220 results about "Urban green space" patented technology

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention provides an urban green land coverage rate dynamic monitoring method and device. The method comprises the following steps: acquiring multispectral satellite remote sensing data, and preprocessing the multispectral satellite remote sensing data; carrying out cloud detection and shadow detection on the preprocessed remote sensing image, and calculating an effective pixel proportion index, a spectrum quality index, a time phase matching degree index and a spatial integrity index; based on the four indexes and the corresponding weights, constructing a fuzzy comprehensive evaluation matrix, and selecting an optimal data combination; calculating a normalized difference vegetation index (NDVI) based on the near infrared band and the red light band in the optimal data combination, and performing illumination angle correction and time sequence smoothing processing on the NDVI to obtain an optimized NDVI; performing green space identification on the optimized NDVI by adopting a first deep learning model, and outputting a green space distribution map; and calculating a green land area change rate, an NDVI difference value and connectivity index change based on the green land distribution map. According to the invention, high-precision dynamic monitoring of the green land coverage rate is realized, and the monitoring efficiency and accuracy are improved.

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention discloses an urban green land carbon sink dynamic accounting method and system based on multi-source data fusion and spatial network modeling. The method comprises the following steps: collecting urban green space carbon sink original data and preprocessing the data, and constructing a space * time * feature structure carbon sink data cube; identifying green land source plaques based on the spatial dimension data; taking the source ground plaque as an initial node, constructing a resistance surface by using a dynamic resistance factor, simulating a carbon flow in combination with a circuit theory, and generating a carbon flow gallery and a carbon flow density grid; inputting multi-source data into the carbon density dynamic calibration model, and outputting a dynamic grid; constructing a multi-scale prediction engine, and outputting carbon density prediction results under different time scales; and combining GIS and BIM technologies, inputting a prediction result and an urban space structure into a three-dimensional visual platform, generating a carbon density thermodynamic diagram and a carbon flow gallery evolution diagram, and realizing carbon sink dynamic accounting. According to the invention, the precision and space-time continuity of carbon sink accounting can be obviously improved.

The invention discloses an urban green land vegetation extraction method based on an unmanned aerial vehicle multispectral remote sensing image. Comprising the following steps: 1) determining an image acquisition area, and performing unmanned aerial vehicle path planning; 2) performing data acquisition through an unmanned aerial vehicle to obtain a high-resolution multispectral image; (3) splicing the acquired images through measurement software Pix4D mapper 4.4 to generate a digital orthoimage; and 4) combining the orthoimages of the spectral bands by using ENVI 6.0 software to obtain images of five bands including blue, green, red, red edge and near-infrared. According to the method, a background area which does not meet the vegetation spectral reflectivity characteristic is effectively inhibited, and the abnormal phenomenon that the reflectivity of a red-edge wave band is lower than that of a red wave band or higher than that of a near-infrared wave band is also solved, so that noise interference is effectively reduced; meanwhile, no obvious intersection phenomenon exists between the gray statistical characteristic values of the vegetation and the non-vegetation characteristics, and the accuracy of green land vegetation collection is improved.

The invention discloses a remote sensing-based canopy scale urban green land vegetation classification method. The method comprises the following steps of: obtaining and fusing multi-source remote sensing data; constructing a canopy scale multi-dimensional feature set fused with multi-source remote sensing data; constructing an urban green land vegetation enhancement sample set; constructing a low-dimensional feature set after optimization; the invention further discloses an urban green land automatic classification method based on the canopy scale of the improved random forest. According to the method, a multi-dimensional feature set is constructed through multi-source high-resolution remote sensing data; an automatic dynamic feature optimization and weight distribution mechanism based on mRMR is introduced, redundancy is effectively reduced, and discriminative features are highlighted; constructing a heterogeneity-driven adaptive sample set in combination with multi-source prior knowledge; and based on an improved random forest algorithm which introduces dynamic weighted node splitting, neighborhood constraint and adaptive category balance, high-precision, canopy-scale and adaptive classification of urban green land vegetation is realized.

