Forest carbon sink dynamic monitoring method based on near-surface multi-source remote sensing data

Abstract

The invention relates to the technical field of remote sensing technology and forest ecological system carbon sink monitoring, and particularly discloses a forest carbon sink dynamic monitoring method based on near-surface multi-source remote sensing data, which comprises the steps of S1, data acquisition, S2, data processing, S3, forest change information monitoring, S4, forest carbon reserve estimation, S5, forest carbon sink dynamic analysis and S6, result verification and uncertainty analysis. By integrating multi-source information such as the multispectral data, the laser radar data and the field survey data, the structure and functional characteristics of the forest ecosystem can be comprehensively and accurately reflected. Compared with a single data source, the comprehensive data acquisition mode has the advantages that the precision and reliability of forest parameter extraction can be remarkably improved, and a solid foundation is provided for subsequent forest carbon reserve estimation and carbon sink dynamic analysis.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing technology and forest ecosystem carbon sink monitoring, and in particular relates to a forest carbon sink dynamic monitoring method based on near-ground multi-source remote sensing data. Background Art

[0002] With the severe challenge of global climate change, forests are important carbon sinks in the earth's ecosystem. Dynamic monitoring of their carbon storage plays a vital role in evaluating the forest carbon sink function and formulating scientific and reasonable emission reduction policies. However, traditional ground survey methods are not only time-consuming and laborious, but also have limited coverage, making it difficult to meet the needs of large-scale, high-precision forest carbon sink monitoring. Therefore, using near-ground multi-source remote sensing data to monitor forest carbon sinks has become an efficient and accurate new method.

[0003] In this regard, the inventors proposed a dynamic monitoring method for forest carbon sinks based on near-ground multi-source remote sensing data to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data, comprising:

[0007] S1. Data collection: using a near-ground remote sensing platform to collect multi-source data of forest areas, including multi-spectral data, lidar data, and field survey data;

[0008] S2. Data processing: pre-process the collected data and extract forest structure and spectral characteristic parameters through data fusion technology;

[0009] S3. Forest change information monitoring: Based on processed multi-source remote sensing data, machine learning algorithms are used to automatically identify forest change information, including changes in forest stand types and forest area;

[0010] S4. Forest carbon stock estimation: Based on the change information, the biomass expansion factor method is used to combine the forest volume, wood density, and biomass carbon content parameters to establish a forest biomass and carbon stock calculation model;

[0011] S5. Dynamic analysis of forest carbon sinks: Based on data from multiple time points, the dynamic changes in forest carbon storage are calculated, and the driving mechanism of carbon sink changes is analyzed in combination with external factors such as land use and climate change;

[0012] S6. Result verification and uncertainty analysis: The estimated carbon storage results were compared and verified with the measured data, the accuracy was evaluated using goodness of fit and root mean square error indicators, and uncertainty analysis was performed on the results.

[0013] Preferably, the multispectral data is used for forest biomass calculation, and the forest biomass calculation expression is:

[0014]

[0015] Among them, ρgreen, ρred and ρblue represent the reflectance of the green light band, red light band and blue light band, respectively. The green light band replaces the near-infrared band in NDVI, and ρred+ρblue replaces the red light band in NDVI.

[0016] Preferably, the lidar data is used to extract forest structure parameters, including tree height, crown width, canopy volume and leaf area index.

[0017] Preferably, the machine learning algorithm is a random forest algorithm, which is used for feature selection and change identification. The formula of the random forest algorithm is:

[0018]

[0019] Among them, Ti(x) represents the prediction result of the i-th decision tree, and N is the total number of decision trees.

[0020] Preferably, the calculation expression of the carbon storage C is:

[0021] C=V×BEF×WD×(1+R)×CF

[0022] Where C is the carbon storage of forest vegetation of a certain forest type, tC;

[0023] V is the unit volume of a forest m 3 ;

[0024] BEF is the biomass expansion factor;

[0025] D is the wood density of a certain tree group td·m·m -3

[0026] R is the ratio of belowground biomass to aboveground biomass of a certain tree group;

[0027] CF is the carbon content of the biomass of a certain tree group, tC / (td·m).

