Regional carbon sink monitoring method and device based on urban forest

Abstract

The invention provides a regional carbon sink monitoring method and device based on an urban forest, and belongs to the technical field of carbon sink monitoring, and the method comprises the steps: obtaining the vegetation spectrum information and ground image data of the urban forest collected by a space-based satellite monitoring end, generating a vegetation map of the urban forest based on the vegetation spectrum information and the ground image data, and dividing a carbon sink monitoring area based on a vegetation map of the urban forest. And performing multi-source data monitoring on the carbon sink monitoring area, and determining biomass information of the carbon sink monitoring area based on the monitored multi-source data. And determining target carbon sink data of the carbon sink monitoring area based on the biomass information. According to the regional carbon sink monitoring method and device based on the urban forest provided by the invention, the problems that the monitoring data acquired by the existing carbon sink monitoring means for the urban forest is not comprehensive enough and the accuracy of the carbon sink data is low can be solved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of carbon sink monitoring, and more specifically, to a method and device for regional carbon sink monitoring based on urban forests. Background Art

[0002] As an important part of the urban ecosystem, urban forests play a key role in mitigating climate change and improving the quality of the ecological environment. Among them, regional carbon sink monitoring is crucial to understanding the ecological service functions of urban forests. Regional carbon sink monitoring aims to accurately assess the ability of urban forests to absorb and store carbon. At present, carbon sink monitoring mainly relies on remote sensing data or sample site surveys. However, due to the environmental complexity of urban forests, the monitoring data obtained by existing carbon sink monitoring methods for urban forests is not comprehensive enough and the accuracy of carbon sink data is low. Summary of the invention

[0003] The purpose of the present disclosure is to provide a regional carbon sink monitoring method and device based on urban forests, so as to solve the problem that the monitoring data obtained by the existing carbon sink monitoring means for urban forests is not comprehensive enough and the accuracy of carbon sink data is low.

[0004] According to a first aspect of an embodiment of the present disclosure, a regional carbon sink monitoring method based on urban forests is provided, comprising: obtaining vegetation spectral information and ground image data of urban forests collected by a space-based satellite monitoring terminal, generating a vegetation map of the urban forest based on the vegetation spectral information and the ground image data, and dividing the carbon sink monitoring area based on the vegetation map of the urban forest.

[0005] Multi-source data monitoring is carried out on the carbon sink monitoring area, and the biomass information of the carbon sink monitoring area is determined based on the monitored multi-source data.

[0006] Determine the target carbon sink data for the carbon sink monitoring area based on biomass information.

[0007] A second aspect of the embodiments of the present disclosure provides a regional carbon sink monitoring device based on urban forests, comprising: The first monitoring module is used to obtain vegetation spectral information and ground image data of urban forests collected by the space-based satellite monitoring terminal, generate a vegetation map of the urban forest based on the vegetation spectral information and ground image data, and divide the carbon sink monitoring area based on the vegetation map of the urban forest.

[0008] The second monitoring module is used to perform multi-source data monitoring on the carbon sink monitoring area and determine the biomass information of the carbon sink monitoring area based on the monitored multi-source data.

[0009] The calculation module is used to determine the target carbon sink data of the carbon sink monitoring area based on the biomass information.

[0010] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for regional carbon sink monitoring based on urban forests when executing the computer program.

[0011] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for regional carbon sink monitoring based on urban forests are implemented.

[0012] The beneficial effects of the regional carbon sink monitoring method and device based on urban forests provided by the embodiments of the present disclosure are: on the one hand, the embodiments of the present disclosure can obtain more comprehensive and detailed urban forest vegetation information by integrating space-based satellite monitoring terminals and ground data sources.

[0013] On the other hand, the disclosed embodiment uses the generated vegetation map to divide the carbon sink monitoring area, realizing a refined assessment of the urban forest carbon sink potential. The regionalized monitoring method helps to identify areas with high carbon sink potential and provide targeted guidance for subsequent carbon sink management.

[0014] On the other hand, the disclosed embodiments can more accurately estimate the carbon sink amount of urban forests by monitoring the carbon sink monitoring area with multi-source data and determining the target carbon sink data based on biomass information. Multi-dimensional data fusion not only improves the accuracy of carbon sink data, but also provides reliable technical support for the measurement, reporting and verification of urban forest carbon sinks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of a flow chart of a regional carbon sink monitoring method based on urban forests provided in an embodiment of the present disclosure; Figure 2 A schematic diagram of a multi-terminal monitoring system provided by an embodiment of the present disclosure; Figure 3 A schematic diagram of a multi-terminal monitoring system provided by another embodiment of the present disclosure; Figure 4 A structural block diagram of a regional carbon sink monitoring device based on urban forests provided in an embodiment of the present disclosure; Figure 5A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0018] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0019] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for monitoring regional carbon sinks based on urban forests provided in one embodiment of the present disclosure. The method may include S101 to S103.

[0020] S101: Obtain vegetation spectral information and ground image data of the urban forest collected by the space-based satellite monitoring terminal, generate a vegetation map of the urban forest based on the vegetation spectral information and the ground image data, and divide the carbon sink monitoring area based on the vegetation map of the urban forest.