The invention provides an urban green land invasion and occupation degradation identification system and method based on computer vision identification, and the method comprises the steps: collecting multi-temporal images of an urban green land, sorting the images according to timestamps, and generating a difference atlas to reflect the dynamic change of the green land; carrying out vectorization processing on the dynamic information, establishing a digital model as a reference standard of a greenbelt boundary, and carrying out superposition comparison with the latest remote sensing image to obtain a corrected greenbelt boundary; using the target detection network to identify a non-vegetation coverage area in the multi-temporal image, generating a preliminary green land invasion location map, then performing shape regularization and size filtering, and optimizing the green land invasion location map; and based on the optimization graph, time and space distribution characteristics of each factor are analyzed, prediction is carried out in combination with the change trend of the non-vegetation coverage area, and a trend prediction result is generated. And finally, carrying out quantitative evaluation on a prediction result, and generating a comprehensive degradation severity report. According to the invention, the identification efficiency and identification precision of urban green land invasion and degradation are improved.

The invention discloses an urban green land carbon sink metering method and system based on multi-source data fusion, and relates to the technical field of environmental monitoring, and the method comprises the steps: collecting urban green land multi-source data, and carrying out the preprocessing; performing parameter completion on vegetation in the building shielding blind area by adopting a dual-channel generative adversarial network to obtain complete green land leaf area index distribution data; simulating a reflected light path of a building glass curtain wall by using a ray tracing algorithm, obtaining a photosynthetically active radiation correction coefficient received by a vegetation canopy, and identifying urban green land carbon sink distribution data through multi-scale data fusion; and obtaining a carbon sink amount error based on the urban green land carbon sink amount distribution data, performing dynamic correction through a Bayesian optimization algorithm, and generating an urban green land carbon sink amount measurement report. According to the method, the adversarial network is generated through two channels, and the cooperative training mechanism of the U-Net generator and the PatchGAN discriminator is utilized, so that the high-precision spatial continuity reconstruction of the leaf area index of the building shielding blind area is realized.

The invention discloses an intelligent monitoring and evaluation system and method for urban green land carbon sequestration, and relates to the technical field of carbon sequestration evaluation, and the monitoring and evaluation method comprises the following steps: constructing a carbon sequestration evaluation system to evaluate each carbon sequestration monitoring record; capturing green land planting types in the area monitored by each carbon sequestration monitoring record to obtain the carbon sequestration capacity of each planting type; extracting influence factors influencing the comprehensive carbon sequestration capability of the region; analyzing the carbon sequestration capability difference in different time periods to obtain the influence degree of each influence factor; obtaining a theoretical carbon sink condition of each region, and generating a plurality of abnormal regions; based on the influence degree of each influence factor, carrying out green land adjustment reminding on any abnormal region, and giving an adjustment suggestion; by effectively analyzing the carbon sequestration capacity of each planting type, the carbon sequestration condition of each region of the city is accurately analyzed, and workers are helped to reasonably plan the green land of the city.

The invention discloses an interactive garden design method and system based on landscape garden simulation, particularly relates to the technical field of garden landscape design and computer graphic simulation, and relates to a plurality of modules of environmental data acquisition, three-dimensional modeling, plant growth simulation, water simulation, interactive design, microclimate prediction and the like. According to the method, terrain, climate and soil data are collected through a laser radar and multiple sensors, and a standardized environmental map is constructed; generating a plant topological structure based on a context sensitive type L system, and simulating a growth process by adopting a Logistic function in combination with an environmental factor; the method comprises the following steps: realizing dynamic simulation of a water body by utilizing an SPH particle method and a Navier-Stokes equation, and establishing a plant-water body feedback model; and real-time operation and design optimization are supported through a three-dimensional interaction interface. The system has the advantages of being high in ecological adaptability, high in visualization degree, perfect in feedback mechanism and the like, the intelligence and scientificity of garden design are remarkably improved, and the system is suitable for various scenes such as urban green lands and ecological restoration.

