Urban crown distribution calculation method and device based on Gaofen-7 satellite image

Abstract

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.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing data application technology, and in particular to a method and apparatus for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery. Background Technology

[0002] Urban canopy cover has a significant impact on the radiative thermal stress of urban streets and public spaces, especially in areas with significant differences between winter and summer. The phenological changes of deciduous trees lead to marked differences in street canopy cover between winter and summer. Meanwhile, the continuous development of domestically produced high-resolution multi-view satellites provides fundamental data support for urban planning and management.

[0003] However, in related technologies, the 10m resolution of Sentinel-2 satellite data makes it impossible to represent the details of tree canopy distribution and coverage. The 0.5m resolution tree canopy height data released by Meta and the World Resources Institute cannot reflect the seasonal changes in vegetation. The use of drone platforms has limited coverage capabilities in urban areas, a large amount of data, high processing complexity, and limited applicability in low-altitude flight control areas. As a result, it is difficult to accurately quantify the seasonal changes in tree canopy coverage in urban areas, which in turn limits the accuracy of urban land cover mapping and urban thermal environment simulation, and urgently needs to be improved. Summary of the Invention

[0004] This application provides a method and apparatus for calculating urban canopy distribution based on Gaofen-7 satellite imagery, in order to solve the problem that in related technologies, mainstream open-source remote sensing data (such as Sentinel-2, Landsat-8) cannot meet the needs of high spatial resolution monitoring, and UAV solutions are difficult to achieve large-scale and efficient urban-scale applications, which makes it difficult to accurately quantify the seasonal changes in urban canopy cover, thus limiting the accuracy of urban land cover mapping and urban outdoor thermal environment simulation.

[0005] The first aspect of this application provides a method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, comprising the following steps: acquiring Gaofen-7 satellite images covering the same urban area in winter and summer; preprocessing the Gaofen-7 satellite images to obtain image data that meets preset conditions; generating a surface digital model raster file based on the image data, obtaining vegetation height based on the surface digital model raster file, and calculating a normalized vegetation index based on the image data to extract vegetation range; distinguishing between trees and shrubs based on the vegetation range and the vegetation height using a preset height threshold to determine the winter and summer tree canopy distributions, and calculating the canopy distributions of evergreen trees and deciduous trees based on the winter and summer tree canopy distributions.

[0006] Through the aforementioned technical means, this application embodiment can acquire Gaofen-7 satellite image data, generate a digital model raster file of the land surface based on the image data to obtain vegetation height, calculate the normalized vegetation index to extract vegetation range, use a preset height threshold to distinguish trees from grasses and shrubs to determine the canopy distribution in winter and summer, and further calculate the canopy distribution of evergreen and deciduous trees. This fully leverages the sub-meter-level stereo mapping advantages of the Gaofen-7 satellite to achieve accurate identification and classification of urban vegetation, accurately distinguish evergreen and deciduous trees and quantify their spatial distribution, providing data support with both seasonal dynamics and spatial accuracy for refined urban greening modeling and vegetation type statistics, and contributing to the scientific planning and management of urban greening.

[0007] Optionally, in one embodiment of this application, the step of generating a digital land model raster file based on the image data and obtaining vegetation height based on the digital land model raster file includes: performing stereo matching based on the image data to generate point cloud data, and converting the point cloud data into a digital land model raster file; constructing a grid of a preset resolution to calculate a ground elevation datum, and calculating the vegetation height based on the ground elevation datum and the digital land model raster file.

[0008] Through the above-mentioned technical means, the embodiments of this application can generate high-precision point cloud data through stereo matching technology and establish an accurate ground elevation benchmark based on gridding processing, thereby reducing systematic errors in the vegetation height calculation process, accurately calculating the vertical height information of urban vegetation, and providing high-precision vegetation height parameters for subsequent differentiation of trees and shrubs through height thresholds.

[0009] Optionally, in one embodiment of this application, the step of calculating the normalized vegetation index (NWRI) based on the image data to extract the vegetation range includes: calculating the NWRI based on the image data and resampling it to obtain high-resolution NWRI raster data; and dividing the high-resolution NWRI raster data according to preset thresholds for winter and summer NWRIs respectively to obtain the vegetation range.

[0010] Through the above-mentioned technical means, the embodiments of this application can generate high-resolution normalized vegetation index raster images and adopt a seasonal adaptive threshold strategy to achieve accurate extraction of vegetation range in winter and summer. This not only maintains the integrity of vegetation boundary details but also effectively overcomes the influence of spectral differences caused by seasonal changes, ensuring the consistency of vegetation extraction results in different seasons and providing an accurate data foundation for subsequent canopy classification.

