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Accurate identification of individual tree species (ITS) is crucial to forest management. However, current ITS identification methods are mainly based on traditional image features or deep learning. Traditional image features are more interpretative, but the generalization and robustness of such methods are inferior. In contrast, deep learning based approaches are more generalizable, but the extracted features are not interpreted; moreover, the methods can hardly be applied to limited sample sets. In this study, to further improve ITS identification, typical spectral and texture image features were weighted to assist deep learning models for ITS identification. To validate the hybrid models, two experiments were conducted; one on the dense forests of the Huangshan Mountains, Anhui Province and one on the Gaofeng forest farm, Guangxi Province, China. The experimental results demonstrated that with the addition of image features, different deep learning ITS identification models, such as DenseNet, AlexNet, U-Net, and LeNet, with different limited sample sizes (480, 420, 360), were all enhanced in both study areas. For example, the accuracy of DenseNet model with a sample size of 480 were improved to 87.67% from 85.41% in Huangshan. This hybrid model can effectively improve ITS identification accuracy, especially for UAV aerial imagery or limited sample sets, providing the possibility to classify ITS accurately in sample-poor areas. [ABSTRACT FROM AUTHOR]

In ordeo te study tee effects of Eucalyptus mixed plantation with different mixed tree species and mixed propotion on soii nutrient accumulation and facility quality, Eucalyptus - Mytilaria laosceis and Eucalyptus -- Magnolia sumatrana mixed plantations in Guangei State-owned Qipo Forest Farm were selected as the research objecta. Based on 15 conventionat soil feyility indexes, the minimum data set and membership function method were used te evaluate the soil 0611X10 quality ot mixed plantations with different atructures. The resulta showed that compared with pure forest- the soii oreanic mattee, totat nitrogen, totat potassium, alkali hydrolyzable nitrogen, available phosphorus available potassium and othes nutuenta ot mixed plantations with different treatmenta were significantly increased, and the contenta ot Cu and Zn in tracc elementa were most aignificantly increased. Compared with pure forest- the soil feyilitu qualita t mixed plantations witU different structures was significantly irnproved. The treatment witU the highest soil fed lity qualita was 25% ol' Eucalyptus --M. sumatrana mixed plantation. When formulating production and manaaement strate/ies, it can refee te construction measures oS the mixed plantation te ensure the sustainability oi forest soil fertility, and promote the healthy and sustainable development oi Eucalyptus plantation and downstream industucs in Guangxi. [ABSTRACT FROM AUTHOR]

[Objective] By studying growth and plant diversity characteristics of different Eucalyptus plantations mixed forests could provide reference for the scientific management of Eucalyptus mixed forests. [Method] A total of 4 Eucalyptus stands in the 18-year-old (Eucalyptus and Quercus griffithii mixed plantation, Eucalyptus and CastOJJ.opsis hystrix mixed plantation, Eucalyptus and Alnus forrnosana mixed plantation, Eucalyptus plantation ) in Gaofeng forest farm of Guangxi as the research plots, the growth indexes such as height, height under branches, individual volume and stand volume, as well as the species composition, important values and diversity index of shrub and grass under the forest were investigated. [ Result] The diameter at breast height, height and individual volume growth of Eucalyptus and C. hystrix mixed plantation were higher than that in Eucalyptus plantation. The Eucalyptus and C. hystrix mixed plantation can promote the growth of Eucalyptus. The total volume of four Eucalyptus plantations were Eucalyptus and C. hystrix mixed plantation(373. 733 m3 / hm2 ),Eucalyptus and Q. griffithii mixed plantation ( 366. 042 m3 / hm2 ), Eucalyptus plantation ( 339. 860 m3 / hm2 ), Eucalyptus and A. formosana mixed plantation ( 249. 041 m3 / hm2 ) . There were no significantly difference in the total volume total among Eucalyptus and A. formosana mixed plantation, Eucalyptus and Q. griffithii mixed plantation and Eucalyptus plantation( ? > 0. 05),but they were extremely significantly greater than Eucalyptus and A. fomwsana mixed plantation(P <0. 01) . The results show that there were 139 species in 112 genera of 69 families of undergrowth plants in 4 Eucalyptus stands ;96 species in 77 genera of 47 families in the shrub layer,43 species in 35 genera of 22 families in the herb layer. The species number and species richness index of herb layer were lower than those of shrub layer, Euphorbiaceae and Gramineae are the most species in the shrub and herb layer respectively. There were 95 species in 82 genera of 58 families of undergrowth plants in Eucalyptus and C. hystrix mixed plantation. The Jaccard coefficient among Eucalyptus and C. hystrix mixed plantation, Eucalyptus and A. fornwsana mixed plantation and Eucalyptus plantation, which had higher Jaccard coefficient than that of Eucalyptus and Q. griffithii mixed plantation. Different mixed modes of Eucalyptus had a significant impact on understory plant diversity, and plant diversity per stand large to small was as follows: Eucalyptus and C. hystrix mixed plantation >Eucalyptus plantation >Eucalyptus and A. fornwsana mixed plantation > Eucalyptus >Eucalyptus and Q. griffithii mixed plantation. [Conclusion] According to the comparative of stand growth and plant diversity, Eucalyptus and C. hystrix mixed plantation can be popularized as a reference model of Eucalyptus mixed plantation in the future since it has advantages in Eucalyptus growth, stand accumulation, total and understory diversity. [ABSTRACT FROM AUTHOR]

