CN-122022014-A - Urban forest vegetation carbon reserve current situation assessment and prediction method

CN122022014ACN 122022014 ACN122022014 ACN 122022014ACN-122022014-A

Abstract

A current situation assessment and prediction method for urban forest vegetation carbon reserves comprises the steps of obtaining global built-up area data, administrative division data of a target city, remote sensing image data of earth coverage in past years, actual measurement meteorological data in past years and month average prediction data of future weather, preprocessing, extracting built-up area range of the target city, generating a forest vegetation coverage distribution map, calculating total newly-increased forest vegetation data Sum （m,j） in the built-up area range, calculating biomass B t of newly-increased forest vegetation coverage in the built-up area range based on the actual measurement meteorological data in past years, constructing a carbon fixation process model, calculating total newly-increased forest vegetation carbon density C density(m,j) in the built-up area range, and obtaining current situation and prediction data of total newly-increased forest vegetation carbon reserves C storage(m,a) in the built-up area range. The invention integrates the process of the land coverage change and the growth mechanism in the past year to evaluate and predict the current situation of the carbon reserves of the target city, thereby realizing the dynamic evaluation and prediction of the carbon reserves of the forests of the newly added city.

Inventors

WANG XIAOBIAO
XU PENG
LIU XINYI
YANG YANG
ZHU MENGLAN
ZHANG QIAN
ZENG FEIXIANG
Shao han
HUANG RUI

Assignees

中国电建集团贵阳勘测设计研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20251230

Claims (10)

1. The current situation assessment method for the urban forest vegetation carbon reserves is characterized by comprising the following steps of: Acquiring global built-up area data, administrative division data of a target city, remote sensing image data of the land coverage of the past year and the weather data of the past year, wherein the weather data of the past year comprises average annual temperature Avg t(y) and rainfall Avg r(y) of the past year; extracting the built-up area range of the target city according to the global built-up area data and the administrative division data of the target city; Preprocessing the remote sensing image data covered by the land of the past year based on the range of the built-up area, extracting a pixel Value i to generate a forest vegetation coverage distribution map, and calculating total newly-increased forest vegetation data Sum （m,j） in the range of the built-up area, wherein the pixel Value i is forest vegetation category data; Calculating the maximum biomass B max , the intrinsic growth rate V 0 and the biomass B t of the newly-increased forest vegetation coverage in the built-up area based on the measured meteorological data of the past year; setting initial carbon fixation parameters, and constructing a carbon fixation process model, wherein the initial carbon fixation parameters comprise tree ages The carbon fixation process model is used for obtaining total newly-increased forest vegetation carbon density C density(m,j) in the built-up area, wherein m represents the name of the built-up area, and j represents the year of the newly-increased forest vegetation; And calculating the current data of the total newly-increased forest vegetation carbon reserves C storage(m,a) in the range of the built-up area by combining the newly-increased forest vegetation data Sum （m,j） to realize the current assessment of the urban forest vegetation carbon reserves, wherein the calculation expression is as follows: ; wherein a represents the current year, b represents the initial year of the calendar year data, and b≤j≤a.
2. The method for evaluating the current situation of urban forest vegetation carbon reserves according to claim 1, wherein preprocessing the remote sensing image data of the calendar land cover comprises the following contents: Taking the built-up area range as a mask range, and performing mask cutting on the remote sensing image data covered by the aged land according to a specified period; extracting pixel values of forest vegetation categories according to the classification standard of the remote sensing images Recording forest vegetation as 1, recording background as 0, and generating a forest vegetation distribution map; Performing difference operation on the forest vegetation distribution map of the adjacent period to obtain pixel values of the vegetation distribution variation of the adjacent period The expression is: the forest vegetation distribution change comprises the steps of adding forest vegetation and reducing forest vegetation, wherein i represents the current cycle number, and i-1 represents the previous cycle number of the current cycle.
3. The method for evaluating the current situation of urban forest vegetation carbon reserves according to claim 2, wherein the classification criteria of the remote sensing images comprise 9 categories, specifically 1 is cultivated land, 2 is woodland, 3 is shrub forest, 4 is grassland, 5 is water body, 6 is snow or glacier, 7 is bare land, 8 is impermeable layer and 9 is wetland.
4. A method for assessing the current status of urban forest vegetation carbon reserves as claimed in claim 3, wherein the total newly added forest vegetation data in the area of the built-up area The calculation process of (1) comprises the following steps: Pixel values according to the distribution change of the forest vegetation in the adjacent period Extracting a newly-increased forest vegetation range; performing multiple different times of difference calculation to obtain distribution variation pixel values of forest vegetation coverage of different times The expression is Wherein e is the total number of cycles of mask cutting, i is more than or equal to 1 and less than or equal to e; extracting pixel values of newly-increased forest vegetation coverage which always exists in the built-up area range The expression is = ; Performing regional rasterization processing to obtain newly increased forest vegetation data count of the subareas in the built-up area, and calculating total newly increased forest vegetation data in the built-up area ; ; Wherein, the The method is characterized in that the method is used for representing the vegetation area of a newly increased forest in the range of m city built-up areas in j years, n is the number of built-up areas in the range of a target city, n is more than or equal to 1, and count represents the number of newly increased forest vegetation in each subarea all the time.
5. The method for assessing the current status of urban forest vegetation carbon reserves as recited in claim 1, wherein the maximum biomass And the intrinsic growth rate By the annual average air temperature And rainfall amount Calculating and obtaining; the maximum biomass The calculated expression of (2) is: ; the intrinsic growth rate The calculated expression of (2) is: ; Wherein MAT represents an annual average temperature, mat= MAP represents annual average rainfall, map= 。
6. The method for assessing the current status of urban forest vegetation carbon reserves of claim 5, wherein the biomass of newly added forest vegetation coverage The calculated expression of (2) is: ; Wherein, the Is vegetation biomass (t×hm -2 ) with the age of t; Maximum biomass, t 0 is the initial age.
7. The method for evaluating the current situation of urban forest vegetation carbon reserves according to claim 6, wherein the expression of the carbon sequestration process model is: ; Wherein, the Representing biomass in m city build-up areas for j years.
8. A method for predicting urban forest vegetation carbon reserves, which is realized based on the current situation assessment method of urban forest vegetation carbon reserves as claimed in any one of claims 1 to 7, and comprises the following contents: Acquiring month-average forecast data of future weather and biomass of forest vegetation coverage of built-up area of target city Is the current status data of (2); Preprocessing month average forecast data of the future weather, and calculating the year average air temperature of the future weather Average annual rainfall ; Setting tree ages t corresponding to the predicted years, and combining the average annual air temperature of the future weather Average annual rainfall Calculating total newly-increased forest vegetation carbon reserves in the built-up area of the target city by adopting the current situation assessment method of the urban forest vegetation carbon reserves Acquiring a potential value of carbon reserves in the periphery of the built-up area of the target city; The method for evaluating the current situation of the carbon reserves of the forest vegetation in the urban area comprises the steps of replacing the measured meteorological data of the past year with the average predicted data of the future meteorological data, and obtaining the initial vegetation biomass Replaced by the biomass Is a current status data of (a).
9. The method for predicting urban forest vegetation carbon reserves of claim 8, wherein preprocessing the month-average prediction data of the future weather comprises unifying temperature units and year-average rainfall units, wherein the temperature units are unifying in degrees celsius, and the rainfall units are unifying in millimeters per day.
10. A method for predicting urban forest vegetation carbon reserves as claimed in claim 9, wherein the annual average air temperature The calculated expression of (2) is: ; Average annual rainfall The calculated expression of (2) is: ; wherein y represents year, w is grid number; Pixel values for each grid cell; for each grid unit pixel value, day is the total number of days in y years.