[0011] Optionally, in one embodiment of this application, the step of calculating the canopy distribution of the evergreen trees and the deciduous trees based on the winter canopy distribution and the summer canopy distribution includes: generating height raster and cover raster data of the evergreen trees and the deciduous trees based on the canopy distribution of the evergreen trees and the deciduous trees, so as to generate high-precision urban land cover raster data containing evergreen trees and deciduous trees.

[0012] Through the above-mentioned technical means, the embodiments of this application can generate high-precision urban land cover raster data containing evergreen trees and deciduous trees, transform the classification results into geospatial data that can be directly applied, and intuitively display the distribution pattern of different types of trees using a structured data output format, which greatly facilitates practical applications in urban planning, green space management, landscape environment and ecological benefit assessment.

[0013] Optionally, in one embodiment of this application, it further includes: constructing a digital surface model of the tree canopy based on the winter and summer tree canopy distributions to simulate the urban thermal environment in winter and summer.

[0014] Through the above-mentioned technical means, the embodiments of this application can construct a seasonally specific digital surface model of tree canopy, providing key input parameters for urban thermal environment simulation that take into account vegetation phenological changes, thereby improving the scientificity and practicality of urban thermal environment simulation results, taking into account the impact of winter and summer vegetation phenological changes on the seasonal thermal environment, and improving the accuracy of urban near-ground pedestrian height outdoor thermal environment simulation.

[0015] A second aspect of this application provides an urban canopy distribution calculation device based on Gaofen-7 satellite imagery, comprising: an acquisition module for acquiring winter and summer Gaofen-7 satellite imagery covering the same urban area, preprocessing the Gaofen-7 satellite imagery to obtain image data that meets preset conditions; an extraction module for generating a surface digital model raster file based on the image data, obtaining vegetation height based on the surface digital model raster file, and calculating a normalized vegetation index based on the image data to extract vegetation range; and a calculation module for distinguishing trees from grasses and shrubs based on the vegetation range and the vegetation height using a preset height threshold to determine the winter and summer canopy distributions, and calculating the canopy distributions of evergreen trees and deciduous trees based on the winter and summer canopy distributions.

[0016] Through the aforementioned technical means, this application embodiment can acquire Gaofen-7 satellite image data, generate a digital model raster file of the land surface based on the image data to obtain vegetation height, calculate the normalized vegetation index to extract vegetation range, use a preset height threshold to distinguish trees from grasses and shrubs to determine the canopy distribution in winter and summer, and further calculate the canopy distribution of evergreen and deciduous trees. This fully leverages the sub-meter-level stereo mapping advantages of the Gaofen-7 satellite to achieve accurate identification and classification of urban vegetation, accurately distinguish evergreen and deciduous trees and quantify their spatial distribution, providing data support with both seasonal dynamics and spatial accuracy for refined urban greening modeling and vegetation type statistics, and contributing to the scientific planning and management of urban greening.

[0017] Optionally, in one embodiment of this application, the extraction module includes: a matching unit, configured to perform stereo matching based on the image data to generate point cloud data, and convert the point cloud data into a digital land model raster file; and a construction unit, configured to construct a grid of preset resolution to calculate a ground elevation datum, and calculate the vegetation height based on the ground elevation datum and the digital land model raster file.

[0018] Through the above-mentioned technical means, the embodiments of this application can generate high-precision point cloud data through stereo matching technology and establish an accurate ground elevation benchmark based on gridding processing, thereby reducing systematic errors in the vegetation height calculation process and accurately calculating the vertical height information of vegetation, providing high-precision vegetation height parameters for subsequent differentiation of trees and shrubs through height thresholds.

[0019] Optionally, in one embodiment of this application, the extraction module includes: a sampling unit, configured to calculate the normalized vegetation index (NVI) and resample based on the image data to obtain high-resolution NVI raster data; and a division unit, configured to divide the high-resolution NVI raster data according to preset thresholds for winter and summer NVI, respectively, to obtain the vegetation range.

[0020] Through the above-mentioned technical means, the embodiments of this application can generate high-resolution normalized vegetation index and adopt a seasonal adaptive threshold strategy to achieve accurate extraction of vegetation range. This not only maintains the integrity of vegetation boundary details but also effectively overcomes the influence of spectral differences caused by seasonal changes, ensuring the consistency of vegetation extraction results in different seasons and providing an accurate data foundation for subsequent canopy classification.

[0021] Optionally, in one embodiment of this application, a generation module is included, configured to calculate the canopy distribution of the evergreen trees and the deciduous trees based on the winter canopy distribution and the summer canopy distribution, and generate height raster and cover raster data of the evergreen trees and the deciduous trees based on the canopy distribution of the evergreen trees and the deciduous trees, so as to generate high-precision urban land cover raster data containing evergreen trees and deciduous trees.

[0022] Through the above-mentioned technical means, the embodiments of this application can generate high-precision urban land cover raster data containing evergreen trees and deciduous trees, transform the classification results into geospatial data that can be directly applied, and intuitively display the distribution pattern of urban evergreen and deciduous trees using a structured data output format, which greatly facilitates practical applications in urban planning, green space management and ecological benefit assessment.