In order to understand the characteristics of nutrient changes in the needles and roots of Masson pine plantations with different ages and densities, this article selected four forest ages (young forest, middle-age forest, mature forest and overmature forest) in Zhenlong Forest Farm, Hengxian County, Nanning City, Guangxi. Forest) and four densities (low-density forest, medium-low-density forest, medium-high-density forest and high-density forest) Masson pine forests, a total of eight forest stands, analyzed the CNP content and ratio of masson pine needles and roots and their relationship with soil nutrients relation. The results showed that: (1) The N:P ratio of masson pine needles in all age forests and density forests was greater than 16, indicating that masson pine is obviously restricted by P in this area, and young forests are more obvious. (2) The C content in the needles of Masson pine gradually increases and then decreases with the growth of the forest age. The content of N and P shows a weak downward trend, resulting in a slight upward trend in the C:N ratio, C:P ratio and N:P ratio, but no Reached a significant level; the root C content, P content and C:N ratio gradually increased, and the N content, C:P ratio and N:P ratio were U-shaped and both were the largest in young forests; needles and roots were in mature forests All stages have higher P content and highest C content. (3) The masson pine needles in the medium-density forest have higher C and N content and the highest P content. The C:N ratio is lower and the C:P ratio and N:P ratio are the lowest; the root system has higher CN and P content, and C:N ratio C:P ratio and N:P ratio are lower. (4) The root nutrients of Masson pine, especially P content, change more drastically between different age forests and different density forests than needles, and its correlation with soil nutrients is stronger than that of needles. The above results show that the Pinus massoniana plantation is restricted by P. Strengthening the management of P fertilizer in young forests and choosing the appropriate forest density (medium density) will help alleviate the status of Pinus massoniana being restricted by P. [ABSTRACT FROM AUTHOR]

• Proposing a solution for tree height estimation based on trunk curve. • Estimating the trunk volume combining segmentation measurement and cylinder fitting. • Achieving automatic measurement of the individual tree parameters using CRP data. The forest plots survey's primary measurement factors are the diameter at breast height (DBH), tree height, and the individual tree volume. The reconstruction of a 3D forest scene based on dense images can contribute to the automatic extraction of individual tree parameters. Close-range photogrammetry (CRP) can achieve the reconstruction of a 3D scene from the sequence images at a highly efficient and low cost. However, the reconstructed point clouds often miss the trees' upper part, resulting in the tree height and volume not being measured directly. This paper proposes an integrating extraction framework and methods of the DBH, tree height, and individual tree volume for achieving automatic measurement of the individual tree parameters with high accuracy based on CRP. Four plantation plots in Wangyedian Forest Farm, Inner Mongolia, and Gaofeng Forest Farm, Guangxi Zhuang, are selected to obtain sequence close-up photographs and construct 3D scenes. The DBH, tree height, and trunk volume of the individual tree are extracted using circle fitting, stem curve fitting, growth equation, and segmentation measurement. The extracted individual tree parameters from Terrestrial laser scanning (TLS) are used as validation data to evaluate the accuracy. It shows a good performance in two Larix gmelinii plots, one Pinus tabulaeformis plot, and one Eucalyptus grandis plot with the accuracy of extracted DBH above 90%, which is 90.18%, 96.54%, 90.17%, and 95.00%, respectively. The accuracy of extracted tree height of the four plots is all above 85%, which is 93.34%, 91.71%, 90.32%, and 85.96%, respectively, and the accuracy of trunk volume is 91.55%, 91.94%, 89.86%, and 89.6%, respectively. The results show that our proposed integrated parameter extraction frame and methods can achieve high accuracy economically, efficiently, and conveniently, and be used for automatic measurements of individual tree factors in forest plot surveys. [ABSTRACT FROM AUTHOR]

It is essential to classify tree species accurately for the sustainable management of forest resources and effective monitoring of species diversity. Airborne hyperspectral images have high spatial and spectral resolution, and consequently, the large quantity of information on spectral and spatial structures is effective for tree species classification. In this research, Gaofeng Forest Farm in Nanning, Guangxi Province, China, was used as the study site, and the airborne hyperspectral images were used as the data source. The spectral and textural information extracted by wavelet analysis and edge information extracted by mathematical morphological analysis composed a feature set. The feature set was filtered through a random forest, and object-oriented methods were used to classify tree species through a support vector classifier. The results showed that spectral features extracted by wavelet analysis were highly effective in classifying tree species that had the greatest spectral separability. Horizontal and vertical textures had no positive effect on the classification accuracy, while diagonal textures improved the classification accuracy of tree species. Texture features were not sensitive to stands with small areas and broken distributions, while the edge structure features extracted from mathematical morphology were sensitive to the complex forests. The overall accuracy of tree species classification by combining spectral, textural, and edge structural features was 96.54%, with a Kappa coefficient of 0.96. In the comparative test, the first-derivative and second-derivative of the hyperspectral image and texture features composed a feature set. Using the same classification methods, the OA was 80.91% and Kappa was 0.7711. Therefore, the wavelet analysis and mathematical morphology can significantly improve the tree species classification accuracy of hyperspectral images. Accurate tree species classification can provide basic scientific data for forest resource monitoring and management measures. • Wavelet analysis and mathematical morphological analysis were evaluated. • Wavelet analysis could classify tree species with high spectral separability. • Diagonal (not horizontal or vertical) textures improved classification accuracy. • Complex forests were sensitive to edge structure features of mathematical morphology. • This approach facilitates the sustainable management of forest resources and effective monitoring of species diversity. [ABSTRACT FROM AUTHOR]

