Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin’an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (<1.2 m), a lower NDVI (115 mm), or a shorter DTRD (<500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the “very high” category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation.

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.

Site index (SI) estimation for loblolly pine (Pinus taeda L.) plantations is important for the successful management of this important commercial tree species in the West Gulf Coastal Plain of the United States. This study evaluated various SI models for intensively managed loblolly plantations in the West Gulf Coastal Plain using data collected from permanent plots installed in intensively managed loblolly pine plantations across east Texas and western Louisiana. Six commonly used SI models (Cieszewski GADA model, both Chapman-Richards ADA and GADA models, both Schumacher ADA and GADA models, and McDill-Amateis GADA model) were fit to the data and compared. The Chapman-Richards GADA model and the McDill-Amateis GADA model were similar and best in their fit statistics. These two models were further compared to the existing models (Diéguez-Aranda et al. 2006 (DA2006), Coble and Lee 2010 (CL2010)) commonly used in the region. Both the Chapman-Richards GADA and the McDill-Amateis GADA models consistently predicted greater heights up to age 25 than the models of DA2006 and CL2010, with larger height differences for the higher quality sites, but predicted shorter heights thereafter. Ultimately, the McDill-Amateis GADA model was chosen as the best model for its consistency in predicting reasonable heights extrapolated beyond the range of the data. Foresters can use this model to make more informed silvicultural prescriptions for intensively managed loblolly pine plantations in the West Gulf Coastal Plain.

Forest growing stock volume (GSV, m³/ha) is an indispensable variable for forest resources supervision. Precise measurement of GSV is conducive to monitoring forest dynamics. Nevertheless, little research has explored the pattern pixel values and the function of texture features derived from a vegetation index (VI) for GSV prediction. In this study, we investigated combining linear regression or Random Forest with Sentinel-2 spectral predictors and image textures derived from spectral bands and vegetation indices, which were based on standard deviation values or mean values at the plot level, for predicting GSV of Masson pine, theropencedrymion and all the survey plots in Anji County, China. Specifically, ten groups of experiments encompassing combinations of spectral parameters and texture measures were established for detecting the potential of image textures based on two different image pixel statistics. The results showed that texture measures derived from VI were superior to spectral parameters or texture measures derived from spectral bands for estimating the GSV of single tree species. Moreover, texture features based on shortwave infrared bands or their related VI and the standard deviation at pixel level for spectral bands/indices were emphasized. Finally, the mean value at the plot level exhibited slightly stronger potential than the standard deviation in general.