[0028] Preferably, the calculation expression for the dynamic change of forest carbon storage ΔC is:

[0029] ΔC=Ct2-Ct1

[0030] Among them, Ct1 and Ct2 represent the forest carbon storage at two time points respectively.

[0031] Preferably, the expression of the goodness of fit is:

[0032]

[0033] The expression of the root mean square error is:

[0034]

[0035] Among them, yi is the measured value, is the predicted value, is the measured average value, and n is the total number of samples.

[0036] Preferably, the preprocessing includes radiation correction, geometric correction and noise removal operations.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The present invention can comprehensively and accurately reflect the structural and functional characteristics of forest ecosystems by integrating multi-source information such as multispectral data, lidar data, and field survey data. Compared with a single data source, this comprehensive data collection method can significantly improve the accuracy and reliability of forest parameter extraction, providing a solid foundation for subsequent forest carbon storage estimation and carbon sink dynamic analysis.

[0039] (2) The present invention uses machine learning algorithms to automatically identify forest change information, such as changes in forest stand types and forest area, which not only improves monitoring efficiency but also enables real-time tracking of dynamic changes in forest ecosystems. This helps to timely discover and respond to adverse factors such as forest degradation and deforestation, and provides a scientific basis for forest management and protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a flow chart of a method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Embodiment 1:

[0043] See also Figure 1 As shown, a method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data includes the following steps:

[0044] S1. Data collection: using a near-ground remote sensing platform to collect multi-source data of forest areas, including multi-spectral data, lidar data, and field survey data;

[0045] S2. Data processing: pre-process the collected data and extract forest structure and spectral characteristic parameters through data fusion technology;

[0046] S3. Forest change information monitoring: Based on processed multi-source remote sensing data, machine learning algorithms are used to automatically identify forest change information, including changes in forest stand types and forest area;

[0047] S4. Forest carbon stock estimation: Based on the change information, the biomass expansion factor method is used to combine the forest volume, wood density, and biomass carbon content parameters to establish a forest biomass and carbon stock calculation model;

[0048] S5. Dynamic analysis of forest carbon sinks: Based on data from multiple time points, the dynamic changes in forest carbon storage are calculated, and the driving mechanism of carbon sink changes is analyzed in combination with external factors such as land use and climate change;

[0049] S6. Result verification and uncertainty analysis: The estimated carbon storage results were compared and verified with the measured data, the accuracy was evaluated using goodness of fit and root mean square error indicators, and uncertainty analysis was performed on the results.

[0050] Specifically, the multispectral data is used for forest biomass calculation, and the forest biomass calculation expression is:

[0051]

[0052] Among them, ρgreen, ρred and ρblue represent the reflectance of the green light band, red light band and blue light band respectively. The green light band replaces the near-infrared band in NDVI, and ρred+ρblue replaces the red light band in NDVI. In order to ensure that the value is between -1 and 1, the green light band is multiplied by 2.

[0053] Specifically, the lidar data is used to extract forest structure parameters, including tree height, crown width, canopy volume and leaf area index.

[0054] Specifically, the machine learning algorithm is a random forest algorithm, which is used for feature selection and change identification. The formula of the random forest algorithm is:

[0055]

[0056] Among them, Ti(x) represents the prediction result of the i-th decision tree, and N is the total number of decision trees.

[0057] Specifically, the calculation expression of the carbon storage C is:

[0058] C=V×BEF×WD×(1+R)×CF

[0059] Where C is the carbon storage of forest vegetation of a certain forest type, tC;

[0060] V is the unit volume of a forest m 3 ;

[0061] BEF is the biomass expansion factor;

[0062] D is the wood density of a certain tree group td·m·m -3

[0063] R is the ratio of belowground biomass to aboveground biomass of a certain tree group;

[0064] CF is the carbon content of the biomass of a certain tree group, tC / (td·m).