A decision system (100) for planning urban green spaces in cities, wherein the system comprises: a remote sensing unit (102) configured to acquire satellite and aerial imagery for the assessment of land surface temperature (LST), land use, land cover and urban green areas; a unit (104) of a geographic information system (GIS) integrated with a remote sensing unit (102) and configured to collect and process spatial data, including land surface temperature (LST) data, land use data and green space distribution data; a computing unit (106) connected to the remote sensing unit (102) and configured to estimate the green space requirement based on LST reduction, wherein the computing unit (106) uses a curve fitting technique using historical and observed data sets to reduce the LST; a prediction unit (108) connected to a computing unit (106) and configured to predict the optimal grassland area to achieve a desired LST reduction, wherein the prediction unit (108) uses a third-order polynomial equation that correlates the grassland area with the land surface temperature; a data validation unit (110) used to validate generated outputs using statistical methods, such as the coefficient of determination (R²). 2 ), of the mean square error (RMSE) and the mean absolute error (MAE), is configured to ensure accuracy and reliability; a site suitability analysis unit (112) integrated into the GIS unit (104), configured to identify suitable locations for urban green spaces based on spatial distribution, land use patterns and LST data; A processing unit (114) is configured so that urban planners and political decision-makers can enter parameters such as the desired temperature reduction and generate reports with detailed information on the need for green spaces and suitable locations for implementation.

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The invention discloses an urban green land plant growth state monitoring and analyzing system and method, relates to the technical field of big data analysis, and can accurately track the health condition of plants in each sub-region and improve the scientificity and effectiveness of urban green land management through detailed region division and coordinate marking. By means of the plant growth simulation model and the ideal environment growth model which are constructed through the neural network, the system can dynamically predict the plant growth state and conduct personalized management on plant growth conditions in different areas. Various collection means such as environmental factors and remote sensing data are integrated, the growth state of urban green land plants is comprehensively monitored, and the limitation of a traditional method is overcome. By setting the state abnormality threshold and combining the growth deviation trend chart, the system can realize a real-time early warning mechanism, quickly identify potential growth abnormality and ensure that problems are found and solved in time, so that the monitoring error of the remote sensing technology is reduced, and the management efficiency is improved.

The embodiment of the invention provides an urban green land community ecosystem regulation service optimization factor identification method and device based on a fusion algorithm and electronic equipment, and belongs to the technical field of ecological data processing, and the method comprises the steps: collecting a plant community, an elevation model and meteorological monitoring data to form a basic data set; uniformly quantifying the data and evaluating the ecological service in combination with the measurement and calculation model to form a standardized data set; inputting the features into a plurality of machine learning models, and screening out a key feature set through operations such as guided aggregation and the like; constructing an IOFESRM-LMEM model, outputting an optimal model through a series of operations, and analyzing key factors; and finally, outputting an optimization result, a key factor and an evaluation result by using a multi-dimensional index evaluation model. According to the invention, data support is provided for improving the ecological function of the urban green land, formulating ecological planning and optimizing urban management.

The invention discloses an intelligent optimization method and system for a green space structure of a block, and relates to the technical field of urban planning, landscape gardens and artificial intelligence, and the method comprises the steps: obtaining a planning region, carrying out the unit division based on the planning region, obtaining a plurality of block units, obtaining the related index data of each block unit, and carrying out the calculation of the related index data; inputting the related index data of each block unit into a pre-established regression model, and outputting to obtain an optimized regression model; calculating variable importance for the optimized regression model, and performing weight correction and coupling degree calculation based on the variable importance to obtain a corrected weight and a block unit coupling degree; vacant land images are obtained, grid identification is carried out on the vacant land images, then superposition is carried out to obtain a vacant land expansion area, land grid vacancy probability prediction is carried out based on the optimized regression model, and a vacant land probability is obtained; generating a block initial database based on the vacant land use probability, generating an initial population for the block initial database based on a genetic algorithm, and determining a block green space structure optimization result through the fitness of an optimal individual in each generation of initial population based on a reinforcement learning algorithm; and diversified choices can be provided for urban green space planning.

The embodiment of the invention provides a green land carbon sink benefit optimization method and device. The method comprises the following steps: acquiring data related to carbon sequestration of a plurality of functional zones in a research area, and for each functional zone, determining vegetation carbon sequestration benefits of the functional zone based on the data related to the carbon sequestration; calculating the importance percentage of each item in the influence factor set of the function partition on the vegetation carbon sink benefit, representing the action degree of the influence factors on the greenbelt carbon sink through the importance percentage, realizing the quantification of the carbon sink influence factors, and determining a key factor from multiple influence factors according to the importance percentage, calculating a local standardized regression coefficient of the key factor to analyze the spatial influence of the key factor on the vegetation carbon sink benefit, determining an optimization strategy of the greenbelt carbon sink benefit of the function partition according to the key factor and an analysis result, and formulating the optimization strategy of the greenbelt carbon sink benefit for different function partitions in a targeted manner. And the comprehensive improvement of the urban green land carbon sequestration capability can be realized.

