8 results about "Urban tree" patented technology

The invention provides an urban tree species classification method, system and device, and belongs to the technical field of tree species classification, and the method comprises the steps: firstly, selecting candidate wavebands in a red-edge spectrum range, traversing all dual-waveband combinations, and defining a vegetation index through a preset formula; secondly, for each index, generating a vegetation mask by using a plurality of candidate thresholds, and calculating a confusion matrix evaluation index by comparing the vegetation mask with a real mask; and finally, based on the index, automatically screening out an index with optimal performance and a threshold pair. Wherein the index defined by the optimal dual-band combination is the final vegetation index, and according to the final vegetation index, classification and identification of urban tree species are carried out through a machine learning model. The method solves the problem that classification of the classic vegetation index on the urban tree species is not accurate.

The application discloses a method for calculating tree height and determining planting point by using a map, comprising the following steps: selecting two satellite images of the same place taken at different times in a map, and determining tangent values of their deformation azimuths; obtaining coordinates of the same point on the crown of the same tree in the two satellite images; obtaining a cosine value of the shooting angle of one of the satellite images; determining real coordinates of the point before deformation; establishing an equation group according to the relationship between deformation and tree height, and solving tree height and base point coordinates of the tree, which represent planting point coordinates. The method can obtain tree height and planting point without field measurement, is simple to operate, short in time consumption, and can be applied to sampling detection of different urban trees.

The invention relates to an urban tree annual ecological value assessment method and system. The urban tree annual ecological value assessment method comprises the steps of sequentially obtaining an annual photosynthetic carbon sequestration value, an annual maximum dust retention value, an annual heat absorption value, an annual shading value, an annual oxygen release value, an annual biodiversity support value and an annual maximum noise reduction value of urban trees; setting a region weight according to a region where the urban trees are located; setting landscape weights according to landscapes where the urban trees are located; setting an aggregation weight according to the aggregation condition of the urban trees; and obtaining the urban tree annual ecological value based on the annual photosynthetic carbon sequestration value, the annual maximum dust retention value, the annual heat absorption value, the annual shading value, the annual oxygen release value, the annual biodiversity support value, the annual maximum noise reduction value, the region weight, the landscape weight and the aggregation weight. According to the method, the annual ecological value of the urban trees can be correctly estimated, and the actual ecological benefits of the urban trees can be correctly reflected.

This invention relates to the field of multimodal remote sensing technology, and particularly to an emergency monitoring system and method for urban tree encroachment based on a large-scale Internet of Things (IoT) model. The method includes: identifying potential target areas; acquiring monitoring data corresponding to the potential target areas; determining pruning areas based on the monitoring data and generating comprehensive pruning parameters corresponding to the pruning areas; generating pruning instructions based on the comprehensive pruning parameters and sending them to intelligent pruning equipment; controlling the intelligent pruning equipment based on the pruning instructions and the comprehensive pruning parameters to control an intelligent pruning robot and / or a drone to proceed to the pruning area, and controlling the opening and closing of the valve of the inhibitor injection pump carried by the intelligent pruning robot to precisely inject into the soil, and / or driving the mechanical shears mounted on the drone to physically cut branches, thereby pruning the trees within the pruning area. This invention enables proactive prevention and collaborative governance of the risk of conflict between urban greening and infrastructure.

The invention relates to an urban crown distribution calculation method and device based on Gaofen-7 satellite images, and the method comprises the steps: obtaining the Gaofen-7 satellite images covering the same urban region in winter and summer, and carrying out the preprocessing of the Gaofen-7 satellite images, so as to obtain image data meeting a preset condition; generating an earth surface digital model grid file based on the image data, obtaining a vegetation height, and calculating a normalized vegetation index based on the image data to extract a vegetation range; based on the vegetation range and the vegetation height, trees and grass and shrubs are distinguished through a preset height threshold value, winter crown distribution and summer crown distribution are determined, and crown distribution of evergreen trees and deciduous trees is calculated. Therefore, the problems that in the related technology, due to the fact that existing open-source remote sensing data cannot meet the high-spatial-resolution monitoring requirement and an unmanned aerial vehicle scheme is difficult to achieve city scale application, city tree crown seasonal coverage changes are difficult to accurately quantify, and then city land coverage mapping and city thermal environment simulation accuracy are limited are solved.

The invention provides an urban tree species classification and mapping method based on LiDAR and hyperspectral fusion. The method comprises the steps of multi-source data acquisition and collaborative preprocessing; vegetation area extraction and non-tree ground object masking; individual tree crown segmentation and contour optimization are carried out; performing multi-dimensional feature extraction and feature engineering; feature selection and classification model construction; multi-category tree species identification and classification result generation; carrying out precision verification and uncertainty analysis; the advantages of LiDAR data in the aspect of representing a canopy three-dimensional structure and the characteristics of hyperspectral data in the aspect of detecting vegetation biochemical parameters are fully utilized, information complementation is achieved through feature level fusion, and the classification precision is remarkably improved.

The invention discloses an urban tree species identification method, system and device and a storage medium, and the method comprises the steps: carrying out the shadow identification of an urban region according to multi-channel satellite remote sensing image data, and extracting the information data of the shadow region; performing shadow area spectrum recovery according to the shadow area information to obtain an image after shadow recovery; and carrying out tree species identification on the image after shadow recovery. Furthermore, an improved MSASDNet-M model is provided for shadow recognition, a fusion method of multiband radiation correction and boundary smoothing is provided for shadow region spectrum recovery, and an NAM-DeepLabV3 + network model is provided for tree species recognition. According to the method, the shadow region identification precision, the spectrum compensation effect and the overall tree species identification precision can be remarkably improved. Meanwhile, the method has high cross-domain adaptability and can be widely applied to tree species monitoring and ecological planning of other urban green lands.

The invention discloses an urban tree above-ground biomass estimation method and system, and relates to the technical field of biomass monitoring, and the method comprises the following steps: carrying out the judgment of original point cloud data, and obtaining a vegetation point cloud; generating a ground elevation through the ground point cloud, performing canopy height analysis based on a difference value between the elevation of the vegetation point cloud and the ground elevation, and constructing a rough canopy height model; performing individual tree boundary segmentation through the rough canopy height model and the crown to obtain the boundary of the individual tree; segmenting the original point cloud data based on the boundary of the single tree to obtain a corresponding point cloud cluster; and extracting a point cloud index for evaluating the biomass based on the point cloud cluster, and inputting the point cloud index into a pre-trained above-ground biomass estimation model to obtain an above-ground biomass estimation value of the single tree. According to the method, the single tree is efficiently and accurately segmented from the ALS point cloud, extra sample labeling is disordered, and a robust correlation model between the tree point cloud index and the AGB is established.