[0023] Optionally, in one embodiment of this application, it further includes: a simulation module for constructing a digital surface model of the tree canopy based on the winter and summer canopy distributions, in order to simulate the urban thermal environment in winter and summer.

[0024] Through the above-mentioned technical means, the embodiments of this application can construct a seasonally specific digital surface model of the tree canopy, providing key input parameters for urban thermal environment simulation that take into account vegetation phenological changes, thereby improving the accuracy and precision of urban thermal environment simulation results, taking into account the impact of winter and summer vegetation phenological changes on urban seasonal thermal environment and microclimate, and improving the accuracy of urban near-ground pedestrian height outdoor thermal environment simulation.

[0025] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the urban tree canopy distribution calculation method based on Gaofen-7 satellite imagery as described in the above embodiments.

[0026] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery.

[0027] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery.

[0028] This application's embodiments can acquire Gaofen-7 satellite imagery data, generate a digital model raster file of the land surface based on the imagery data to obtain vegetation height, calculate the normalized vegetation index to extract vegetation range, use a preset height threshold to distinguish trees from grasses and shrubs to determine the canopy distribution in winter and summer, and further calculate the canopy distribution of evergreen and deciduous trees. This fully leverages the sub-meter-level stereo mapping advantages of the Gaofen-7 satellite to achieve accurate identification and classification of urban vegetation, accurately distinguish between evergreen and deciduous trees and quantify their spatial distribution. This provides data support with both seasonal dynamics and spatial accuracy for refined urban greening modeling and vegetation type statistics, contributing to the scientific planning and management of urban greening. Therefore, it solves the problem in related technologies where mainstream open-source remote sensing data (such as Sentinel-2, Landsat-8) cannot meet the needs of high spatial resolution monitoring, and UAV solutions are difficult to implement on a large scale and with high efficiency at the urban scale, making it difficult to accurately quantify seasonal changes in urban canopy cover, thus limiting the accuracy of urban land cover mapping and urban thermal environment simulation.

[0029] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0030] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, according to an embodiment of this application. Figure 2 A point cloud model and a magnified example of a local 3D scene are generated based on Gaofen-7 satellite imagery (May 30, 2021) provided in one embodiment of this application; Figure 3 A schematic diagram of the geographical extent of a calculation example provided according to an embodiment of this application; Figure 4 A schematic diagram of tree canopy distribution and height in winter and summer calculated based on an example provided in one embodiment of this application; Figure 5 A schematic diagram comparing winter and summer Google images with local calculation results is provided according to an embodiment of this application; Figure 6 A schematic diagram of the distribution of evergreen and deciduous trees calculated based on seasonal canopy distribution, provided according to an embodiment of this application; Figure 7 This is a flowchart illustrating a method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, according to an embodiment of this application. Figure 8 This is a schematic diagram of a device for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, according to an embodiment of this application. Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0031] Figure label: 10-Urban tree canopy distribution calculation device based on Gaofen-7 satellite imagery; 100-Acquisition module, 200-Extraction module, 300-Calculation module; 901-Memory, 902-Processor, 903-Communication interface. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0033] The following describes, with reference to the accompanying drawings, a method and apparatus for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, according to embodiments of this application. In response to the aforementioned technologies mentioned in the background section, existing open-source remote sensing data cannot meet the monitoring requirements for high spatial resolution, and UAV solutions are difficult to implement on a large scale and with high efficiency at the urban scale. This results in the difficulty of accurately quantifying seasonal changes in urban tree canopy cover, thus limiting the accuracy of urban land cover mapping and urban thermal environment simulation. This application provides a method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery. In this method, Gaofen-7 satellite imagery data can be acquired, a digital model raster file of the land surface can be generated based on the imagery data to obtain vegetation height, a normalized vegetation index can be calculated to extract vegetation range, a preset height threshold can be used to distinguish between trees and shrubs to determine the canopy distribution in winter and summer, and the canopy distribution of evergreen and deciduous trees can be further calculated. This fully leverages the sub-meter-level stereo mapping advantages of Gaofen-7 satellite to achieve accurate identification and classification of urban vegetation, accurately distinguish between evergreen and deciduous trees and quantify their spatial distribution. This provides data support with both seasonal dynamics and spatial accuracy for refined urban greening modeling and vegetation type statistics, contributing to the scientific planning and management of urban greening. This solves the problem in related technologies where existing open-source remote sensing data cannot meet the monitoring requirements for high spatial resolution, and drone solutions are difficult to implement on a large scale and with high efficiency at the urban scale, making it difficult to accurately quantify seasonal changes in urban canopy cover, thus limiting the accuracy of urban land cover mapping and urban thermal environment simulation.

