371 results about "Woodlot" patented technology

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention discloses a forest land ecosystem health assessment system, and belongs to the technical field of environmental protection. Comprising the following modules: an intelligent sensing module for realizing omnibearing and high-resolution monitoring of the forest land ecological environment; the feature extraction module automatically extracts, standardizes and dynamically corrects the multi-dimensional features of the ecological system, and constructs a unified ecological index mapping model; the coupling analysis module is used for accurately identifying structural evolution characteristics and key conversion nodes of the ecological system; the health assessment module is used for constructing a dynamic self-adaptive ecological health assessment model, assessing the overall health state of the system and simulating the vulnerability and potential risk of the ecological system under different climate change scenes; the decision and visualization module is used for realizing efficient intelligent interaction and multi-scene decision analysis of ecological big data; and the restoration and regulation and control module intelligently generates a restoration path and an intervention strategy based on ecological risk assessment so as to improve the restoration capability and toughness of the ecological system.

The invention relates to an airborne and SLAM point cloud automatic registration method for a forest sample plot, and belongs to the field of laser point cloud data processing. The method comprises the following steps: cooperatively acquiring point cloud data of a forest land area through an airborne laser radar and SLAM equipment, and performing feature extraction on SLAM point cloud by adopting a random forest model to obtain crown edge contour points; segmenting the airborne point cloud by using a multi-level watershed algorithm and extracting crown edge contour points; using an FPFH algorithm to match position features of computer-borne data crown edge contour points and SLAM data crown edge contour points; and a transformation matrix is calculated through an RANSAC method, and the source point cloud is transformed to the reference of the target point cloud, so that complete spatial information description of the forest land scene is realized. According to the method, the edge contour points of the tree point cloud can be automatically extracted, the airborne point cloud and the SLAM point cloud data are registered, and the forest point cloud vertical structure information is fused, so that the forest structure information is finely inverted.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention relates to the technical field of remote sensing, and discloses a forest land health state analysis method and system based on multi-source remote sensing image analysis. The system comprises a multi-source remote sensing acquisition module, a feature extraction and fusion module, a health assessment module, a traceability analysis module, a strategy matching module, a visual reconstruction module, an early warning decision module and an execution feedback module, and constructs a multi-modal forest land observation data set by fusing multi-source remote sensing data of multispectrum, hyperspectrum, radar and thermal infrared. The comprehensive extraction of multi-dimensional information such as vegetation coverage, canopy biochemical characteristics, under-forest structures and surface thermal environments is realized, the limitation of single data source analysis is overcome, and the comprehensiveness and accuracy of forest land health condition evaluation are improved; through dynamic comparison of multi-stage remote sensing images and health risk level mapping, early identification and early warning of forest growth abnormity, degeneration trend and pest and disease risk are realized.

The invention provides an annotation method and system based on forest land remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data of a forest land; processing the multi-source remote sensing data; an improved Mask R-CNN network is adopted to extract optical image features, the weight of a near-infrared band is enhanced through a compression and excitation SE module, and feature fusion is carried out to obtain a multi-modal enhanced feature map; carrying out feature extraction by adopting a 3D convolutional network to obtain a multi-level three-dimensional space feature map; inputting the processed data into a Transform model to obtain a forest growth curve graph; performing initial labeling based on the feature map and the curve map to obtain an initial labeling result; performing morphological filtering on the initial labeling result to remove small-area noise, and optimizing a labeling boundary in combination with a superpixel segmentation algorithm to obtain an optimized labeling result; and generating a vector annotation file according to the optimized annotation result. Multi-dimensional information complementation of spectral features, three-dimensional structure features and time sequence dynamic features is achieved, and data availability and reliability are remarkably improved.