[0021] In this embodiment, the space-based satellite monitoring terminal may include a satellite system located in a space orbit for monitoring urban forests. For example, the space-based satellite monitoring terminal can carry visible light, multi-spectral, hyperspectral, thermal infrared and other payloads to effectively provide full-spectral spectrum, multi-spectral spectrum, infrared spectrum and other information, and provide data support for human development activity monitoring, vegetation parameters, environmental parameters monitoring, etc. Space-based satellites have the advantages of a wide monitoring range, continuous data space, and multiple monitoring indicators, and provide basic data support for human activity monitoring and ecological status assessment in important ecological spaces.

[0022] Vegetation spectral information refers to the reflection, absorption and other characteristics of vegetation under electromagnetic radiation of different wavelengths, which can reflect the growth status, health and coverage of vegetation.

[0023] Vegetation spectral information can include reflectance values ​​in different bands, vegetation indices (such as normalized difference vegetation index), chlorophyll content related parameters, etc.

[0024] Ground image data is the surface images of urban forest areas taken by satellites from space. For example, high-resolution ground images can clearly show the outlines and distribution of trees.

[0025] Vegetation maps are generated by processing and analyzing vegetation spectral information and ground image data. Vegetation maps can show the distribution of vegetation in urban forests. The parameters in vegetation maps can include vegetation type classification, vegetation coverage, forest patch size, etc.

[0026] The carbon sink monitoring area is a specific area of ​​carbon sink to be monitored, which is divided according to the characteristics on the vegetation map.

[0027] For example, space-based satellites receive electromagnetic radiation reflected and emitted by urban forests through onboard sensors to obtain vegetation spectrum information and ground image data. The collected data is sent to a ground receiving station through data transmission. Data processing and analysis are performed on the ground to identify different vegetation types and distributions. Vegetation maps are generated with the help of software such as geographic information systems (GIS). Carbon sink monitoring areas are divided according to vegetation characteristics, terrain and other factors on the vegetation maps, so that carbon sink monitoring and assessment can be carried out more targetedly.

[0028] S102: Perform multi-source data monitoring on the carbon sink monitoring area, and determine the biomass information of the carbon sink monitoring area based on the monitored multi-source data.

[0029] like Figure 2 and Figure 3 As shown, in this embodiment, multi-source data monitoring refers to the use of a multi-terminal monitoring system to conduct comprehensive observations of the carbon sink monitoring area and collect data of various types and scales. The multi-terminal monitoring system may include: a space-based satellite monitoring terminal, an air-based remote sensing monitoring terminal, an aerial drone monitoring terminal, a mobile monitoring vehicle monitoring terminal, and a ground monitoring terminal. Data can be collected using space-based satellite monitoring terminals, air-based remote sensing monitoring terminals, aerial drone monitoring terminals, mobile monitoring vehicle monitoring terminals, and ground monitoring terminals. Give full play to the characteristics and advantages of each observation platform and technology, and connect each node in the monitoring process through multi-party collaborative technical methods such as equipment, data, and applications to build a complex monitoring network and business model that covers equipment, data, algorithms, and services, with multiple dimensions, multiple nodes, and nonlinearity.

[0030] Biomass information is an indicator that reflects the total amount of organic matter in vegetation and soil within the carbon sink monitoring area. Biomass information can include: vegetation biomass (such as dry weight and fresh weight of vegetation at different levels such as trees, shrubs, and herbs) and soil carbon storage (such as the content and density of organic carbon in the soil).

[0031] Exemplarily, multi-source data is obtained through air-based remote sensing monitoring terminals, near-ground mobile monitoring terminals, and ground monitoring terminals. Air-based remote sensing can quickly obtain images and terrain information over a large area, near-ground mobile monitoring can supplement high-resolution details, and ground monitoring provides the most accurate field data. The data from these different sources are integrated and analyzed, and the vegetation biomass is calculated by establishing an empirical model of biomass, tree diameter at breast height, and tree height, combined with parameters such as the vegetation index of remote sensing images; soil carbon storage is calculated using soil sampling analysis results combined with terrain data, and the biomass information of the carbon sink monitoring area is determined based on vegetation biomass and soil carbon storage.

[0032] S103: Determine target carbon sink data of the carbon sink monitoring area based on the biomass information.

[0033] In this embodiment, the target carbon sink data refers to the data reflecting the carbon storage and absorption capacity of the region obtained by analyzing and calculating the biomass information of the carbon sink monitoring area. For example, the target carbon sink data may include total carbon storage, annual carbon absorption, carbon sink contribution of different vegetation types, etc. The total carbon storage reflects the total amount of carbon currently stored in the region; the annual carbon absorption reflects the amount of carbon fixed by the region through vegetation photosynthesis, etc. within a year; the carbon sink contribution of different vegetation types can help understand the role of various types of vegetation in carbon sinks.

[0034] For example, the relationship between biomass and carbon content is used to calculate regional carbon sink data based on biomass information and the carbon content ratio of different vegetation.