[0034] Specifically, Figure 1This is a flowchart illustrating a method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, provided in an embodiment of this application.

[0035] like Figure 1 As shown, the method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery includes the following steps: In step S101, Gaofen-7 satellite images covering the same urban area in winter and summer are acquired, and the Gaofen-7 satellite images are preprocessed to obtain image data that meets preset conditions.

[0036] It is understood that the preset conditions in this application embodiment can be Gaofen-7 satellite imagery with low cloud cover and geometric accuracy error of less than 0.65m, to ensure the reliability of subsequent calculation data. The preset conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0037] In practical implementation, this application embodiment can acquire winter and summer Gaofen-7 satellite images covering the same urban area. The Gaofen-7 satellite is conventionally equipped with one dual-line array stereo camera to record forward and backward panchromatic stereo images, with an image swath width of 20 km. It can continuously observe and acquire ground stereo images of overlapping areas. The forward-looking camera can acquire forward panchromatic images with a resolution of 0.8 m, while the backward-looking camera can acquire backward panchromatic images with a resolution of 0.65 m and multispectral images with a resolution of 2.6 m, including red, blue, green, and infrared bands. Data preprocessing is required, including radiometric calibration, atmospheric correction, and orthorectification. Image registration is performed based on the panchromatic backward-looking images. Radiometric calibration converts digital quantization values ​​into surface reflectance. Atmospheric correction models are used to eliminate the influence of aerosols and water vapor in the atmosphere. Orthorectification is then performed to generate orthorectified images with accurate geographic coordinates.

[0038] The embodiments of this application can obtain high-quality, comparable multi-seasonal satellite imagery data through a standardized preprocessing process, providing a reliable data foundation for subsequent canopy distribution analysis and avoiding classification errors caused by data quality issues.

[0039] In step S102, a digital land model raster file is generated based on the image data, and the vegetation height is obtained based on the digital land model raster file. The normalized vegetation index is calculated based on the image data to extract the vegetation range.

[0040] It is understood that the surface digital model in this application embodiment can be a raster file containing the top elevations of all surface features (trees, buildings, roads, etc.), which can provide a benchmark for vegetation height calculation; the normalized vegetation index is a vegetation growth status index calculated by the ratio of the near-infrared band to the red band.

[0041] For example, this application embodiment can utilize the stereo observation capabilities of the Gaofen-7 satellite to match highly overlapping fore- and post-view panchromatic stereo images, generating high-density three-dimensional point cloud data. The point cloud is then interpolated to generate a high-precision digital surface model raster file for further extraction of surface object heights. The normalized vegetation index (NVI) is calculated based on multispectral imagery and resampled to match the panchromatic band resolution to extract vegetation extent. The NVI is the most commonly used remote sensing index for detecting green vegetation. It quantifies vegetation growth by measuring the difference between the near-infrared band (strongly reflected by vegetation leaves) and the red band (strongly absorbed by vegetation leaves). When the NVI > 0, a high value indicates dense vegetation, a low value indicates sparse or no vegetation (such as rocks or bare soil), and a negative value represents clouds, water, or snow.

[0042] The embodiments of this application can obtain the three-dimensional structural information and spectral features of vegetation. By using the dual constraints of height and spectrum, the height and range of vegetation in winter and summer can be accurately extracted, providing more comprehensive feature support for subsequent vegetation classification and significantly improving the accuracy of vegetation identification.

[0043] Optionally, in one embodiment of this application, generating a digital surface model raster file based on image data and obtaining vegetation height based on the digital surface model raster file includes: performing stereo matching based on image data to generate point cloud data, and converting the point cloud data into a digital surface model raster file; constructing a grid with a preset resolution to calculate a ground elevation benchmark, and calculating vegetation height based on the ground elevation benchmark and the digital surface model raster file.

[0044] It is understood that the ground elevation benchmark in this application embodiment can be the actual ground elevation surface of non-vegetated areas (such as bare soil or hardened ground) within the grid.

[0045] For example, embodiments of this application can calculate the DSM based on the forward and backward panchromatic images of the Gaofen-7 satellite. The calculation can use the ENVI 5.3's Generate Point Clouds and DSM by Dense Image Matching plugin to extract point cloud data from highly overlapping stereo image pairs between two images. Figure 2 As shown, the point cloud data is further processed into a 1m resolution digital surface model raster file with elevation information of ground features, such as... Figure 3 As shown, this is used to further estimate the height of the tree crown.

[0046] Furthermore, a square grid with a resolution of 30 meters is established, and the minimum elevation (grid_min) within each grid is calculated as the ground elevation near the vegetation. The vegetation height (vegetation_height) is obtained by subtracting grid_min from the vegetation elevation (vegetation_elevation), as shown in the following formula:

[0047] The size of the 30m grid can be selected from 10m to 100m based on the urban vegetation density and the pixel size of the digital model of the ground. Since the ground in the default grid is not completely covered by the tree canopy and there is no significant elevation change, in areas with extremely dense trees, the lowest value of the vegetation canopy height may be mistaken for the ground elevation, thus underestimating the tree height. Therefore, it is only applicable to urban areas with less dense trees.

