Urban park habitat identification and division method based on unmanned aerial vehicle multi-source telemetry technology

Abstract

The invention provides an urban park habitat identification and division method based on an unmanned aerial vehicle multi-source telemetry technology. The method comprises the steps of building a habitat classification system, collecting and preprocessing multi-source remote sensing image data, performing vector division on the multi-source remote sensing image data, integrating the multi-source remote sensing image data and drawing a habitat type distribution diagram. According to the method, a multi-source remote sensing image is obtained by taking an unmanned aerial vehicle as a carrier, and ground surface features are finely classified by using remote sensing and data processing technologies such as ortho-incidence, multispectral, radar and elevation, so that the classification accuracy and precision are improved. Compared with a small-scale habitat space identification artificial interpretation method, the method provided by the invention has the advantages that the speed is higher, the manual workload is reduced, and the tedious field investigation work is avoided; according to the method, a new technical path is provided for high-precision recognition and division of the urban park green space habitat, monitoring and optimization of an urban park vegetation community structure are facilitated, a habitat environment for human-friendly wild animals is built, and the urban biodiversity is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing identification, and in particular relates to a method for urban park habitat identification and type classification based on unmanned aerial vehicle multi-source telemetry technology. Background Art

[0002] At present, drones with multi-source remote sensing technology are widely used in geological disaster management, mineral resources, urban construction, precision agriculture and other fields, and can identify land types on a large scale. However, on a small scale in urban parks, due to the complex and intertwined habitats, diverse interfaces, and many types of vegetation with complex distribution, traditional multispectral remote sensing technology cannot accurately identify habitat types. If the habitat is identified by visual interpretation alone, although the accuracy is improved, the workload is large and time-consuming and labor-intensive.

[0003] The common habitat spatial classification method is to classify habitat types based on high-resolution satellite remote sensing data combined with field surveys. It is generally large-scale, low-precision, and poorly timely. Based on high-spatial-resolution satellite remote sensing data, it is impossible to distinguish vegetation canopies, and similar forest layers are difficult to separate from each other, which can easily lead to misclassification and large classification errors. The scale of habitat unit division in urban park green spaces is small, and traditional high-resolution images and image processing methods are difficult to meet the needs of high-precision habitat identification and classification at the scale of urban parks.

[0004] Urban parks are important green spaces that carry urban biodiversity. Their rewilding is the key to improving the diversity of wild herbaceous plants in parks and optimizing wildlife habitats. High-precision identification of park ecological spaces and accurate classification of habitat types are important basic work to improve the quality of urban park green space. Based on the water body index, vegetation index, and leaf area index, a method for identifying and classifying urban park habitats is established, and combined with drone multi-source telemetry technology, it can effectively classify and divide surface objects in a fine manner. Summary of the invention

[0005] The main purpose of the present invention is to provide a method for urban park habitat identification and division based on unmanned aerial vehicle multi-source telemetry technology, so as to solve the problems of low habitat identification accuracy and inaccurate division in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology, comprising the following steps: S1 Establish a multi-level habitat classification system based on the sample characteristics of the study area; S2 obtains multi-source remote sensing data of the study area and performs data preprocessing. The multi-source remote sensing data includes orthophoto, multispectral and lidar data; S3 performs secondary processing according to the categories of the pre-processed multi-source remote sensing data and divides the vector range; S4 integrates the vector data divided in S3 and uses the integrated data to draw a habitat type distribution map.

[0007] In step S1, a multi-level habitat classification system is established based on the sample characteristics of the study area, including: According to the characteristics of urban park green space, on the basis of land use type as the major habitat category, park habitat types are divided into primary unit, secondary unit, tertiary unit and quaternary unit according to land use type, canopy structure, functional characteristics and canopy density; The first-level unit includes green space, bare land, paved land, buildings, and water bodies; The secondary unit is divided into green space, pavement and water body on the basis of the primary unit. Green space is divided into woodland, shrubs, grassland, etc., pavement is divided into roads and squares, and water body is divided into revetments and water areas. The third-level unit is divided into forest land, revetment and water area based on the second-level unit. The forest land is divided into evergreen forest and deciduous forest, the revetment is divided into hard revetment and natural revetment, and the water area is divided into clear water and turbid water. The fourth-level unit divides the evergreen forest into evergreen dense forest, evergreen medium-density forest and evergreen sparse forest based on the third-level unit.

