CN-122024037-A - Garden carbon sink function partitioning method integrating multisource remote sensing data

CN122024037ACN 122024037 ACN122024037 ACN 122024037ACN-122024037-A

Abstract

The invention provides a garden carbon sink function partitioning method integrating multisource remote sensing data, and relates to the technical field of garden carbon sink assessment and remote sensing monitoring. The method comprises the steps of obtaining multi-source remote sensing data such as optical remote sensing, radar remote sensing and laser radar, carrying out radiometric calibration, atmospheric correction and geometric correction pretreatment on the data, extracting vegetation characteristic parameters such as normalized vegetation index, leaf area index and surface temperature, calculating garden carbon sink efficiency parameters based on the vegetation characteristic parameters, calculating garden carbon sink function division parameters by combining the topographic data, carrying out carbon sink function division on a garden area by adopting a clustering algorithm, dividing high, medium and low carbon sink function grade areas, and outputting a carbon sink function division space distribution diagram and a data report. According to the invention, the comprehensive evaluation is improved through multi-source data fusion, the accurate quantification is realized through innovative parameter calculation, the scientificity and the repeatability are improved through an objective partitioning method, and the fine management of garden carbon sink is effectively supported.

Inventors

XU HUA
SONG JIANGPING
SHEN YI
XU GEGE

Assignees

绍兴理工学院

Dates

Publication Date: 20260512
Application Date: 20251219

Claims (10)

1. A garden carbon sink function division method integrating multisource remote sensing data is characterized by comprising the following steps of: S1, acquiring multi-source remote sensing data, wherein the multi-source remote sensing data comprises optical remote sensing data, radar remote sensing data and laser radar data; s2, preprocessing the multi-source remote sensing data, including radiation calibration, atmospheric correction and geometric correction, so as to eliminate data errors and improve data quality; S3, extracting vegetation characteristic parameters from the preprocessed multi-source remote sensing data, wherein the vegetation characteristic parameters comprise normalized vegetation indexes NDVI, leaf area indexes LAI and surface temperature; s4, calculating a garden carbon sink efficiency parameter based on the vegetation characteristic parameter; S5, calculating a garden carbon sink function division parameter based on the garden carbon sink efficiency parameter and the topographic data; S6, according to the garden carbon sink function division parameters, carrying out carbon sink function division on a garden area by adopting a clustering algorithm, and dividing areas with different carbon sink function grades; and S7, outputting a garden carbon sink function division result, including a spatial distribution diagram and a data report.
2. The method for determining a garden carbon sink function according to claim 1, wherein the step of calculating the garden carbon sink efficiency parameter in S4 comprises: based on the vegetation characteristic parameters, calculating a garden carbon sink efficiency parameter CEI, wherein the calculation formula is as follows: Wherein CEI represents a garden carbon sink efficiency parameter, Represents the light energy utilization rate constant, NDVI represents the normalized vegetation index, LAI represents the leaf area index, T represents the surface temperature, Represents the optimum temperature of the landscape plant, and sigma represents the standard deviation of temperature tolerance.
3. The method for calculating the garden carbon sink function partition parameter according to claim 1, wherein the step of calculating the garden carbon sink function partition parameter in S5 comprises: S501, obtaining terrain data comprising a gradient S, a slope A and an elevation E; s502, calculating garden carbon sink function division parameters CFZP based on the garden carbon sink efficiency parameters CEI and the topographic data, wherein the calculation formula is as follows: Wherein CFZP represents a garden carbon sink function division parameter, CEI represents a garden carbon sink efficiency parameter, S represents a gradient, A represents a gradient direction, Represents the optimal slope direction, E represents the altitude, Representing the reference altitude.
4. The method for determining a garden carbon sink function region by fusing multi-source remote sensing data according to claim 1, wherein the specific step of obtaining the multi-source remote sensing data in S1 comprises the following steps: S101, acquiring optical remote sensing data from a satellite remote sensing platform, wherein the optical remote sensing data comprises multispectral data and hyperspectral data; s102, acquiring radar remote sensing data from an airborne or spaceborne radar system, wherein the radar remote sensing data comprises synthetic aperture radar SAR data; s103, obtaining laser radar data from a laser radar system, wherein the laser radar data comprise point cloud data and Digital Elevation Model (DEM) data.
5. The method for a garden carbon sink function division fused with multi-source remote sensing data according to claim 1, wherein the specific step of preprocessing the multi-source remote sensing data in S2 comprises: s201, performing radiometric calibration and atmospheric correction on the optical remote sensing data to eliminate atmospheric scattering and absorption effects; S202, performing speckle noise filtering and geometric correction on radar remote sensing data to improve data consistency; and S203, performing point cloud classification and interpolation processing on the laser radar data to generate high-precision topographic data.
6. The method for determining a garden carbon sink function region by fusing multisource remote sensing data according to claim 1, wherein the specific step of extracting the vegetation characteristic parameters in S3 comprises: S301, calculating a normalized vegetation index NDVI from optical remote sensing data; s302, inverting leaf area index LAI from laser radar data; S303, inverting the surface temperature from the multispectral remote sensing data.
7. The method for carbon sink function division in gardens with multi-source remote sensing data fusion according to claim 1, wherein the specific step of adopting a clustering algorithm to conduct carbon sink function division on the garden area in S6 comprises the following steps: s601, taking garden carbon sink function division parameters as input characteristics; S602, grouping areas by using a K-means clustering algorithm; and S603, dividing the carbon sink function grade according to the clustering result, wherein the carbon sink function grade comprises a high carbon sink region, a medium carbon sink region and a low carbon sink region.
8. The method for a garden carbon sink function block with multisource remote sensing data fusion according to claim 1, wherein the specific step of outputting the garden carbon sink function block result in S7 includes: S701, generating a carbon sink function division space distribution diagram, and marking different function grades in the diagram; And S702, generating a data report comprising area statistics and carbon sink parameter summary of each functional area.
9. The method for a garden carbon sink function compartment fused with multi-source remote sensing data according to claim 1, further comprising the steps of: and S8, according to the carbon sink function division result, setting a garden management strategy, and implementing differential maintenance measures for areas with different function grades.
10. The method for a garden carbon sink function block with multi-source remote sensing data fusion according to claim 6, wherein the step of inverting the leaf area index LAI from the laser radar data in S302 comprises: S3021, classifying vegetation and non-vegetation on the laser radar point cloud data; S3022, calculating a leaf area index LAI based on the point cloud density and the altitude information.

