Urban high-sensitization plant evaluation method
By employing ArcGIS and Google Earth Engine with Sentinel-2 data and random forest models, the method effectively identifies and maps allergenic tree species, addressing the challenge of unknown sources and risks from urban allergenic pollen.
Patent Information
- Application Number
- CN202510391616.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-15
AI Technical Summary
The increasing prevalence of allergenic pollen from trees in urban areas poses a significant health risk due to high concentrations of allergenic tree pollen, which existing plant species identification methods struggle to accurately identify and monitor, leading to unknown sources and unmanaged risks.
A method combining ArcGIS and Google Earth Engine platforms with Sentinel-2 satellite data and random forest classification models to identify and map allergenic tree species at high spatial resolution, using full spectral bands and vegetation indices for precise tree species classification.
This approach enables accurate identification and mapping of allergenic tree species, enhancing the precision and efficiency of allergenic tree species recognition, thereby supporting targeted risk management and reducing health hazards.
Smart Images
Figure CN120318686A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of plant remote sensing identification, in particular to an urban highly allergenic plant evaluation method. Background Art
[0002] Urban green space plants are an essential part of urban ecological environment construction, and have multiple positive impacts on urban landscape value, ecological functions, residents' lives and public health. However, airborne allergenic plant pollen can easily cause human allergies, ranging from sneezing, coughing, runny nose, and skin rashes to asthma, tracheitis, and cor pulmonale, which can even threaten life. In the process of urban development, the level of green space construction has been continuously improved. For example, the green space area has increased and the plant diversity has increased, resulting in an increasing number of pollen species and quantities. The pollen of allergenic plants has also increased rapidly, and the pollen allergy rate of urban residents has continued to rise. The problem of pollen allergy needs to be solved urgently. As the main body of urban green space, woody plants are an important part of urban vegetation. Among them, the pollen yield and pollen allergenic potential of pollen allergenic woody plants are outstanding. The improvement of urban greening rate and green space construction level also means that the types and number of pollen allergenic tree species are gradually increasing, and the distribution of tree species in urban green space space shows a trend of strong aggregation and high coverage. In addition, residents often come into contact with pollen-allergenic tree species in their activities and suffer from pollen allergies. Therefore, the large-scale distribution of highly pollen-allergenic tree species brings the risk of allergies to urban residents due to high concentrations of allergenic pollen invading the human body.
[0003] The emergence of plant remote sensing identification technology, with its powerful advantages such as wide data coverage, efficient processing flow, and low difficulty in acquisition, has effectively alleviated the negative impact of traditional plant species surveys, such as low efficiency, limited implementation scope in time and space, high cost, and certain difficulties. In the process of the development of plant remote sensing identification and classification technology, the types of satellite remote sensing data with high spectral resolution and high temporal and spatial resolution are constantly increasing, and the application exploration of multi-source remote sensing data with multi-scale and long time series is constantly improving, which makes the support, update and accessibility of remote sensing technology for plant species identification results continue to improve, and promote the sustainable development of plant distribution research. One of the hot development trends in the existing tree species remote sensing classification and identification algorithm research is to rely on the strengthening and updating of big data computing power and algorithms to explore ways to greatly improve the accuracy and speed of remote sensing classification, and to continue to try to apply and refine monitoring and analysis in large-scale spatial and temporal ranges such as regions and the world. This research trend has gradually spread to the research field of pollen allergenic tree species, providing new ideas and methods for the identification and monitoring of allergenic tree species. It is of great significance for judging whether there is a pollen allergy problem and determining its source on a large scale, as well as assessing the risk of pollen allergy. Summary of the invention
[0004] The object of the present invention is to obtain the specific types, distribution locations and areas of highly pollen-sensitizing tree species from the perspective of remotely sensing and identifying plants, so as to establish new ideas and methods for spatial analysis of the pollen sensitization risk of highly pollen-sensitizing tree species, in order to better solve the problems such as unknown sources of urban pollen sensitization problems, unknown harm levels, and no basis for prevention and early warning, meet the identification and classification requirements of highly pollen-sensitizing tree species at specific spatio-temporal scales and with high precision, and further become the quantitative analysis basis for scientifically and reasonably reducing the pollen sensitization risk of urban tree species.
