CN115082274A - Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing - Google Patents
Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing Download PDFInfo
- Publication number
- CN115082274A CN115082274A CN202210649062.XA CN202210649062A CN115082274A CN 115082274 A CN115082274 A CN 115082274A CN 202210649062 A CN202210649062 A CN 202210649062A CN 115082274 A CN115082274 A CN 115082274A
- Authority
- CN
- China
- Prior art keywords
- carbon
- remote sensing
- satellite
- estimation
- sink
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 248
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 248
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000009825 accumulation Methods 0.000 claims abstract description 37
- 238000012937 correction Methods 0.000 claims description 25
- 230000005855 radiation Effects 0.000 claims description 18
- 238000004177 carbon cycle Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000002310 reflectometry Methods 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 230000001788 irregular Effects 0.000 claims description 3
- 230000003595 spectral effect Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 claims 1
- 238000001228 spectrum Methods 0.000 claims 1
- 230000001932 seasonal effect Effects 0.000 abstract 1
- 238000003860 storage Methods 0.000 description 12
- 230000007774 longterm Effects 0.000 description 10
- 230000002776 aggregation Effects 0.000 description 6
- 238000004220 aggregation Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 230000007246 mechanism Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
- G01N21/274—Calibration, base line adjustment, drift correction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/004—CO or CO2
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0098—Plants or trees
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/127—Calibration; base line adjustment; drift compensation
- G01N2201/12746—Calibration values determination
Landscapes
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Business, Economics & Management (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Mathematical Physics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Combustion & Propulsion (AREA)
- Strategic Management (AREA)
- Primary Health Care (AREA)
- Botany (AREA)
- Wood Science & Technology (AREA)
- Image Processing (AREA)
Abstract
本发明公开了一种基于卫星遥感的地表植被碳汇估算与交易方法及系统，包括：基于对地观测卫星遥感观测技术进行地表植被碳汇(包括森林碳汇、农作物碳汇、草地碳汇、湿地碳汇)定量估算与核算，使用地基点位观测碳汇数据校对验证卫星遥感大数据反演的碳汇结果，并直接将特定区域面积的植被碳汇提交碳交易市场供碳排放企业购买。本发明可快速、高效完成地表植被在月、季、年尺度上的碳积存和碳排放量的估算，对有植被碳汇估算需求相关的碳交易环节统一到完整的框架下，形成碳汇估算到碳交易的业务闭环，从而活跃碳交易市场、提高碳交易效率、增加碳交易透明度。
The invention discloses a method and system for estimating and trading surface vegetation carbon sinks based on satellite remote sensing. Wetland carbon sinks) quantitative estimation and accounting, use ground-based point observation carbon sink data to verify the carbon sink results retrieved from satellite remote sensing big data, and directly submit vegetation carbon sinks in specific areas to the carbon trading market for carbon emission companies to purchase. The method can quickly and efficiently complete the estimation of the carbon accumulation and carbon emission of the surface vegetation on the monthly, seasonal and annual scales, and unify the carbon trading links related to the estimation of vegetation carbon sinks under a complete framework to form a carbon sink estimation To the closed loop of carbon trading business, so as to activate the carbon trading market, improve the efficiency of carbon trading, and increase the transparency of carbon trading.
Description
技术领域technical field
本发明涉及一种实现地表植被碳汇估算以提高碳交易效率和透明度的方法，尤其涉及基于卫星遥感的地表植被碳汇估算与交易方法及系统。The invention relates to a method for realizing surface vegetation carbon sink estimation to improve carbon trading efficiency and transparency, in particular to a method and system for surface vegetation carbon sink estimation and trading based on satellite remote sensing.
背景技术Background technique
随着全国碳排放权交易市场正式上线交易，标志着我国碳排放交易体系的总体设计基本完成。碳交易市场成交量情况来，成交量整体呈现初期增速较快、中期市场热度下降、近年来再现增长的变化，但碳交易市场的成交金额在试点阶段保持持续增长趋势。究其原因，部分归咎于碳定价方法、碳税和碳交易市场体系与碳汇碳源估算脱节造成的。要充分发挥碳交易市场作为低成本碳减排的市场化核心政策工具功能，必须完善碳定价机制；而完善碳定价机制的基础，则离不开科学、有效的碳汇碳源评估技术作为支撑。近年来快速发展的卫星遥感对地观测技术及其在陆地生态系统动态监测方面的广泛应用无疑为完善碳定价机制、提高碳交易效率和透明度提供了无可替代的技术保障。With the official launch of the national carbon emission trading market, it marks that the overall design of my country's carbon emission trading system has been basically completed. In terms of the transaction volume of the carbon trading market, the overall trading volume showed a rapid growth in the initial stage, a decline in the market popularity in the mid-term, and a re-growth in recent years. However, the transaction value of the carbon trading market maintained a continuous growth trend in the pilot stage. The reason is partly due to the disconnection between carbon pricing method, carbon tax and carbon trading market system and carbon sink carbon source estimation. In order to give full play to the carbon trading market as a market-oriented core policy tool for low-cost carbon emission reduction, the carbon pricing mechanism must be improved; and the basis for improving the carbon pricing mechanism cannot be separated from the scientific and effective carbon sink carbon source assessment technology as the support . In recent years, the rapid development of satellite remote sensing earth observation technology and its wide application in dynamic monitoring of terrestrial ecosystems undoubtedly provide an irreplaceable technical guarantee for improving carbon pricing mechanism and improving carbon trading efficiency and transparency.