[0048] The embodiments of this application can generate high-precision point cloud data through stereo matching technology and establish an accurate ground elevation benchmark based on gridding processing, thereby reducing systematic errors in the vegetation height calculation process and accurately calculating the vertical height information of vegetation, providing high-precision vegetation height parameters for subsequent differentiation of trees and shrubs through height thresholds.

[0049] Optionally, in one embodiment of this application, calculating the normalized vegetation index based on image data to extract vegetation range includes: calculating the normalized vegetation index based on image data and resampling it to obtain high-resolution normalized vegetation index raster data; dividing the high-resolution normalized vegetation index raster data according to preset thresholds for winter and summer normalized vegetation index respectively to obtain vegetation range.

[0050] It is understood that in the embodiments of this application, the preset threshold for the normalized vegetation index in winter is greater than 0.45, and the preset threshold for the normalized vegetation index in summer is greater than 0.3. These thresholds can be adaptively adjusted according to the vegetation phenological characteristics of the calculation area. The preset thresholds can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0051] In practical implementation, the embodiments of this application can calculate the Normalized Difference Vegetation Index (NDVI) based on Gaofen-7 multispectral imagery. The NDVI is the most commonly used remote sensing index for detecting green vegetation. It quantifies vegetation growth by measuring the difference between the near-infrared band (strongly reflected by vegetation leaves) and the red band (strongly absorbed by vegetation leaves). When the NDVI > 0, a high value indicates dense vegetation, a low value indicates sparse vegetation or no vegetation (such as rocks or bare soil), and a negative value represents clouds, water bodies, or snow.

[0052] The red band of the Gaofen-7 satellite has a wavelength range of 0.63-0.69 μm, and the infrared band has a wavelength range of 0.77-0.89 μm. These wavelengths are similar to those of Landsat-8 (red, 0.64-0.67 μm and infrared, 0.85-0.88 μm) and Sentinel-2 (red, 0.65-0.68 μm and infrared, 0.79-0.90 μm), which are commonly used for calculating the Normalized Difference Vegetation Index (NDVI). The red and infrared bands can be used to calculate the NDVI at a resolution of 2.6 m, which can be used to extract the distribution range of leafy vegetation in winter and summer. Figure 4 As shown. The calculation formula is as follows: , in, The normalized difference vegetation index (NDVI) is used, with Red representing the red light band and NIR representing the near-infrared band. After calculation, it is resampled to a 1-meter resolution.

[0053] Using the superimposed portion of two Gaofen-7 satellite images of a part of Beijing's urban area during winter and summer as an example for calculation, such as... Figure 3 As shown, the overlapping area is located within the 6th Ring Road of Beijing, covering an area of ​​373 km². One image was taken on May 30, 2021 (summer), and the other on December 3, 2021 (winter). The spatial distribution of vegetation was obtained from high-resolution normalized vegetation index (NVI). Based on the NVI threshold, areas with a summer NVI greater than 0.3 and a winter NVI greater than 0.45 were defined as the vegetation zones, using the following formula: , in, For a certain grid pixel position in summer The normalized vegetation index value, For the position of a certain grid pixel in winter The normalized vegetation index value, This is a Boolean mask for the vegetation area (1 indicates that there is vegetation, and 0 indicates that there is no vegetation).

[0054] The embodiments of this application can generate high-resolution normalized vegetation indices and adopt a seasonal adaptive threshold strategy to achieve accurate extraction of vegetation range. This not only maintains the integrity of vegetation boundary details but also effectively overcomes the influence of spectral differences caused by seasonal changes, ensuring the consistency of vegetation extraction results in different seasons and providing an accurate data foundation for subsequent canopy classification.

[0055] In step S103, based on the vegetation range and vegetation height, trees and grasses and shrubs are distinguished using a preset height threshold to determine the winter canopy distribution and summer canopy distribution, and the canopy distribution of evergreen trees and deciduous trees is calculated based on the winter canopy distribution and summer canopy distribution.

[0056] It is understood that the preset height threshold in this application embodiment can be 2 meters, and can be adjusted within the range of 1.5m-3m according to local forestry or urban greening standards. The preset height threshold can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0057] In practical implementation, this application embodiment can extract the range and height information of leafy vegetation in different seasons, and then, based on the vegetation range and height, use a preset height threshold to distinguish between trees and shrubs to determine the winter and summer canopy distributions, as shown in the following formula: , In this study, vegetation taller than 2m was classified as trees, while vegetation shorter than 2m was classified as grassland and shrubs, thus obtaining the distribution of leafy tree canopies in winter and summer within the calculation range. Figure 4 As shown.

