73 results about "Tree canopy" patented technology

The invention discloses a single tree detection method and system based on a pre-trained large model and a ladder side tuning strategy, and relates to the technical field of forestry remote sensing intelligent monitoring, and the method comprises the steps: obtaining a remote sensing image, preprocessing the remote sensing image, and constructing a reference sample data set; using the pre-trained large model as a global feature encoder, constructing a bypass model as a local feature encoder based on a ladder side tuning strategy, and using the encoder to extract and fuse global and local features of the preprocessed remote sensing image stage by stage to obtain multi-level features; a segmentation threshold and a canopy processing strategy are preset for different scenes; constructing a double-flow feature decoder, processing the multi-level features according to a segmentation threshold value and a canopy processing strategy, and further processing the multi-level features by adopting a full convolutional network to obtain a tree canopy segmentation result; and outputting individual tree detection information in the target area. According to the method, the detection adaptability to complex scenes is improved, the dependence on large-scale annotation data is reduced, the single tree detection cost is reduced, and the efficiency is improved.

The invention provides a cross-source fusion modeling method and system for a forest three-dimensional point cloud model. The method comprises the following steps: acquiring first information based on camera equipment carried by an unmanned aerial vehicle, wherein the first information comprises tree canopy layer image data above a preset height of a forest and forest underlayer image data below the preset height of the forest; performing image processing based on the first information, and preprocessing point cloud data obtained by processing to obtain preprocessed point cloud data; determining canopy layer forest data and understory forest data based on the preprocessed point cloud data, and obtaining position data of each forest; and carrying out primary registration on the position data of each forest based on an improved Delou triangulation network algorithm, and carrying out secondary registration on a primary registration result through an optimization function to obtain a forest three-dimensional point cloud model. According to the method, the efficiency and precision of forest three-dimensional modeling are remarkably improved.

The invention discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, and the method comprises the following steps: multi-source data collection: obtaining a monthly vegetation coverage image through a satellite remote sensing platform, collecting the sensor data of soil temperature and humidity, air CO2 concentration and the like through a laid ground sensor network, and carrying out the real-time prediction of the carbon sink increment of an ecological restoration area; adopting an unmanned aerial vehicle laser radar to obtain vegetation canopy point cloud data according to a preset period, and collecting biomass actual measurement data of a restoration area in a historical preset age limit; preprocessing data, performing radiometric calibration, atmospheric correction and cutting splicing on a monthly vegetation coverage image acquired by a satellite remote sensing platform, and extracting a vegetation coverage and vegetation index time sequence; denoising, ground point separation and single tree canopy segmentation are carried out on vegetation canopy point cloud data acquired by the unmanned aerial vehicle laser radar, and the height and crown breadth of single plant vegetation are calculated. According to the invention, carbon sink increment prediction can be realized more accurately.

The invention discloses a forestry remote sensing image recognition method and system based on image recognition, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a LiDAR point cloud and a multispectral optical image, and obtaining a three-dimensional forest point cloud; forest feature segmentation is carried out through spectral difference analysis, forest feature segmentation boundary points are redistributed based on spectral dissimilarity, a core spectral signature is calculated, and a single-tree point cloud entity set is output; and carrying out topological analysis on the single tree point cloud entity set, and outputting a vector result. According to the method, the three-dimensional forest point cloud is directly operated, each tree is identified as a single-tree point cloud entity containing complete space structure and spectrum information, the terrain influence is fundamentally eliminated, the three-dimensional structure parameters of the single tree can be accurately obtained, a two-stage strategy of geometric rough division and spectrum refinement is creatively provided, and the method is suitable for the three-dimensional tree point cloud entity of the single tree. The deep fusion perfectly combines the macroscopic accuracy of geometric segmentation and the microscopic identification of spectrum segmentation, and greatly improves the segmentation precision of the adhered tree crowns.

