classification satellite imagery dataset, NWPU RESISC-45 was also trained and validated in the paper. ResNet achieved a high validation accuracy of 87.90% after training 100 epochs in NWPU RESISC-45. The paper shows in forest tree species classification based on remote sensing images and deep learning that (1) PCA and NDVI can be combined to improve the accuracy of classification; (2) ResNet is more suitable than other deep learning algorithms including DenseNet, EfficientNet, MobileNet, and ShuffleNet in remote sensing classification; and (3) being too shallow or deep in ResNet does not perform better in the FTSD, that is, 50 layers are better than 34 and 101 layers.

Tree crown width relates directly to wood quality and tree growth. The traditional method used to measure crown width is labor-intensive and time-consuming. Pairing imagery taken by an unmanned aerial vehicle (UAV) with a deep learning algorithm such as a faster region-based convolutional neural network (Faster-RCNN) has the potential to be an alternative to the traditional method. In this study, Faster-RCNN outperformed single-shot multibox detector (SSD) for crown detection in a young loblolly pine stand but performed poorly in a dense, mature loblolly pine stand. This paper proposes a novel Faster-RCNN algorithm for tree crown identification and crown width extraction in a forest stand environment with high-density loblolly pine forests. The new algorithm uses Residual Network 101 (ResNet101) and a feature pyramid network (FPN) to build an FPN_ResNet101 structure, improving the capability to model shallow location feature extraction. The algorithm was applied to images from a mature loblolly pine plot in eastern Texas, USA. The results show that the accuracy of crown recognition and crown width measurement using the FPN_ResNet101 structure as the backbone network in Faster-RCNN (FPN_Faster-RCNN_ResNet101) was high, being 95.26% and 0.95, respectively, which was 4.90% and 0.27 higher than when using Faster-RCNN with ResNet101 as the backbone network (Faster-RCNN_ResNet101). The results fully confirm the effectiveness of the proposed algorithm.

With the progress of computer vision and the development of unmanned aerial vehicles (UAVs), UAVs have been widely used in forest resource investigation and tree feature extraction. In the field of crown width measurement, the use of traditional manual measurement methods is time-consuming and costly and affects factors such as terrain and weather. Although the crown width extraction method based on the segmentation of UAV images that have recently risen in popularity extracts a large amount of information, it consumes long amounts of time for dataset establishment and segmentation. This paper proposes an improved YOLOv7 model designed to precisely extract the crown width of Metasequoia glyptostroboides. This species is distinguished by its well-developed terminal buds and distinct central trunk morphology. Taking the M. glyptostroboides forest in the Qingshan Lake National Forest Park in Lin’an District, Hangzhou City, Zhejiang Province, China, as the target sample plot, YOLOv7 was improved using the simple, parameter-free attention model (SimAM) attention and SIoU modules. The SimAM attention module was experimentally proved capable of reducing the attention to other irrelevant information in the training process and improving the model’s accuracy. The SIoU module can improve the tightness between the detection frame and the edge of the target crown during the detection process and effectively enhance the accuracy of crown width measurement. The experimental results reveal that the improved model achieves 94.34% mAP@0.5 in the task of crown detection, which is 5% higher than that achieved by the original model. In crown width measurement, the R² of the improved model reaches 0.837, which is 0.151 higher than that of the original model, thus verifying the effectiveness of the improved algorithm.

Diameter at breast height (DBH) is the main metric for standing tree growth measurements. Developing fast and efficient measurement devices for measuring DBH is of great values in forest resource surveys. In this paper, we propose a new tree diameter measurement instrument developed using self-reset displacement sensor, which is equipped with a Personal Computer (PC) terminal to enable the measurement, transmission, storage, and analysis of tree diameters in one. The measurements showed an overall bias of 1.44% and an accuracy of 97.35% compared to the caliper, a conventional diameter measuring instrument. The overall bias was −0.52% compared to the diameter tape, and the measurement accuracy was 98.43%. Compared to the conventional devices, the proposed device is small and easy to carry, the measurement efficiency is significantly improved, and the measurement data can be directly derived for analysis. Compared with related electronic measuring instruments, this proposed device has the advantages of small volume, easy operation, large measurement range, and wider applicability.

The site quality evaluation of plantations has consistently been the focus in matching tree species with sites. This paper studied the site quality of Chinese fir (Cunninghamia lanceolata) plantations in Lin’an District, Zhejiang Province, China. The site quality model was constructed using the algebraic difference approach (ADA) to classify the site quality grades. The rough set algorithm was used to screen out the key site factors affecting the site rank of Chinese fir plantations. Site quality classification models based on random forest were established, and the importance of key site factors was evaluated. The results are as follows. The random forest model based on the rough set algorithm had small scale and low complexity, and the training and testing accuracies of the model were 92.47% and 78.46%, respectively, which were better than the model without attribute reduction. The most important factors affecting Chinese fir growth in the study area were the slope aspect, slope grade, and canopy closure. The least important factors were the humus layer thickness, soil layer thickness, naturalness, and stand origin. The attribute reduction method proposed in this study overcame the subjectivity of traditional site factor selection, and the site quality classification model constructed improved the model accuracy and reduced the complexity of the algorithm. The methods used in this study can be extended to other tree species to provide a basis for matching tree species with sites and to improve the level of forest management in the future.

Estimating tree height at the national to regional scale is crucial for assessing forest health and forest carbon storage and understanding forest ecosystem processes. It also aids in formulating forest management and restoration policies to mitigate global climate change. Extensive ground-survey data offer a valuable resource for estimating tree height. In tree height estimation modeling, a few comparative studies have examined the effectiveness of global-based versus local-based models, and the spatial heterogeneity of independent variable parameters remains insufficiently explored. This study utilized ~200,000 ground-survey data points covering the entire provincial region to compare the performance of the global-based Ordinary Least Squares (OLS) and Random Forest (RF) model, as well as local-based Geographically Weighted Regression (GWR) model, for predicting the average tree height of Chinese fir forests in Zhejiang Province China. The results showed that the GWR model outperformed both OLS and RF in terms of predictive accuracy, achieving an R-squared (R²) and adjusted R² of 0.81 and MAE and RMSE of 0.93 and 1.28, respectively. The performance indicated that the local-based GWR held advantages over global-based models, especially in revealing the spatial non-stationarity of forests. Visualization of parameter estimates across independent variables revealed spatial non-stationarity in their impact effects. In mountainous areas with dense forest coverage, the parameter estimates for average age were notably higher, whereas in forests proximate to urban areas, the parameters were comparatively lower. This study demonstrates the effectiveness of large ground-survey data and GWR in tree height estimation modeling at a provincial scale.