Description

Garden carbon sink function partitioning method integrating multisource remote sensing data Technical Field The invention relates to the technical field of garden carbon sink assessment and remote sensing monitoring, in particular to a garden carbon sink function partitioning method integrating multisource remote sensing data. Background With the acceleration of the urban process and the deep promotion of ecological civilization construction, the carbon sink function evaluation and optimization management of garden greenbelts are increasingly emphasized. The accurate division of garden carbon sink functional areas is a basis for realizing scientific management, and has important significance for improving urban carbon sink capacity and optimizing green space layout. At present, the technical method in the field still has a plurality of limitations, and the accurate evaluation and effective management of the garden carbon sink function are restricted. In the prior art, garden carbon sink assessment relies primarily on a single remote sensing data source, such as using only optical remote sensing data. The method has obvious defects that optical data is easily influenced by cloud and rain weather, the continuity and the integrity of the data are difficult to ensure, and meanwhile, the three-dimensional structure and the physiological state of vegetation are difficult to comprehensively reflect by simply relying on vegetation indexes, so that the evaluation result of carbon sink is inaccurate. Although the laser radar can acquire vegetation structural parameters, radar data has penetrability, a single data source cannot fully utilize the advantages of each remote sensing technology, and the problem of insufficient information utilization exists. The existing garden carbon sink function partitioning method is mostly based on simple vegetation index threshold segmentation, and lacks comprehensive consideration of multidimensional environmental factors. The traditional partitioning method only considers vegetation coverage, but ignores the influence of environmental factors such as topography, microclimate and the like on the carbon sink function. Terrain features such as gradient and slope direction can obviously influence illumination conditions and water distribution, and further influence photosynthesis efficiency of vegetation, but the existing method rarely brings the factors into a quantitative model of carbon sink functional division, so that a division result is deviated from actual carbon sink capacity. In terms of parameter calculation, the prior art generally adopts a simplified empirical formula, and the interaction between the physiological characteristics of vegetation and environmental factors cannot be fully considered. For example, the effect of temperature on plant photosynthesis is typically reduced to a linear relationship, whereas in practice there is a significant non-linear characteristic of the carbon absorption efficiency of plants under different temperature conditions. The simplified processing leads the evaluation result of the carbon sink efficiency to have larger difference with the actual situation, and influences the scientificity of the functional division. In addition, the existing functional partitioning method has obvious defects in a data processing link. The fusion processing of the multi-source remote sensing data lacks a systematic technical scheme, the registration precision among different data sources is not high, and the problem of inconsistent data scale is outstanding. This results in large errors in subsequent feature extraction and parameter calculation, affecting the reliability and accuracy of the overall technical process. The links such as radiation correction, atmosphere correction and the like in the data preprocessing process also lack unified quality control standards, so that the usability of the data is further reduced. In the implementation level of functional partitioning, the existing method mostly depends on manual experience partitioning or simple statistical classification, and lacks objective and quantitative partitioning basis. This more subjective partitioning approach results in poor repeatability of results, and different operators may give completely different partitioning results. Meanwhile, the traditional method is difficult to process massive remote sensing data and complex environmental factors, and the practical application requirements are difficult to meet in the aspects of efficiency and precision. Another significant problem is the lack of verification mechanisms of the system in the prior art. The accuracy of the carbon sink function division result is difficult to verify by reliable technical means, most methods are stopped at a theoretical level, and the feasibility of practical application is lacked. The complete technical chain from data acquisition to final functional division is not established yet, and the links are not tight