[0035] Exemplarily, the vegetation spectral information and ground image data of the urban forest are collected through the space-based satellite monitoring terminal to generate a detailed vegetation map. According to the vegetation map, the urban forest is divided into different carbon sink monitoring areas, such as the main tree forest area, shrub forest area, etc. are divided separately. Multi-source data monitoring is carried out on each monitoring area, including satellite image data, ground soil and meteorological data, high-resolution images collected by drones, etc. These multi-source data are used to determine the biomass information of each area, for example, the total biomass of trees in a specific tree forest area is calculated. The target carbon sink data is then determined based on the biomass information. In practical applications, these data can provide a scientific basis for urban planning, such as avoiding the destruction of high carbon sink areas in urban construction, rationally planning parks and green spaces, and selecting tree species suitable for local growth and with strong carbon sink capacity for afforestation.

[0036] From the above, it can be concluded that, on the one hand, this embodiment realizes comprehensive and accurate monitoring of the urban forest carbon sink monitoring area by integrating multiple data collection methods such as space-based satellites, air-based remote sensing, and ground observation. The multi-source data monitoring method not only improves the accuracy and reliability of the data, but also makes the monitoring results more comprehensive, which helps to have a deeper understanding of the carbon sink capacity of urban forests.

[0037] On the other hand, this embodiment achieves a refined assessment of carbon sink potential by generating a detailed vegetation map and dividing the carbon sink monitoring area according to vegetation characteristics, terrain and other factors. This regionalized monitoring and assessment method helps to identify areas with high carbon sink potential and provide targeted guidance for subsequent carbon sink management and urban planning.

[0038] On the other hand, this embodiment provides a scientific basis for carbon sink measurement, reporting and verification of urban forests by calculating biomass information and target carbon sink data. This embodiment has broad application prospects and can not only be used for carbon sink monitoring and evaluation of urban forests, but also provide strong support for urban planning, environmental protection policy formulation, etc. Scientific data analysis and application will help promote green urban development.

[0039] In one embodiment of the present disclosure, a vegetation map of an urban forest is generated based on vegetation spectrum information and ground image data, and a carbon sink monitoring area is divided based on the vegetation map of the urban forest, including: The vegetation distribution data and vegetation types of urban forests are determined based on vegetation spectral information.

[0040] The terrain data and land use type are determined based on the ground image data.

[0041] Generate vegetation maps based on vegetation distribution data and terrain data.

[0042] Carbon sink monitoring areas are divided on vegetation maps based on vegetation types and land use types.

[0043] In this embodiment, the vegetation distribution data represents the specific spatial location and range of various vegetation types in the urban forest. The vegetation distribution data may include vegetation coverage, area proportions of different vegetation types, size and shape of vegetation patches, and the like.

[0044] Vegetation type refers to the classification of different plant species present in urban forests. The relevant parameters of vegetation type can include tree species name, types of shrubs and herbaceous plants, growth habits of vegetation, etc.

[0045] Topographic data includes the topographic and geomorphic features of the area where the urban forest is located. Parameters related to topographic data may include altitude, slope, and slope direction.

[0046] Land use type reflects the land use and functional division of the area where the urban forest is located. Land use types can include forest land, grassland, construction land, water area, etc., and related parameters can include the area and proportion of different land use types.

[0047] Exemplarily, for vegetation spectral information, by analyzing the reflectance characteristics of different vegetation in specific spectral bands, remote sensing image processing technology is used to determine the distribution and type of vegetation. For example, multispectral satellite images are used to identify tree species based on the differences in spectral characteristics of different vegetation. Ground image data is obtained through high-resolution satellite images or aerial photography. Terrain data is extracted using image recognition algorithms, such as determining altitude and slope through stereo image pair analysis. At the same time, the land use type is determined based on the features of the objects in the image. Vegetation distribution data and terrain data are integrated, and a vegetation map is generated using Geographic Information System (GIS) technology to intuitively display the spatial distribution and topography of vegetation. According to the vegetation type and land use type, areas with different carbon sink characteristics are divided on the vegetation map as carbon sink monitoring areas.

[0048] This embodiment combines vegetation spectral information and ground image data to accurately generate vegetation maps of urban forests and scientifically divide carbon sink monitoring areas. This embodiment not only improves the accuracy and efficiency of carbon sink monitoring, but also helps to fully understand the vegetation distribution, types and terrain characteristics of urban forests, providing strong support for the formulation of targeted ecological protection measures, effectively promoting the improvement of urban greening and carbon sink capacity, and promoting the sustainable development of urban ecological environment.

[0049] In one embodiment of the present disclosure, multi-source data monitoring is performed on a carbon sink monitoring area, including: Multi-source data monitoring of the carbon sink monitoring area is carried out based on air-based remote sensing monitoring terminals, near-ground mobile monitoring terminals and ground monitoring terminals.

[0050] In this embodiment, the air-based remote sensing monitoring terminal can be composed of a variety of monitoring payloads mounted on a high tower platform or a high building, and can be customized and deployed according to the characteristics of the regional ecological conditions (forests, grasslands, wetlands, deserts, etc.) and the focus of human interference activities (mineral development, tree felling, vehicle entry, etc.). For example, it can include ultra-long-range infrared monitoring systems, high-definition fog-penetrating cameras, hyperspectral imaging cameras, laser radars, etc. The air-based remote sensing monitoring terminal can achieve nearly 360-degree high-precision observation, with a spatial resolution of decimeters and a time frequency of half an hour or even minutes. It has the advantages of high spatiotemporal resolution of monitoring data, abnormal warning and fast emergency response, and effectively makes up for the shortcomings of insufficient timeliness and precision of space-based satellite monitoring.

