Forest Farm Research Articles

First report of Colletotrichum siamense causing leaf spot on Machilus pauhoi in China.

Machilus pauhoi Kaneh. is an excellent evergreen broad-leaved tree species widely grown in China for its ornamental and economic value (He et al. 2022). In September 2021, a leaf spot was observed on M. pauhoi plants on Guantian forest farm (27°06'15.6″N, 114°34'20.72″E) in ji' an city, Jiangxi province, China. The disease incidence was estimated to be above 20%. The symptoms began as brown irregular spots, then the spots gradually expand over time, with a gray-to-brown center and dark brown-to-black edges. Small infected tissues (3 to 5 mm2) were surface-sterilized in 70% ethanol for 30 s and 2% NaClO for 60 s, and rinsed three times with sterile water (Ju et al. 2021). Tissues were placed on potato dextrose agar (PDA) and incubated at 25°C. Pure cultures were obtained by transferring hyphal tips to new PDA plates. Twenty-two isolates of Colletotrichum ssp. were obtained (isolation frequency about 78%). Three representative single-spore isolates (PN-1, PN-4, and PN-9) were used for morphological studies and phylogenetic analyses. Colonies on the PDA of the three isolates were white to gray with cottony mycelia and grayish-white on the undersides of the culture. Conidia were single-celled, straight, hyaline, cylindrical, clavate, and measured 11.4-16.8 ×4.1-5.5 µm (13.2 ± 1.0 × 4.4 ± 0.3 µm, n = 100). Appressoria were brown to dark brown, ovoid to clavate, slightly irregular to irregular, and ranged from 5.2-8.8 × 4.1-6.2 µm (6.7 ± 0.2 × 5.1 ± 0.3 µm, n=100). Morphological features were similar to Colletotrichum gloeosporioides species complex (Weir et al. 2012). The internal transcribed spacer (ITS) regions, actin (ACT), calmodulin (CAL), beta-tubulin 2 (TUB2), chitin synthase (CHS-1), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were amplified from genomic DNA for the three isolates using primers ITS1/ITS4, ACT-512F/ACT-783R, CL1/CL2, T1/Bt2b, CHS-79F/CHS-354R and GDF/GDR (Weir et al. 2012), respectively. All sequences were deposited into GenBank (ITS, ON176154 - ON176156; ACT, ON185554 - ON185556; GAPDH, ON185563 - ON185565; TUB2, ON185566 - ON185568; CHS-1, ON185560 - ON185562; CAL, ON185557 - ON185559). A maximum likelihood and Bayesian posterior probability analyses using IQtree v. 1.6.8 and Mr. Bayes v. 3.2.6 with the concatenated sequences placed PN-1, PN-4, and PN-9 in the clade of C. siamense. Based on the multi-locus phylogeny and morphology, three isolates were identified as C. siamense. The pathogenicity of three isolates was tested on nine M. pauhoi plants, which were grown in the field. Healthy leaves were wounded with a sterile needle and inoculated with 10 µL of spore suspension (106 conidia/mL). The spore suspension of each isolate was inoculated onto six leaves. Another three plants inoculated with ddH2O served as the control (Wan et al. 2022). All the inoculated leaves were covered with plastic bags to keep them moist for 2 days (relative humidity > 80%). All the inoculated leaves showed similar symptoms to those observed in the field, whereas control leaves were asymptomatic for 7 days. C. siamense was reisolated from the lesions, whereas no fungus was isolated from control leaves. Up to now, Pestalotiopsis chamaeropis, Corynespora cassiicola and Arthrinium arundinis could infect M. pauhoi plants (Zhang et al. 2021), and cause leaf spots in China. To our knowledge, this is the first report of C. siamense causing leaf spots on M. pauhoi. This work provided crucial information for epidemiologic studies and appropriate control strategies for this newly emerging disease.

Species asynchrony stabilizes productivity over 20 years in Northeast China.