在全球气候变化研究框架内，采用卫星遥感地对观测技术开展了一系列陆地生态系统和海洋生态系统碳汇碳源研究，在定量反演生态系统碳积存和碳排放方面取得了显著进展，基于卫星遥感定量反演模型的碳循环理论框架和解析方法已可精确估算地球生物圈特定位置、特定生态系统、特定面积、特定时间段内的碳积存和碳蓄积量。因此，本发明结合已经过科学验证、发展较为成熟的定量遥感反演模型，集成长时间序列、具有高可靠性的卫星遥感科学数据集，根据当前碳交易市场对精确碳汇估算的需求，形成一整套采用卫星遥感手段实现地表植被碳汇估算以提高碳交易效率和透明度的系统和方法。Within the framework of global climate change research, a series of studies on carbon sinks and carbon sources in terrestrial ecosystems and marine ecosystems have been carried out using satellite remote sensing geo-pair observation technology, and significant progress has been made in quantitatively inverting ecosystem carbon storage and carbon emissions. The carbon cycle theoretical framework and analytical methods of the satellite remote sensing quantitative inversion model can accurately estimate the carbon accumulation and carbon accumulation in a specific location, a specific ecosystem, a specific area, and a specific time period in the Earth's biosphere. Therefore, the present invention combines a scientifically verified and relatively mature quantitative remote sensing inversion model, integrates long-term, high-reliability satellite remote sensing scientific data sets, and according to the current carbon trading market demand for accurate carbon sink estimation, forms A set of systems and methods for the estimation of surface vegetation carbon sinks by means of satellite remote sensing to improve the efficiency and transparency of carbon trading.
发明内容SUMMARY OF THE INVENTION
为了解决上述至少一个技术问题，本发明提出了一种基于卫星遥感的地表植被碳汇估算与交易方法及系统。In order to solve at least one of the above technical problems, the present invention proposes a method and system for estimating and trading surface vegetation carbon sinks based on satellite remote sensing.
本发明第一方面提供了一种基于卫星遥感的地表植被碳汇估算与交易方法，包括：A first aspect of the present invention provides a method for estimating and trading surface vegetation carbon sinks based on satellite remote sensing, including:
步骤S1，完成多源对地观测遥感卫星数据之间的辐射定标，并进行卫星数据产品地表反射率产品归一化，以生成长时间序列的具有统一辐射定标体系的卫星对地观测数据产品；Step S1, complete the radiometric calibration between the multi-source earth observation remote sensing satellite data, and normalize the satellite data product surface reflectance products to generate long-term satellite earth observation data with a unified radiometric calibration system product;
步骤S2，基于定量遥感反演模型估算不同陆地生态系统(森林、农作物、草地、湿地)在特定地理位置(低纬、中纬、高纬)、特定生境条件下(洪涝、湿润、干旱)、特定时间段内(逐日、月度、年际)碳蓄积量；Step S2, based on the quantitative remote sensing inversion model to estimate different terrestrial ecosystems (forests, crops, grasslands, wetlands) in specific geographic locations (low latitude, middle latitude, high latitude), specific habitat conditions (flood, humid, drought), Carbon storage in a specific time period (daily, monthly, inter-annual);
步骤S3，使用地面点位碳循环(包括碳源、碳汇)观测站对卫星遥感反演获取的碳汇成果进行验证并用于遥感定量反演模型输入参数的校正；Step S3, using the ground point carbon cycle (including carbon source and carbon sink) observation station to verify the carbon sink results obtained by the satellite remote sensing inversion and use it for the correction of the input parameters of the remote sensing quantitative inversion model;
步骤S4，以现势对地观测遥感卫星影像作为输入变量，使用校正后的、针对不同陆地生态系统的、用于碳汇估算卫星遥感定量反演模型，估算特定区域、特定时段内的碳蓄积量；Step S4, taking the current situation of the earth observation remote sensing satellite image as the input variable, using the corrected satellite remote sensing quantitative inversion model for different terrestrial ecosystems and used for carbon sink estimation, to estimate the carbon accumulation in a specific area and a specific period of time. ;
步骤S5，将基于对地观测遥感卫星反演估算的碳汇估算成果提交至全国碳排放权交易市场的登记和结算中心的全国碳交易注册登记系统；Step S5, submit the carbon sink estimation result based on the inversion estimation of the earth observation remote sensing satellite to the national carbon trading registration system of the registration and settlement center of the national carbon emission trading market;
步骤S6，与市场碳排放配额一起完成配额汇总，参与碳排放权市场交易。Step S6, complete the quota aggregation together with the market carbon emission quota, and participate in the carbon emission right market transaction.
本方案中，所述步骤S1中，生成统一的卫星辐射定标体系，具体为：In this solution, in the step S1, a unified satellite radiation calibration system is generated, specifically:
进行对地观测遥感卫星数据预处理，使用卫星传感器辐射定标参数将卫星原始数据转换为辐射亮度值，高斯滤波等方法去除卫星影像上的不规则噪声和亮斑；Carry out preprocessing of earth observation remote sensing satellite data, use satellite sensor radiometric calibration parameters to convert satellite raw data into radiance values, and remove irregular noise and bright spots on satellite images by Gaussian filtering and other methods;
使用直方图匹配及地面辐射校正场，在不同对地观测遥感卫星影像之间建立关键点链接；Use histogram matching and ground radiation correction field to establish key point links between different earth observation remote sensing satellite images;
通过不同卫星影像的关键点辐射亮度值构建两两卫星之间的相对辐射校正模型，并获取模型系数，实现不同卫星遥感影像的辐射亮度物理量上的统一；The relative radiation correction model between two satellites is constructed by the radiance values of key points of different satellite images, and the model coefficients are obtained to achieve the unification of the physical quantities of radiance of different satellite remote sensing images;
使用大气辐射传输模型，将不同卫星遥感影像的辐射亮度转化归一化的地表反射率，生成长时间序列的多源遥感卫星的地表反射率产品。Using the atmospheric radiative transfer model, the radiance of different satellite remote sensing images is converted into the normalized surface reflectance to generate a long-term series of surface reflectance products of multi-source remote sensing satellites.