[0065] Specifically, the calculation expression for the dynamic change of forest carbon storage ΔC is:

[0066] ΔC=Ct2-Ct1

[0067] Among them, Ct1 and Ct2 represent the forest carbon storage at two time nodes respectively.

[0068] Specifically, the expression of the goodness of fit is:

[0069]

[0070] The expression of the root mean square error is:

[0071]

[0072] Among them, yi is the measured value, is the predicted value, is the measured average value, and n is the total number of samples.

[0073] Specifically, the preprocessing includes radiation correction, geometric correction and noise removal operations.

[0074] From the above, we can see that by integrating multi-source information such as multispectral data, lidar data, and field survey data, we can comprehensively and accurately reflect the structural and functional characteristics of forest ecosystems. Compared with a single data source, this comprehensive data collection method can significantly improve the accuracy and reliability of forest parameter extraction, providing a solid foundation for subsequent forest carbon storage estimation and carbon sink dynamic analysis.

[0075] The use of machine learning algorithms to automatically identify forest change information, such as changes in forest stand types and forest area, not only improves monitoring efficiency, but also enables real-time tracking of dynamic changes in forest ecosystems. This helps to promptly detect and respond to adverse factors such as forest degradation and deforestation, and provides a scientific basis for forest management and protection.

[0076] The forest biomass and carbon storage calculation model established by combining the biomass expansion factor method with relevant parameters can accurately estimate forest carbon storage and dynamically analyze the changing trend of carbon storage. This helps to assess the important role of forests in responding to global climate change and provide data support for the formulation of carbon reduction policies and climate change response strategies.

[0077] In addition, by combining the impact of external factors such as land use and climate change on forest carbon sink changes, we can more deeply reveal the driving mechanism of carbon sink changes. This will help us better understand the dynamic changes of forest carbon sinks and provide a theoretical basis for formulating scientific and reasonable forest management and protection strategies.

[0078] Embodiment 2:

[0079] Carbon storage in the tree layer of the Pudacuo fixed plot

[0080] The forest ecosystem in Yunnan Province has a high carbon fixation capacity, and the carbon density of the fir forest ecosystem is the highest. According to calculations, the total carbon storage of the tree layer in the Pudacuo fixed sample plot is 23.99t, the average single tree carbon storage is 0.064t, the minimum single tree carbon storage is 0.00844t, and the maximum single tree carbon storage is 0.56t. The average carbon storage of the data set is low, but there is a large dispersion (large standard deviation), and the data distribution is skewed to the right and has a peak trend. The difference between the minimum and maximum values ​​is large, indicating that there is significant variability in the data set. There is a certain big tree effect. The median is much smaller than the mean, which further supports the view that the data is skewed, as shown in Table 1 below:

[0081] Table 1 Carbon storage in the tree layer of the Pudacuo fixed plot

[0082]

[0083]

[0084] Table 1

[0085] From the above, we can see that the confidence information shows that at the 95% confidence level, there is a certain uncertainty in the estimation of average carbon storage.

[0086] Embodiment three:

[0087] Carbon storage in the tree layer of the fixed plot in Ailao Mountain

[0088] The average single-tree carbon storage in the tree layer of the fixed plot in Ailao Mountain is 0.018313t, and the range of carbon storage is from a minimum of 0.001019t to a maximum of 0.141487t, showing significant variability in carbon storage within the plot. And there is a large degree of dispersion (standard deviation is 0.021827). The data is skewed to the right (skewness is 2.008117), which means that most observations are less than the average, but a few larger observations pull up the average. The data presents a peak distribution (kurtosis is 4.468674), indicating that the data is relatively concentrated near the average, as shown in Table 2 below:

[0089] Table 2 Carbon storage in the tree layer of the fixed plot in Ailao Mountain

[0090]

[0091]

[0092] Table 2

[0093] Embodiment 4:

[0094] Carbon storage in the tree layer of fixed plots in Xishuangbanna

[0095] The average carbon storage of a single tree in the tree layer of the fixed sample plot in Xishuangbanna is 0.016678 t, and the range of carbon storage is from a minimum of 0.000516 t to a maximum of 0.083487 t.