The stability of forest productivity can reflect the functioning of forest ecosystems. It is a crucial topic to understand the relationship between biodiversity and ecosystem functions in ecology. Although previous studies have made great progress in understanding the effects of diversity, species asynchrony, and other factors on community biomass and productivity, few studies have explored how these factors affect the temporal stability of productivity. In this study, we hypothesized that diversity, species asynchrony, and topography would directly or indirectly impact the temporal stability of productivity. To test this hypothesis, we used a multiple regression model and a piecewise structural equation model based on the inventory data over 20 years (5-year intervals) from 1992 to 2012 at Jingouling Forest Farm in Northeast China. Our results showed that species asynchrony was the main driving factor affecting the temporal stability of productivity. Structural diversity significantly decreased community stability, while species diversity had a nonsignificant effect on it. We found the combination of a multiple regression model and a piecewise structural equation model is an effective method for evaluating the factors that influence community stability. The effect of species asynchrony is crucial for understanding the ecological mechanisms underlying the diversity-stability relationship in mixed forests.

An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT

Monitoring and early warning technology for forest fires is crucial. An early warning/monitoring system for forest fires was constructed based on deep learning and the internet of things. Forest fire recognition was improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer fire-image features were extracted, improving the input conversion by building a forest fire risk prediction model based on an improved dynamic convolutional neural network. The proposed back propagation neural network fire (BPNNFire) algorithm calculated the image processing speed and delay rate, and data were preprocessed to remove noise. The model recognized forest fire images, and the classifier classified them to distinguish images with and without fire. Fire images were classified locally for feature extraction. Forest fire images were stored on a remote server. Existing algorithms were compared, and BPNNFire provided real-time accurate forest fire recognition at a low frame rate with 84.37% accuracy, indicating superior recognition. The maximum relative error between the measured and actual values for real-time online monitoring of forest environment indicators, such as air temperature and humidity, was 5.75%. The packet loss rate of the forest fire monitoring network was 5.99% at Longshan Forest Farm and 2.22% at Longyandong Forest Farm.

Temporal and Spatial Analysis of the Changes in the Supply of Ecological Products in State-Owned Forest Farms after the Reform

To reveal the changes in the supply efficiency of forest farm ecological products after the reform of state-owned forest farms, this study uses the ecological product accounting method to calculate the physical quantity and value of state-owned forest farm ecological products using data from 2015 and 2020. Through exploratory spatial data analysis, the spatial distribution of the supply efficiency of ecological products of state-owned forest farms and its primary index are studied to explore the temporal and spatial evolution of ecological products from state-owned forest farms in China. The results show the following: (1) Through the reform of state-owned forest farms, the supply capacity of forest farms’ ecological products has been greatly improved, and the physical quantity and value quantity of ecological products have shown an increasing trend, with growth rates of 31.7% and 24.2%, respectively. (2) There is strong spatial heterogeneity in the supply efficiency of ecological products from state-owned forest farms in various provinces, and the overall distribution pattern is gradually decreasing from the northwest to the southeast coastal provinces. (3) There is a negative correlation between the supply capacity of ecological products and provincial GDP. The supply capacity of ecological products in provinces with high per capita GDP is relatively low. (4) The comprehensive index of the supply efficiency of ecological products and its first-level index of state-owned forest farms are characterized by different degrees of agglomeration in space, which are mainly represented by three distribution modes: “H–H”, “L–L”, and “L–H”. Therefore, it is necessary to clarify the ecological function orientation of state-owned forest farms, improve the endogenous power of ecological product supply, actively explore and connect social ecological needs, and achieve coordinated progress in terms of ecological environmental protection and economic development.

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

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.

Improvement of Treetop Displacement Detection by UAV-LiDAR Point Cloud Normalization: A Novel Method and A Case Study

Normalized point clouds (NPCs) derived from unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have been applied to extract relevant forest inventory information. However, detecting treetops from topographically normalized LiDAR points is challenging if the trees are located in steep terrain areas. In this study, a novel point cloud normalization method based on the imitated terrain (NPCIT) method was proposed to reduce the effect of vegetation point cloud normalization on crown deformation in regions with high slope gradients, and the ability of the treetop detection displacement model to quantify treetop displacements and tree height changes was improved, although the model did not consider the crown shape or angle. A forest farm in the mountainous region of south-central China was used as the study area, and the sample data showed that the detected treetop displacement increased rapidly in steep areas. With this work, we made an important contribution to theoretical analyses using the treetop detection displacement model with UAV-LiDAR NPCs at three levels: the method, model, and example levels. Our findings contribute to the development of more accurate treetop position identification and tree height parameter extraction methods involving LiDAR data.