本方案中，所述步骤S2中，通过多源卫星遥感地表反射率产品估算特定陆地生态系统在特定时间段内碳蓄积量，具体为：In this scheme, in the step S2, the carbon accumulation of a specific terrestrial ecosystem in a specific time period is estimated by using a multi-source satellite remote sensing surface reflectance product, specifically:
根据全球和区域尺度的碳循环研究框架，选择适合不同生态系统的自下而上的定量遥感反演模型作为碳汇估算模型；According to the carbon cycle research framework at the global and regional scales, a bottom-up quantitative remote sensing inversion model suitable for different ecosystems is selected as the carbon sink estimation model;
将辐射校正、大气校正及不同传感器相对辐射校正后的卫星遥感影像作为输入变量，使用碳汇估算模型，完成特定区域在特定时间段内的碳蓄积量初步估算。Using the satellite remote sensing images after radiation correction, atmospheric correction and relative radiation correction of different sensors as input variables, the carbon sink estimation model is used to complete the preliminary estimation of carbon accumulation in a specific area in a specific time period.
本方案中，所述步骤S4中，使用校正后的遥感定量反演模型估算特定区域、特定时段内的碳蓄积量，具体为：In this solution, in the step S4, the corrected remote sensing quantitative inversion model is used to estimate the carbon accumulation in a specific area and a specific time period, specifically:
根据特定需求和数据可获取的便利程度，采集不同空间分辨率、波谱分辨率的卫星遥感影像，并对影像数据进行预处理，与其他卫星传感器遥感影像进行相对辐射校正；According to specific needs and the convenience of data acquisition, collect satellite remote sensing images of different spatial resolutions and spectral resolutions, preprocess the image data, and perform relative radiometric correction with other satellite sensor remote sensing images;
使用处理后的卫星遥感影像作为输入参数驱动用于碳汇估算的卫星遥感定量反演模型，计算特定区域、特定时段内的碳蓄积量；Use the processed satellite remote sensing images as input parameters to drive the satellite remote sensing quantitative inversion model for carbon sink estimation, and calculate the carbon accumulation in a specific area and a specific period of time;
将计算得到的特定区域、特定时段内的碳蓄积量与其他可获取的站点、实测碳蓄积量进行比对，核算该区域在特定区域、特定时段内的碳蓄积量。Compare the calculated carbon accumulation in a specific area and a specific period with other available sites and measured carbon accumulation, and calculate the carbon accumulation in a specific area and in a specific period of time.
本方案中，所述步骤S6中，进行卫星遥感碳汇核算与全国碳交易注册登记系统的联通，具体为：In this scheme, in the step S6, the connection between the satellite remote sensing carbon sink accounting and the national carbon trading registration system is performed, specifically:
经卫星遥感估算的特定区域、特定时间段内的碳汇量在完成碳汇核算并经认证后录入全国碳交易注册登记系统；The carbon sinks estimated by satellite remote sensing in a specific area and within a specific time period are entered into the national carbon trading registration system after the carbon sink accounting is completed and certified;
将录入全国碳交易注册登记系统的增量碳汇与市场碳排放配额一起完成配额汇总，在全国碳排放权交易市场，供对碳排放权有需求的企业选择，并进行交易。The incremental carbon sinks entered into the national carbon trading registration system will be combined with the market carbon emission quotas to complete the quota aggregation. In the national carbon emission rights trading market, companies that need carbon emission rights can choose and trade them.
本发明第二方面还提供了一种基于卫星遥感的地表植被碳汇估算与交易系统，该系统包括：存储器、处理器，所述存储器中包括基于卫星遥感的地表植被碳汇估算与交易方法程序，所述基于卫星遥感的地表植被碳汇估算与交易方法程序被所述处理器执行时实现如下步骤：A second aspect of the present invention also provides a satellite remote sensing-based surface vegetation carbon sink estimation and trading system, the system comprising: a memory and a processor, wherein the memory includes a satellite remote sensing-based surface vegetation carbon sink estimation and trading method program , the method program for estimating and trading surface vegetation carbon sinks based on satellite remote sensing is executed by the processor to achieve the following steps:
完成多源对地观测遥感卫星数据之间的辐射定标，并进行卫星数据产品地表反射率产品归一化，以生成长时间序列的具有统一辐射定标体系的卫星对地观测数据产品；Complete the radiometric calibration between multi-source earth observation remote sensing satellite data, and normalize the surface reflectance products of satellite data products to generate long-term satellite earth observation data products with a unified radiometric calibration system;
基于定量遥感反演模型估算不同陆地生态系统在特定地理位置、特定生境条件下、特定时间段内碳蓄积量；Estimation of carbon storage in different terrestrial ecosystems in specific geographical locations, specific habitat conditions, and specific time periods based on quantitative remote sensing inversion models;
使用地面点位碳循环观测站对卫星遥感反演获取的碳汇成果进行验证并用于遥感定量反演模型输入参数的校正；Use ground-based carbon cycle observation stations to verify the carbon sink results obtained by satellite remote sensing inversion and use it to correct the input parameters of the remote sensing quantitative inversion model;
以现势对地观测遥感卫星影像作为输入变量，使用校正后的、针对不同陆地生态系统的、用于碳汇估算卫星遥感定量反演模型，估算特定区域、特定时段内的碳蓄积量；Using the current Earth observation remote sensing satellite image as the input variable, using the corrected satellite remote sensing quantitative inversion model for carbon sink estimation for different terrestrial ecosystems, to estimate the carbon accumulation in a specific area and a specific period of time;
将基于对地观测遥感卫星反演估算的碳汇估算成果提交至全国碳排放权交易市场的登记和结算中心的全国碳交易注册登记系统；Submit the carbon sink estimation results based on the inversion estimation of earth observation remote sensing satellites to the National Carbon Trading Registration System of the Registration and Settlement Center of the National Carbon Emissions Trading Market;
与市场碳排放配额一起完成配额汇总，参与碳排放权市场交易。Complete quota aggregation together with market carbon emission quotas and participate in carbon emission rights market transactions.