The invention discloses an urban low-altitude landing risk assessment method based on three-dimensional space grids, and belongs to the technical field of low-altitude flight risk assessment. The objective of the invention is to solve the problem of accurate assessment of the low-altitude falling risk in a complex city scene. The method comprises the steps of performing three-dimensional rasterization processing on a low-altitude airspace of an evaluation airspace to obtain voxels of the evaluation airspace, and obtaining a voxel set of the evaluation airspace; building shelter formed by population density grids or mobile signaling inversion population, sensitive facility vectors, tree canopy shielding and three-dimensional building models is quantified, values are assigned to voxels of an evaluation airspace, and a voxelization environment parameter database covering the whole domain is obtained; constructing an aircraft failure rate model; carrying out an aircraft falling trajectory uncertainty simulation test to obtain landing coverage probability density distribution and tail end speed of the aircraft; and carrying out voxel risk synthesis by considering the probability of collision with people, and carrying out risk grading on the obtained voxel risk, thereby completing urban low-altitude landing risk assessment based on the three-dimensional space grid.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The present invention discloses a quantitative analysis method of solar shortwave radiation of a tree canopy based on a computer simulation model, comprising: first, collecting tree laser point cloud data, combining machine vision algorithm to separate branches and leaves and segment single leaves on the tree laser point cloud, and using elliptical and cylindrical geometric units of appropriate size to fit each leaf and skeleton respectively to reconstruct a real model of the tree; secondly, using computer graphics methods, combining the longitude and latitude of the research site and time to simulate the incident light of the sun, and introducing the bidirectional reflection and transmission distribution function of physics and the Monte Carlo ray tracing algorithm to simulate the collision of reflected and transmitted light with leaves in the canopy; finally, according to the instrument measurement, the average roughness and refractive index of leaves of different tree species are obtained, and the shortwave radiation distribution calculation in the tree canopy and the light interception efficiency evaluation of the forest canopy are realized in combination with the ray tracing algorithm. The present invention can accurately obtain the interception amount of direct, reflected and transmitted light energy by the tree canopy.

The invention discloses a natural secondary forest tree species classification method based on multi-modal data fusion. The method comprises the following steps: S1, building a tree species space-spectrum feature extraction module; s2, building a tree canopy global context sensing fusion module; s3, building a tree perception detail optimization module; s4, building a tree species classification network based on multi-modal data fusion; s5, after the tree species classification network with multi-modal data fusion is built, corresponding hyperspectral and laser radar data sets are used for training and testing; according to the method, the problems of fuzzy crown boundary, difficulty in distinguishing tree species with similar structures and low classification precision caused by difficulty in considering long-distance global dependence modeling and fine-grained local detail reservation when an existing tree species classification technology is used for processing a complex forest stand environment are solved.

The invention discloses a forest combustible load measuring and calculating method and system, and the method comprises the steps: carrying out the segmentation processing of an image corresponding to each region of a forest according to the RGB ranges of the forest combustible in different layers, and obtaining the images corresponding to different layers of each region of the forest; calculating parameter information of different layers of each region of the forest based on the images corresponding to the different layers of each region of the forest; biomass models of different levels are adopted to calculate biomass information of different levels of each region of the forest based on the parameter information sets of different levels of each region of the forest; based on the biomass information of the different layers of each region of the forest, obtaining the combustible loading capacity of the different layers of each region of the forest; and calculating the combustible loading capacity of each region of the forest based on the combustible loading capacity of different layers of each region of the forest. By utilizing the scheme of the invention, the influence of environmental factors can be avoided, the influence of the tree canopy layer on the data acquisition of other levels is also avoided, the required time is short, and the workload is small.

The embodiment of the invention relates to the technical field of wind field simulation, and discloses a tree canopy wind field assessment method based on wind environment simulation, comprising: acquiring a three-dimensional data model of a to-be-assessed target environment; hydrodynamic simulation is executed to simulate wind field distribution when wind flows through the three-dimensional data model, and the tree model of the at least one tree is configured to be a porous medium model in the wind field simulation; obtaining a result based on wind field analog simulation; and determining a risk assessment result for the corresponding tree according to the wind load response index. According to the wind field evaluation method in the embodiment of the invention, at least one wind load response index, such as wind speed, pressure, wind load and the like, related to the corresponding tree is calculated according to a wind field simulation result. The quantified indexes can visually reflect the wind action condition of the tree in the wind field, the mechanical response of the tree in different wind environments can be deeply known, and a specific numerical basis is provided for evaluating the wind resistance of the tree.