[0008] Furthermore, the acquisition of multi-source remote sensing image data includes: using a multi-rotor drone to conduct aerial surveys of the study area and obtain multi-source remote sensing image raw data, including multispectral data, lidar data, and visible light orthophoto data.

[0009] Furthermore, the preprocessing includes: contrast and brightness adjustment, sharpening filtering, radiation calibration, atmospheric correction and geometric correction.

[0010] Further, in step S2, based on the principle of meeting the view clarity, orthophoto, multispectral and lidar each preset a data accuracy threshold; The preprocessing method of the visible light orthophoto data includes differential decomposition, parameter calculation and coordinate conversion, data sorting and aerial triangulation processing; The preprocessing method of the laser radar data includes tightly coupled differential analysis of point cloud trajectories, point cloud solution, flight strip adjustment, coordinate conversion, redundancy removal, noise removal and point cloud coloring; The multispectral data preprocessing method includes reflectance calibration and multi-band image stitching.

[0011] Furthermore, the preset data accuracy thresholds of orthophoto, multispectral, and lidar after preprocessing are 0.05m, 0.1m, and 284 points / m respectively. 2 .

[0012] Through the above-mentioned preprocessing operations on remote sensing image data, the visual effect and interpretability can be significantly improved; by eliminating noise, distortion and other problems in the image, the image can be made clearer, more realistic, and easier to observe and interpret; preprocessing technology can highlight the target objects and feature information in the image through contrast and brightness adjustment, sharpening filtering and other enhancement technologies, making it easier to identify and extract; preprocessing can reduce the cost of data storage and transmission, and improve processing speed and analysis efficiency. Through preprocessing, the difficulty and cost of subsequent analysis work can be reduced, thereby improving overall work efficiency; preprocessing steps such as radiation calibration, atmospheric correction and geometric correction can help eliminate differences between different images and ensure that they are comparable at the same physical scale, thereby improving the accuracy and availability of remote sensing images.

[0013] Further, in step S3, secondary processing is performed according to the category of the preprocessed multi-source remote sensing data, specifically: the water body index of the preprocessed multispectral data is calculated in ENVI or ArcGIS; the preprocessed lidar point cloud data is processed, including resampling, denoising, ground point classification, generation of digital elevation model (DEM) and digital surface model (DSM) and ground point normalization, to generate a canopy height model (CHM); the visible light orthophoto data is processed to obtain a high-precision orthophoto map and a digital surface model (DSM).

[0014] Further, in step S3, the method of performing habitat division according to the processed multi-source remote sensing data is to extract and divide different habitats according to the park water body index, vegetation index, leaf area index and canopy density, including: (1) Multispectral data: The preprocessed orthophoto, multispectral, and lidar data were imported into ArcGIS for registration, cut according to the park green space boundary, and the park green space scope was clipped out.

[0015] In the cropped multispectral data, water bodies and land areas (including other non-water land within the park) are extracted according to the water body index. Specifically, the FUI water color index is used to divide the extracted water body area into clear water and turbid water. The park land area is divided according to the vegetation index, and green space, bare land and artificial surfaces within the park land area are extracted.

[0016] (2) LiDAR point cloud data: Based on the canopy height model, the green area is reclassified into vector ranges of trees, shrubs, and grassland.

[0017] The vector range of trees was imported into the LiDAR360 software to calculate the leaf area index of the forest land, and then deciduous forest and evergreen forest were divided.