[0096] Table 3 Carbon storage in the tree layer of the fixed plot in Xishuangbanna

[0097]

[0098] Table 3

[0099] Embodiment five:

[0100] Taking the fixed sample plot in Ailao Mountain, Yunnan Province as the research object, the forest carbon sink data were collected using an unmanned aerial vehicle platform equipped with a multispectral sensor and a lidar system. Combined with the field measurement data, forest carbon stock estimation and dynamic monitoring were implemented.

[0101] Implementation steps

[0102] 1. Data Collection

[0103] Remote sensing data collection:

[0104] Platform: The drone is equipped with multi-spectral sensors (red, green, blue, and near-infrared bands) and airborne laser radar (LiDAR).

[0105] Scope: A 20-hectare fixed sample plot in Ailao Mountain was selected for data collection.

[0106] Accuracy:

[0107] Multispectral image resolution: 0.2m;

[0108] LiDAR point cloud density: 10 points / m 2 .

[0109] Field measurement data:

[0110] Three replicate plots (20 m × 20 m each) were set up.

[0111] Measurement parameters: tree species, diameter at breast height (DBH), tree height (H), and crown diameter (CW).

[0112] 2. Data processing

[0113] LiDAR data: Extracting forest 3D structure information through point cloud data:

[0114] Tree height (average tree height): 17.8m;

[0115] Crown diameter (average value): 4.2m;

[0116] Canopy volume: calculated to be 325m 3 / ha.

[0117] Spectral data: Extract vegetation index:

[0118] NDVI average value: 0.75, coefficient of variation: 0.06.

[0119] VDVI index (Modified Vegetation Index): 0.62.

[0120] Data fusion: Geometric calibration of spectral data and lidar data is performed to generate forest parameter raster data.

[0121] 3. Forest change information monitoring

[0122] Use Random Forest (RF) algorithm for feature selection and change detection:

[0123] Characteristic parameters: tree height, crown width, NDVI, VDVI.

[0124] RF model accuracy:

[0125] Goodness of fit (R 2 ): 0.89;

[0126] Root mean square error (RMSE): 5.6t / ha.

[0127] Change information results:

[0128] Forest structural parameters changed significantly (such as tree height increased by 1.5m / year), and vegetation coverage increased by 3.2%.

[0129] 4. Forest carbon stock estimation

[0130] Based on the biomass expansion factor method (BEF) combined with field measured data, the carbon storage calculation formula is:

[0131] C=V×BEF×WD×(1+R)×CF

[0132] Parameter selection:

[0133] Parameter value description

[0134] Unit storage capacity (V) 180m 3 / ha Average forest volume

[0135] Biomass expansion factor (BEF) 1.93 Mixed coniferous and broad-leaved forest

[0136] Wood density (WD) 0.475t / m 3 Measured wood density of dominant tree species

[0137] Ratio of underground to aboveground biomass (R) 0.24 Ratio of underground to aboveground biomass

[0138] Biomass carbon content (CF) 0.49 Average carbon content

[0139] Calculation results:

[0140] C=180×1.93×0.475×(1+0.24)×0.49=78.93t / ha

[0141] Total forest carbon stock value:

[0142] Total carbon storage in the study area = 78.93t / ha × 20ha = 1578.6t

[0143] 5. Dynamic analysis of carbon sinks

[0144] Changing trend: Combining the monitoring data in 2018 and 2023:

[0145] Carbon storage in 2018: 72.5t / ha;

[0146] Carbon storage in 2023: 78.93t / ha.

[0147] Average annual increase in carbon storage: ΔC = 78.93-72.55 = 1.29 t / ha / year

[0148] Driving factor analysis:

[0149] Increased vegetation cover;

[0150] Climate factors (average annual precipitation increased by 5.2%);

[0151] Implementation of forest stand protection policies (such as natural forest protection projects).