[0058] Furthermore, the area covered by the winter canopy is considered as evergreen, and the portion of the summer canopy outside the winter canopy is considered as deciduous, such as... Figure 5 As shown, and can be used to further enrich land cover map types, such as Figure 6 As shown.

[0059] , in, The area covered by an evergreen canopy. The area covered by the deciduous tree canopy. For the winter canopy coverage, The summer canopy coverage is represented by the distribution of evergreen trees, which is the spatial distribution map of trees in winter. The distribution of deciduous trees is the difference between the summer tree distribution map and the winter tree distribution map.

[0060] The embodiments of this application can distinguish between trees and grasses and shrubs by height thresholds, and determine the types of evergreen and deciduous trees by comparing the distribution of tree crowns in winter and summer, so as to realize the refined classification and statistics of urban tree crown distribution and provide an important basis for the refined management of urban vegetation.

[0061] Optionally, in one embodiment of this application, calculating the canopy distribution of evergreen and deciduous trees based on the canopy distribution in winter and summer includes: generating height raster and cover raster data of evergreen and deciduous trees based on the canopy distribution of evergreen and deciduous trees, so as to generate high-precision urban land cover raster data containing evergreen and deciduous trees.

[0062] It is understood that in the embodiments of this application, the height grid can be a grid image that records the height of the vegetation canopy, and the land cover grid can be grid data that represents land cover information including grasses, shrubs, evergreen trees and deciduous trees.

[0063] In actual implementation, the embodiments of this application can generate height grids and cover grids of evergreen and deciduous trees based on the canopy distribution of evergreen and deciduous trees. The height grids can retain the height information of each type of tree, and the cover grid data can record the projection range of the canopy, so as to generate high-precision urban land cover grid data containing evergreen and deciduous trees.

[0064] The embodiments of this application can generate high-precision urban land cover raster data containing evergreen and deciduous trees, transform the classification results into directly applicable geospatial data, and intuitively display the distribution patterns of different types of trees using a structured data output format, which greatly facilitates practical applications in urban planning, green space management, and ecological benefit assessment.

[0065] Optionally, in one embodiment of this application, it further includes: constructing a digital surface model of the tree canopy based on the winter and summer tree canopy distributions to simulate the urban thermal environment in winter and summer.

[0066] It is understood that the tree canopy digital surface model in this application embodiment can be a digital model that expresses the elevation of the top of the vegetation canopy, retains the elevation information of the tree canopy area in winter and summer, and forms a digital surface model that specifically reflects the spatial morphology of the tree canopy. Its core function is to quantify the range and intensity of the tree canopy's shading of solar radiation.

[0067] In actual implementation, the embodiments of this application can construct a digital surface model of the tree canopy based on the winter and summer tree canopy distributions. This model can be further imported into SOLWEIG, ENVI-met, or other urban thermal environment analysis software to consider the impact of seasonal vegetation changes on radiative thermal stress and to conduct urban thermal environment simulations in winter and summer.

[0068] The embodiments of this application can construct a seasonally specific digital surface model of tree canopy, providing key input parameters for urban thermal environment simulation that take into account vegetation phenological changes, thereby improving the scientificity and practicality of urban thermal environment simulation results, taking into account the impact of winter and summer vegetation phenological changes on the seasonal thermal environment, and improving the accuracy of urban near-ground pedestrian height outdoor thermal environment simulation.

[0069] Specifically, it can be combined with Figure 7 As shown, a specific embodiment is used to illustrate in detail the working principle of the urban tree canopy distribution calculation method based on Gaofen-7 satellite imagery in this application.

[0070] like Figure 7 As shown, the embodiments of this application can be divided into four stages: GF-7 (Gaofen-7) data preprocessing stage, vegetation distribution and height calculation stage, winter and summer canopy distribution and height calculation stage, and evergreen and deciduous tree distribution calculation stage.

[0071] Specifically, the GF-7 data preprocessing stage includes: panchromatic band processing: using forward and backward panchromatic images, point cloud files (.las format) are generated through ENVI, and further a DSM (Digital Surface Model) with a resolution of 1 meter is generated; multispectral band processing: for the red and near-infrared bands, an NDVI (Normalized Difference Vegetation Index) with a resolution of 2.6 meters is calculated; at the same time, the multispectral data needs to go through a preprocessing process of radiometric calibration + atmospheric correction → orthorectification → image registration.

[0072] The vegetation distribution and height calculation stage combines the acquisition of vegetation height and the calculation of vegetation range to generate a vegetation DSM with a resolution of 1 meter, thus achieving preliminary integration of the overall spatial and height information of vegetation.

[0073] In the stage of calculating the canopy distribution and height in summer and winter, the shrubland DSM and the canopy DSM (CDSM) were separated from the vegetation DSM. Subsequently, the canopy DSM was further subdivided into summer tree DSM and winter tree DSM, which correspond to the spatial height models of trees in summer and winter, respectively.