The invention belongs to the technical field of fruit tree cultivation, and particularly discloses a durian micro-spraying humidity control cultivation method which comprises the following steps: selecting a durian tree planting field, and digging planting holes; a distributed micro-spray head array is arranged around each durian tree, and the micro-spray heads are vortex atomization type micro-spray heads; installing temperature regulation and control equipment in the planting land; installing a soil moisture content sensor in the soil, and arranging a canopy temperature and humidity sensor on the canopy of the durian tree; the bottoms of the planting holes are filled with decomposed organic fertilizer, and durian seedlings are planted; regulation and control are carried out according to requirements of durian on temperature and humidity in different growth stages; the durian tree is patrolled regularly, and when the signs of leaf tip burning and dry heat burning are found, cultivation intervention is conducted in combination with temperature and humidity regulation and control. According to the micro-spraying humidity-control cultivation method for the durians, through an accurate micro-spraying system and a humidity control strategy, a suitable humidity environment is created for growth of the durians, the growth speed of the durians is increased, and the occurrence probability of scorched tips of leaves, burning caused by dry heat and runt seedlings is reduced.

The invention discloses a FasterNet and BiFPN fused litchi tree canopy segmentation method, and relates to the technical field of deep learning and intelligent agriculture. A lightweight FasterNet module is used as a backbone network and is deeply integrated with a bidirectional feature pyramid network BiFPN, an efficient multi-scale feature extraction and fusion architecture is constructed, and the method is used for segmenting the canopy of a litchi tree. The FasterNet module adopts a combination strategy of partial channel deep convolution and point-by-point convolution, preferentially performs deep convolution operation on partial channels of an input feature map, captures spatial textures and edge details of litchi crown leaves, realizes cross-channel information interaction and dimension transformation through light-weight point-by-point convolution, and obtains the spatial texture and edge details of the litchi crown leaves. According to the design, the calculation load brought by dense convolution operation is remarkably reduced, so that the model can process high-resolution input of 5280 * 3956 pixels at a high speed, the BiFPN network aggregates multi-stage features which are extracted by FasterNet and come from different depths through a bidirectional cross-scale connection path, and collaborative optimization of calculation efficiency and multi-scale feature representation capability is realized.

The embodiment of the invention provides a method for determining a bamboo forest cultivation target by using the canopy density of an upper-layer arbor vegetation type, and relates to the technical field of forestry, the method comprises the following steps: evaluating growth related indexes of square bamboos planted under different vegetation types under different site conditions, and determining the bamboo forest cultivation target according to an evaluation result. And in combination with related cutting technical regulations, allowing the corresponding forest category, the forest origin and the forest species to be adjusted to an upper-layer arbor closing range, and determining an under-forest bamboo cultivation target of the corresponding forest category, the forest species and the origin under each site condition. And according to the determined square bamboo cultivation target, in combination with factors such as forest category, forest species and origin, cultivation site screening can be assisted, so that a scientific basis is provided for bamboo industry development planning.

This utility model discloses an agricultural remote sensing measurement device, including a control box, a solar charging device, and a tree measurement device. This agricultural remote sensing measurement device uses measuring branches to measure data such as leaf density, light radiation within the tree canopy, temperature, humidity, and stem flow. The controller stores the corresponding data in a memory, and periodically uploads the data in the memory to a remote control center via a 5G communication module. This facilitates researchers' study of tree transpiration, and the remote control center can view the current data at any time via the 5G communication module for convenient sampling. The use of tree branches facilitates the installation of the measuring branches and control box, ensuring installation stability for long-term monitoring. The solar charging device charges the battery, reducing the measurement device's dependence on external power and is energy-saving and environmentally friendly.

This invention discloses a method for assessing urban low-altitude fall risks based on a three-dimensional spatial grid, belonging to the field of low-altitude flight risk assessment technology. It aims to address the problem of accurately assessing low-altitude fall risks in complex urban scenarios. The invention includes: 1) 3) Rasterizing the low-altitude airspace of the assessment area into voxels, obtaining a voxel set; 2) quantifying population density grids or mobile signaling-derived population, sensitive facility vectors, tree canopy shading, and building shelters formed by three-dimensional building models, and assigning these values ​​to the voxels of the assessment airspace, resulting in a voxel-based environmental parameter database covering the entire area; 3) constructing an aircraft failure rate model; 4) conducting an uncertainties simulation experiment of the aircraft's fall trajectory to obtain the probability density distribution of the aircraft's landing coverage and terminal velocity; 5) considering the probability of collision with people, synthesizing voxel risks, and classifying the voxel risks to complete an urban low-altitude fall risk assessment based on a three-dimensional spatial grid.