本发明解决了背景技术中存在的缺陷，本发明具备以下有益效果：The present invention solves the defects existing in the background technology, and the present invention has the following beneficial effects:
(1)基于成熟的卫星对地观测遥感技术，依托质量可靠的、可溯源核算的、长时间序列的卫星遥感数据，驱动经学术界和工业界共同认可的陆地生态系统碳汇模型，进行碳汇估算与核算；(1) Based on the mature satellite earth observation remote sensing technology, relying on reliable quality, traceable accounting, long-term satellite remote sensing data, driving the terrestrial ecosystem carbon sink model recognized by both academia and industry to carry out carbon foreign exchange estimation and accounting;
(2)使用经地面观测站点数据验证核算后的碳汇结果，并经生态环境部门认定，然后将卫星遥感技术核算的碳汇增量注册到中碳登系统，供碳排放企业选择，并在全国碳排放权交易市场进行交易；(2) Use the carbon sink results verified and calculated by the data of the ground observation station, and confirmed by the ecological environment department, and then register the carbon sink increment calculated by satellite remote sensing technology into the China Carbon Registration System for carbon emission enterprises to choose, and in the Trading in the national carbon emissions trading market;
(3)在系统的实施中最大程度将陆地生态系统的逐年碳汇增量市场化、货币化、资产化(3) Marketize, monetize and capitalize the annual carbon sink increment of terrestrial ecosystem to the greatest extent in the implementation of the system
(4)在市场层面增加了全国碳排放权交易市场的可交易额度；在全市场碳定价机制层面，促进了碳交易科学定价机制，从而提高全国碳排放权交易市场的碳交易效率和透明度。(4) At the market level, the tradable quota of the national carbon emissions trading market has been increased; on the market-wide carbon pricing mechanism level, the scientific pricing mechanism of carbon trading has been promoted, thereby improving the carbon trading efficiency and transparency of the national carbon emissions trading market.
附图说明Description of drawings
图1示出了本发明基于卫星遥感的地表植被碳汇估算与交易方法的流程图；Fig. 1 shows the flow chart of the present invention's method for estimating and trading surface vegetation carbon sinks based on satellite remote sensing;
图2示出了本发明将陆地生态系统核算后的碳汇融入全国碳排放权交易市场的方法流程图；Fig. 2 shows the flow chart of the method of the present invention for integrating the carbon sink after terrestrial ecosystem accounting into the national carbon emission trading market;
图3示出了本发明基于卫星遥感的地表植被碳汇估算与交易系统的框图。Fig. 3 shows a block diagram of the satellite remote sensing-based surface vegetation carbon sink estimation and trading system of the present invention.
具体实施方式Detailed ways
为了能够更清楚地理解本发明的上述目的、特征和优点，下面结合附图和具体实施方式对本发明进行进一步的详细描述。需要说明的是，在不冲突的情况下，本申请的实施例及实施例中的特征可以相互组合。In order to understand the above objects, features and advantages of the present invention more clearly, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是，本发明还可以采用其他不同于在此描述的其他方式来实施，因此，本发明的保护范围并不受下面公开的具体实施例的限制。Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific details disclosed below. Example limitations.
图1示出了本发明一种基于卫星遥感的地表植被碳汇估算与交易方法的流程图。Figure 1 shows a flow chart of a method for estimating and trading surface vegetation carbon sinks based on satellite remote sensing according to the present invention.
如图1所示，本发明第一方面提供了一种基于卫星遥感的地表植被碳汇估算与交易方法，包括：As shown in Figure 1, a first aspect of the present invention provides a method for estimating and trading surface vegetation carbon sinks based on satellite remote sensing, including:
步骤S1，完成多源对地观测遥感卫星数据之间的辐射定标，并进行卫星数据产品地表反射率产品归一化，以生成长时间序列的具有统一辐射定标体系的卫星对地观测数据产品；Step S1, complete the radiometric calibration between the multi-source earth observation remote sensing satellite data, and normalize the satellite data product surface reflectance products to generate long-term satellite earth observation data with a unified radiometric calibration system product;
步骤S2，基于定量遥感反演模型估算不同陆地生态系统(森林、农作物、草地、湿地)在特定地理位置(低纬、中纬、高纬)、特定生境条件下(洪涝、湿润、干旱)、特定时间段内(逐日、月度、年际)碳蓄积量；Step S2, based on the quantitative remote sensing inversion model to estimate different terrestrial ecosystems (forests, crops, grasslands, wetlands) in specific geographic locations (low latitude, middle latitude, high latitude), specific habitat conditions (flood, humid, drought), Carbon storage in a specific time period (daily, monthly, inter-annual);
步骤S3，使用地面点位碳循环(包括碳源、碳汇)观测站对卫星遥感反演获取的碳汇成果进行验证并用于遥感定量反演模型输入参数的校正；Step S3, using the ground point carbon cycle (including carbon source and carbon sink) observation station to verify the carbon sink results obtained by the satellite remote sensing inversion and use it for the correction of the input parameters of the remote sensing quantitative inversion model;
步骤S4，以现势对地观测遥感卫星影像作为输入变量，使用校正后的、针对不同陆地生态系统的、用于碳汇估算卫星遥感定量反演模型，估算特定区域、特定时段内的碳蓄积量；Step S4, taking the current situation of the earth observation remote sensing satellite image as the input variable, using the corrected satellite remote sensing quantitative inversion model for different terrestrial ecosystems and used for carbon sink estimation, to estimate the carbon accumulation in a specific area and a specific period of time. ;
步骤S5，将基于对地观测遥感卫星反演估算的碳汇估算成果提交至全国碳排放权交易市场的登记和结算中心的全国碳交易注册登记系统；Step S5, submit the carbon sink estimation result based on the inversion estimation of the earth observation remote sensing satellite to the national carbon trading registration system of the registration and settlement center of the national carbon emission trading market;
步骤S6，与市场碳排放配额一起完成配额汇总，参与碳排放权市场交易。Step S6, complete the quota aggregation together with the market carbon emission quota, and participate in the carbon emission right market transaction.