[0005] The technical solution of the present invention is as follows:
[0006] An evaluation method for highly pollen-sensitizing plants in cities, comprising the following steps:
[0007] Step 1: Vectorize patch sample data. In the ArcGIS software, convert the research samples of highly pollen-sensitizing tree species into.shp vector format, and assign sample category attributes to distinguish tree species, obtaining a sample vector data set.
[0008] Step 2: On the Google Earth Engine (GEE) remote sensing big data processing and presentation platform, call the Sentinel-2 remote sensing data set of the target time and region, and upload local patch samples of highly pollen-sensitizing tree species, arbors, shrubs and grasses vegetation, and boundary vector data. The identification, classification and mapping of highly pollen-sensitizing tree species are realized by writing and running codes. The main processes are as follows:
[0009] Step 2.1: Preprocess remote sensing image data.
[0010] Step 2.1.1: Screening. Select remote sensing images according to the target time range, perform atmospheric correction processing, exclude the interference of atmospheric scattering and absorption, cloud, aerosol, light and other factors on image interpretation, and obtain the true spectral information of ground objects reflected to the sensor.
[0011] Step 2.1.2: Cloud removal. Perform cloud and noise removal on the images. After obtaining all images with cloud cover below a certain percentage, create and synthesize a composite remote sensing satellite image.
[0012] Step 2.1.3: Fusion. Traverse the median values of all images at the same pixel, and calculate to obtain a multi-spectral fusion image presented in the form of pixel medians.
[0013] Step 2.1.4: Mosaic and clipping. Mosaic and splice multiple adjacent images together to obtain a seamless, continuous, large-scale remote sensing image covering the entire region. According to the vector boundary of the target area, accurately clip the range of the mosaicked image to obtain the corresponding image data. Using the visual interpretation data of arbors, shrubs and grasses vegetation classification as a mask, obtain the vegetation remote sensing image data of the target area, and uniformly resample all data to the same spatial resolution.
[0014] Step 2.2: Remote sensing feature extraction and combination. Obtain the multi-spectral band reflectance information of the remote sensing image, and calculate the vegetation index for calculation. Use the band information and vegetation index as the classification feature values of highly pollen-sensitizing tree species, and select the reflectance values of all bands of the remote sensing image data and the vegetation index to set up a remote sensing feature set for classification.
[0015] Step 2.3: Construct a random forest classification model.
[0016] Step 2.3.1: Random sampling. Conduct random sampling with replacement on the initial training data set, obtaining data from the original data that may be repeated or not selected at all to establish a new data training set, and by taking the average of multiple random subsets, reduce the risk of overfitting and enhance the generalization ability of the model to new data.
[0017] Step 2.3.2: Randomly select feature attribute parameters. On the premise that not all features are used for model training, randomly select a feature subset from all feature attributes, and let each decision tree select an optimal feature attribute from the feature subset as the splitting node. Randomly select the features of each node, only considering the random feature subset rather than all feature attributes.
[0018] Step 2.3.3: Majority voting. Output the classification labels of each decision tree respectively, and conduct majority voting on all labels to obtain the final classification result, ensuring that the final prediction result is the integration of the results of all decision trees.
[0019] Step 2.4: Training and validation for the identification and classification of highly pollen-sensitizing tree species. Divide the sample data into two categories, training and validation, and input them into the random forest classification model to obtain the classification operation result.
[0020] Step 2.5: Accuracy evaluation and spatial distribution mapping for the remote sensing identification and classification of highly pollen-sensitizing tree species. Use a confusion matrix to measure the accuracy of the classification model. First, separately count the number of result values of incorrect and correct classifications in the classification model, then display them all in a table, and finally obtain a matrix form result table of rows and columns. Determine the correct or incorrect classification situation of a model in terms of predicted and true classes through the confusion matrix, and whether the classification model confuses two classes.