[0018] Leaf area index calculation method: For each statistical unit, a three-dimensional grid is constructed according to the average point spacing of the point cloud, and the total number of three-dimensional grids and the number of grids containing laser points in each layer are counted, and the frequency of laser points in each layer is calculated. The expression is: ; Where, N(s) is the laser point frequency of the sth layer, n I (s) is the total number of grids containing laser points in the sth layer, n T (s) is the total number of three-dimensional grids in the sth layer, and then the leaf area index of the sth layer is calculated according to the above formula: ; Where α(θ) is the blade inclination correction factor, set to 1.1, I s is the leaf area index of the sth layer; finally, the leaf area index of each layer is accumulated to obtain the leaf area index of the entire statistical unit: ; Among them, LAI is leaf area index.

[0019] According to the canopy density index, the LiDAR360 airborne forestry module was used to divide evergreen trees into evergreen dense forest, evergreen medium-density forest, and evergreen sparse forest.

[0020] (3) Visible light orthophoto data: According to DSM, artificial surfaces are divided into building pavement and revetment; based on orthophoto data, habitat subcategories such as revetment type and pavement type are divided.

[0021] Furthermore, in step S4, the vector data divided in S3 are integrated, and a habitat type distribution map is drawn for the integrated data, that is, the vector data of each habitat type divided in S3 is set as a layer in ArcGIS software, and the integrated data is used to draw a habitat type distribution map, and finally the habitat map of each park is completed.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. The urban park habitat identification and division method of the present invention is a new method for automatic division of urban park habitat types based on unmanned aerial vehicle multi-source telemetry technology. It is a new method for automatic division based on spectral analysis by coupling multiple data. Compared with computer classification, it has high accuracy and precision and does not require a large amount of sample training; it only requires spring or winter data and does not require multi-season image comparison to divide evergreen and deciduous forests; compared with the method of manual interpretation of small-scale habitat space identification, it is faster, reduces manual workload, and avoids tedious field investigation work.

[0023] 2. The present invention relies on unmanned aerial vehicles as carriers to obtain multi-source remote sensing images, and uses orthophoto, multispectral, radar, and other telemetry and data processing technologies to finely classify surface objects, solving the technical problems of poor park habitat recognition accuracy and inaccurate division in the prior art, thereby providing a new path for high-precision identification and division of urban park habitats, helping to monitor and optimize the vegetation community structure of urban parks, build a habitat environment for human-friendly wild animals, and improve urban biodiversity. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 It is the technical roadmap of the present invention; Figure 2 It is the orthophoto data, lidar data, and multispectral data map of the research area of ​​the present invention; Figure 3 The habitat type distribution map of Xuhui Runway Park of the present invention; Figure 4 It is the habitat type distribution map of Chunshen Park of the present invention; Figure 5 The distribution map of habitat types in the Peace Park of the present invention; Figure 6 The habitat type distribution map of Luxun Runway Park of the present invention; Figure 7 The habitat type distribution map of Mianqing Park of the present invention; Figure 8 The habitat type distribution map of Gaodong Ecological Park of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the content of the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It is understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all the contents.

[0026] like Figure 1 As shown, this embodiment provides a method for identifying and dividing urban park habitats based on drone multi-source telemetry technology, including the following steps: S1 Establish a multi-level habitat classification system based on the sample characteristics of the study area; Combined with the characteristics of urban park green space, on the basis of land use type as the major habitat category, the main land use types such as green space, bare land, pavement, building, and water body are selected as the first-level classification units for the division of urban park green space habitat; taking full account of the characteristics of each land use type, the canopy structure and the characteristics of each land use type are selected as the second-level division basis for each element. Green space is divided into woodland, shrubs, and grassland according to the canopy structure, pavement is divided into roads and squares according to its functional characteristics, and water bodies are divided into revetments and water areas according to their natural characteristics; the tertiary units are divided according to the characteristics of the corresponding habitat types, and evergreen and deciduous vegetation, revetment form, water body quality, etc. are selected as the tertiary division basis for each element. Forest land is divided into evergreen forest and deciduous forest, revetments are divided into hard revetments and natural revetments, and water areas are divided into clear water bodies and turbid water bodies; the fourth-level unit is mainly for evergreen forest land. The canopy density is used as a supplementary classification basis (Table 1).