The invention discloses an urban green land soil fertility index evaluation system and method based on instant data, and relates to the technical field of soil fertility evaluation. Extracting input features of the prediction model of the indoor detection indexes; training a prediction model of the indoor detection indexes by using the extracted input features; obtaining real-time data of the indoor detection indexes, and comprehensively evaluating the soil fertility according to the real-time data of the on-site rapid detection indexes and the real-time data of the indoor detection indexes; on-site rapid detection indexes related to soil fertility are monitored in real time, indoor detection indexes which can only be obtained through experimental analysis are obtained according to the change condition of the on-site rapid detection indexes, soil fertility indexes can be generated in time, the soil fertility can be evaluated, treatment and adjustment can be conducted in time according to the change condition of the soil fertility, and the purposes of maintaining the soil fertility and improving the soil fertility quality are achieved. The yield and the quality of crops are improved.

The invention relates to an urban green land high-resolution remote sensing monitoring method. The method comprises the following steps: data acquisition and preprocessing: synchronously acquiring point cloud data, hyperspectral data and multi-temporal remote sensing data; feature engineering: constructing a multi-dimensional feature space, and optimizing the multi-dimensional feature space through feature selection; model training: on the basis of the optimized multi-dimensional feature space, performing filling, differential normalization processing and regularization improvement on a U-net + model through pixel values of the edges of mirror images in sequence for urban green land features, and establishing the urban green land-oriented and multi-dimensional feature-based U-net + model, so that the urban green land-oriented and multi-dimensional feature-based U-net + model is obtained; adding the multi-dimensional feature data of the training sample into the U-net + model for training, predicting the spatial distribution of the urban green land after the training is completed, obtaining a prediction result of the U-net + model, and combining a mixed loss function with an adaptive training strategy; and post-processing and result generation: applying adaptive CRFs to optimize a U-net + model prediction result, generating a high-resolution urban green land distribution map, and outputting a precision evaluation report.

The invention provides a healthy plant community construction method based on an urban green land nutrition relationship, and relates to a construction method of an urban green land plant community. The construction method comprises the following steps: selecting plant communities to construct species according to urban green land types and functional requirements; according to the plant community establishment species, determining the nutrition relationship between the phytophagous arthropods corresponding to the establishment species plants and the nutrition relationship between the phytophagous arthropods corresponding to the natural enemies; and selecting functional plants according to the corresponding nutrition relationship, and configuring the functional plants around the colonized plants to construct and form a healthy plant community based on the urban green land nutrition relationship. According to the construction method, healthy growth of plants in the urban green land, nutritional relation coupling and ecological system balance of the urban green land can be realized.

The invention provides an urban green land ecological benefit assessment method and system, and belongs to the technical field of ecological environment. The urban green land ecological benefit assessment method comprises the following steps: determining an establishment principle of an assessment system, and measuring and improving the value of an urban green land by taking scientificity, comprehensiveness and operability as references; determining a green land ecological benefit evaluation target, determining a purpose and an application range of an evaluation system, and determining corresponding evaluation key points and indexes according to different evaluation targets; determining green land ecological benefit evaluation content, defining an evaluation range and content, and determining the purpose and application range of an evaluation system. According to the method, the purpose and the application range of the evaluation system can be determined by determining the green land ecological benefit evaluation target, corresponding evaluation key points and indexes are determined according to different evaluation targets, the evaluation pertinence is improved, the problem is accurately focused, resource allocation is optimized, and the evaluation efficiency is improved. The explicit assessment goals may help determine which aspects require more resource investment.