[0021] Step 3: Analyze the spatial distribution of the remote sensing identification and classification results of highly pollen-sensitizing tree species, and count the areas of various sensitizing tree species in the ArcGIS software to obtain the spatial distribution characteristics of highly pollen-sensitizing tree species in the city. The main process is as follows:
[0022] Step 3.1: Spatial distribution analysis of highly pollen-sensitizing tree species. Analyze the areas, administrative regions, green space types, specific locations, etc. where highly pollen-sensitizing tree species are densely distributed.
[0023] Step 3.2: Statistical analysis of the spatial distribution area of highly pollen-allergenic tree species. Using the raster area calculation function of ArcGIS software, calculate the distribution area of each tree species, and further calculate the number of species, distribution area, and proportion of highly pollen-allergenic tree species in a specific area.
[0024] The beneficial effects of the present invention are as follows:
[0025] 1. The present invention proposes a spatial recognition technology for highly pollen-allergenic tree species based on full-band information and six types of vegetation indices, accurately determining information such as the types, distribution locations, areas, and characteristics of highly pollen-allergenic tree species in cities, achieving fine recognition of highly pollen-allergenic tree species at a spatial resolution of 10 m, and providing a new spatial analysis idea and method for research related to urban pollen allergy problems from the perspective of tree species.
[0026] 2. The present invention comprehensively uses traditional remote sensing software and emerging remote sensing processing code platforms, and utilizes the random forest in supervised classification, making the technical solution steps clear, operation efficient, ensuring accuracy, and improving the spatial recognition efficiency and accuracy of highly pollen-allergenic tree species. Description of the Drawings
[0027] Figure 1 is the technical flow chart of the present invention;
[0028] Figure 2 is the spatial mapping of the classification and recognition results of highly pollen-allergenic tree species in the urban area of Beijing based on full-band and vegetation index eigenvalues of the present invention. Detailed Embodiment
[0029] The following further describes the detailed embodiment of the present invention in conjunction with the drawings:
[0030] Embodiment 1
[0031] As Figure 1 shown, Step 1: In the ArcGIS software, vectorize the field survey sample data of 10 common highly pollen-allergenic tree species and 1 type of other plant patches from the.kml format to the.shp format, and ensure that the number of samples in each category is uniform, with the number of samples for each tree species between 400 and 500, obtaining a vector dataset of highly pollen-allergenic tree species patch samples.
[0032] Step 2: Write and run code on the GEE remote sensing processing platform, call the Sentinel-2 remote sensing dataset during the high-incidence period of common pollen allergy risks from March to June and September to October in 2022, and upload the local tree species patch samples, arbors, shrubs, and herbs, and the vector data of the target area boundary in the target area to carry out remote sensing recognition. The specific process is as follows:
[0033] Step 2.1: Eliminate the radiation error through the FLAASH atmospheric correction model.
[0034] Step 2.2: Use the QA60 cloud mask band to remove clouds and noise, convert the affected location scene subset to the apparent reflectance at the top of the atmosphere, and obtain the median of the minimum cloud pixels according to the simple cloud fraction to get the composite remote sensing image with all cloud amounts less than 20%.
[0035] Step 2.3: Perform image fusion operations on multiple remote sensing images using the pixel median replacement method.
[0036] Step 2.4: First, mosaic and splice multiple adjacent fused images, then read the vector boundary data of the target area and crop to obtain the remote sensing image of the target area.
[0037] Step 2.5: Read the arbor, shrub, and grass vegetation data as a mask to extract the vegetation remote sensing image data of the target area
[0038] Step 2.6: Based on the multi-spectral band reflectance values of the remote sensing image, calculate a total of 6 vegetation indices, namely NDVI, GNDVI, SAVI, OSAVI, TVI, and EVI, which are common, easy to calculate, and representative. Their descriptions and calculation formulas are shown in Table 1. Set all the band information from B1 - B9 and the 6 vegetation indices as the remote sensing feature set for classification.