[0027] Table 1 Classification of urban park green space habitats

[0028] S2 acquires multi-source remote sensing data and performs data preprocessing. The multi-source remote sensing data includes orthophoto, multispectral and lidar data; (1) Acquisition of multi-source remote sensing image data: A high-precision multi-rotor drone was used to conduct aerial survey of the study area. The drone was equipped with visible light payload, lidar payload, and multispectral payload to obtain raw data such as orthophoto, multispectral, and lidar ( Figure 2 ).

[0029] (2) Multi-source remote sensing image data preprocessing: Ortho data preprocessing: The final output is achieved through differential decomposition, parameter calculation and coordinate conversion, data sorting, aerial triangulation and other steps. The direct results of ortho data include quick puzzle, DSM and true ortho data. LiDAR data preprocessing: point cloud data is generated through steps such as tightly coupled differential analysis of point cloud trajectories, point cloud solution, flight zone adjustment, coordinate transformation, redundancy removal, denoising, and point cloud coloring; The multispectral data includes: generating multispectral image data through steps such as reflectance calibration and multi-band image stitching; Through the above preprocessing, the preset data accuracy thresholds of orthophoto, multispectral, and lidar are 0.05m, 0.1m, and 284 points / m respectively. 2 .

[0030] S3 performs secondary processing according to the categories of the pre-processed multi-source remote sensing data and divides the vector range.

[0031] (1) Multispectral data processing: The preprocessed orthophoto, multispectral, and lidar data were imported into ArcGIS for registration, and the research data were clipped according to the boundaries of the park green space.

[0032] In the cropped multispectral data, the water body index was used to classify the study area and extract the water body extent and land area (including other non-water body land within the park) within the park.

[0033] The water index is the MNDWI improved normalized water index, and its expression is: ; Among them, p(Green) refers to the green band, and p(MIR) refers to the shortwave infrared band.

[0034] Specifically, the FUI water color index is used to divide the water quality range of the extracted water body into clear water bodies and turbid water bodies.

[0035] The expression of the FUI water color index is: ; Among them, Rrs_blue represents the reflectivity of the blue light band, and Rrs_green represents the reflectivity of the green light band. The value range of the FUI index is usually between -1 and 1. The larger the value, the higher the concentration of chlorophyll a in the water body, and the more eutrophic the water body is; the smaller the value, the lower the concentration of chlorophyll a in the water body, and the clearer and more transparent the water body is. When the FUI index is close to 0, it means that the water body is in a medium nutrient state.

[0036] In view of the diverse and complex habitats of urban parks, the vegetation index is used to divide the land area of ​​the park. The vegetation index includes the normalized difference vegetation index NDVI or the green normalized vegetation index GNDVI, which integrates multi-band data to construct NDVI images and GNDVI images for assisting three-channel visible light image recognition.

[0037] The normalized difference vegetation index NDVI can directly reflect the background influence of plant canopy and extract bare land, artificial objects and green land; its expression is: ; Among them, NIR refers to the reflectivity of the near-infrared band, and Red refers to the reflectivity of the red light band.

[0038] The green normalized vegetation index GNDVI can accurately evaluate vegetation coverage and growth conditions, and its expression is: ; Among them, NIR refers to the reflectivity of the near-infrared band, and Green refers to the reflectivity of the green light band.

[0039] Through the above vegetation index analysis, green space, bare land and artificial surfaces within the park’s land area were extracted.

[0040] (2) LiDAR point cloud data processing: LiDAR360 software was used to process the LiDAR data, including resampling, denoising, ground point classification, generation of DEM and DSM, ground point normalization, generation of CHM, and importing CHM into ArcGIS software. The green space part was reclassified according to the canopy height to generate the vector range of trees, shrubs, and grassland.

[0041] The vector range of trees was imported into the LiDAR360 software to calculate the leaf area index of the forest land, and then deciduous forest and evergreen forest were divided.