The invention discloses an actual evapotranspiration calculation method based on multi-source remote sensing data. Comprising the following steps: S1, acquiring multi-source remote sensing data; S2, based on an energy balance principle; S3, adopting a dynamic data fusion algorithm based on machine learning; S4, constructing an actual evapotranspiration calculation model adapted by regions; the method can accurately estimate the actual evapotranspiration of the heterogeneous region through multi-source remote sensing data fusion and a dynamic calculation model, is suitable for scenes such as agricultural areas, wetlands, urban green lands and forests, keeps high calculation precision and adaptability under various weather conditions, and has a wide application prospect. The problems of low resolution, data missing, lack of regional adaptability, low calculation efficiency and the like in the prior art are effectively solved, the precision, adaptability and efficiency of evapotranspiration calculation are improved, and more efficient technical support is provided for large-scale water resource management, ecological assessment and agricultural production.

The invention discloses a method and system for measuring and calculating the coupling degree of a green space structure and a built environment, and relates to the technical field of urban planning, landscape gardens and artificial intelligence. Through establishment of a coupling structure, based on a random forest regression model, a significant relation between urban observation data and a green space structure-built environment correlation index is established; urban green space performance evaluation is converted into a series of coupling values, the problems that an urban green space performance evaluation system is huge, indexes are redundant and quantification is difficult are solved, and the benefit evaluation accuracy and the measurement and calculation efficiency are improved.

The invention provides a method for improving urban green land biological source emission based on a high-resolution global mode, and provides a technical scheme for optimizing a biological source emission list by using high-resolution land coverage data aiming at the problem of urban green land emission deficiency in a current numerical model. The technologies comprise a weak supervision global land coverage mapping method, spectral information of images can be fused, and features extracted by a visual large model are used for drawing high-resolution land coverage images; the vegetation function type matching method is used for converting the high-resolution land coverage data into vegetation function types; and preparing a high-resolution numerical mode input file, converting the file into an input format suitable for a high-resolution earth system mode, carrying out numerical simulation, and estimating global biological source emission, especially urban green space emission.

The present invention relates to the technical field of urban green space identification, and specifically to a high-resolution urban green space identification method. The present invention can identify urban green spaces more finely by utilizing the two steps of "preliminary identification" and "fine identification", especially the green spaces on both sides of roads. The present invention also utilizes the progress prediction and extraction of deep learning models, and has higher accuracy and migration capabilities than the prior art, and has a faster processing speed, thereby providing a high-quality and efficient method for solving the deficiencies in urban greening identification.

The invention discloses a multi-type urban green land ecosystem-oriented carbon sink measurement system and method, belongs to the technical field of carbon sink measurement, and solves the problem of inaccurate evaluation result caused by non-uniform data distribution or insufficient sample size when a feature selection algorithm is adopted to extract feature factors in the existing method. The method comprises the following steps: collecting sequential urban green land carbon sink amount associated data, determining characteristic factors influencing the total carbon sink amount in a carbon sink preprocessing set based on a characteristic factor extraction model, analyzing a characteristic factor set by combining a carbon reserve method and the characteristic factor extraction model, and calculating the arbor and shrub biomass of the urban green land; according to the method, the feature factor extraction model is constructed by combining the GBR model and global sensitivity analysis, so that the feature factors influencing the total carbon sink amount in the carbon sink preprocessing set are accurately determined, the uncertainty in carbon sink estimation can be reduced, and the accuracy of an evaluation result can be ensured when data distribution is uneven or the sample size is insufficient.

The invention relates to the technical field of environmental data processing, in particular to an environmental monitoring data processing method and system based on big data analysis. The method comprises the following steps: carrying out statistical marking on an urban environment to-be-monitored area, and obtaining block vegetation type data, block environment humidity data and block soil condition data; carrying out block vegetation growth analysis and vegetation carbon storage influence analysis on the basis of the block vegetation type data to obtain actual block vegetation carbon storage; performing soil microflora abundance analysis and soil organic carbon storage estimation on the block soil condition data to obtain actual organic carbon storage of block soil, and performing time period window division and carbon sink capacity comparison division to obtain an urban green land efficient carbon sink block and an urban green land low-efficiency carbon sink block; and performing targeted optimization adjustment on the urban green land low-efficiency carbon sink block until the urban green land low-efficiency carbon sink block is converted into an urban green land high-efficiency carbon sink block. According to the invention, the regional carbon sink capability can be effectively improved and managed.