Rastrojo: Re(in)surgent forests

<i>Rastrojo</i>: Re(in)surgent forests

A new method for constructing roads map in forest area using UAV images

High definition map is a map used for automatic driving. Its absolute precision is sub meter level, that is, the error between the position of an element on the map and the position of the element in the real world is less than 100 cm. In addition, compared with ordinary maps, high definition maps contain more geographic information, such as road width, slope, curvature, etc. At present, high definition roads map construction is largely for the main traffic road. However, there are few studies on the construction of high definition forest roads map for forestry production and management. Thus, it is imperative and helpful to explore an efficient and low-cost method for constructing high definition forest roads map that can provide accurate basic map services for forestry management activities. In this study, firstly, the DJI Phantom 4 RTK UAV was used for aerial photography to obtain the forest oblique images. Then, combined with close range photogrammetry, the high-resolution images under the forest are acquired and generated into dense point cloud images. Next, the two images are fused using iterative closest point (ICP) algorithm combined with manual registration; Finally, the fine-grained 3D scene model is generated by using the Context Capture 3D modeling software, based on which a high definition forest roads map was constructed using human-computer interaction model. Taking Menggongshan Forest Farm as an example, the construction of the fine 3D scene model was carried out to solve the problem of loopholes and ambiguities of the road model under forest, based on which the high definition forest roads map was constructed. To evaluate the accuracy of the forest roads map, the accuracy check was conducted. The results showed that the point position on the forest roads map, compared to the actual point, had a medium, minimum and maximum error of 0.076 m, 0.020 m and 0.170 m in plane, respectively, a medium, minimum and maximum error of 0.122 m, 0.005 m and -0.214 m in elevation, respectively. The accuracy of the high definition forest roads map meets the need of forestry production and management activities. It is feasible to adopt the method of UAV oblique photogrammetry combined with close-range photogrammetry, fine 3D scene model construction and human-computer interactive for constructing high definition forest road map. And this method can provide a low-cost and high-efficiency way for constructing high definition forest roads map, and also can better provide basic map data for forest management activities.

Extraction of tree crown parameters of high-density pure Cunninghamia lanceolata plantations by combining the U-Net model and watershed algorithm.

As one of the important timber species in China, Cunninghamia lanceolata is widely distributed in southern China. The information of tree individuals and crown plays an important role in accurately monitoring forest resources. Therefore, it is particularly significant to accurately grasp such information of individual C. lanceolata tree. For high-canopy closed forest stands, the key to correctly extract such information is whether the crowns of mutual occlusion and adhesion can be accurately segmented. Taking the Fujian Jiangle State-owned Forest Farm as the research area and using the UAV image as the data source, we developed a method to extract crown information of individual tree based on deep learning method and watershed algorithm. Firstly, the deep learning neural network model U-Net was used to segment the coverage area of the canopy of C. lanceolata, and then the traditional image segmentation algorithm was used to segment the individual tree to obtain the number and crown information of individual tree. Under the condition of maintaining the same training set, validation set and test set, the extraction results of the canopy coverage area by the U-Net model and traditional machine learning methods [random forest (RF) and support vector machine (SVM)] were compared. Then, two individual tree segmentation results were compared, one using the marker-controlled watershed algorithm, and the other using the combination of the U-Net model and marker-controlled watershed algorithm. The results showed that the segmentation accuracy (SA), precision, IoU (intersection over union) and F1-score (harmonic mean of precision and recall) of the U-Net model were higher than those of RF and SVM. Compared with RF, the value of those four indicators increased by 4.6%, 14.9%, 7.6% and 0.05, respectively. Compared with SVM, the four indicators increased by 3.3%, 8.5%, 8.1% and 0.05, respectively. In terms of extracting the number of trees, the overall accuracy (OA) of the U-Net model combined with the marker-controlled watershed algorithm was 3.7% higher than that of the marker-controlled watershed algorithm, with the mean absolute error (MAE) being decreased by 3.1%. In terms of extracting crown area and crown width of individual tree, R2 increased by 0.11 and 0.09, mean squared error decreased by 8.49 m2 and 4.27 m, and MAE decreased by 2.93 m2 and 1.72 m, respectively. The combination of deep learning U-Net model and watershed algorithm could overcome the challenges in accurately extracting the number of trees and the crown information of individual tree of high-density pure C. lanceolata plantations. It was an efficient and low-cost method of extracting tree crown parameters, which could provide a basis for developing intelligent forest resource monitoring.

