Mangrove forest ecological environment monitoring method based on stable isotope

Abstract

The invention provides a mangrove forest ecological environment monitoring method based on a stable isotope, and the method comprises the following monitoring steps: S1, carbon sink function evaluation: (1) employing a DJIM300RTK + Micro Altum-PT multispectral camera unmanned aerial vehicle and a Thermo Scientific MAT 253 isotope ratio mass spectrometer to carry out evaluation operation, enabling the flight height of the unmanned aerial vehicle to be 80m, enabling the overlapping rate to be 80%, generating an NDVI / NDRE index map, and carrying out the analysis of a carbon sink function; according to the method, the isotope, the unmanned aerial vehicle and the sensor technology are intensively applied, the multi-scale accurate monitoring capability can be achieved, meanwhile, manual sampling is replaced by the unmanned aerial vehicle, the field workload is reduced by 60%, the timeliness of traditional isotope monitoring can be improved by 10-100 times, and the method is suitable for popularization and application. And meanwhile, the comprehensive cost is reduced by 30-50%, and the method is particularly suitable for normalized monitoring requirements of mangrove forest protection areas.

Description

Technical Field

[0001] The present invention relates to the technical field of mangrove ecological environment monitoring, and in particular to a mangrove ecological environment monitoring method based on stable isotopes. Background Art

[0002] Mangroves, as unique ecosystems in coastal wetlands, provide core functions such as wind and wave protection, water purification, carbon sequestration, and biodiversity maintenance. Monitoring their ecology is crucial for assessing coastal health and providing early warning of ecological degradation risks. Globally, mangroves cover nearly 15 million hectares, but are disappearing at an average annual rate of 0.13%. Monitoring can quantify the impacts of reclamation, pollution, and climate change (e.g., sea level rise of 3.3 mm / year). For example, monitoring heavy metal levels in sediments (e.g., lead and cadmium exceeding standards by 3-8 times) can trace land-based pollution, while tracking fluctuations in water salinity (ranging from 15‰ to 35‰) can provide early warning of vegetation degradation caused by saltwater intrusion. Mangroves have a carbon sequestration capacity of up to 1023 MgCO2e / ha. Monitoring changes in soil organic carbon density (average 2.5-5.0 kg / m³) and vegetation biomass (e.g., annual carbon sequestration by Kandelia candel trees at 4.8 t / ha) can provide data support for blue carbon trading.

[0003] Existing mangrove ecological environment monitoring methods are limited by a single technical path, high reliance on manual labor, and insufficient integration of isotope tracing technology. Traditional methods mostly focus on visible light remote sensing vegetation cover (NDVI index) and artificial sample plot surveys (such as setting up three 5m×5m sample plots per hectare to count biomass). They have weak ability to analyze hidden ecological processes such as pollutant migration and carbon and nitrogen cycle pathways, especially the lack of stable isotopes (δ¹³C, δ¹ 5 The systematic integration of fingerprint technologies such as N, etc. makes it difficult to accurately trace the source of heavy metal pollution (for example, the difference in lead isotope ratios between industrial emissions and geological background can reach 1.5-2.0), quantify the contribution of organic matter (the boundary between marine and terrestrial organic carbon δ¹³C values is approximately -22‰), or analyze the energy flow structure of the food web (δ¹ 5 Each 3.4‰ increase in N represents a trophic level. Only 5% of existing monitoring studies involve the use of isotopes, and most are limited to discrete laboratory sampling (e.g., δ³ taken every 10 cm of sediment column). 4 S analysis), no in-situ continuous isotope sensor network was built, and it was impossible to monitor the pore water salinity under tidal driving in real time -δ¹ 8 O dynamic coupling relationship (each 1 psu increase in salinity corresponds to δ¹ 8 O increased by about 0.2‰. Artificial isotope sampling requires time-consuming sample pre-treatment (such as freeze-drying and grinding plant tissue for 48 hours) and mass spectrometry detection (single sample costs over US$100), which makes it difficult to accurately calculate the mangrove carbon sink (δ¹³C distinguishes between atmospheric CO2 and soil respiration sources) and the migration flux of methylmercury (δ² 0Key process data such as Hg fractionation coefficient tracer are missing, and the error of blue carbon model parameters is over 30%. Summary of the Invention

[0004] Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a mangrove ecological environment monitoring method based on stable isotopes, which solves the problems of existing mangrove ecological environment monitoring methods such as single technical path, high dependence on manual labor and insufficient integration of isotope tracing technology.