The invention discloses a large-scale forest land carbon reserve estimation method and system based on an active and passive remote sensing technology. The method comprises the following steps: acquiring satellite-borne laser radar data of different sensors in a research area, SAR data of an L wave band and a C wave band, multispectral remote sensing image data, airborne laser radar data, topographic data and land coverage category data containing forest land; discontinuous combined spaceborne laser radar canopy height products are obtained by using spaceborne laser radar data of different sensors; improving a geographic weighted regression model to estimate the canopy height of a continuous scale; extracting polarization parameters by using C-band SAR data, extracting vegetation indexes and texture indexes by using multispectral remote sensing image data, constructing characteristic variables together with topographic data, and screening the characteristic variables based on a variance reduction criterion; and constructing an overground carbon reserve inversion model, and carrying out large-scale forest land carbon reserve estimation. According to the method, the forest land carbon reserve estimation precision of the large-scale regional broken plot is improved.

The present invention discloses a method for detecting forest disturbances based on a full-polarization SAR image, which includes obtaining a full-polarization SAR image within a target area, performing image classification for the reference time phase and the monitoring time phase, performing pre-processing including introduction of the SAR image, polarization matrix transformation of the SAR image, Refined Lee filtering, and false-color synthesis on the classified full-polarization SAR image to obtain a false-color synthesized SAR image, inputting the false-color synthesized SAR image into an optimized surface change detection model to extract surface change patches, combining with a radar vegetation index RVI image to obtain initial forest disturbance patches, performing an overlay check on the initial forest disturbance patches to obtain forest disturbance patches within the target area, and generating a visualization result. It has efficient, accurate, and reliable detection capabilities, can observe the ground all day long, all-weather, and penetrate clouds and fog, can be widely applied in fields such as forest resource monitoring and environmental protection, and can better serve ecological protection restoration supervision and management.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention discloses a sparse forest land single tree segmentation method and system based on NDVI weighted attention, and the method comprises the steps: making a data set containing an image-label-NDVI image array of a sparse forest land single tree, and dividing the data set into a training set, a test set and a verification set; constructing a sparse forest land individual tree segmentation network model based on NDVI weighted attention; performing training, hyper-parameter adjustment and generalization ability evaluation on the constructed sparse forest land single tree segmentation network model by using the training set, the verification set and the test set; and performing individual tree segmentation by using the trained sparse forest land individual tree segmentation network model. According to the method, small target recognition, accurate edge and lightweight inference in the sparse forest land scene are realized, and a new technical path is provided for single-tree-level monitoring in a complex earth surface environment.

The embodiment of the invention provides a tree species identification method, device and equipment based on a dense time sequence image, and the method comprises the steps: obtaining a dense long time sequence multispectral remote sensing image and topographic data of a research area; carrying out image wave band fusion according to the dense long-time-sequence multispectral remote sensing image to construct 12 vegetation spectral indexes; performing land utilization classification on ground features in the dense long-time-sequence multispectral remote sensing image of the research area by utilizing a linear spectrum hybrid model in combination with topographic data, dividing the ground features into buildings, water bodies, other ground features and a forest land data set, and performing pre-classification based on the extracted forest land data set; dividing the forest land data set into an evergreen forest and a deciduous forest by combining an RVI threshold method with a linear spectrum hybrid model; according to the forest land data set, Savitzaky-Golay filtering transformation and spectral differential transformation are carried out, and an NDVI transformation image set is obtained; and performing combination according to the vegetation spectral index NDVI transformation image set as classification features, and discussing the classification effect of the tree species in the research area through a random forest model.

The invention discloses a forest stand tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining data which comprises forest land remote sensing image data and ground data; carrying out preprocessing on the obtained data; a dual-path CBAM-UNet network model is constructed; inputting the preprocessed data into the double-path CBAM-UNet network model to carry out tree species classification prediction and obtain a tree species classification chart; performing image splicing on the tree species classification map to obtain a tree species classification grid map; the system comprises a data acquisition module, a data preprocessing module, a prediction module and an image processing module. According to the forest stand tree species identification method and system based on multi-source remote sensing data fusion provided by the invention, multi-scale feature capture and feature fusion of multi-source heterogeneous data can be realized through the constructed dual-path CBAM-UNet network model, high-precision identification can be performed on forest stand tree species under complex links, and high-quality data can be provided for forest management.