[0051] In this embodiment, the near-ground mobile monitoring terminal may include an aerial drone monitoring terminal and a mobile monitoring vehicle monitoring terminal, and the ground monitoring terminal may include a ground observation station. The ground observation station may use data acquisition sensors, cameras and other devices to collect data.

[0052] For example, the monitoring end of aerial drones can accurately monitor key ecological and environmental indicators such as vegetation type distribution, vegetation growth status, ecosystem anomalies, and human interference activities through high-precision sensors such as visible light cameras, multispectral cameras, hyperspectral cameras, airborne laser radars, and thermal infrared imagers carried by drones, which is the main way to conduct regular patrol monitoring. Aerial drones have the advantages of flexibility, strong accessibility, high spatial resolution, and rich information capture. They have the ability to fly at low altitudes under clouds, which makes up for the defect that satellite optical remote sensing is often blocked by clouds and cannot obtain images, and fills the space-time blind spots of space-based satellite and air-based remote sensing monitoring.

[0053] Mobile patrol monitoring vehicles can use special vehicles as the main monitoring platform, carry low-altitude drone monitoring systems, and configure a variety of vehicle-mounted and airborne payloads for monitoring business needs to conduct comprehensive monitoring of near-surface ecological conditions. As one of the important components of the "five-base" monitoring system, patrol vehicles have the advantages of rich monitoring information (including multi-element two- and three-dimensional information), high monitoring accuracy and efficiency, and the ability to be linked with patrol law enforcement. They can achieve large-scale, rapid and continuous monitoring near the ground, fill the temporal and spatial blind spots of space-based satellite and air-based remote sensing monitoring, and are the main means of conducting regular patrol monitoring.

[0054] This embodiment realizes all-round, high-precision, and high-frequency monitoring of carbon sink areas through multi-source data monitoring of air-based remote sensing monitoring, near-ground mobile monitoring, and ground monitoring. This embodiment not only improves monitoring efficiency, but also ensures the temporal and spatial resolution and accuracy of data, effectively makes up for the shortcomings of space-based satellite monitoring, realizes accurate capture and rapid response of ecological and environmental indicators, provides strong technical support for carbon sink monitoring and ecological protection, and helps promote ecological and environmental protection and sustainable development.

[0055] In one embodiment of the present disclosure, the multi-source data includes image data and terrain data.

[0056] Multi-source data monitoring of the carbon sink monitoring area is carried out based on air-based remote sensing monitoring terminals, near-ground mobile monitoring terminals and ground monitoring terminals, including: The primary image data and primary terrain data of the carbon sink monitoring area are collected based on the air-based remote sensing monitoring terminal.

[0057] The carbon sink monitoring area is divided based on the primary image data and primary terrain data, and the primary monitoring area is determined.

[0058] The secondary image data and secondary terrain data of the primary monitoring area are collected based on the near-ground mobile monitoring terminal. Both the primary image data and the secondary image data are image data, and both the primary terrain data and the secondary terrain data are terrain data.

[0059] In this embodiment, the multi-source data also includes ecological environment data.

[0060] Multi-source data monitoring of the carbon sink monitoring area is carried out based on air-based remote sensing monitoring terminals, near-ground mobile monitoring terminals and ground monitoring terminals, including: The data monitoring frequency of the carbon sink monitoring area is determined based on the secondary image data and secondary terrain data.

[0061] Control the ground monitoring terminal to dynamically collect ecological environment data based on the data monitoring frequency.

[0062] In this embodiment, the primary image data and primary terrain data are relatively macroscopic images and terrain information about the carbon sink monitoring area collected by the air-based remote sensing monitoring terminal. The primary image data may include spectral reflectance of different bands, image resolution, etc.; the primary terrain data may include altitude range, slope distribution, etc.

[0063] The primary monitoring area is a further division of the carbon sink monitoring area based on the primary image data and primary terrain data, resulting in areas that require detailed monitoring.

[0064] Secondary image data and secondary terrain data are more detailed image and terrain information collected by the near-ground mobile monitoring terminal for the primary monitoring area. The resolution of secondary image data is higher than that of primary image data, and secondary image data has richer texture features than primary image data; secondary terrain data has more accurate micro-topography changes than primary terrain data.

[0065] Ecological and environmental data are data that reflect the ecological and environmental conditions of the carbon sink monitoring area. Ecological and environmental data can include vegetation index data (such as normalized vegetation index), tree growth data (tree height, breast diameter, etc.), near-ground spectral data, meteorological data (temperature, precipitation, etc.), and soil data (soil organic carbon content, texture, etc.).

[0066] The data monitoring frequency is the time interval for collecting data in the carbon sink monitoring area determined based on the secondary image data and secondary terrain data. For example, the data monitoring frequency can be set to different time intervals such as once a day, once a week, etc.

[0067] The ground monitoring terminal is a device or system that collects data on the ground. The ground monitoring terminal can be equipped with sensors, such as sensors for measuring soil moisture and sensors for measuring atmospheric carbon dioxide concentration.