[0074] In the distribution calculation stage of evergreen and deciduous trees, based on the summer tree DSM and winter tree DSM, the deciduous tree DSM and evergreen tree DSM are distinguished and generated, and the spatial distribution and height characteristics of the two types of trees are accurately extracted.

[0075] The overall process starts with raw satellite data and gradually refines it to specific distribution models of evergreen and deciduous trees through layered processing, clearly presenting the logical relationship between data processing and canopy classification.

[0076] The method for calculating urban canopy distribution based on Gaofen-7 satellite imagery proposed in this application can acquire Gaofen-7 satellite imagery data, generate a digital land model raster file based on the imagery data to obtain vegetation height, calculate the normalized vegetation index to extract vegetation range, use a preset height threshold to distinguish trees from grasses and shrubs to determine the canopy distribution in winter and summer, and further calculate the canopy distribution of evergreen and deciduous trees. This fully leverages the sub-meter-level stereo mapping advantages of the Gaofen-7 satellite to achieve accurate identification and classification of urban vegetation, accurately distinguish between evergreen and deciduous trees and quantify their spatial distribution. This provides data support with both seasonal dynamics and spatial accuracy for refined urban greening modeling and vegetation type statistics, contributing to the scientific planning and management of urban greening. Therefore, this solves the problem in related technologies where existing open-source remote sensing data cannot meet the high spatial resolution monitoring requirements, and UAV solutions are difficult to implement on a large scale and with high efficiency at the urban scale, resulting in the difficulty in accurately quantifying seasonal changes in urban canopy cover, thus limiting the accuracy of urban land cover mapping and urban thermal environment simulation.

[0077] Secondly, referring to the accompanying drawings, an urban tree canopy distribution calculation device based on Gaofen-7 satellite imagery is proposed according to an embodiment of this application.

[0078] Figure 8 This is a schematic diagram of the structure of the urban tree canopy distribution calculation device based on Gaofen-7 satellite imagery, according to an embodiment of this application.

[0079] like Figure 8 As shown, the urban tree canopy distribution calculation device 10 based on Gaofen-7 satellite imagery includes: an acquisition module 100, an extraction module 200, and a calculation module 300.

[0080] The acquisition module 100 is used to acquire winter and summer Gaofen-7 satellite images covering the same urban area, and to preprocess the Gaofen-7 satellite images to obtain image data that meets preset conditions.

[0081] The extraction module 200 is used to generate a digital land model raster file based on image data, obtain vegetation height based on the digital land model raster file, and calculate the normalized vegetation index based on image data to extract vegetation range.

[0082] The calculation module 300 is used to distinguish between trees and grasses and shrubs based on vegetation range and vegetation height using a preset height threshold, in order to determine the winter and summer canopy distribution, and to calculate the canopy distribution of evergreen trees and deciduous trees based on the winter and summer canopy distribution.

[0083] Optionally, in one embodiment of this application, the extraction module 200 includes a matching unit and a construction unit.

[0084] The matching unit is used to perform stereo matching based on image data to generate point cloud data and convert the point cloud data into a digital model raster file of the land surface.

[0085] The building unit is used to construct a grid at a preset resolution to calculate the ground elevation benchmark, and to calculate vegetation height based on the ground elevation benchmark and the surface digital model raster file.

[0086] Optionally, in one embodiment of this application, the extraction module 200 includes a sampling unit and a division unit.

[0087] The sampling unit is used to calculate the normalized vegetation index based on image data and perform resampling to obtain high-resolution normalized vegetation index raster data.

[0088] The division unit is used to divide the high-resolution normalized vegetation index raster data according to the preset thresholds of the winter normalized vegetation index and the summer normalized vegetation index, respectively, so as to obtain the vegetation range.

[0089] Optionally, in one embodiment of this application, the urban tree canopy distribution calculation device 10 based on Gaofen-7 satellite imagery includes: a generation module.

[0090] The generation module is used to calculate the canopy distribution of evergreen and deciduous trees based on the canopy distribution in winter and summer, and to generate height raster and cover raster data of evergreen and deciduous trees based on the canopy distribution of evergreen and deciduous trees, so as to generate high-precision urban land cover raster data containing evergreen and deciduous trees.

[0091] Optionally, in one embodiment of this application, the urban tree canopy distribution calculation device 10 based on Gaofen-7 satellite imagery further includes a simulation module.

[0092] The simulation module is used to construct a digital surface model of the tree canopy based on the winter and summer canopy distributions, in order to simulate the urban thermal environment during the winter and summer seasons.

[0093] It should be noted that the foregoing explanation of the embodiment of the urban tree canopy distribution calculation method based on Gaofen-7 satellite imagery also applies to the urban tree canopy distribution calculation device based on Gaofen-7 satellite imagery in this embodiment, and will not be repeated here.

