Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development.

To investigate forest resources, it is necessary to identify the tree species. However, it is a challenge to identify tree species using 3D point clouds of trees collected by light detection and ranging (LiDAR). PointNet++, a point cloud deep learning network, can effectively classify 3D objects. It is important to establish high-quality individual tree point cloud datasets when applying PointNet++ to identifying tree species. However, there are different data processing methods to produce sample datasets, and the processes are tedious. In this study, we suggest how to select the appropriate method by designing comparative experiments. We used the backpack laser scanning (BLS) system to collect point cloud data for a total of eight tree species in three regions. We explored the effect of tree height on the classification accuracy of tree species by using different point cloud normalization methods and analyzed the effect of leaf point clouds on classification accuracy by separating the leaves and wood of individual tree point clouds. Five downsampling methods were used: farthest point sampling (FPS), K-means, random, grid average sampling, and nonuniform grid sampling (NGS). Data with different sampling points were designed for the experiments. The results show that the tree height feature is unimportant when using point cloud deep learning methods for tree species classification. For data collected in a single season, the leaf point cloud has little effect on the classification accuracy. The two suitable point cloud downsampling methods we screened were FPS and NGS, and the deep learning network could provide the most accurate tree species classification when the number of individual tree point clouds was in the range of 2048–5120. Our study further illustrates that point-based end-to-end deep learning methods can be used to classify tree species and identify individual tree point clouds. Combined with the low-cost and high-efficiency BLS system, it can effectively improve the efficiency of forest resource surveys.

Accurate tree species information is a prerequisite for forest resource management. Combining light detection and ranging (LiDAR) and image data is one main method of tree species classification. Traditional machinelearningmethods rely on expert knowledge to calculatea large number of feature parameters.Deep learning technology can directly use the original image and pointclouddata to classify tree species. However, data with different patterns require the use of different types of deeplearningmethods. In this study, a multimodal deeplearningframework (TSCMDL) that fuses 2D and 3D features was constructed and then used to combine data from multiple sources for tree species classification. This framework uses an improved version of the PointMLP model as its backbone network and uses ResNet50 and PointMLP networks to extract the image features and pointcloudfeatures, respectively. The proposed framework was tested using UAV LiDAR data and RGB orthophotos. The results showed that the accuracy of the tree species classification using the TSCMDL framework was 98.52%, which was 4.02% higher than that based on pointcloudfeatures only. In addition, when the same hyperparameters were used for training the model, the efficiency of the model training was not significantly lower than for models based on pointcloudfeatures only. The proposed multimodal deeplearningframework extracts features directly from the original data and integrates them effectively, thus avoiding manual feature screening and achieving more accurate classification. The feature extraction network used in the TSCMDL framework can be replaced by other suitable frameworks and has strong application potential.

Forest carbon flux is critical to climate change, and the accurate modeling of forest carbon flux is an extremely challenging task. The remote sensing model (the MODIS MOD_17 gross primary productivity (GPP) model (MOD_17)) has strong practicability and is widely used around the world. The ecological process (the Biome-BioGeochemical Cycles Multilayer Soil Module model (Biome-BGCMuSo)) model can describe most of the vegetation’s environmental and physiological processes on fine time scales. Nevertheless, complex parameters and calibrations pose challenges to the application and development of models. In this study, we optimized all the input parameters of the MOD_17 model for the calibration of the Biome-BGCMuSo model to obtain GPP with continuous spatiality. To determine the contribution of input parameters to the GPP of different forest types, an Extended Fourier Amplitude Sensitivity Test (EFAST) was performed on the Biome-BGCMuSo model firstly. Then, we selected the sample points of each forest type and its different ecological gradients (30 for each type), using the GPP simulation value of the optimized MOD_17 model corresponding to the time and space scale to calibrate the Biome-BGCMuSo model, to drive the calibrated Biome-BGCMuSo, and we simulated the different forest types’ net primary productivity (NPP). According to dendrochronological measurements, the NPP simulation results were verified on the whole regional scale. The results showed that the GPP values of different forest types were highly sensitive to C:N_leaf (C:N of leaf), SLA1 (canopy average specific leaf area in phenological phase 1), and FLNR (fraction of leaf N in Rubisco). The coefficient of determination (R²) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m²/year. Our study aims to reduce uncertainty in forest carbon fluxes simulated by the Biome-BGCMuSo model, providing feedback for understanding forest ecosystem carbon cycling, vegetation productivity, and climate change.

Malachite green (MG) is highly toxic, persistent, and carcinogenic, and its widespread use is a danger to the ecosystem and a threat to public health and food safety, making it necessary to develop new sensitive multimode molecular spectroscopy methods. In this work, a new copper-based nanomaterial (CuNM) was prepared by a high-temperature roasting using a copper metal–organic framework (CuMOF) as precursor. The as-prepared CuNM was characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy, transmission electron microscopy (TEM), and BET surface area analysis. CuNM was found to catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) by H₂O₂ to produce the oxidation product TMBOX; however, subsequently, the MG aptamer (Apt) could be adsorbed on the CuNM surface by intermolecular interaction, which would inhibit the catalytic performance. After the addition of MG to be tested, the CuNM previously adsorbed by the Apt was transformed into its free state, thus restoring its catalytic activity. This new nanocatalytic indicator reaction could be monitored by surface-enhanced Raman scattering (SERS)/resonance Rayleigh scattering (RRS)/fluorescence (FL)/absorption (Abs) quadruple-mode methods. The SERS determination range was 0.004–0.4 nmol L⁻¹ MG, with a limit of detection of 0.0032 nM. In this way, a rapid, stable, and sensitive method for the determination of MG residues in the environment was established.

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R² = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R² = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R² = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.