An efficient and accurate forest sample plot survey is of great significance to understand the current status of forest resources at the stand or regional scale and the basis of scientific forest management. Close-range photogrammetry (CRP) technology can easily and quickly collect sequence images with high overlapping to reconstruct the 3D model of forest scenes and extract the individual tree parameters automatically and, therefore, can greatly improve the efficiency of forest investigation and has great application potential in forestry visualization management. However, it has some issues in practical forestry applications. First, the imaging quality is affected by the illumination in the forest, resulting in difficulty in feature matching and low accuracy of parameter extraction. Second, the efficiency of 3D forest model reconstruction is limited under complex understory vegetation or the topographic situation in the forest. In addition, the density of point clouds by dense matching directly affects the accuracy of individual tree parameter extraction. This research collected the sequence images of sample plots of four tree species by smartphones in Gaofeng Forest Farm in Guangxi and Wangyedian Forest Farm in Mongolia to analyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes. First, we compared the image enhancement effects of median–Gaussian (MG) filtering, single-scale retinex (SSR) and multi-scale retinex (MSR) filtering algorithms. Then, an improved algorithm combining Harris corner detection with speeded-up robust features (SURF) feature detection (Harris+SURF) is proposed, and the feature matching effect is compared with that of a scale invariant feature transform (SIFT) operator. Third, according to the morphological characteristics of the trees in the sequence images, we used the iterative interpolation algorithm of a planar triangulation network based on geometric constraints (GC-based IIPTN) to increase the density of point clouds and reconstruct the 3D forest model, and then extract the position and DBH of the individual trees. The results show that MSR image enhancement can significantly increase the number of matched point pairs. The improved Harris+SURF method can reduce the reconstruction time of the 3D forest model, and the GC-based IIPTN algorithm can improve the accuracy of individual tree parameter extraction. The extracted position of the individual tree is the same as the measured position with the bias within 0.2 m. The accuracy of extracted DBH of Eucalyptus grandis, Taxus chinensis, Larix gmelinii and Pinus tabuliformis is 94%, 95%, 96% and 90%, respectively, which proves that the proposed 3D model reconstruction method based on image enhancement has great potential for tree position and DBH extraction, and also provides effective support for forest resource investigation and visualization management in the future. [ABSTRACT FROM AUTHOR]

Airborne hyperspectral remote sensing data with both rich spectral and spatial features can effectively improve the classification accuracy of vegetation species. However, the spectral data of hundreds of bands brings about problems such as dimensional explosion, which poses a huge challenge for hyperspectral remote sensing classification based on classical parameters models. Deep learning methods have been used for remotely sensed images classification in recent years, but the popular HSI datasets including Kennedy Space Center, Indian Pines, Pavia University scene and Salinas scene, have low spatial resolution, significant differences between categories, and regular boundaries. When applied to the classification of forestry tree species, the accuracy often decreases because the spectral response of different plants of the same family and genus are very similar, especially under the fragmented species distribution, complex topography and the occluded canopy. So we collect new data sets, selected Gaofeng State Owned Forest Farm in Guangxi province in south China as the research area and adopted the airborne hyperspectral data obtained by the LiCHy system of the Chinese Academy of Forestry to explore an improved three-dimensional convolutional neural network(3D-CNN) model for tree species classification. The proposed model uses raw data as input without dimension reduction or feature screening, and simultaneously extracts spectral and spatial features. After the 3D convolutional layer, the captured high-level semantic concept is a joint spatial spectral feature representation, so we can turn it into a one-dimensional feature as a new input to learn a more abstract level of expression. The widely used earlystop method is also used to prevent overfitting. The proposed model is a lightweight, generalized, and fast convergence classification model, by which the short-time and large-area of multiple tree species classification with high-precision can be realized. The result shows that the 3D-1D CNN model can shorten the training time of the 3D CNN model by 60% and achieve a classification accuracy of 93.14% within 50 ha in 6.37 min, which provides a basis for the classification of tree species, the mapping of forest form and the inventory of forest resources. • The 3D CNN is applied to tree species classification of actual hyperspectral images. • The improved model accelerates the computational efficiency while ensuring accuracy. • The model can be used for classifications with only a very small number of samples. • Using feature map and t-SNE to provide the possibility to explore the CNN black box. [ABSTRACT FROM AUTHOR]

Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China. [ABSTRACT FROM AUTHOR]