Development of a protocol to identify land function based on multifunctionality and suitability analysis: A case study of the Nanyuntai Forest Farm, China

Accurately identifying regional land functions is important for balancing and coordinating multiple ecosystem services. Identification of land functions should be guided by the ecological suitability analysis framework, highlighting the primary ecological function and considering the temporal and spatial trends of land functions to resolve the contradictions among various needs of land ecology, production, and life. Taking Nanyuntai Forest Farm in Lianyungang City, Jiangsu Province, as a case study, we used the Analytical Hierarchical Process to construct a systematic and complete land multi-function evaluation and spatial suitability evaluation system, and used the ArcGIS spatial analysis technology to analyze its land function changes from 2011 to 2021. Finally, the results of multifunctionality and suitability analysis were integrated to identify the land's ecological, production, and living functions by constructing the algorithm using Python. Our results showed that the proportions of ecological, production, and living functions of the land in the study area were 56.82, 34.03, and 9.15%, respectively. This study highlighted that the results of land function identification based on comprehensive land-change-trend and spatial suitability analyses were more precise and coordinated. This comprehensive evaluation is a valuable and practical method for decision-makers and land managers to create land use maps and determine high-priority areas that require conservation measures for sustainable management. Moreover, this approach can be practically applied to other planning units.

Soil organic carbon regulation from chemistry in top- but microbial community in subsoil in eastern coastal China poplar plantations

Although patterns in the vertical distribution of soil organic carbon (SOC) are key to assessing soil C sequestration potential, they remain poorly understood. We sampled 18 soil profiles at one meter depth at the Dongtai Forest Farm to investigate the vertical distribution of SOC, the stoichiometric relationship between SOC and total nitrogen (TN) and the controlling factors of SOC at different soil depths. We found that SOC content decreased significantly with increasing soil depth. Approximately 67% of SOC was stored in the top 30 cm. The N-C scaling slope (i.e., the slope of the relationship between log-transformed N and C not significantly different from 1.0) revealed significant differences between top- and subsoil with N and C scaled isometrically in topsoil (0–30 cm), but not in subsoil (30–100 cm). SOC content was co-regulated by soil physiochemical and microbial properties at the site level with soil chemical and microbial properties dominant in the top- and subsoil, respectively. Topsoil SOC increased with soil TN, available phosphorus (AP) and fungal abundance. Subsoil SOC increased with the fungal-to-bacterial ratio, fungal abundance and soil dissoluble organic carbon (DOC). Our study highlights the dominance of microbial community in regulating SOC in the subsoil and advances our understanding of the variation in mechanisms regulating SOC along the soil profile.

A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds

Spatially continuous mapping forest aboveground biomass (AGB) is crucial for better understanding the capacities of carbon sequestration capacities of forest ecosystems at both individual tree and landscape levels. Collecting field data is one of the most labor-intensive and time-consuming tasks in biomass mapping using airborne laser scanning (ALS) data. Building on a LiDAR biomass index (LBI) developed for use with terrestrial laser scanning (TLS) data, we successfully developed an improved and robust LBI-based approach to estimate forest AGB at both individual tree and plot levels while minimizing the effort required for field data collection. This approach was tested for larch, birch, and eucalyptus over three forest farms in Northeast China and one in Southern China. The results showed that LBI was highly correlated with the diameter, height, and AGB of larch trees. AGB estimates derived using LBI-based models for the three tree species were close to ground measurements at the individual tree level. They explained 81% to 95% of the variance of independent test data not used to calibrate those models. Tree level AGB estimates are required by many applications, but they could not be provided by commonly used plot-based biomass mapping approaches like LiDAR metrics-based regression (LMR) or Random Forest (RF). Calibrated with small fractions of the trees needed to calibrate LMR and RF models, LBI-based biomass models produced plot level biomass estimates comparable to or better than those produced using the two plot-based methods. More importantly, the LBI-based models generalized far better than LMR and RF among the three larch forest farms located hundreds of kilometers apart. These promising results warrant more research on the effectiveness of the LBI-based approach for other forest types and tree species not considered in this study. As LiDAR technology and related algorithms are evolving rapidly, further improvements to this approach might be feasible. A robust LBI-based approach applicable to a wide range of tree species and forest types across the globe will greatly facilitate the use of increasingly better and more affordable ALS data to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) and other forest-based climate mitigation initiatives.