The invention relates to the technical field of atmospheric mode and numerical simulation, in particular to a tree canopy drag wind speed attenuation parameterization method based on a turbulence physical model, which comprises the following steps: calculating a three-dimensional equivalent leaf area index by acquiring street geometric parameters and tree structure data, and decomposing the three-dimensional equivalent leaf area index into leaf area density changing along with height; further converting into equivalent windward area density, substituting the equivalent windward area density into a momentum equation, defining a canopy drag source item and calculating a canopy turbulence production item; and finally, coupling a canopy turbulence production item with a turbulence equation, and carrying out numerical solution by combining the speed and shear stress conditions of the ground, the crown edge and the roof to obtain a street height stratification wind speed profile. According to the three-dimensional parameterization method, the wind speed attenuation simulation capability of the tree streets is remarkably improved, the wind speed profile is more continuous and reasonable and highly coincides with the CFD simulation result, and the method is suitable for urban wind environment assessment and air pollution simulation.

1. Name of the product in this design: Assembled Toy (Tree Crown). 2. Purpose of this design: Assembled toys for entertainment and play. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model.

There are approximately 35,000 abandoned and unplugged oil and gas wells in New York with no known location. Unplugged wells emit methane, a strong greenhouse gas, which has the potential to significantly contribute to global climate change and act as a pollutant chemical. A long-range UAV equipped with methane sensors, MagPike (atomic magnetometer), and LiDAR sensors successfully detected unmarked well sites using characteristic magnetic signals generated by vertical metal piping preserved in the ground. The optimal flight altitude and transect spacing was determined for detection driven by the total field strength of the Earth's magnetic field and the height of tree canopies determined by LiDAR. Traditional methods of identifying oil and gas wells are costly and less powerful in acquisition of data such as using large magnetometers attached to helicopters.

The present application relates to the technical field of atmospheric model and numerical simulation, and particularly relates to a tree crown drag wind speed attenuation parameterization method based on a turbulent flow physical model, which obtains street geometric parameters and tree structure data, calculates a three-dimensional equivalent leaf area index, and decomposes the three-dimensional equivalent leaf area index into a leaf area density varying with height; after being further converted into an equivalent windward area density, the equivalent windward area density is substituted into a momentum equation, a crown layer drag source term is defined and a crown layer turbulent production term is calculated; finally, the crown layer turbulent production term is coupled with a turbulent flow equation, and velocity and shear stress conditions of the ground, the tree crown edge and the roof are combined to obtain a street height layered wind speed profile by numerical solution. The present application significantly improves the wind speed attenuation simulation capability of a street with trees by a three-dimensional parameterization method, makes the wind speed profile more continuous and reasonable, and is highly consistent with a CFD simulation result, and is suitable for urban wind environment evaluation and air pollution simulation.

This utility model relates to the field of environmental monitoring equipment technology, and discloses an air quality monitoring device for tree maintenance, including a housing, an air monitoring unit, a fan, and an air duct. The housing is used to connect to the tree and has a cavity, an air inlet, and an air outlet. The air inlet and air outlet are both connected to the cavity. The air monitoring unit and the fan are both connected to the cavity. The fan is used to draw air from outside the housing into the cavity. The air duct is connected to the housing and is connected to the air inlet. The air duct extends to or beyond the tree canopy. When the fan operates, it causes the air from or beyond the canopy to form an airflow that enters the cavity through the air duct and the air inlet and exits through the air outlet. During this process, the air monitoring unit detects this airflow to obtain the composition information of the air in or beyond the canopy. In other words, the air quality monitoring device for tree maintenance can monitor the air around the tree canopy, providing data support for the scientific maintenance of trees.