[0042] Leaf area index calculation method: Leaf area index (LAI) calculation method: First, voxelize the tree point cloud, and then use the voxel-based canopy contour method to calculate LAI. That is, for each statistical unit, first calculate the standard deviation (STD) of the average distance between each point and its neighboring points. If the distribution obtained is a Gaussian distribution, the average distance of all points is greater than 1.5×STD of the average distance to the query point as a noise point; according to the average point spacing of the point cloud, a three-dimensional grid is constructed with 1.5 times the average point spacing of the point cloud (1.5×STD); then, based on the constructed three-dimensional grid, the total number of three-dimensional grids in each layer and the number of grids containing laser points are counted, and the frequency of laser points in each layer is calculated. The expression is: ; Where, N(s) is the laser point frequency of the sth layer, n I (s) is the total number of grids containing laser points in the sth layer, n T (s) is the total number of three-dimensional grids in the sth layer, and then the leaf area index of the sth layer is calculated according to formula (1): ; Among them, α(θ) is the leaf inclination correction factor, which is set to 1.1 according to Li et al. (Li et al., 2016). Finally, the leaf area index of each layer is accumulated to obtain the leaf area index of the entire statistical unit: ; Among them, LAI is leaf area index.

[0043] According to the canopy density index, the LiDAR360 airborne forestry module is used to divide evergreen trees into evergreen dense forest, evergreen medium canopy density forest, and evergreen sparse forest. Canopy density calculation: For a certain statistical unit, the canopy density is the ratio of the number of vegetation points to the total number of points: ; Among them, CC is the canopy closure, nveg is the number of vegetation points, n total is the total number of points. During the calculation process, points greater than the height threshold are considered vegetation points, and the height threshold is set to 0.5m.

[0044] (3) Visible light orthophoto data processing: After processing the visible light orthophoto data, high-precision orthophotos and DSM heights are obtained. According to the DSM, the artificial surface range is divided into building pavement and revetment; according to the orthophoto data, habitat subcategories such as revetment type and pavement type are divided.

[0045] S4 integrates the vector data divided in S3 and uses the integrated data to draw a habitat type distribution map; In ArcGIS software, the vector data of each habitat type divided in S3 was set as a layer, and the integrated data was used to draw a habitat type distribution map ( Figure 3-8 ).

[0046] According to the drawn park habitat classification map, the sample plot method was used to verify the accuracy of the method of the present invention, specifically: 5 sample plots were delineated in the typical park green space (Table 2), the method of the present invention was used to draw the urban park habitat distribution map, and the habitat patch data (Table 3) was extracted. The accuracy was compared with the accurate habitat type distribution patch area drawn through manual interpretation combined with on-site verification.

[0047] The accuracy test adopted the sample plot method, and multiple sample plots were taken for manual interpretation combined with on-site verification to draw accurate habitat type distribution maps, which were imported into ArcGIS software for patch comparison with the park habitat classification map drawn by the method of the present invention to calculate the accuracy, accuracy = (total area-overlapping area) / total area. The results showed that the accuracy of the automatic division method of the present invention could reach more than 95%, and the average accuracy reached 84.32%. This shows that the method of the present invention can fully explore the deep information of green space in the sample plots and effectively improve the classification accuracy of urban park habitats.

[0048] Table 2 List of typical urban parks and green spaces in Shanghai

[0049] Table 3 Habitat data of urban park green space

[0050] In recent years, the commonly used method for small-scale habitat spatial identification is to import high-resolution satellite remote sensing image data into ArcGIS software, obtain land use information after image registration and digitization, and set up layers for each habitat type in ArcGIS in combination with topographic maps and field survey data. Finally, the habitat map of each park is completed. However, the classification accuracy is too dependent on manual field surveys and the classification accuracy is poor.

[0051] The method for identifying and dividing urban park habitat types using unmanned aerial vehicle multi-source telemetry technology provided by the present invention is a new method based on spectral analysis and automatic division by coupling multiple data in ArcGIS software. Compared with traditional computer classification methods, the method of the present invention effectively improves the accuracy and precision of urban park habitat identification and division. Compared with the method of manual interpretation of small-scale habitat space identification, the method of the present invention is faster, reduces manual workload, and avoids tedious field investigation work.