[0068] In this embodiment, the data monitoring frequency of the carbon sink monitoring area is determined based on the secondary image data and the secondary terrain data, including: The primary monitoring area is divided into multiple secondary monitoring areas.

[0069] The number of vegetation species and vegetation coverage in each secondary monitoring area are calculated based on the secondary image data.

[0070] The terrain type and terrain complexity in each secondary monitoring area are calculated based on the secondary image data.

[0071] A first data monitoring frequency is determined based on the number of vegetation types, vegetation coverage, and terrain complexity, and the first data monitoring frequency is used as the data monitoring frequency of the corresponding area.

[0072] Each secondary monitoring area is divided into multiple tertiary monitoring areas based on vegetation type and terrain type. Each tertiary monitoring area contains one type of vegetation and one type of terrain.

[0073] The first data monitoring frequency is adjusted based on the vegetation type and terrain type of the third-level monitoring area, and the second data monitoring frequency is determined, and the second data monitoring frequency is used as the data monitoring frequency of the corresponding area.

[0074] The vegetation growth data in the third-level monitoring area is determined based on the second-level image data, the second data monitoring frequency is adjusted based on the vegetation growth data, the third data monitoring frequency is determined, and the third data monitoring frequency is used as the data monitoring frequency of the corresponding area.

[0075] Considering that different vegetation may have different growth cycles and responses to the environment, a rich variety of vegetation types means more variables need to be tracked. If the secondary image data shows that the vegetation coverage in the carbon sink monitoring area is high and the vegetation types are rich, such as the presence of a variety of different forest community structures, then more frequent data collection is required to accurately monitor changes in vegetation growth status.

[0076] Considering that differences in terrain will lead to uneven distribution of ecological factors such as light, water, and soil conditions, which in turn affect vegetation growth and carbon sequestration processes. Therefore, when the secondary terrain data shows that the micro-topography changes in the region are complex, such as the presence of more valleys, ridges, steep slopes and other terrains, a higher data monitoring frequency is also required. For example, in the valley area, there is sufficient water and vegetation grows vigorously, but it is also more susceptible to factors such as soil erosion. Vegetation in the ridge area may grow slowly due to factors such as drought. Vegetation changes in these different terrain areas need to be closely monitored.

[0077] Considering the stability and rate of change of ecosystems, when the analysis of secondary image data and secondary terrain data shows that the ecosystem stability in the carbon sink monitoring area is low, such as the presence of more signs of human interference (such as construction activities in the surrounding area, traces of reclamation on the edge of the forest, etc.) or natural disaster risks (such as proximity to landslide-prone areas, areas prone to forest fires, etc.), the frequency of data monitoring should be increased accordingly. Because these factors may lead to rapid changes in ecological and environmental elements such as vegetation and soil, timely data collection can better capture the impact of these changes on carbon sinks.

[0078] In addition, if the vegetation in the monitoring area grows rapidly, such as in young forests or in a fast-growing season, more frequent data collection is also required. The rate of change of the vegetation index can be determined based on the secondary image data of the historical period, and the vegetation growth rate can be determined based on the rate of change of the vegetation index. For fast-growing vegetation, its biomass, photosynthesis and other carbon sink-related processes also change rapidly, so the monitoring frequency should be increased.

[0079] For example, in the monitoring of forest carbon sinks in a mountainous area, the first-level monitoring area of ​​the area is divided into multiple second-level monitoring areas. The number of vegetation species, coverage, terrain type and complexity of each second-level area are calculated through the second-level image data to determine the first data monitoring frequency. Then the third-level monitoring area is divided. If a certain area has a specific tree species and rugged terrain, the second data monitoring frequency is adjusted. If it is further determined through the second-level image that the vegetation in the third-level area grows rapidly, the third data monitoring frequency is adjusted again based on the vegetation growth data, so as to grasp the carbon sink dynamics in the area more timely and provide an accurate basis for forest management and protection.

[0080] In this embodiment, considering that urban forests are usually in a complex urban environment, buildings, roads, etc. in the carbon sink monitoring area or the primary monitoring area may interfere with the observation of satellites and drones. For example, the shadow of a building may affect the measurement of the vegetation spectrum, and appropriate image processing technology can be used to eliminate these interferences, or the near-ground mobile monitoring terminal and the ground monitoring terminal can be used to monitor the relevant data to ensure the accuracy and comprehensiveness of the data.

[0081] Consider the impact of human activities: Frequent human activities in cities, such as park maintenance and management, garbage disposal, etc., may change soil properties and vegetation growth conditions. During the monitoring process, the potential impact of these human activities on carbon sink data should be considered. The impact of human activities can be assessed by setting up control areas or conducting long-term monitoring comparisons.

[0082] Considering the environmental complexity of urban forests, the data obtained by different monitoring terminals are different. Therefore, it is necessary to regularly calibrate the equipment at each monitoring terminal and verify the monitoring data through field sampling and laboratory analysis to ensure the reliability of the data.

[0083] Considering that urban forests are relatively small in area and scattered in distribution, a multi-scale monitoring method is needed, including macro satellite and airborne remote sensing monitoring to understand the overall distribution and changing trends of urban forests, and micro ground observation and drone monitoring to obtain detailed information inside the forests.