This invention relates to the field of automated litchi harvesting technology, specifically a high-low staggered harvesting device and method for harvesting litchi from the entire tree canopy. The device includes a walking mechanism, a high-low staggered double robotic arm, a flexible clamping mechanism, a curved guide rail mechanism, a shearing device, a control device, and an identification device. The identification device and shearing device are mounted on the robotic arms. The low-position 6-DOF robotic arm is mounted on the slide of the curved guide rail mechanism. The high-position 3-DOF robotic arm consists of three linear motion modules along the X, Y, and Z axes. The flexible clamping mechanism is placed on the X-axis linear motion mechanism, which is placed on the Y-axis linear motion mechanism, and the Y-axis linear motion mechanism is placed on the Z-axis linear motion mechanism. The Z-axis linear motion mechanism, control device, and curved guide rail mechanism are mounted on the walking mechanism. This invention can cover the entire litchi canopy for fruit harvesting, is suitable for harvesting in high-branch scenarios, and significantly improves the efficiency of litchi harvesting.

This application relates to a method and system for detecting the diameter at breast height (DBH) of individual trees in forests based on the organic fusion of multi-source sensors, belonging to the field of forestry remote sensing technology. It performs terrain filtering, orthorectification, and independent tree instance segmentation on airborne lidar point cloud data and multispectral satellite imagery, respectively. Then, through local semantic topology graph construction and cross-modal graph matching, object-level identity binding is achieved to obtain a set of homologous binding pairs for individual trees. Subsequently, a Gaussian weight field for the tree canopy is constructed using the 3D point cloud spatial density distribution of the bound trees. A pixel-by-pixel probabilistic soft-assignment weighting is performed on the multispectral image object blocks to generate a soft-fusion feature vector for individual trees. Finally, this feature vector is input into a regression model constrained by the forest allometric growth law for DBH detection. This application effectively suppresses edge noise pollution caused by spatiotemporal misalignment of multi-source data through object-level flexible anchoring and probabilistic feature fusion, improving the accuracy and robustness of DBH detection in complex forest areas.

The invention relates to the technical field of graphic attribute analysis, and discloses a high-canopy forest carbon reserve variation remote sensing measurement method, which comprises the following steps of: firstly, carrying out single-tree crown segmentation on a forest, matching trees in different time phases, and then judging the survival state change of the trees in two time phases by utilizing the change of the number of point cloud data points; the method comprises the following steps: classifying trees into normal trees, morning trees, post-morning trees, cut trees and new trees based on survival states of different trees, formulating different carbon reserve variable quantity metering methods one by one, and substituting remote sensing data into a model to calculate the carbon reserve variable quantity of each tree after modeling; the carbon reserve variable quantity of each tree is calculated and then added to obtain accurate carbon reserve variable quantity data, so that the adverse effect of upper canopy shielding on remote sensing is overcome. The ground survey data is only used for modeling, does not need to be obtained from the forest to be measured, is not affected by the terrain of the forest to be measured, and completely gets rid of the ground survey data after modeling is completed.

The invention relates to the field of intelligent agriculture, and particularly discloses a macadamia nut disease detection and recognition method based on image recognition, which comprises the following steps: S100: image data acquisition: acquiring high-definition digital images of leaves and fruits at the middle and upper parts of a macadamia nut tree canopy from multiple angles under a natural light condition, the method comprises the following steps: collecting an object covering healthy leaves and disease samples which are confirmed as anthracnose, leaf blight and stem canker on site by agricultural experts, and accurately marking each collected image to generate a data set containing image file names and corresponding disease category labels, wherein the disease samples comprise different disease severity degrees, leaf growth stages and background environments; s200, performing image input processing, and taking the data set obtained in S100 as input data; s300, image processing is carried out; s400, obtaining a final recognition result; according to the technical scheme, disease features can be learned in a self-adaptive mode, complex environment interference is resisted, and recognition precision and efficiency are both considered.

The invention discloses a stay wire type precise pesticide spraying unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicles, the stay wire type precise pesticide spraying unmanned aerial vehicle comprises an unmanned aerial vehicle body, the unmanned aerial vehicle body is provided with a pesticide box and an airborne intelligent camera, the lower end of the unmanned aerial vehicle body is provided with at least one profiling pesticide spraying arm, and the profiling pesticide spraying arms can conduct profiling according to tree crowns; sprayers and position sensors are distributed on the profiling pesticide spraying arm, an outlet of the pesticide box is communicated with the sprayers through pipelines, a transmission box, a sensor control system and a pesticide spraying arm control system are arranged at the position, corresponding to the profiling pesticide spraying arm, of the upper end of the unmanned aerial vehicle body, the transmission box is connected with the profiling pesticide spraying arm, and the position sensors are electrically connected with the sensor control system. The sensor control system is electrically connected with the pesticide spraying arm control system, and the pesticide spraying arm control system is electrically connected with the transmission case. Through profiling of the profiling pesticide spraying arm, the position of the spray head on the profiling pesticide spraying arm can be controlled to be matched with the shape of a fruit tree crown, the comprehensiveness and uniformity of the spraying coverage range are ensured, and precise pesticide spraying is achieved.