[0052] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology, characterized in that: The method for identifying and dividing urban park habitats comprises: S1 Establish a multi-level habitat classification system based on the sample characteristics of the study area; S2 obtains multi-source remote sensing data of the study area and performs data preprocessing. The multi-source remote sensing data includes: visible light orthophoto, multispectral and lidar data; S3 performs secondary processing according to the categories of the pre-processed multi-source remote sensing data and divides the vector range; S4 integrates the vector data divided in S3 and uses the integrated data to draw a habitat type distribution map.

2. The method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology according to claim 1 is characterized in that: In step S1, a multi-level habitat classification system is established based on the sample characteristics of the study area, including: According to the characteristics of urban park green spaces, on the basis of land use type as the major habitat category, park habitat types are divided into primary units, secondary units, tertiary units and quaternary units according to land use type, canopy structure, functional characteristics and canopy density.

3. The method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology according to claim 1 is characterized in that: In step S2, the preprocessing includes: contrast and brightness adjustment, sharpening filtering, radiation calibration, atmospheric correction and geometric correction.

4. The method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology according to claim 1 is characterized in that: In step S2, based on the principle of meeting the view clarity, orthophoto, multispectral and lidar data accuracy thresholds are preset respectively; The preprocessing method of the visible light orthophoto data includes differential decomposition, parameter calculation and coordinate conversion, data sorting and aerial triangulation processing; The preprocessing method of the laser radar data includes tightly coupled differential analysis of point cloud trajectories, point cloud solution, flight strip adjustment, coordinate conversion, redundancy removal, noise removal and point cloud coloring; The multispectral data preprocessing method includes reflectance calibration and multi-band image stitching.

5. The method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology according to claim 4 is characterized in that: The preset data accuracy thresholds for orthophoto, multispectral and lidar after preprocessing are 0.05m, 0.1m and 284 points / m respectively. 2 .

6. The method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology according to claim 1 is characterized in that: In step S3, secondary processing is performed according to the category of the pre-processed multi-source remote sensing data, specifically: calculating the water index of the processed multispectral data in ENVI or ArcGIS; processing the processed lidar point cloud data, including resampling, denoising, ground point classification, generation of digital elevation model and digital surface model, and ground point normalization processing, to generate a canopy height model; After image correction and fusion of visible light orthophoto data, high-precision orthophoto maps and digital surface models are obtained.

7. The method for identifying and dividing urban park habitats based on multi-source telemetry technology of unmanned aerial vehicles according to claim 6 is characterized in that: Based on the processed multi-source remote sensing data, after secondary processing, different habitats were extracted and divided according to the park water body index, vegetation index, leaf area index and canopy closure index.

8. The method for identifying and dividing urban park habitats based on multi-source telemetry technology of unmanned aerial vehicles according to claim 7 is characterized in that: The calculation of the leaf area index includes: for each statistical unit, a three-dimensional grid is constructed according to the average point spacing of the point cloud, and the total number of three-dimensional grids and the number of grids containing laser points in each layer are counted, and the frequency of laser points in each layer is calculated. The expression is: ； Where, N(s) is the laser point frequency of the sth layer, n I (s) is the total number of grids containing laser points in the sth layer, n T (s) is the total number of three-dimensional grids in the sth layer, and then the leaf area index of the sth layer is calculated according to the above formula: ； Where α(θ) is the blade inclination correction factor, which is set to 1.1, I s is the leaf area index of the sth layer; finally, the leaf area index of each layer is accumulated to obtain the leaf area index of the entire statistical unit: ； Among them, LAI is leaf area index.

9. The method for identifying and dividing urban park habitats based on unmanned aerial vehicle multi-source telemetry technology according to claim 1 is characterized in that: In step S4, the vector data of each habitat type divided in S3 is set as a layer in ArcGIS software, and the integrated data is used to draw a habitat type distribution map, and finally the habitat map of the park in the study area is completed.

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