The invention relates to the technical field of forest fertilization, in particular to a forest precise fertilization decision-making method and system based on soil nutrient profit and loss, and the method comprises the steps: obtaining basic information of a target forest land, and dividing forest growth stages based on the basic information; the method comprises the following steps: collecting an undisturbed soil sample and a mixed soil sample of a 0-40cm soil layer of a target forest land by adopting an S-shaped point distribution method, separating the mixed soil sample, and respectively measuring the soil nutrient content, the bioavailability, the soil enzyme activity and the microbial functional gene. According to the method, by measuring the dimension indexes, constructing a five-dimensional profit and loss evaluation index system for determining the reference value and dynamically adjusting the weight according to the forest land data, calculating the comprehensive profit and loss index, judging the nutrient state and generating the personalized fertilization scheme, the defects that an existing fertilization scheme is single in basis index and lacks an effective dynamic adjustment link are effectively overcome; the requirements of multi-generation continuous planting forest land are met, and the environmental risks such as fertilizer waste, soil acidification and water eutrophication are effectively reduced.

The invention relates to the technical field of resource accounting, and discloses a dynamic index accounting system for displacement of cultivated land resources and forest land resources. The system comprises a resource data acquisition unit for acquiring spatial distribution data of cultivated land resources, types and coverage data of forest land resources and replacement constraint condition data and performing space-time alignment to generate a resource multi-dimensional data set; the replacement index accounting unit is used for executing resource adaptability reconstruction processing based on the resource multi-dimensional data set and separating target resource data from abnormal resource data; the replacement scheme generation unit is used for generating an accounting result containing a replacement area, a weight and a priority by combining a preset replacement rate and a development rate; and the dynamic correction unit is used for identifying an exception type based on the exception resource data and correcting an accounting result in real time. The system can comprehensively capture resource dynamics, improves the accuracy and timeliness of replacement accounting, and adapts to complex resource management requirements.

The invention relates to the technical field of land data processing, in particular to a land data updating and analyzing method based on spatial-temporal dynamic change, which comprises the following steps: S1, data collection and arrangement; s2, space-time dynamic evolution analysis based on a GIS model; s3, performing data analysis based on a Python script tool; s4, land data updating; according to the method, the dynamic evolution of land utilization in the time sequence in 2009-2024 is accurately presented, meanwhile, the spatial distribution change is carefully presented, through the combination of the GIS model and a Python script tool, the spatial and temporal change rule of various land features such as cultivated land, garden land, forest land and construction land is accurately revealed, a reliable basis is provided for scientific planning, and the method is suitable for popularization and application. The invention provides an LSTM-CNN fusion model based on deep learning for solving the problem that the abandoned farmland monitoring precision is insufficient, and the abandoned farmland recognition accuracy reaches 94.7% through transfer learning optimization parameters.

The utility model relates to the technical field of enclosures, in particular to a vegetation recovery enclosure device which comprises a vertical rod installation structure. A transverse rod mounting structure is mounted on the vertical rod mounting structure in an angle-adjustable mode, a plurality of angle adjusting butt joint structures are mounted on the vertical rod mounting structure and the transverse rod mounting structure, and an enclosure telescopic stop rod structure is mounted between the two angle adjusting butt joint structures. The vertical rod mounting structure ensures that the enclosure device can be stably mounted on a flat ground or an uneven soil slope under various topographic conditions, the enclosure device is not prone to displacement or toppling in the using process, the reliability of long-term use is ensured, and the angle adjusting butt joint structure and the enclosure telescopic stop lever structure are convenient to use. The device shows excellent flexibility and adaptability, the device can be flexibly installed and adjusted according to actual requirements no matter in soil slopes, mountainous regions or forest lands with different heights, the installation process can be simplified, and the working efficiency is further improved.