The invention relates to the technical field of green land benefit evaluation, and discloses a crown coverage benefit evaluation method and system, and the method comprises the following steps: 1, constructing a crown coverage benefit comprehensive evaluation system; green ecological benefit indexes and social and economic benefit indexes are set in the crown covering benefit comprehensive evaluation system; step 2, collecting topographic features, ecological features and land utilization features of crown covering plates in the target area; and step 3, based on the data collected in the step 2, calculating green ecological benefit indexes and social economic benefit indexes of the crown covering plate. According to the method, the crown covering benefit can be accurately evaluated, reliable information reference can be provided for investigation and optimization of the current situation of the crown covering benefit, the improvement of the urban environment quality is facilitated, and the welfare of people is improved.

The invention provides a fire prediction method and device based on multi-source data, and the method comprises the steps: obtaining the multi-source data of a target region, and calculating the parameters of a tree canopy of the target region, for the target region where the fire prediction is expected; performing feature extraction on point cloud data and meteorological data in the multi-source data to obtain spatial features corresponding to the point cloud data and meteorological time sequence features corresponding to the meteorological data, and performing feature fusion on the spatial features and the meteorological time sequence features to obtain a multi-dimensional fusion feature vector of the target area; and respectively inputting the tree canopy parameters, the point cloud data and the multi-dimensional fusion feature vector into a fire prediction model, and outputting a fire prediction result of the target area. Through the method, the accuracy and efficiency of fire prediction are improved, and the practicability and flexibility of fire prediction are further improved.

The invention relates to a light suppression effect analysis method and system for a tree canopy, and the method comprises the steps: carrying out the three-dimensional structure reconstruction of the tree canopy, introducing a time dimension, and obtaining a canopy structure model; based on the canopy structure model, performing light distribution simulation on the tree canopy to obtain light distribution characteristics; establishing a photosynthetic productivity evaluation model of the tree canopy according to the light distribution characteristics; and according to the photosynthetic productivity evaluation model, carrying out photoinhibition effect analysis on a to-be-analyzed tree canopy to obtain an analysis result. According to the method, through accurate canopy three-dimensional reconstruction and efficient light distribution simulation, the prediction precision of the photosynthetic productivity of the coniferous tree is remarkably improved.

This disclosure relates to a firebreak clearing device and a control method for the firebreak clearing device. The firebreak clearing device includes: an off-road chassis assembly (1) having wheels (11) and a non-load-bearing frame (12); a boom assembly (2) connected to the frame (12) and capable of telescoping and rotating relative to the frame (12); a preparation assembly (3) connected to the frame (12) and configured to prepare a flame retardant; a spraying assembly (4) disposed at the end of the boom assembly (2) and connected to the preparation assembly (3) via piping, and configured to spray the flame retardant; and a processor (5) configured to cause the spraying assembly (4) to spray the flame retardant onto the tree canopy during the movement of the off-road chassis assembly (1) to form a firebreak. The firebreak clearing device sprays while driving, meeting the requirements of rapid response in fire prevention and effectively preventing the spread of fire.

A system and method for automated timber inventory. The system and method use photogrammetry and an artificial neural network to produce digital maps and models of highly accurate tree canopy delineation, species prediction, tree height modeling, and subsequent merchantable timber values. In one aspect, a method for automated timber inventory involves acquiring a plurality of aerial images for a timber stand of interest, photogrammetrically generating a three-dimensional representation of the timber stand of interest (e.g., an orthomosaic map, a reflectance map, a digital terrain model, a digital surface model, or any combination of the same) from the plurality of aerial images, identifying one or more tree canopies in the three-dimensional representation using an iteratively-improving artificial intelligence convolutional neural network, and generating a digital canopy map of the timber stand of interest using the convolutional neural network, where the digital canopy map includes labels for the tree canopies.