[0039] Step 2.7: Call the random forest classification model, add a random attribute with a value of a random number from 0 to 1 to the training feature set to generate training samples. Use the generated random numbers greater than 0.7 as test data, otherwise as training data, thus dividing the data into two parts. Use 70% of the data for training and 30% for validation.
[0040] Step 2.8: Perform classification operations on the remote sensing image according to the set various data and parameters.
[0041] Step 2.9: Predict the test data and create a confusion matrix for accuracy evaluation. The specific indicators include overall accuracy, user accuracy, producer accuracy, Kappa coefficient, and the harmonic mean accuracy of user accuracy and producer accuracy. Their calculation formulas and descriptions are shown in Table 2. After calculating the classification accuracy of the 5 accuracy evaluation indicators, generate a result data table as shown in Table 3, achieving a high-precision spatial fine remote sensing identification of high-pollen sensitizing tree species with a total classification accuracy of 85.93%.
[0042] Step 2.10: Set the output parameters such as a spatial resolution of 10m, a range of the target area, a projection of EPSG:4326 (WGS84), and a maximum number of pixels of 1e13 to obtain the raster data of the classification result of high-pollen sensitizing tree species, and its spatial distribution is as Figure 2 shown.
[0043] Step 3: Statistically analyze the spatial distribution area of highly pollen-allergenic tree species to obtain the aggregated spatial distribution characteristics and distribution area of highly pollen-allergenic tree species in the city. The allergenic tree species are relatively dispersed in the central part of the Beijing urban area and relatively concentrated in the surrounding areas, accounting for 55.80% of the total green space vegetation area. Among them, Sophora japonica has the widest distribution, and the park green spaces between the Third Ring Road and the Fifth Ring Road are the aggregation areas of pollen-allergenic tree species. The specific process is as follows:
[0044] Step 3.1: In the ArcGIS software, use the raster area calculation function. Take the raster area of each tree species as its spatial distribution area, and take the sum of the raster areas of all highly pollen-allergenic tree species and other plants as the green space area, as shown in Table 4.
[0045] Step 3.2: Calculate the number of species, distribution area, and their proportion in the green space of highly pollen-allergenic tree species in a specific area, as shown in Table 5.
[0046] Table 1 Calculation formulas and explanations of common vegetation indices of the present invention
[0047]
[0048]
[0049] Table 2 Calculation formulas and explanations of accuracy evaluation indicators of the present invention
[0050]
[0051]
[0052] Table 3 Classification accuracy evaluation results of highly pollen-allergenic tree species in the Beijing urban area based on full-band and vegetation index eigenvalues of the present invention
[0053] Table 4 Spatial recognition and distribution area of highly pollen-allergenic tree species in the Beijing urban area bounded by the Third Ring Road of the present invention
[0054] Table 5 Spatial recognition and classification area of highly pollen-allergenic tree species in the Beijing urban area of the present invention
[0055]
[0056] Example 2
[0057] In the computer system, the raster data of the classification results of highly pollen-allergenic tree species obtained in Example 1 (that is, Figure 2)Select the grid with NDVI values between 0.60 and 0.70 as the area to be verified, calculate and check the DN value of the 665nm band in the hyperspectral remote sensing image of this area to be verified, select the grid with DN values between 300 and 500 as the Mikania micrantha climbing area, and exclude the Mikania micrantha climbing area from the identified area of highly pollen-allergenic tree species.