Description

Urban forest vegetation carbon reserve current situation assessment and prediction method Technical Field The invention relates to the technical field of ecological environment monitoring, in particular to a current situation assessment and prediction method for urban forest vegetation carbon reserves. Background Cities are the most intensive areas for human activities and the most concentrated energy consumption, are the main emission sources of greenhouse gases, and meanwhile, an important ecological system, namely urban forests, is inoculated. Urban forests are known as 'urban lung', improve urban local climate, relieve heat island effect, purify air, and are a huge-potential 'urban carbon sink'. Compared with natural forests, urban forests have the characteristics of high spatial heterogeneity, broken plaque distribution, complex tree species composition, dense artificial interference and the like. This makes conventional carbon reserve estimation methods based on spot check a great challenge. The traditional method is high in precision, but is time-consuming, labor-consuming, high in cost and destructive to a certain extent, and large-scale and high-frequency continuous monitoring of a wide and dynamically-changed urban forest is difficult to realize. The development of efficient, accurate and repeatable urban forest carbon reserve monitoring and predicting technology has become a key scientific problem to be solved in the crossing fields of ecology, forests and urban science. In recent years, remote sensing technology has become the mainstream technical means for estimating forest carbon reserves by virtue of macroscopic, rapid and dynamic observation advantages. Around the remote sensing evaluation and potential prediction of urban forest carbon reserves, scholars at home and abroad have conducted a great deal of research, and have made remarkable progress. The core of remote sensing carbon reserve estimation is to establish a quantitative relationship between remote sensing information and vegetation biomass (and thus conversion to carbon reserves). Many scholars have calculated forest vegetation carbon reserves by adopting optical remote sensing data, laser radar data and combining a multi-source fusion and machine learning method, and on the basis of current situation assessment, prediction of future carbon sink potential is a key for realizing prospective city planning and management, but only macroscopic prediction of forest carbon sink potential on a national scale is realized, and researches on high-resolution land coverage change of long-time sequences and a process model based on a growth mechanism are freshly carried out to dynamically predict the carbon sink potential of a newly added city forest of a specific province. The patent document with the publication number of CN120833559A specifically discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology, which comprises the following steps of acquiring multi-source remote sensing data, respectively acquiring optical remote sensing data and radar remote sensing data, classifying and identifying tree species, classifying the forest in a research area to realize the spatial distribution information extraction of tree species scale, calculating vegetation biomass, combining biomass model parameters acquired by field investigation for different tree species, establishing a biomass estimation model based on the tree species, wherein the model input parameters are biomass model parameters, outputting the biomass model parameters as vegetation biomass, calculating carbon reserves, calculating the vegetation carbon reserves by utilizing inverted vegetation biomass and combining carbon content coefficients of different tree species, estimating soil carbon reserves, and finally obtaining the total carbon reserves of a forest ecological system. The patent document with the publication number of CN120278404A specifically discloses a method and a system for urban green space carbon sink quantity measurement based on multi-source data fusion, which comprise the steps of collecting urban green space multi-source data and preprocessing, adopting a dual-channel generation countermeasure network to carry out parameter complementation on vegetation in a building shelter area to obtain complete green space leaf area index distribution data, using a light ray tracing algorithm to simulate a building glass curtain wall reflection light path, obtaining a photosynthetic effective radiation correction coefficient received by a vegetation canopy, identifying the urban green space carbon sink quantity distribution data through multi-scale data fusion, obtaining a carbon sink quantity error based on the urban green space carbon sink quantity distribution data, and carrying out dynamic correction through a Bayesian optimization algorithm to generate an urban green space carbon sink quantity measu