Study on Rural Ecological Resilience Measurement and Optimization Strategy Based on PSR-“Taking Weiyuan in Gansu Province as an Example”

Under the dual impact of urbanization and ecological crisis, rural ecological resilience research can improve the system’s level of resisting external pressure and restoring ecological balance and provide a new perspective for sustainable rural development. This study establishes a rural ecological resilience measurement system in Weiyuan based on the PSR framework, evaluates the level of rural ecological resilience in Weiyuan in 2021 using the entropy method and the GWR model and detects its driving factors. The results show that (1) the spatial characteristics of rural ecological resilience diverge significantly, with the ecological resilience level of the three southern forest farms being higher overall the high values of resilience in Qingyuan, Wuzhu and Xiacheng being distributed in the central villages, while other villages are at low and medium values (2) X5 and X7 have negative driving effects on village ecological resilience, and X1, X5, X9 and X10 have positive driving effects on village ecological resilience (3) the dominant drivers and characteristics, we construct a scheme on stressor repair, state adaptation transformation and response efficiency optimization to provide ideas for improving rural ecological resilience.

The Trade-Offs and Synergies of Ecosystem Services in Pinus massoniana Lamb. Plantations in Guangxi, China

A scientific understanding of the synergistic and trade-off relationships among ecosystem services (ESs) is essential for maintaining the structure, function, and health of plantation forest ecosystems. This understanding facilitates effective ecosystem management practices, and helps identify the types, intensities, and spatial and temporal distribution characteristics of interactions among ESs, which is critical for regional development planning, ecological supplementation, and the maximization of economic benefits. In this study, we used correlation analysis, bivariate spatial autocorrelation, and hot spot analysis to comprehensively analyze the synergistic and trade-off relationships between ESs in Pinus massoniana (PM) plantations in Guangxi Paiyang Forest Farm from 2009 to 2018, across both time and space. The study showed that the ESs in PM plantations in Guangxi Paiyang Forest Farm maintained significant positive correlation (synergy), with a mutually reinforcing relationship among services. Notably, the regulating services shifted from weak synergy to weak trade-off relationships over time. From the bivariate spatial autocorrelation analysis, it is clear that the overall trade-off synergistic relationship among the four ESs is basically consistent with the correlation analysis results. From the distribution of multiple ES hot spots, we determined that the number of small groups that can provide three to four service hot spots in Guangxi Paiyangshan Forest Farm is greater. The spatial distribution of cold–hot spots of various ESs varied, and the distribution of cold–hot spots of supply services and regulation services of carbon sequestration and oxygen release was similar.

AMDNet: A Modern UAV RGB Remote-Sensing Tree Species Image Segmentation Model Based on Dual-Attention Residual and Structure Re-Parameterization