[0094] The urban canopy distribution calculation device based on Gaofen-7 satellite imagery proposed in this application can acquire Gaofen-7 satellite imagery data, generate a digital model raster file of the land surface based on the imagery data to obtain vegetation height, calculate the normalized vegetation index to extract vegetation range, use a preset height threshold to distinguish trees from grasses and shrubs to determine the canopy distribution in winter and summer, and further calculate the canopy distribution of evergreen and deciduous trees. This fully leverages the sub-meter-level stereo mapping advantages of the Gaofen-7 satellite to achieve accurate identification and classification of urban vegetation, accurately distinguish between evergreen and deciduous trees and quantify their spatial distribution. It provides data support with both seasonal dynamics and spatial accuracy for refined urban greening modeling and vegetation type statistics, contributing to the scientific planning and management of urban greening. This solves the problem in related technologies where existing open-source remote sensing data cannot meet the high spatial resolution monitoring requirements, and UAV solutions are difficult to implement on a large scale and with high efficiency at the urban scale, resulting in the difficulty in accurately quantifying seasonal changes in urban canopy cover, thus limiting the accuracy of urban land cover mapping and urban thermal environment simulation.

[0095] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0096] When the processor 902 executes the program, it implements the method for calculating the urban tree canopy distribution based on Gaofen-7 satellite imagery provided in the above embodiments.

[0097] Furthermore, electronic devices also include: Communication interface 903 is used for communication between memory 901 and processor 902.

[0098] The memory 901 is used to store computer programs that can run on the processor 902.

[0099] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0100] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0101] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0102] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0103] This application also provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery.

[0104] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, characterized in that, Includes the following steps: Acquire winter and summer Gaofen-7 satellite images covering the same urban area, and preprocess the Gaofen-7 satellite images to obtain image data that meets preset conditions; Based on the image data, a digital model raster file of the land surface is generated, and the vegetation height is obtained according to the digital model raster file of the land surface. The normalized vegetation index is calculated based on the image data to extract the vegetation range. Based on the vegetation range and the vegetation height, trees and grasses and shrubs are distinguished using a preset height threshold to determine the winter and summer canopy distributions, and the canopy distributions of evergreen and deciduous trees are calculated based on the winter and summer canopy distributions.

2. The method according to claim 1, characterized in that, The step of generating a digital land model raster file based on the image data, and obtaining vegetation height based on the digital land model raster file, includes: Based on the image data, stereo matching is performed to generate point cloud data, and the point cloud data is converted into a digital model raster file of the land surface; A grid with a preset resolution is constructed to calculate the ground elevation benchmark, and the vegetation height is calculated based on the ground elevation benchmark and the surface digital model raster file.

3. The method according to claim 1, characterized in that, The step of calculating the normalized vegetation index based on the image data to extract vegetation extent includes: Based on the image data, the normalized vegetation index is calculated and resampled to obtain high-resolution normalized vegetation index raster data. The high-resolution normalized vegetation index raster data is divided according to the preset thresholds for winter and summer normalized vegetation index, respectively, to obtain the vegetation range.

4. The method according to claim 1, characterized in that, The calculation of the canopy distribution of the evergreen and deciduous trees based on the winter and summer canopy distributions includes: Based on the canopy distribution of the evergreen and deciduous trees, height and cover raster data of the evergreen and deciduous trees are generated to produce high-precision urban land cover raster data containing evergreen and deciduous trees.

5. The method as described in claim 1, characterized in that, Also includes: A digital surface model of the tree canopy is constructed based on the winter and summer canopy distributions to simulate the urban thermal environment during the winter and summer seasons.

6. A device for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery, characterized in that, include: The acquisition module is used to acquire winter and summer Gaofen-7 satellite images covering the same urban area, and to preprocess the Gaofen-7 satellite images to obtain image data that meets preset conditions. The extraction module is used to generate a digital land model raster file based on the image data, obtain vegetation height based on the digital land model raster file, and calculate the normalized vegetation index based on the image data to extract the vegetation range. The calculation module is used to distinguish between trees and grasses and shrubs based on the vegetation range and the vegetation height using a preset height threshold, so as to determine the winter canopy distribution and the summer canopy distribution, and to calculate the canopy distribution of evergreen trees and deciduous trees based on the winter canopy distribution and the summer canopy distribution.

7. The apparatus according to claim 6, characterized in that, The extraction module includes: A matching unit is used to perform stereo matching based on the image data to generate point cloud data and convert the point cloud data into a digital model raster file of the land surface. A construction unit is used to construct a grid with a preset resolution to calculate a ground elevation benchmark, and to calculate the vegetation height based on the ground elevation benchmark and the surface digital model raster file.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the method for calculating urban tree canopy distribution based on Gaofen-7 satellite imagery as described in any one of claims 1-5.

Images