A device and method for introducing new vegetation into a forest floor includes seeds, seedlings, and / or saplings carried within a plurality of tapered shells having polygonal cross sections, possibly mixed with soil, sand, gravel, fertilizer, SAP granules, and / or water. The tapered shells are inserted into the ground through the hydrophobic layer by dropping or propelling them from an aircraft, or manually pressing on them. One or more holes proximate the bottoms of the tapered shells enable rain water and germinating roots to penetrate into soil beneath the hydrophobic layer. A perforated lid can prevent the seeds or seedlings from being ejected upon impact with the forest floor. A weighted tip can improve penetration of the tapered shells into the forest floor. A plurality of tapered shells can extend downward from a tray or frame. The tapered shell and / or perforated lid can be made from biodegradable materials.

The invention discloses a forest land restoration and carbon sink management system and method based on a three-dimensional cadastral model, and the method comprises the steps: integrating an earth surface forest right, an underground space right and a block chain information management right through the three-dimensional cadastral model, and collecting forest biomass and soil carbon library data in real time in combination with an Internet of Things sensor network; and data storage and carbon point transaction are realized based on a block chain multi-chain architecture. According to the scheme, the problems of unclear ownership and low data credibility in traditional forest land restoration are solved, the carbon sink value is converted into economic benefits, and the method is suitable for low-efficiency forest land ecological restoration and carbon asset management.

The invention discloses a forest land litter water-holding capacity automatic observation method, which comprises the following steps: identifying topographic factor distribution of different slope gradients and altitudes, calculating vegetation structure parameters by combining vegetation indexes, dividing forest land environment heterogeneity regions according to the topographic factor distribution and the vegetation structure parameters, and generating a spatial distribution map layer, generating an observation point position layout scheme with regional representativeness, deploying a sensing device according to the observation point position layout scheme, and obtaining the dynamic change moment and the change amplitude of the litter water-holding capacity; and decomposing the dynamic change moment and the change amplitude of the litter water-holding capacity, generating continuous spatial distribution data of the forest land litter water-holding capacity, constructing a water-holding capacity prediction model, and calculating litter water-holding capacity observation values of different areas of the forest land in real time according to parameter coefficients of the prediction model. According to the invention, accurate observation of the water holding characteristic of the litter in the forest land is realized.

The invention discloses a bidirectional driving control method and system for a concentrated transportation machine, and belongs to the technical field of engineering machinery. The method aims at solving the safety problem of the two-way concentrated transportation vehicle during forest farm operation. The method comprises the steps that a starting signal is obtained after an engine is started; triggering detection of road condition information and seat direction based on the starting signal; if the direction of the seat is the forward direction, a control signal of the driving unit is generated to activate an operation handle and a front accelerator pedal of the vehicle; if the direction of the seat is reverse, a control signal of the driving unit is generated to activate a rear accelerator pedal of the vehicle; selecting a driving mode according to the road condition information; according to the driving mode and the direction of the seat, a control signal of a steering unit is generated so as to adjust vehicle steering. The driving mode, steering control and driving control related execution mechanisms can be automatically switched according to the seat direction and the road condition information, the manual operation complexity is reduced, various terrain requirements are met, and the safety of forest land driving is improved.

The invention belongs to the technical field of crop planting, and provides a mushroom crop efficient planting method, which comprises the following steps: 1, land planning: selecting a soil position with sufficient shading and sufficient water supply, and preferably selecting a fruit forest land, a commodity forest land, a nut forest land and an arbor tea tree land; step 2, land preparation: selecting a target open space with the width of 0.8-1.2 m along the wind direction by utilizing the line spacing of fruit trees; carrying out soil preparation, chemical disinfection and physical disinfection on the soil; the problem that bacteria and microorganisms in soil affect the mushroom sticks is avoided, meanwhile, when the mushroom sticks are cultivated and planted, the soil at the planting position of the mushroom sticks is subjected to multiple times of sterilization treatment, the mushroom sticks are covered with protective films for protection, in addition, the soil outside the mushroom sticks is subjected to biological disinfection treatment, and therefore the mushroom sticks are protected. Therefore, the influence of other organisms in the soil on the cultivation and growth of the mushroom sticks is avoided, and the purpose of excellent cultivation and growth of the mushroom sticks is achieved.