Tree species classification is an important and challenging task in image recognition and the management of forest resources. Moreover, the task of tree species classification based on remote sensing images can significantly improve the efficiency of the tree species survey and save costs. In recent years, many large models have achieved high accuracy in the task of tree species classification in an airborne remote-sensing manner, but due to their fixed geometric structure, traditional convolutional neural networks are inherently limited to the local receptive field and can only provide segmental context information. The limitation of insufficient context information greatly affects the segmentation accuracy. In this paper, a dual-attention residual network (AMDNet) and a re-parameterized model approach are proposed to capture the global context information, fuse the weight, reduce the model volume, and maintain the computational efficiency. Firstly, we propose MobileNetV2 as the backbone network for feature extraction to further improve the feature identification by modeling semantic dependencies in the spatial dimension and channel dimension and adding the output of the two attention modules. Then, the attention perception features are generated by stacking the attention modules, and the in-depth residual attention network is trained using attention residual learning, through which more accurate segmentation results can be obtained. Secondly, we adopt the approach of structure re-parameterization, use a multi-branch topology for training, carry out weighted averaging on multiple trained models, and fuse multiple branch modules into a completely equivalent module in inference. The proposed approach results in a reduction in the number of parameters and an accelerated inference speed while also achieving improved classification accuracy. In addition, the model training strategy is optimized based on Transformer to enhance the accuracy of segmentation. The model was used to conduct classification experiments on aerial orthophotos of Hongya Forest Farm in Sichuan, China, and the MIOU of tree species recognition using the test equipment reached 93.8%. Compared with current models such as UNet, our model exhibits a better performance in terms of both speed and accuracy, in addition to its enhanced deployment capacity, and its speed advantage is more conducive to real-time segmentation, thereby representing a novel approach for the classification of tree species in remote sensing imagery with significant potential for practical applications.

Untargeted Metabolite Profiling of Camellia tetracocca’s Response to an Empoasca onukii Attack Using GC-MS and LC-MS

Camellia tetracocca, a kind of tea with local popularity, is unique to southwest China, where it has an important natural heritage and cultural heritage. However, the tea plant and its sprout are frequently attacked on a large-scale by Empoasca onukii. The metabolic mechanisms of the unique plant for defending against these pest insects are unclear. Therefore, we used untargeted gas chromatography–mass spectrometry (GC-MS) and high performance liquid chromatography–mass spectrometry (LC-MS) to compare the metabolite profiles between E. onukii-attacked leaves and healthy leaves. Using GC-MS, 56 metabolites (24 up-regulated, 32 down-regulated) were preliminarily identified. Additionally, 576 metabolites (280 up-regulated, 287 down-regulated) were rudimentarily identified with LC-MS. Differentially abundant metabolites were mainly enriched in the biosynthesis of specialized metabolites. Fourteen accumulated specialized metabolites are related to insect resistance. Mainly, momordicin I and arabidopside B are reportedly involved in the resistance to the insect. Therefore, we conjectured that the accumulation of momordicin I and arabidopside B is involved in the C. tetracocca’s resistance to E. onukii. Our results indicate that these specialized metabolites may be served as candidate biocontrol agents against the pest of E. onukii of C. tetracocca located in the State-owned Pubai Forest Farm.

Spatial scale applicability of 4-Scale geometrical optics model canopy reflectance simulation.

Accurately clarifying the applicable spatial scale of 4-Scale model is conducive to improving the accuracy of its application in canopy reflectance simulation of different vegetation types, and to further improving the inversion accuracy of leaf area index, canopy density, and other parameters. Two forest plots (one for broad-leaved forest and one for mixed forest) with each area of 100 m×100 m in Maoershan Experimental Forest Farm, Shangzhi, Heilongjiang, were divided into the spatial scales of 10, 20, 30, 40 and 50 m, respectively. The 4-Scale model was used to simulate forest canopy reflectance. Local mean method, the nearest neighbor method, bilinear interpolation method, and cubic convolution method were used to convert Sentinel-2 images with spatial resolution of 10 m to other scales, with the results being evaluated. The simulated canopy reflectance and remote sensing pixel reflectance were compared and analyzed. The spatial scale of mixed forest and broad-leaved forest suitable for high-precision inversion parameters of 4-Scale model was determined. The results showed that the 4-Scale model underestimated the pixel forest canopy reflectance as a whole. The canopy reflectance of mixed forest and broad-leaved forest had the worst simulation effect at the 20 m scale. Both the root mean square error (RMSE) and the mean absolute error from (MAE) of red and near-infrared band were large. When the scale was >20 m, the simulation effect became better. The applicability of the model was the best when the mixed forest was 40 m and the broad-leaved forest was 30 m. The mean and standard deviation of the reflectance difference between the simulated value and the remote sensing pixel were the minimum in the red and near near-infrared bands, with the minimum RMSE and MAE. The simulation results of mixed forest and broad-leaved forest at 10 m scale were not stable, the rule of mean and standard deviation was inconsistent, and the difference between RMSE and MAE was large under the same band.