其中，在步骤S1中，根据多源对地观测遥感卫星传感器的参数设置，需实现不同卫星传感器的辐射定标与归一化，生成统一的卫星辐射定标体系，并用于长时间序列卫星地面反射率产品的生产，具体为：Among them, in step S1, according to the parameter settings of the multi-source earth observation remote sensing satellite sensors, it is necessary to realize the radiometric calibration and normalization of different satellite sensors to generate a unified satellite radiometric calibration system, which is used for long-time sequence satellite ground Production of reflectivity products, specifically:
进行对地观测遥感卫星数据预处理，使用卫星传感器辐射定标参数将卫星原始数据转换为辐射亮度值，高斯滤波等方法去除卫星影像上的不规则噪声和亮斑；Carry out preprocessing of earth observation remote sensing satellite data, use satellite sensor radiometric calibration parameters to convert satellite raw data into radiance values, and remove irregular noise and bright spots on satellite images by Gaussian filtering and other methods;
使用直方图匹配及地面辐射校正场，在不同对地观测遥感卫星影像之间建立关键点链接；Use histogram matching and ground radiation correction field to establish key point links between different earth observation remote sensing satellite images;
通过不同卫星影像的关键点辐射亮度值构建两两卫星之间的相对辐射校正模型，并获取模型系数，实现不同卫星遥感影像的辐射亮度物理量上的统一；The relative radiation correction model between two satellites is constructed by the radiance values of key points of different satellite images, and the model coefficients are obtained to achieve the unification of the physical quantities of radiance of different satellite remote sensing images;
使用大气辐射传输模型，将不同卫星遥感影像的辐射亮度转化归一化的地表反射率，生成长时间序列的多源遥感卫星的地表反射率产品。Using the atmospheric radiative transfer model, the radiance of different satellite remote sensing images is converted into the normalized surface reflectance to generate a long-term series of surface reflectance products of multi-source remote sensing satellites.
其中，在步骤S2中，通过多源卫星遥感地表反射率产品估算特定陆地生态系统在特定时间段内碳蓄积量，所述定量遥感反演模型为卫星遥感地表反射率产品之一，定量遥感反演模型可以选取归一化植被指数、差值植被指数、比值植被指数、土壤调整植被指数与碳汇量进行相关性分析，得到其相关性，按照其相关性显著性建立回归模型，反映土地的利用情况及植被覆盖情况；Wherein, in step S2, the carbon accumulation of a specific terrestrial ecosystem in a specific time period is estimated by using a multi-source satellite remote sensing surface reflectance product, and the quantitative remote sensing inversion model is one of the satellite remote sensing surface reflectance products. Quantitative remote sensing inversion Normalized vegetation index, difference vegetation index, ratio vegetation index, soil-adjusted vegetation index and carbon sinks can be selected for correlation analysis to obtain their correlation, and then a regression model can be established according to the significance of their correlation to reflect the land value. Utilization and vegetation coverage;
根据全球和区域尺度的碳循环研究框架，选择适合不同生态系统的自下而上(Bottom-up)的定量遥感反演模型作为碳汇估算模型；According to the carbon cycle research framework at the global and regional scales, a bottom-up quantitative remote sensing inversion model suitable for different ecosystems was selected as the carbon sink estimation model;
将辐射校正、大气校正及不同传感器相对辐射校正后的卫星遥感影像作为输入变量，使用碳汇估算模型，完成特定区域在特定时间段内的碳蓄积量初步估算。Using the satellite remote sensing images after radiation correction, atmospheric correction and relative radiation correction of different sensors as input variables, the carbon sink estimation model is used to complete the preliminary estimation of carbon accumulation in a specific area in a specific time period.
其中，在步骤S3中，以地面点位碳循环观测站记录为对卫星遥感反演获取的碳汇成果进行验证并用于遥感定量反演模型输入参数的校正，具体为：Among them, in step S3, the carbon cycle observation station record at the ground point is used to verify the carbon sink results obtained by satellite remote sensing inversion and to correct the input parameters of the remote sensing quantitative inversion model, specifically:
依具体需要，根据特定区域所属的陆地生态系统类型，优先选择中国通量观测研究联盟(ChinaFLUX)所属的长期碳循环观测站点作为地面验证站，若在特定区域内，无相近类型的陆地生态系统ChinaFLUX监测站存在，建议选择当地省级生态环境监测部门所设立的碳循环监测站作为地面验证站；According to specific needs, according to the type of terrestrial ecosystem to which a specific area belongs, the long-term carbon cycle observation site belonging to China Flux Observation and Research Alliance (ChinaFLUX) is preferentially selected as the ground verification station. If there is no similar type of terrestrial ecosystem in a specific area ChinaFLUX monitoring station exists, it is recommended to select the carbon cycle monitoring station established by the local provincial ecological environment monitoring department as the ground verification station;
待选取碳循环地面验证站后，使用该站点所处陆地生态系的长期观测数据作为真值，逐步校正步骤S2所使用的碳汇估算模型系数，从而得到针对特定陆地生态系统、特定区域、特定生境模式下的碳汇估算模型的更新(校正)系数。After the carbon cycle ground verification station is selected, the long-term observation data of the terrestrial ecosystem where the station is located is used as the true value, and the carbon sink estimation model coefficient used in step S2 is gradually corrected, so as to obtain a specific terrestrial ecosystem, specific area, specific Update (correction) coefficients for carbon sink estimation models in habitat models.