The invention discloses an under-forest ecological efficient cultivation method for abrus cantoniensis hance, and belongs to the technical field of abrus cantoniensis hance planting. By interplanting the abrus cantoniensis hance under the arbor forest, the optimal interplanting mode of interplanting the abrus cantoniensis hance under the Chinese fir forest is found, and the mode can improve the utilization efficiency of the forest land, reduce the planting cost and increase the economic benefit; the harm of weeds and diseases and pests is reduced, the use of weeding labor and pesticides is reduced, water and soil loss is reduced, water and soil are kept, and good ecological benefits are achieved. According to the method, a scientific basis is provided for selection of a cultivation mode for interplanting the abrus cantoniensis hance under the forest. In ecological management, if the yield and the nutrient content of the abrus cantoniensis hance are improved as a target, a cedarwood forest stand is preferentially selected for interplanting.

The invention discloses an under-forest cultivation method of a clitocybe maxima Shenshi No.2 strain, and belongs to the technical field of edible mushroom cultivation. The method comprises the steps that a forest land with the canopy density of 60%-75% is selected, and when the air temperature is stabilized at 23 DEG C or above in summer, bag-removed Shenmushroom No.2 fungus bags are placed in a furrow bed at the interval of 8-15 cm and covered with soil; a simple arched shed covered with a plastic film and a sunshade net is built above the furrow bed, and ventilation and humidity are regulated and controlled by opening and closing the two ends of the simple arched shed. According to the method, through two core measures of interval placement and arched shed regulation and control, the problems that fungus bags are prone to fungus burning and rainstorm scouring in the high-temperature period under the forest in summer are systematically solved, efficient and stable production of the high-temperature-resistant variety Shenmushroom No.2 under the forest in summer is achieved, and the blank of under-forest cultivation in the high-temperature season is effectively filled up.

The invention belongs to the technical field of forestry ecological restoration, and discloses a method for transforming a pinus massoniana low-quality and low-efficiency water source forest into a coniferous and broad-leaved mixed forest, which comprises the following steps: (1) forest stand diagnosis and planning; (2) three-dimensional complementary planting; and (3) innovation of a mixed mode. The biological characteristics of different fireproof tree species are utilized, upper, middle and lower mixed spaces are matched, shade-tolerant and sun-loving tree species are matched, and the mutual promotion effect is achieved. The characteristics of various tree species are effectively utilized, matching of deep and shallow root system tree species is fully considered, the improvement of the water and soil conservation function of forest land soil is effectively ensured, and the water and soil conservation function of a water source forest region is particularly promoted.

The invention relates to the technical field of ecological construction engineering. The invention provides a forest small watershed-based under-forest adaptive planting method for different sites in a mountainous area in the eastern Liaoning province. The method comprises the following steps: (1) carrying out site division on the small watershed of the forest in the mountainous area in the eastern Liaoning province according to landforms, slope positions and slope directions; (2) collecting data and determining an optimization scheme by taking a soil condition as a guiding index; (3) collecting data and determining an optimization scheme by taking the canopy density as a guiding index; and (4) based on the optimization schemes in the step (2) and the step (3), selecting a site suitable for forest fruits, forest fungi, forest vegetables and forest medicines, and carrying out under-forest adaptive planting. By means of the technical scheme, forest land resources in mountainous areas in the eastern Liaoning province can be fully utilized, economic plants or fungi can be planted under different site forests in small watersheds of forests according to local conditions, and the economic benefits of under-forest planting are brought into play to a greater extent; under-forest adaptive planting improves the functions of forest soil conservation, water conservation, biological diversity and the like, and ecological benefits are improved.