Classification of Individual Tree Species Using UAV LiDAR Based on Transformer

Tree species surveys are crucial in forest resource management and can provide references for forest protection policymakers. Traditional tree species surveys in the field are labor-intensive and time-consuming. In contrast, airborne LiDAR technology is highly capable of penetrating forest vegetation; it can be used to quickly obtain three-dimensional information regarding vegetation over large areas with a high level of precision, and it is widely used in the field of forestry. At this stage, most studies related to individual tree species classification focus on traditional machine learning, which often requires the combination of external information such as hyperspectral cameras and has difficulty in selecting features manually. In our research, we directly processed the point cloud from a UAV LiDAR system without the need to voxelize or grid the point cloud. Considering that relationships between disorder points can be effectively extracted using Transformer, we explored the potential of a 3D deep learning algorithm based on Transformer in the field of individual tree species classification. We used the UAV LiDAR data obtained in the experimental forest farm of Northeast Forestry University as the research object, and first, the data were preprocessed by being denoised and ground filtered. We used an improved random walk algorithm for individual tree segmentation and made our own data sets. Six different 3D deep learning neural networks and random forest algorithms were trained and tested to classify the point clouds of three tree species. The results show that the overall classification accuracy of PCT based on Transformer reached up to 88.3%, the kappa coefficient reached up to 0.82, and the optimal point density was 4096, which was slightly higher than that of the other deep learning algorithms we analyzed. In contrast, the overall accuracy of the random forest algorithm was only 63.3%. These results show that compared with the commonly used machine learning algorithms and a few algorithms based on multi-layer perceptron, Transformer-based networks provide higher accuracy, which means they can provide a theoretical basis and technical support for future research in the field of forest resource supervision based on UAV remote sensing.

Does forest farm carbon sink projects affect agricultural development? Evidence from a Quasi-experiment in China

Forest Farm Carbon Sink (FFCS) projects are one of the effective ways to achieve carbon neutrality and mitigate global warming. Though the existing literature has widely discussed the effect of FFCS on the allocation of agricultural factors, such as land, labor employment structure and income structure, little is known about whether FFCS projects could have an effect on agricultural development. Based on the panel data of 140 counties in Sichuan province, China, from 2002 to 2018, we examined the causal effect on agricultural total factor productivity (TFP), and revealed their dynamic effect and underlying mechanisms. Propensity score matching and the difference in difference (PSM-DID) method were used to address the endogeneity problem of FFCS implementation. Results showed that FFCS projects increased agricultural TFP by 1.7%–2.4%. Health, saving and industrial structure were the important channels through which FFCS projects affect agricultural TFP. Our findings suggest that policies promoting FFCS projects can increase agricultural TFP while achieving environmental goals—a win-win situation.

Smallholder perceptions of and willingness to participate in Forest Farmers' Organizations: Insights from case studies in Ethiopia and Tanzania

Forest Farmers' Organizations (FFOs) are gaining scholarly traction as a means to mobilize non-industrial private farmers to upscale tree-growing practices, thereby contributing to securing the increasing demand for wood products. However, how farmers perceive FFOs and what drives their willingness to participate in FFOs are not yet fully understood. Using two case studies, this study aimed to examine smallholder tree growers' perceptions of FFOs and the main factors influencing their willingness to participate in FFOs. Data were collected from 185 and 190 farm forestry households in Ethiopia and Tanzania, respectively, and triangulated with focus group discussions and key informant interviews. Grounded in the social exchange theory, we employed regression analysis and thematic analysis to analyze the data. The results showed that 74% and 90% of smallholders would be willing to participate in an FFO in the Ethiopian and Tanzanian cases, respectively. In Ethiopia, farmers understood an FFO as a tree marketing cooperative or a collective wood marketing enterprise. Household heads in the middle age group and those who experienced price satisfaction in the last woodlot sales would be more likely to participate in an FFO. In Tanzania, tree growers associations focused on social learning and wood production improvement. Those more likely to participate had larger household sizes and difficulty accessing the wood market. We proposed a hypothesis and further research agenda, calling for a panel study to complement our conceptual assertion.