[0058] Significance of Example 2
[0059] The way that highly pollen-allergenic tree species cause human pollen allergy is through the dissemination of pollen. Mikania micrantha is a highly invasive substance with extremely strong spreading ability and climbing ability on other vegetation. It is found that the growth of highly pollen-allergenic tree species is poor and they are not easy to spread pollen after being climbed by Mikania micrantha. In addition, it is also found that BCP, which is an antipruritic agent for humans, will be disseminated on the surface of Mikania micrantha. Therefore, the overlapping positions between the Mikania micrantha spreading area and the identified area of highly pollen-allergenic tree species are identified through remote sensing technology, and then the overlapping areas are excluded from the identified area of highly pollen-allergenic tree species, so as to make a more refined and effective evaluation of the urban highly pollen-allergenic plant area. Furthermore, it makes the remote sensing evaluation closer to the field pollen allergy monitoring data. In other words, it makes the remote sensing evaluation more accurate.
[0060] Working principle of Example 2
[0061] In practice, it is found that the NDVI value of Mikania micrantha in hyperspectral remote sensing images is generally higher than 0.60, but significantly lower than that of other tree species. Therefore, intercepting the grid with NDVI values between 0.60 and 0.70 can simply and efficiently roughly delimit the area where the edges of trees may have been climbed by Mikania micrantha. Then, the DN value characteristics of Mikania micrantha in the 665nm band of hyperspectral remote sensing images are used to further accurately determine the climbed area, and finally this part of the area is removed to improve the accuracy of the urban highly pollen-allergenic plant evaluation method.
[0062] Example 3
[0063] In the computer system, the raster data of the classification results of highly pollen-allergenic tree species obtained in Example 1 (that is Figure 2 ) Identify the Mikania micrantha climbing area through the following image processing methods, and then exclude the Mikania micrantha climbing area from the identified area of highly pollen-allergenic tree species.
[0064] 1. Data collection and preprocessing
[0065] Data collection: Obtain high-resolution satellite remote sensing images;
[0066] Preprocessing:
[0067] Image enhancement: Adjust the brightness and contrast to enhance the characteristics of Mikania micrantha;
[0068] Geometric correction: Eliminate image distortion;
[0069] Denoising: Using filtering techniques to remove noise;
[0070] 2. Feature Extraction
[0071] Spectral features: Extract the unique reflectance features of Mikania micrantha in the near-infrared band;
[0072] Texture features: Use the Gray-Level Co-Occurrence Matrix (GLCM) to extract texture features;
[0073] Spatial features: Extract spatial features through edge detection and morphological operations;
[0074] 3. Model Selection and Training
[0075] Model selection: Select the UNet deep learning model;
[0076] Training:
[0077] Label data: Label the Mikania micrantha regions in the images;
[0078] Data augmentation: Increase data diversity through operations such as rotation and scaling;
[0079] Model training: Use the labeled data to train the model and optimize the loss function;
[0080] 4. Model Inference and Post-Processing
[0081] Inference: Input the new image into the model to obtain the predicted regions of Mikania micrantha;
[0082] Post-processing:
[0083] Binarization: Convert the prediction results into binary images;
[0084] Remove small regions: Remove small regions caused by noise;
[0085] Morphological operations: Smooth the boundaries through operations such as closing;
[0086] 5. Result Evaluation and Optimization
[0087] Evaluation: Use metrics such as IoU, precision, and recall to evaluate the model performance;
[0088] Optimization: Adjust the model parameters or structure according to the evaluation results to improve the recognition effect.
[0089] The above embodiments and descriptions in the specification only illustrate the principles and best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed.
Claims
1. Evaluation method for highly sensitizing plants in cities, characterized in that, It includes the following steps: Step 1: Vectorize the patch sample data; Step 2: On the Google Earth Engine remote sensing big data processing and presentation platform, call the Sentinel-2 remote sensing data set of the target time and area, and upload the local patch samples of highly pollen-sensitizing tree species, arbors, shrubs and herbs vegetation, and boundary vector data. By writing and running code, realize the identification classification and mapping of highly pollen-sensitizing tree species; Step 3: Analyze the spatial distribution of the remote sensing identification classification results of highly pollen-sensitizing tree species, count the areas of various sensitizing tree species in the ArcGIS software, and then evaluate the types, distribution locations and areas of highly sensitizing tree species in the city.