[0152] 6. Result verification and uncertainty analysis

[0153] Verification method: Verification is carried out using field measured data:

[0154] Number of observed samples: 150 strains;

[0155] Model validation accuracy:

[0156] R2=0.92

[0157] RMSE = 4.8 t / ha

[0158] Uncertainty analysis: mainly comes from remote sensing data errors and model parameter uncertainties. The results show:

[0159] Total carbon stock uncertainty range: ±3.1%.

[0160] As can be seen from the above, through the application of the technology of this embodiment, the following significant effects are achieved:

[0161] High-precision monitoring: By integrating lidar and multispectral data, the accuracy of carbon stock estimation reaches 92%, which is significantly higher than traditional ground survey methods.

[0162] Improved efficiency: Using drones to collect data, it only takes 2 hours to cover 20 hectares of sample sites, saving 70% of time costs compared to traditional methods.

[0163] Dynamic change analysis: The spatiotemporal dynamic monitoring of carbon storage was successfully achieved, revealing the changing trend of forest carbon sink function and its driving mechanism.

[0164] Scientific decision support: Provide high-resolution forest carbon stock distribution maps to provide accurate data support for forest protection and carbon sink management policies.

[0165] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0166] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0167] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic monitoring of forest carbon sinks based on near-surface multi-source remote sensing data, characterized in that: The following steps are involved: S1. Data collection: using a near-ground remote sensing platform to collect multi-source data of forest areas, including multi-spectral data, lidar data, and field survey data; S2. Data processing: pre-process the collected data and extract forest structure and spectral characteristic parameters through data fusion technology; S3. Forest change information monitoring: Based on processed multi-source remote sensing data, machine learning algorithms are used to automatically identify forest change information, including changes in forest stand types and forest area; S4. Forest carbon stock estimation: Based on the change information, the biomass expansion factor method is used to combine the forest volume, wood density, and biomass carbon content parameters to establish a forest biomass and carbon stock calculation model; S5. Dynamic analysis of forest carbon sinks: Based on data from multiple time points, the dynamic changes in forest carbon storage are calculated, and the driving mechanism of carbon sink changes is analyzed in combination with external factors such as land use and climate change; S6. Result verification and uncertainty analysis: The estimated carbon storage results were compared and verified with the measured data, the accuracy was evaluated using goodness of fit and root mean square error indicators, and uncertainty analysis was performed on the results.

2. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1 is characterized in that: The multispectral data is used for forest biomass calculation, and the forest biomass calculation expression is: Among them, ρgreen, ρred and ρblue represent the reflectance of the green light band, red light band and blue light band, respectively. The green light band replaces the near-infrared band in NDVI, and ρred+ρblue replaces the red light band in NDVI.

3. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1 is characterized in that: The LiDAR data were used to extract forest structural parameters including tree height, crown width, canopy volume and leaf area index.

4. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1 is characterized in that: The machine learning algorithm is a random forest algorithm, which is used for feature selection and change identification. The formula of the random forest algorithm is: Among them, Ti(x) represents the prediction result of the i-th decision tree, and N is the total number of decision trees.

5. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1, characterized in that: The calculation expression of the carbon storage C is: C=V×BEF×WD×(1+R)×CF Where C is the carbon storage of forest vegetation of a certain forest type, tC; V is the unit volume of a forest m 3 ; BEF is the biomass expansion factor; D is the wood density of a certain tree group td·m·m -3 R is the ratio of belowground biomass to aboveground biomass of a certain tree group; CF is the carbon content of the biomass of a certain tree group, tC / (td·m).

6. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1, characterized in that: The calculation expression of the dynamic change of forest carbon storage ΔC is: ΔC=Ct2-Ct1 Among them, Ct1 and Ct2 represent the forest carbon storage at two time nodes respectively.

7. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1, characterized in that: The expression of the goodness of fit is: The expression of the root mean square error is: Among them, yi is the measured value, is the predicted value, is the measured average value, and n is the total number of samples.

8. The method for dynamic monitoring of forest carbon sinks based on near-ground multi-source remote sensing data according to claim 1 is characterized in that: The preprocessing includes radiation correction, geometric correction and noise removal operations.

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