[0006] Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a mangrove ecological environment monitoring method based on stable isotopes, comprising the following monitoring steps:

[0008] S1. Carbon sink function assessment:

[0009] (1) The evaluation was performed using a DJIM300 RTK + Micasense Altum-PT multispectral camera drone and a ThermoScientific MAT 253 isotope ratio mass spectrometer. The drone was flown at an altitude of 80 m with an overlap rate of 80%. NDVI / NDRE index maps were generated, and mangrove leaf samples from 50 calibration points were collected and frozen for preservation.

[0010] (2) Laboratory determination of leaf δ 13 C, establish NDVI-δ 13 C regression model:

[0011] \delta^{13}C = -28.5 + 5.2 \times e^{-2.4 \times NDVI} \quad (R^2=0.91), 20% sample cross-validation, RMSE<0.5%;

[0012] (3) Use ArcGIS Pro spatial interpolation to output a 1m resolution carbon storage distribution map.

[0013] δ 13 The area with C < -27‰ is marked as a high carbon sink;

[0014] S2. Nitrogen pollution source tracing:

[0015] The sensor used is the YSI EXO2 water quality monitoring buoy, and the isotope uses the GasBench-IRMS system. Three buoys are deployed at key nodes in the tidal creek, and data is transmitted to the cloud platform every 15 minutes.

[0016] Collect 500ml water sample, filter it on site, add HgCl2 for corrosion protection, use denitrifying bacteria to convert it into N2O, and measure δ 15 N and δ 18 O;

[0017] Construct a dual-isotope fingerprint library and combine it with a hydrological model to trace the pollution path backwards:

[0018]

[0019] S3. Ecosystem Health Diagnosis:

[0020] (1) The drone uses a FLIR Vue Pro R thermal imager and a LAWA-TDLAS laser analyzer for isotopes. The drone is flown in the early morning to identify stressed plants with canopy temperatures > 32°C and mark their coordinates.

[0021] (2) Collect stem water from stressed plants and measure δ 18 O, calculate water use efficiency;

[0022] S4. Biomass dynamic monitoring:

[0023] (1) Using DJI L1 LiDAR, point density ≥ 200 pts / m 2 , extract tree height, crown width, plant density and other parameters, select 20 standard trees, and measure leaf δ 15 N, established biomass-δ 15 N relational model:

[0024] (2) Classification statistics of each δ 15 Output monthly trend chart of biomass in N intervals;

[0025] S5. System data analysis: NVIDIA Jetson AGX Xavier is used to process real-time drone data, while AWS IoT Core is used to connect sensor data to complete analysis of various data.

[0026] Preferably, the DJIM300 RTK + Micasense Altum-PT multispectral camera drone in S1 uses five bands: blue, green, red, red edge, and near infrared.

[0027] Preferably, the FLIR Vue Pro R thermal imager in the S3 has a resolution of 640×512, an accuracy of ±5°C, and an RTK positioning accuracy of 2 cm.

[0028] Preferably, the threshold value WUE<85 μmol / mol in S3 is determined as water stress.

[0029] Beneficial effects

[0030] The present invention provides a method for monitoring the mangrove ecological environment based on stable isotopes. It has the following beneficial effects:

[0031] The present invention achieves multi-scale precise monitoring capabilities through the centralized application of isotopes, drones, and sensor technologies. At the same time, drones replace manual sampling, reducing field workload by 60%. It can also increase the timeliness of traditional isotope monitoring by 10-100 times, while reducing the overall cost by 30-50%. It is particularly suitable for the routine monitoring needs of mangrove reserves. DETAILED DESCRIPTION

[0032] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0033] Example:

[0034] The present invention provides a method for monitoring the ecological environment of mangroves based on stable isotopes, comprising the following monitoring steps:

[0035] S1. Carbon sink function assessment:

[0036] (1) The evaluation was performed using a DJIM300 RTK + Micasense Altum-PT multispectral camera drone and a ThermoScientific MAT 253 isotope ratio mass spectrometer. The drone was flown at an altitude of 80 m with an overlap rate of 80%. NDVI / NDRE index maps were generated, and mangrove leaf samples from 50 calibration points were collected and frozen for preservation.

[0037] (2) Laboratory determination of leaf δ 13 C, establish NDVI-δ 13 C regression model:

[0038] \delta^{13}C = -28.5 + 5.2 \times e^{-2.4 \times NDVI} \quad (R^2=0.91), 20% sample cross-validation, RMSE<0.5%;

[0039] (3) Use ArcGIS Pro spatial interpolation to output a 1m resolution carbon storage distribution map.

[0040] δ 13 The area with C < -27‰ is marked as a high carbon sink;

[0041] S2. Nitrogen pollution source tracing:

[0042] The sensor used is the YSI EXO2 water quality monitoring buoy, and the isotope uses the GasBench-IRMS system. Three buoys are deployed at key nodes in the tidal creek, and data is transmitted to the cloud platform every 15 minutes.