[0084] Considering that the growth and changes of urban forests are affected by many factors such as urban development and climate change, they are highly dynamic. Therefore, it is necessary to establish a dynamic monitoring mechanism and regularly update carbon sink data in order to keep abreast of changes in urban forest carbon sinks. The situation in different regions is different. Considering the cost and resource consumption issues, the data collection frequency can be set according to the actual situation of different regions.

[0085] For example, in the monitoring of urban forest carbon sinks, the air-based remote sensing monitoring terminal is used to obtain the primary image data and primary terrain data of the carbon sink monitoring area, and the area with high carbon sink potential is preliminarily determined as the primary monitoring area. For example, based on the distribution of altitude and slope, it is found that a certain forest area has a large carbon sink potential. The near-ground mobile monitoring terminal (such as a drone) is used to collect secondary image data and secondary terrain data for the primary monitoring area to further clarify the distribution of trees in the forest and micro-topography changes. The data monitoring frequency is determined based on these data. If the vegetation in the area grows rapidly and the terrain is complex, it can be set to a monitoring frequency of once a week. Sensors at the ground monitoring terminal, such as sensors that measure soil moisture and atmospheric carbon dioxide concentration, dynamically collect ecological and environmental data according to this frequency. Through these multi-source data, the carbon sink capacity of the forest in the area can be evaluated, providing a basis for urban forest planning and management, such as determining whether afforestation or soil improvement is needed to increase carbon sinks.

[0086] This embodiment achieves accurate division and dynamic monitoring of urban forest carbon sink areas through air-based remote sensing, near-ground mobile and ground multi-scale, multi-source data monitoring. This embodiment not only improves the comprehensiveness and accuracy of data acquisition, but also effectively eliminates the interference of complex urban environment on monitoring, ensuring the reliability of data. At the same time, the monitoring frequency is flexibly set according to regional characteristics and vegetation growth conditions, which reduces monitoring costs and improves resource utilization efficiency. Through real-time monitoring and data analysis, it provides a scientific basis for urban forest planning, management and ecological protection, helps promote urban greening, enhance carbon sink capacity, and promote sustainable urban development.

[0087] In one embodiment of the present disclosure, the ecological environment data includes vegetation index data, tree growth data and near-ground spectral data.

[0088] The biomass information of the carbon sink monitoring area is determined based on the monitored multi-source data, including: The biomass information of the carbon sink monitoring area is determined based on vegetation index data, tree growth data and near-ground spectral data.

[0089] In this embodiment, the vegetation index data is a numerical value obtained by calculating the reflectivity of electromagnetic waves in different bands, reflecting the growth status and coverage of vegetation. For example, the normalized vegetation index can measure the density and health status of vegetation. Tree growth data can include indicators such as tree height, breast diameter, and crown width, reflecting the growth and development of individual trees. Tree growth data can reflect the biomass accumulation and growth rate of trees. Near-ground spectral data is spectral information collected at a position close to the ground, which can provide more detailed vegetation feature information, such as chlorophyll content, leaf structure, etc.

[0090] For example, in the actual application of urban forest carbon sink monitoring, vegetation index data, such as the Normalized Difference Vegetation Index (NDVI), are obtained through satellite remote sensing to understand the overall growth status and coverage of the forest. At the same time, field measurements are used to obtain tree growth data, including tree height, diameter at breast height, etc. Near-ground spectral data can be collected through portable spectrometers or small drones. For example, in an urban forest park, biomass information is calculated based on these data. If the NDVI value is greater than the preset vegetation index threshold, and the tree diameter at breast height is greater than the preset diameter at breast height threshold, combined with the healthy vegetation characteristics reflected by the near-ground spectral characteristics, it can be inferred that the biomass in the area is high, and then its carbon sink capacity is evaluated, providing a basis for the management and ecological protection of the park.

[0091] This embodiment can comprehensively and accurately monitor the biomass information of carbon sink areas by integrating vegetation index data, tree growth data and near-ground spectral data, providing a scientific basis for evaluating carbon sink capacity. This embodiment not only improves monitoring efficiency, but also ensures the diversity and accuracy of data, helps to accurately formulate ecological protection measures, optimize resource allocation, promote ecological balance and sustainable development, and provides strong data support and decision-making basis for urban forest management and ecological protection.

[0092] In one embodiment of the present disclosure, the ecological environment data includes meteorological data and soil data.

[0093] The biomass information of the carbon sink monitoring area is determined based on the monitored multi-source data, including: Determine the impact rate of human activities based on multi-source data and historical carbon sink data.

[0094] Based on the impact rate of human activities, meteorological data and soil data, the biomass information of the carbon sink monitoring area is predicted to determine the real-time biomass information and predicted biomass information.

[0095] Real-time biomass information and predicted biomass information are both biomass information.

[0096] In this embodiment, the historical carbon sink data is a record of the total amount of carbon fixed in the carbon sink monitoring area over a period of time in the past, which can reflect the long-term trend of changes in the carbon sink capacity of the area.