The invention discloses an urban green land fine classification method based on a high-resolution remote sensing image. The method comprises the following steps: S1, obtaining a VHR image and monthly Sentinel-2 remote sensing time sequence data; s2, obtaining a greenbelt boundary sample and a greenbelt type sample according to the VHR image and monthly Sentinel-2 remote sensing time sequence data; s3, performing land parcel semantic segmentation on the greenbelt boundary sample to obtain urban greenbelt land parcels, and performing land parcel super-pixel segmentation to obtain super-pixel land parcels so as to obtain greenbelt classification land parcels; s4, performing plot time sequence feature extraction on the green land type samples to obtain time sequence feature pixels; s5, performing morphological feature calculation on the classified land parcels of the green land to obtain a land parcel morphological feature subset, and performing feature engineering extraction on the time sequence feature pixels to obtain a land parcel time sequence element feature subset; and S6, carrying out urban green land fine category classification by adopting a classifier to obtain a classification result. According to the method, fine classification can be accurately carried out on the urban green land, and technical support is provided for urban green land management and ecological protection in the future.

The invention relates to the technical field of urban ecology monitoring and optimization control, and discloses an urban ecology-oriented carbon sink dynamic evaluation and optimization method and system, and the method comprises the steps: obtaining multi-source greenbelt feature data; constructing a light energy utilization rate model to output a photosynthetic carbon fixed quantity sequence; a long-short-term memory network model based on introduction of a weighted trend maintenance loss function and a DropBlock mechanism; setting a situation influence factor to construct a multi-situation evolution result; and generating an optimal greenbelt management regulation and control parameter by adopting a particle swarm algorithm. Compared with the technical problem that high-frequency dynamic carbon sink evaluation and optimization control are difficult to realize for urban different-quality greenbelts especially under the condition of frequent manual management behaviors in the prior art, the method has the advantages that the generalization ability of the model is improved by introducing a trend maintenance loss mechanism and time slice DropBlock regularization, and the multi-situation disturbance simulation is combined, so that the dynamic carbon sink evaluation and optimization control of the urban different-quality greenbelts can be realized, and the dynamic carbon sink evaluation and optimization control of the urban different-quality greenbelts can be realized. And the real-time performance, the accuracy and the regulation and control optimization efficiency of urban carbon sink monitoring are improved.

The invention provides an urban green land soil rapid infiltration device which comprises main boxes, the main boxes are connected through connecting pipelines, the upper ends of the main boxes are provided with filter discs, the lower ends of the main boxes are connected with hopper-shaped connecting pipes, the lower ends of the hopper-shaped connecting pipes are connected with a plurality of water guide main pipes, and the water guide main pipes are provided with a plurality of first water guide branch pipes; a plurality of second water guide branch pipes are arranged on the first water guide branch pipes, and the mounting directions of the first water guide branch pipes and the second water guide branch pipes are obliquely downward. Rainwater is efficiently collected and filtered and evenly distributed into soil, the urban drainage pressure is relieved, and groundwater supply is promoted. The permeation amount can be adjusted according to season requirements through the flexible through hole adjusting design, and maintenance and installation are facilitated through threaded connection. The filter plate ensures water quality, and the natural infiltration process supports plant growth and improves urban microclimate. The modular design adapts to projects of different scales, the adjustable structure meets various environmental requirements, and a sustainable solution is provided for urban landscaping.

The invention relates to the field of urban green land evapotranspiration zoning, in particular to an urban green land surface evapotranspiration zoning method and system based on impedance parameters, and aims to distinguish arbor, shrub and lawn transpiration in an urban green land. The method comprises the following steps: firstly, collecting and preprocessing vegetation morphological parameters, meteorological data, canopy surface temperature, soil heat flux, vegetation transpiration and vortex related flux data; calculating leaf boundary layer impedance and canopy stomatal impedance of each vegetation type by using a backstepping Penman-Monteith formula and an earth surface energy balance equation; determining the proportion of each vegetation type in the flux contribution source area through a flux footprint model, and reversely deducing the total aerodynamic impedance and the total stomatal impedance of the earth surface; solving aerodynamic impedance of each vegetation according to an earth surface energy balance equation and performing fitting correction; and finally, the corrected impedance parameters are substituted into a Penman-Monteith model to calculate the transpiration amount of each vegetation. According to the method, the problem of mismatching between single plant scale data and community scale data is effectively solved, and the evapotranspiration partition calculation precision is improved.