其中，在步骤S4中，采集时间序列的对地观测遥感卫星影像，驱动校正更新后的、针对不同陆地生态系统的、用于碳汇估算的卫星遥感定量反演模型，计算特定区域、特定时段内的碳蓄积量，具体为：Among them, in step S4, the time series of earth observation remote sensing satellite images are collected to drive the corrected and updated satellite remote sensing quantitative inversion model for different terrestrial ecosystems and used for carbon sink estimation, and calculate the specific area and specific time period. The amount of carbon stored in the
根据特定需求和数据可获取的便利程度，采集不同空间分辨率、波谱分辨率的卫星遥感影像，并对影像数据进行几何校正、辐射校正、大气校正等预处理，与其他卫星传感器遥感影像进行相对辐射校正；According to specific needs and the convenience of data acquisition, satellite remote sensing images of different spatial resolutions and spectral resolutions are collected, and the image data is subjected to geometric correction, radiometric correction, atmospheric correction and other preprocessing, and compared with other satellite sensor remote sensing images. radiation correction;
使用处理后的卫星遥感影像作为输入参数驱动用于碳汇估算的卫星遥感定量反演模型，计算特定区域、特定时段内的碳蓄积量；Use the processed satellite remote sensing images as input parameters to drive the satellite remote sensing quantitative inversion model for carbon sink estimation, and calculate the carbon accumulation in a specific area and a specific period of time;
将计算得到的特定区域、特定时段内的碳蓄积量与其他可获取的站点、实测碳蓄积量进行比对，核算该区域在特定区域、特定时段内的碳蓄积量。Compare the calculated carbon accumulation in a specific area and a specific period with other available sites and measured carbon accumulation, and calculate the carbon accumulation in a specific area and in a specific period of time.
其中，在步骤S5中，将得到的碳汇估算/核算成果作为可交易的碳汇额度并入国家核证资源减排量(CCER)，同时提交全国碳排放权交易市场的登记和结算中心(全国碳交易注册登记系统，中碳登)，具体为：Among them, in step S5, the obtained carbon sink estimation/accounting result is incorporated into the national certified resource emission reduction (CCER) as a tradable carbon sink quota, and is submitted to the registration and settlement center of the national carbon emission trading market ( National Carbon Trading Registration System, China Carbon Registration), specifically:
将卫星遥感估算获取的特定区域、特定时段内的碳蓄积量，经生态环境保认证的第三方机构进行碳核算后，认定为可交易的碳汇额度；The carbon accumulation in a specific area and in a specific period of time estimated by satellite remote sensing is recognized as a tradable carbon sink after carbon accounting by a third-party agency certified by ecological environment protection;
将认定后的可交易碳汇额度计入国家核证资源减排量(CCER)，同时提交全国碳排放权交易市场的登记和结算中心(全国碳交易注册登记系统，中碳登)。The identified tradable carbon sinks will be included in the National Certified Resource Emission Reduction (CCER), and submitted to the National Carbon Emissions Trading Market Registration and Settlement Center (National Carbon Trading Registration System, China Carbon Registration).
图2示出了本发明将陆地生态系统核算后的碳汇融入全国碳排放权交易市场的方法流程图。Fig. 2 shows a flow chart of the method of the present invention for integrating the carbon sink after accounting of the terrestrial ecosystem into the national carbon emission trading market.
其中，在步骤S6中，进行卫星遥感碳汇核算与全国碳交易注册登记系统的联通，具体为：Wherein, in step S6, the connection between the satellite remote sensing carbon sink accounting and the national carbon trading registration system is performed, specifically:
经卫星遥感估算的特定区域、特定时间段内的碳汇量在完成碳汇核算并经认证后录入全国碳交易注册登记系统；The carbon sinks estimated by satellite remote sensing in a specific area and within a specific time period are entered into the national carbon trading registration system after the carbon sink accounting is completed and certified;
将录入全国碳交易注册登记系统的增量碳汇与市场碳排放配额(CEA)一起完成配额汇总，在全国碳排放权交易市场，供对碳排放权有需求的企业选择，并进行交易；The incremental carbon sinks entered into the national carbon trading registration system are combined with the market carbon emission allowances (CEA) to complete the quota aggregation, and in the national carbon emission trading market, enterprises that need carbon emission rights can choose and trade them;
碳汇交易过程实现了部分地区(如西南省份)将自然界(陆地生态系统逐年碳汇增量货币化、资产化，未完成了从陆地生态系统，碳汇增量，碳汇估算，碳汇核算，碳市场注册，碳市场交易过程。The carbon sink trading process has realized the monetization and capitalization of the natural (terrestrial ecosystem) carbon sink increment year by year in some regions (such as southwestern provinces), but has not completed the process of terrestrial ecosystem, carbon sink increment, carbon sink estimation, and carbon sink accounting. , carbon market registration, carbon market trading process.
图3示出了本发明基于卫星遥感的地表植被碳汇估算与交易系统的框图。Fig. 3 shows a block diagram of the satellite remote sensing-based surface vegetation carbon sink estimation and trading system of the present invention.
本发明第二方面还提供了一种基于卫星遥感的地表植被碳汇估算与交易系统3，该系统包括：存储器31、处理器32，所述存储器中包括基于卫星遥感的地表植被碳汇估算与交易方法程序，所述基于卫星遥感的地表植被碳汇估算与交易方法程序被所述处理器执行时实现如下步骤：A second aspect of the present invention also provides a satellite remote sensing-based surface vegetation carbon sink estimation and trading system 3, the system comprising: a memory 31 and a
完成多源对地观测遥感卫星数据之间的辐射定标，并进行卫星数据产品地表反射率产品归一化，以生成长时间序列的具有统一辐射定标体系的卫星对地观测数据产品；Complete the radiometric calibration between multi-source earth observation remote sensing satellite data, and normalize the surface reflectance products of satellite data products to generate long-term satellite earth observation data products with a unified radiometric calibration system;
基于定量遥感反演模型估算不同陆地生态系统在特定地理位置、特定生境条件下、特定时间段内碳蓄积量；Estimation of carbon storage in different terrestrial ecosystems in specific geographical locations, specific habitat conditions, and specific time periods based on quantitative remote sensing inversion models;
使用地面点位碳循环观测站对卫星遥感反演获取的碳汇成果进行验证并用于遥感定量反演模型输入参数的校正；Use ground-based carbon cycle observation stations to verify the carbon sink results obtained by satellite remote sensing inversion and use it to correct the input parameters of the remote sensing quantitative inversion model;
以现势对地观测遥感卫星影像作为输入变量，使用校正后的、针对不同陆地生态系统的、用于碳汇估算卫星遥感定量反演模型，估算特定区域、特定时段内的碳蓄积量；Using the current Earth observation remote sensing satellite image as the input variable, using the corrected satellite remote sensing quantitative inversion model for carbon sink estimation for different terrestrial ecosystems, to estimate the carbon accumulation in a specific area and a specific period of time;
将基于对地观测遥感卫星反演估算的碳汇估算成果提交至全国碳排放权交易市场的登记和结算中心的全国碳交易注册登记系统；Submit the carbon sink estimation results based on the inversion estimation of earth observation remote sensing satellites to the National Carbon Trading Registration System of the Registration and Settlement Center of the National Carbon Emissions Trading Market;
与市场碳排放配额一起完成配额汇总，参与碳排放权市场交易。Complete quota aggregation together with market carbon emission quotas and participate in carbon emission rights market transactions.