[0043] Collect 500ml water sample, filter it on site, add HgCl2 for corrosion protection, use denitrifying bacteria to convert it into N2O, and measure δ 15 N and δ 18 O;

[0044] Construct a dual-isotope fingerprint library and combine it with a hydrological model to reversely trace the pollution path:

[0045]

[0046] S3. Ecosystem Health Diagnosis:

[0047] (1) The drone uses a FLIR Vue Pro R thermal imager and a LAWA-TDLAS laser analyzer for isotopes. The drone is flown in the early morning to identify stressed plants with canopy temperatures > 32°C and mark their coordinates.

[0048] (2) Collect stem water from stressed plants and measure δ 18 O, calculate water use efficiency;

[0049] S4. Biomass dynamic monitoring:

[0050] (1) Using DJI L1 LiDAR, point density ≥ 200 pts / m 2 , extract parameters such as tree height, crown width, and plant density, select 20 standard trees, and measure leaf δ 15 N, established biomass-δ 15 N relational model:

[0051] (2) Classification statistics of each δ 15 Output monthly trend chart of biomass in N intervals;

[0052] S5. System data analysis: NVIDIA Jetson AGX Xavier is used to process real-time drone data, while AWS IoT Core is used to connect sensor data to complete analysis of various data.

[0053] The DJIM300 RTK + Micasense Altum-PT multispectral camera drone in the S1 uses five bands: blue, green, red, red edge, and near infrared.

[0054] The FLIR Vue Pro R thermal imager in the S3 has a resolution of 640×512, an accuracy of ±5°C, and an RTK positioning accuracy of 2cm.

[0055] In S3, the threshold value of WUE < 85 μmol / mol was determined as water stress.

[0056] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the ecological environment of mangroves based on stable isotopes, characterized in that: The monitoring steps include: S1. Carbon sink function assessment: (1) The evaluation was performed using a DJIM300 RTK + Micasense Altum-PT multispectral camera drone and a ThermoScientific MAT 253 isotope ratio mass spectrometer. The drone was flown at an altitude of 80 m with an overlap rate of 80%. NDVI / NDRE index maps were generated, and mangrove leaf samples from 50 calibration points were collected and frozen for preservation. (2) Laboratory determination of leaf δ 13 C, establish NDVI-δ 13 C regression model: \delta^{13}C = -28.5 + 5.2 \times e^{-2.4 \times NDVI} \quad (R^2=0.91), 20% sample cross-validation, RMSE<0.5%; (3) Use ArcGIS Pro spatial interpolation to output a 1m resolution carbon storage distribution map. δ 13 The area with C < -27‰ is marked as a high carbon sink; S2. Nitrogen pollution source tracing: The sensor used is the YSI EXO2 water quality monitoring buoy, and the isotope uses the GasBench-IRMS system. Three buoys are deployed at key nodes in the tidal creek, and data is transmitted to the cloud platform every 15 minutes. Collect 500ml water sample, filter it on site, add HgCl2 for corrosion protection, use denitrifying bacteria to convert it into N2O, and measure δ 15 N and δ 18 O; Construct a dual-isotope fingerprint library and combine it with a hydrological model to trace the pollution path backwards: S3. Ecosystem Health Diagnosis: (1) The drone uses a FLIR Vue Pro R thermal imager and a LAWA-TDLAS laser analyzer for isotopes. The drone is flown in the early morning to identify stressed plants with canopy temperatures > 32°C and mark their coordinates. (2) Collect stem water from stressed plants and measure δ 18 O, calculate water use efficiency; S4. Biomass dynamic monitoring: (1) Using DJI L1 LiDAR, point density ≥ 200 pts / m 2 , extract tree height, crown width, plant density and other parameters, select 20 standard trees, and measure leaf δ 15 N, established biomass-δ 15 N relational model: (2) Classification statistics of each δ 15 Output monthly trend chart of biomass in N intervals; S5. System data analysis: NVIDIA Jetson AGX Xavier is used to process real-time drone data, while AWS IoT Core is used to connect sensor data to complete analysis of various data.

2. The method for monitoring the mangrove ecological environment based on stable isotopes according to claim 1, characterized in that: The DJIM300 RTK + Micasense Altum-PT multispectral camera drone in the S1 uses five bands: blue, green, red, red edge, and near infrared.

3. The method for monitoring the mangrove ecological environment based on stable isotopes according to claim 1, characterized in that: The FLIR Vue Pro R thermal imager in the S3 has a resolution of 640×512, an accuracy of ±5°C, and an RTK positioning accuracy of 2cm.

4. The method for monitoring the mangrove ecological environment based on stable isotopes according to claim 1, characterized in that: The threshold value WUE<85 μmol / mol in S3 was determined to be water stress.

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