2. The evaluation method according to claim 1, wherein: The method for vectorizing the patch sample data in Step 1: In the ArcGIS software, convert the research samples of highly pollen-sensitizing tree species into.shp vector format, and assign sample category attributes to distinguish tree species types to obtain a sample vector data set.
3. The evaluation method according to claim 1, characterized in that: The process of Step 2 is as follows: Step 2.1: Preprocess the remote sensing image data; Step 2.2: Extract and combine remote sensing features, obtain the multi-spectral band reflectance value information of the remote sensing image, and calculate the vegetation index for calculation. Use the band information and vegetation index as the classification feature values of highly pollen-sensitizing tree species, and select the reflectance values and vegetation index of all bands of the remote sensing image data to set up a remote sensing feature set for classification; Step 2.3: Construct a random forest classification model; Step 2.4: Training and verification of the identification classification of highly pollen-sensitizing tree species. Divide the sample data into training and verification categories and input them into the random forest classification model to obtain the classification operation results; Step 2.5: Accuracy evaluation and spatial distribution mapping of the remote sensing identification classification of highly pollen-sensitizing tree species. Use the confusion matrix to measure the accuracy of the classification model. First, separately count the number of result values of incorrect and correct classification in the classification model, then display them all in a table, and finally obtain the matrix form result table of rows and columns. Determine the correct or incorrect classification of a model in the predicted and true categories through the confusion matrix, and whether the classification model confuses two categories.
4. The evaluation method according to claim 3, characterized in that: The preprocessing of the remote sensing image data includes: Screening: Select remote sensing images according to the target time range, perform atmospheric correction processing, exclude the interference of atmospheric scattering and absorption, and factors such as clouds, aerosols, and illumination on image interpretation to obtain the true spectral information of ground objects reflected to the sensor; Cloud removal: Perform cloud and noise removal on the image. After obtaining all images with cloud cover below a certain percentage, create and synthesize a remote sensing satellite composite image; Fusion: Traverse the median at the same pixel of all images and calculate to obtain a multi-spectral fusion image presented in the form of pixel median; Mosaicking and cropping: Mosaic and splice multiple adjacent images together to obtain a seamless, continuous, large-scale remote sensing image covering the entire region. Accurately crop the range of the mosaicked image according to the target area vector boundary, obtain the corresponding image data, use the arbors, shrubs and herbs vegetation classification visual interpretation data as a mask to obtain the vegetation remote sensing image data of the target area, and uniformly resample all data to the same spatial resolution.
5. The evaluation method according to claim 3, characterized in that: The construction of the random forest classification model includes: Random sampling, performing random sampling with replacement on the initial training data set, obtaining data from the original data that may be repeated or not selected at all to establish a new data training set, and reducing the risk of overfitting and enhancing the generalization ability of the model to new data by taking the average of multiple random subsets; Randomly selecting feature attribute parameters. On the premise that not all features are used for model training, randomly select a feature subset from all feature attributes, and let each decision tree select an optimal feature attribute as the splitting node from the feature subset, randomly selecting the features of each node, only considering the random feature subset without considering all feature attributes; Majority voting, respectively outputting classification labels for each decision tree, and performing majority voting on all labels to obtain the final classification result, ensuring that the final prediction result is the integration of the results of all decision trees.
6. The evaluation method according to claim 1, wherein: The process of step 3 is as follows: Analysis of the spatial distribution of highly pollen-allergenic tree species, analyzing the areas, administrative regions, green space types, and specific locations where highly pollen-allergenic tree species are densely distributed; Statistics of the spatial distribution area of highly pollen-allergenic tree species, using the raster area calculation function of ArcGIS software to calculate the distribution area of each tree species, and further calculating the number of species, distribution area, and proportion of highly pollen-allergenic tree species in a specific area.
7. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the evaluation method described in any one of claims 1-6.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the evaluation method described in any one of claims 1-6.