在本申请所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。以上所描述的设备实施例仅仅是示意性的，例如，所采用的模型可以是“自下向上的”(即bottom-up)，也可以是“自上向下”的(即 top-down)，需要根据特定的陆地生态系统类型及碳汇/源估算具体需求，分别采用不同的模型方法以实现本实施例方案。单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，如：多个单元或组件可以结合，或可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的各组成部分相互之间的耦合、或直接耦合、或通信连接可以是通过一些接口，设备或单元的间接耦合或通信连接，可以是电性的、机械的或其它形式的。In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The device embodiments described above are only illustrative, for example, the adopted model may be "bottom-up" (ie bottom-up) or "top-down" (ie top-down) , it is necessary to adopt different model methods according to the specific terrestrial ecosystem type and the specific needs of carbon sink/source estimation to realize the solution of this embodiment. 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 can be integrated into another system, or some features can be ignored or not implemented. In addition, the coupling, or direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical or other forms. of.
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的，作为单元显示的部件可以是、或也可以不是物理单元；既可以位于一个地方，也可以分布到多个网络单元上；可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。The unit described above as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit; it may be located in one place or distributed to multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
另外，在本发明各实施例中的各功能单元可以全部集成在一个处理单元中，也可以是各单元分别单独作为一个单元，也可以两个或两个以上单元集成在一个单元中；上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may all be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integration The unit can be implemented either in the form of hardware or in the form of hardware plus software functional units.
本领域普通技术人员可以理解：实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成，前述的程序可以存储于计算机可读取存储介质中，该程序在执行时，执行包括上述方法实施例的步骤；而前述的存储介质包括：移动存储设备、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by program instructions related to hardware, the aforementioned program can be stored in a computer-readable storage medium, and when the program is executed, the execution includes: The steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: a removable storage device, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc. A medium on which program code is stored.
或者，本发明上述集成的单元如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器、或者网络设备等)执行本发明各个实施例所述方法的全部或部分。而前述的存储介质包括：移动存储设备、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products are stored in a storage medium and include several instructions for A computer device (which may be a personal computer, a server, or a network device, etc.) is caused to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic disk or an optical disk and other mediums that can store program codes.
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210649062.XA CN115082274A (en) | 2022-06-09 | 2022-06-09 | Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210649062.XA CN115082274A (en) | 2022-06-09 | 2022-06-09 | Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115082274A true CN115082274A (en) | 2022-09-20 |
Family
ID=83250615
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210649062.XA Pending CN115082274A (en) | 2022-06-09 | 2022-06-09 | Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115082274A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115937692A (en) * | 2023-02-15 | 2023-04-07 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | A method and system for assessing the carbon sink effect of coastal wetlands |
| CN116341986A (en) * | 2023-04-07 | 2023-06-27 | 北京一点五度科技有限公司 | Carbon sink project auditing and grading method for multi-source multi-time phase sensor data fusion |
| CN117494998A (en) * | 2023-11-03 | 2024-02-02 | 南方电网能源发展研究院有限责任公司 | Power consumption carbon footprint management method, device, computer equipment and storage medium |
| CN117951469A (en) * | 2023-12-19 | 2024-04-30 | 宁夏大学 | A sky-air-ground integrated carbon sink monitoring system and detection method for vineyard ecosystem |
| JP7518582B1 (en) | 2023-05-25 | 2024-07-18 | 株式会社ゼロボード | Information processing system, information processing method, and program |
| WO2025000511A1 (en) * | 2023-06-30 | 2025-01-02 | 上海市园林设计研究总院有限公司 | Evaluation method and system for carbon sink effect of coastal wetland |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108647623A (en) * | 2018-05-04 | 2018-10-12 | 中国科学院遥感与数字地球研究所 | A kind of potential organic C storage remote sensing estimation method of forest based on resource constraint |
| CN109946714A (en) * | 2019-04-03 | 2019-06-28 | 海南省林业科学研究所 | A kind of method for building up of the forest biomass model based on LiDAR and ALOS PALSAR multivariate data |
| CN112347992A (en) * | 2020-12-01 | 2021-02-09 | 中国林业科学研究院 | Desert region time sequence AGB remote sensing estimation method |
| CN112348812A (en) * | 2020-12-04 | 2021-02-09 | 南京大学 | Forest stand age information measuring method and device |
| CN113177744A (en) * | 2021-06-09 | 2021-07-27 | 西安建筑科技大学 | Urban green land system carbon sink amount estimation method and system |
| CN113806943A (en) * | 2021-09-22 | 2021-12-17 | 河海大学 | A prediction method of wetland vegetation carbon sequestration rate based on the relationship between key water regime variables and vegetation carbon sequestration rate |
| CN114021348A (en) * | 2021-11-05 | 2022-02-08 | 中国矿业大学(北京) | Mining area vegetation carbon sink remote sensing inversion method of fine land utilization type |
| CN114140235A (en) * | 2021-11-12 | 2022-03-04 | 西南林业大学 | Blockchain-based trusted forestry carbon sink trading system and method |
| CN114548910A (en) * | 2022-01-21 | 2022-05-27 | 浙江农林大学 | A blockchain-based forestry carbon sink measurement monitoring and trading system and method |
-
2022