[0097] The human activity impact rate is an indicator to measure the extent to which human activities affect the carbon sink capacity of the carbon sink monitoring area, such as the proportion of negative effects of urbanization, deforestation, land development and other activities on carbon sinks.

[0098] Meteorological data can include temperature, precipitation, light intensity, wind speed, etc. These factors will affect vegetation growth and photosynthesis, and thus affect carbon sequestration capacity.

[0099] Soil data can include soil organic carbon content, texture, fertility, pH, etc. Good soil conditions are conducive to vegetation growth and carbon storage, thereby affecting carbon sequestration.

[0100] Real-time biomass information is the total amount of organic matter in vegetation and soil in the carbon sink monitoring area at the current moment, reflecting the current carbon sink capacity.

[0101] Predicted biomass information is an estimate of the biomass in the carbon sink monitoring area in the future based on existing data and models, providing forward-looking guidance for planning and management.

[0102] In this embodiment, by collecting multi-source data, including historical carbon sink data, meteorological data, soil data, etc., the impact of human activities on carbon sinks is analyzed, that is, the impact rate of human activities is determined. Then, considering these factors comprehensively, mathematical models and algorithms are used to calculate the current real-time biomass information and predict future biomass.

[0103] For example, satellite remote sensing, ground monitoring stations and other means can be used to obtain meteorological data and soil data; soil characteristics can be determined through field surveys and sampling analysis; and the impact rate of human activities can be calculated by combining historical monitoring data and models. Using statistical models or machine learning algorithms, these data are input into the model to obtain real-time biomass information and predicted biomass information.

[0104] For example, in a forest carbon sink monitoring area around a city, historical carbon sink data for the past two years, as well as current meteorological data and soil data, are collected. Through analysis, it is found that with the expansion of the city, the impact rate of human activities in the area is gradually increasing. The model is used to calculate the current real-time biomass information to be high (greater than the biomass threshold) based on current meteorological conditions (such as suitable temperature and precipitation) and good soil organic carbon content. However, considering that future urban construction will further affect the area, according to the changing trends of human activity impact rate, meteorological and soil data, it is predicted that the biomass information will decrease. Based on this, relevant departments can formulate protection measures, such as demarcating ecological protection red lines and strengthening forest management, to maintain and improve the carbon sink capacity of the region.

[0105] This embodiment achieves accurate prediction and real-time monitoring of biomass information in carbon sink monitoring areas by comprehensively considering multi-source data and the impact rate of human activities, providing a scientific basis for ecological protection. This embodiment not only improves the accuracy and timeliness of carbon sink monitoring, but also helps to timely discover and solve the problem of declining carbon sink capacity, provides forward-looking guidance for the formulation of effective ecological protection measures, and helps maintain ecological balance and promote sustainable development.

[0106] Corresponding to the regional carbon sink monitoring method based on urban forests in the above embodiment, Figure 4 This is a structural block diagram of a regional carbon sink monitoring device based on urban forests provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 4 The regional carbon sink monitoring device 20 based on urban forests includes: a first monitoring module 21, a second monitoring module 22 and a calculation module 23.

[0107] Among them, the first monitoring module 21 is used to obtain vegetation spectral information and ground image data of urban forests collected by the space-based satellite monitoring terminal, generate a vegetation map of the urban forest based on the vegetation spectral information and ground image data, and divide the carbon sink monitoring area based on the vegetation map of the urban forest.

[0108] The second monitoring module 22 is used to perform multi-source data monitoring on the carbon sink monitoring area, and determine the biomass information of the carbon sink monitoring area based on the monitored multi-source data.

[0109] The calculation module 23 is used to determine the target carbon sink data of the carbon sink monitoring area based on the biomass information.

[0110] In an embodiment of the present disclosure, the first monitoring module 21 is specifically configured to determine vegetation distribution data and vegetation types of urban forests based on vegetation spectral information.

[0111] The terrain data and land use type are determined based on the ground image data.

[0112] Generate vegetation maps based on vegetation distribution data and terrain data.

[0113] Carbon sink monitoring areas are divided on vegetation maps based on vegetation types and land use types.

[0114] In one embodiment of the present disclosure, the second monitoring module 22 is specifically used to perform multi-source data monitoring of the carbon sink monitoring area based on an air-based remote sensing monitoring terminal, a near-ground mobile monitoring terminal and a ground monitoring terminal.

[0115] In one embodiment of the present disclosure, the multi-source data includes image data and terrain data. The second monitoring module 22 is further configured to collect primary image data and primary terrain data of the carbon sink monitoring area based on the air-based remote sensing monitoring terminal.

[0116] The carbon sink monitoring area is divided based on the primary image data and primary terrain data, and the primary monitoring area is determined.

[0117] The secondary image data and secondary terrain data of the primary monitoring area are collected based on the near-ground mobile monitoring terminal. Both the primary image data and the secondary image data are image data, and both the primary terrain data and the secondary terrain data are terrain data.

[0118] In an embodiment of the present disclosure, the multi-source data also includes ecological environment data. The second monitoring module 22 is further configured to determine the data monitoring frequency of the carbon sink monitoring area based on the secondary image data and the secondary terrain data.

[0119] Control the ground monitoring terminal to dynamically collect ecological environment data based on the data monitoring frequency.

[0120] In one embodiment of the present disclosure, the ecological environment data includes vegetation index data, tree growth data and near-ground spectral data. The second monitoring module 22 is further used to determine the biomass information of the carbon sink monitoring area based on the vegetation index data, tree growth data and near-ground spectral data.

[0121] In one embodiment of the present disclosure, the ecological environment data includes meteorological data and soil data. The second monitoring module 22 is further configured to determine the impact rate of human activities based on multi-source data and historical carbon sink data.

[0122] Based on the impact rate of human activities, meteorological data and soil data, the biomass information of the carbon sink monitoring area is predicted to determine the real-time biomass information and predicted biomass information.

[0123] Real-time biomass information and predicted biomass information are both biomass information.

[0124] See also Figure 5 , Figure 5 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 5The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 4 The functions of modules 21 to 23 are shown.

[0125] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0126] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0127] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0128] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the regional carbon sink monitoring method based on urban forests provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0129] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0130] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0131] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0135] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0136] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A regional carbon sink monitoring method based on urban forests, characterized in that: include: Acquire vegetation spectrum information and ground image data of the urban forest collected by a space-based satellite monitoring terminal, generate a vegetation map of the urban forest based on the vegetation spectrum information and the ground image data, and divide the carbon sink monitoring area based on the vegetation map of the urban forest; Performing multi-source data monitoring on the carbon sink monitoring area, and determining biomass information of the carbon sink monitoring area based on the monitored multi-source data; The target carbon sink data of the carbon sink monitoring area is determined based on the biomass information.

2. The method for monitoring regional carbon sinks based on urban forests according to claim 1, characterized in that: The step of generating a vegetation map of the urban forest based on the vegetation spectrum information and the ground image data, and dividing the carbon sink monitoring area based on the vegetation map of the urban forest, comprises: Determining vegetation distribution data and vegetation types of urban forests based on the vegetation spectral information; determining terrain data and land use type based on the ground image data; generating the vegetation map based on the vegetation distribution data and the terrain data; A carbon sink monitoring area is divided on the vegetation map based on the vegetation type and the land use type.

3. The regional carbon sink monitoring method based on urban forests according to claim 1, characterized in that: The multi-source data monitoring of the carbon sink monitoring area includes: Multi-source data monitoring of the carbon sink monitoring area is carried out based on an air-based remote sensing monitoring terminal, a near-ground mobile monitoring terminal and a ground monitoring terminal.

4. The method for monitoring regional carbon sinks based on urban forests according to claim 3, characterized in that: The multi-source data includes image data and terrain data; The multi-source data monitoring of the carbon sink monitoring area based on the air-based remote sensing monitoring terminal, the near-ground mobile monitoring terminal and the ground monitoring terminal includes: Collecting primary image data and primary terrain data of the carbon sink monitoring area based on an air-based remote sensing monitoring terminal; Dividing the carbon sink monitoring area based on the primary image data and the primary terrain data to determine a primary monitoring area; The secondary image data and the secondary terrain data of the primary monitoring area are collected based on the near-ground mobile monitoring terminal; the primary image data and the secondary image data are both image data, and the primary terrain data and the secondary terrain data are both terrain data.

5. The method for monitoring regional carbon sinks based on urban forests according to claim 4, characterized in that: The multi-source data also includes ecological environment data; The multi-source data monitoring of the carbon sink monitoring area based on the air-based remote sensing monitoring terminal, the near-ground mobile monitoring terminal and the ground monitoring terminal also includes: Determining the data monitoring frequency of the carbon sink monitoring area based on the secondary image data and the secondary terrain data; The ground monitoring terminal is controlled to dynamically collect the ecological environment data based on the data monitoring frequency.

6. The method for monitoring regional carbon sinks based on urban forests according to claim 5, characterized in that: The ecological environment data include vegetation index data, tree growth data and near-ground spectral data; The determining of the biomass information of the carbon sink monitoring area based on the monitored multi-source data includes: The biomass information of the carbon sink monitoring area is determined based on the vegetation index data, tree growth data and near-ground spectral data.

7. The method for monitoring regional carbon sinks based on urban forests according to claim 5, characterized in that: The ecological environment data includes meteorological data and soil data; The determining of the biomass information of the carbon sink monitoring area based on the monitored multi-source data includes: Determine the human activity impact rate based on the multi-source data and historical carbon sink data; Predicting the biomass information of the carbon sink monitoring area based on the human activity impact rate, the meteorological data and the soil data, and determining the real-time biomass information and the predicted biomass information; The real-time biomass information and the predicted biomass information are both biomass information.

8. A regional carbon sink monitoring device based on urban forests, characterized in that: include: A first monitoring module is used to obtain vegetation spectrum information and ground image data of urban forests collected by a space-based satellite monitoring terminal, generate a vegetation map of the urban forest based on the vegetation spectrum information and the ground image data, and divide the carbon sink monitoring area based on the vegetation map of the urban forest; A second monitoring module is used to perform multi-source data monitoring on the carbon sink monitoring area, and determine the biomass information of the carbon sink monitoring area based on the monitored multi-source data; A calculation module is used to determine the target carbon sink data of the carbon sink monitoring area based on the biomass information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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