The invention discloses a forest land soil data management method and system based on unmanned aerial vehicle multispectral inversion, and particularly relates to the technical field of soil ecology, and the method comprises the steps: S1, management region determination: determining target forest land soil data as a target management region, and then dividing the target management region into sub-regions according to an equal-time division mode, the sub-regions in the target management region are sequentially numbered as 1, 2,..., i,..., n according to equal time; s2, unmanned aerial vehicle image data acquisition, which is used for acquiring unmanned aerial vehicle image data of a target management area; the system not only comprises soil physical and chemical indexes, but also covers unmanned aerial vehicle image data so as to obtain spectrum related data, inversion is carried out after multi-source data fusion, the soil ecological function is comprehensively described, and a more comprehensive management result is provided.

Provided in the present invention is an artificial-afforestation area extraction method based on a high-resolution remote-sensing image. The method comprises: acquiring a remote-sensing image, and preprocessing the remote-sensing image; inputting the preprocessed remote-sensing image into an intelligent forest extraction model, so as to obtain a distribution map for all forests; performing edge detection and Hough transform line detection on the distribution map for all the forests, so as to obtain artificial afforestation regions and natural forest regions; and calculating the areas of the artificial afforestation regions and the areas of the natural forest regions, so as to obtain statistical results for artificial afforestation and natural forests. The present invention can solve the shortcomings of conventional monitoring means in terms of efficiency, cost and accuracy, and provides technical supports for large-scale forestry investigation and ecological monitoring.

The utility model provides a timber crusher based on an excavator, which belongs to the technical field of crushers, and comprises an excavator body, an excavator forearm and a timber crushing device, the excavator forearm is connected to the excavator body, and the timber crushing device replaces a bucket and is connected to the lower end of the excavator forearm through a rotating shaft; the wood crushing device comprises a main frame, a crushing part, an upper feeding part and a lower feeding part, the main frame is connected with the excavator small arm, a crushing cavity is formed in the main frame, the crushing part is arranged in the crushing cavity, the upper feeding part and the lower feeding part are arranged at a feeding port of the crushing cavity, the lower feeding part is arranged below the upper feeding part, and the crushing cavity is connected with the excavator small arm. A discharging port in the bottom end of the smashing cavity is provided with a discharging conveying component, and one end of the discharging conveying component is provided with a conveying belt driving component. After wood is crushed by the wood crusher on a forest land, finished products are transported by a truck to be stored in a centralized manner, the overall crushed material transportation density is high, the transportation efficiency is high, and the transportation cost is reduced.

The invention discloses a multi-task forest land identification method based on a high-resolution remote sensing image, and the method comprises the steps: S1, obtaining forest land remote sensing data, and determining an autonomous input channel; s2, reading sample training data and sample test data in an original data source through a user autonomous input channel, interpreting TFReccord data from the sample training data and the sample test data through data decoding, and extracting remote sensing image data and label data contained in each sample data; s3, performing screening and data enhancement on the remote sensing image data and the label data; s4, based on a double-branch multi-task segmentation identification deep learning model, carrying out forest land and non-forest land identification on the preprocessed remote sensing image data in a large range; and step S5, visual output of an identification result is carried out. The problems that in the prior art, forest land recognition precision is not high, adaptability to forest land fragmented areas is poor, and effective utilization of time sequence change information is lacked are solved.

The invention discloses a tree positioning calculation method based on unmanned aerial vehicle remote sensing, and relates to the technical field of agriculture and forestry vegetation positioning. According to the method, firstly, a digital orthoimage and a digital earth surface model (DSM) are generated through aerial photography of an unmanned aerial vehicle, a crown mask is obtained by using an instance segmentation technology, and a trunk position is accurately positioned by calculating a fusion score in the mask by using a joint resolving mechanism of a DSM local peak domain and an iterative corrosion morphological core. The method improves the positioning precision and efficiency through a DSM highest point * iterative corrosion combined optimization strategy, and improves the positioning stability of dense crowns through the corrosion depth constraint space position. According to the method, the positioning precision and efficiency of a complex forest environment are remarkably improved, and the problems of insufficient positioning precision, difficulty in positioning trunks under dense crowns and the like in the existing unmanned aerial vehicle remote sensing technology are solved.