- 2022-06-09 CN CN202210649062.XA patent/CN115082274A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108647623A (en) * | 2018-05-04 | 2018-10-12 | 中国科学院遥感与数字地球研究所 | A kind of potential organic C storage remote sensing estimation method of forest based on resource constraint |
| CN109946714A (en) * | 2019-04-03 | 2019-06-28 | 海南省林业科学研究所 | A kind of method for building up of the forest biomass model based on LiDAR and ALOS PALSAR multivariate data |
| CN112347992A (en) * | 2020-12-01 | 2021-02-09 | 中国林业科学研究院 | Desert region time sequence AGB remote sensing estimation method |
| CN112348812A (en) * | 2020-12-04 | 2021-02-09 | 南京大学 | Forest stand age information measuring method and device |
| CN113177744A (en) * | 2021-06-09 | 2021-07-27 | 西安建筑科技大学 | Urban green land system carbon sink amount estimation method and system |
| CN113806943A (en) * | 2021-09-22 | 2021-12-17 | 河海大学 | A prediction method of wetland vegetation carbon sequestration rate based on the relationship between key water regime variables and vegetation carbon sequestration rate |
| CN114021348A (en) * | 2021-11-05 | 2022-02-08 | 中国矿业大学(北京) | Mining area vegetation carbon sink remote sensing inversion method of fine land utilization type |
| CN114140235A (en) * | 2021-11-12 | 2022-03-04 | 西南林业大学 | Blockchain-based trusted forestry carbon sink trading system and method |
| CN114548910A (en) * | 2022-01-21 | 2022-05-27 | 浙江农林大学 | A blockchain-based forestry carbon sink measurement monitoring and trading system and method |
Non-Patent Citations (1)
| Title |
|---|
| 殷炜达等: "基于遥感技术的城市绿地碳储量估算应用", 《风景园林》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115937692A (en) * | 2023-02-15 | 2023-04-07 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | A method and system for assessing the carbon sink effect of coastal wetlands |
| CN115937692B (en) * | 2023-02-15 | 2023-05-16 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Coastal wetland carbon sink effect evaluation method and system |
| CN116341986A (en) * | 2023-04-07 | 2023-06-27 | 北京一点五度科技有限公司 | Carbon sink project auditing and grading method for multi-source multi-time phase sensor data fusion |
| CN116341986B (en) * | 2023-04-07 | 2024-01-12 | 北京一点五度科技有限公司 | Carbon sink project auditing and grading method for multi-source multi-time phase sensor data fusion |
| JP7518582B1 (en) | 2023-05-25 | 2024-07-18 | 株式会社ゼロボード | Information processing system, information processing method, and program |
| WO2025000511A1 (en) * | 2023-06-30 | 2025-01-02 | 上海市园林设计研究总院有限公司 | Evaluation method and system for carbon sink effect of coastal wetland |
| CN117494998A (en) * | 2023-11-03 | 2024-02-02 | 南方电网能源发展研究院有限责任公司 | Power consumption carbon footprint management method, device, computer equipment and storage medium |
| CN117951469A (en) * | 2023-12-19 | 2024-04-30 | 宁夏大学 | A sky-air-ground integrated carbon sink monitoring system and detection method for vineyard ecosystem |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115082274A (en) | Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing | |
| US20220027992A1 (en) | Non-fungible cryptographic tokens for tracking trees | |
| He et al. | Joint operation mechanism of distributed photovoltaic power generation market and carbon market based on cross-chain trading technology | |
| CN112053152B (en) | Distributed energy grid-connected authentication and transaction method based on green rights and interests consensus mechanism | |
| CN108055119A (en) | Safe motivational techniques and system based on block chain in a kind of intelligent perception application | |
| KR102462089B1 (en) | System for distributing profits of renewable energy to residents | |
| WO2017044737A1 (en) | Spatial modeling and other data analytics enabled energy platform | |
| Albersen et al. | Estimation of agricultural production relations in the LUC model for China | |
| CN114548910B (en) | Forestry carbon sink measurement monitoring and transaction system and method based on block chain | |
| CN111461888A (en) | A research method of integrated operation mechanism of electricity and carbon trading market | |
| US20130191296A1 (en) | Method and system for obtaining ghg reduction credits associated with ghg reduction efforts | |
| US20160042416A1 (en) | Method and system for collecting commodity consumption data and a method and system for generating an offer | |
| CN115327662A (en) | Statistical learning-based regional ionized layer TEC prediction method | |
| CN110110025A (en) | Regional population's density analog method based on characteristic vector space filter value | |
| CN112488819A (en) | Fast farming loan business credit line verification method, system, equipment and medium | |
| CN110929979A (en) | A method and system for measuring the transaction scale of excess consumption of renewable energy | |
| CN113076855A (en) | Distributed photovoltaic resource integration method, system, device and storage medium | |
| CN117217409A (en) | Power contract-based user side carbon emission evaluation method and device | |
| JP2025512872A (en) | System and method for bilateral trading of greenhouse gases and environmental rights | |
| CN114969425A (en) | Noctilucent remote sensing data space downscaling method based on geographical weighted regression | |
| KR102360383B1 (en) | System for providing bigdata based reservation price probability distribution prediction service for procurement auction | |
| Lu et al. | Eco‐Economic Environment Coupling Based on Urban RSEI Theory | |
| Hao et al. | Blockchain-enabled parametric solar energy insurance via remote sensing | |
| CN117078401A (en) | Credit approval data processing method, device, equipment and readable storage medium | |
| Cenky et al. | Rooftop Photovoltaic Potential Estimation Using QGIS and Simple Building Shadow Analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |