A multi-scale three-dimensional monitoring method and system for agro-pastoral ecotone ecological hydrology

Abstract

The application discloses a kind of agro-pastoral ecotone ecological hydrology multiscale stereoscopic monitoring method and system, comprising: using multi-source satellite source, unmanned aerial vehicle aerial survey and "point-line-surface" three-level sensing network acquisition space-based data, air-based data and ground-based data;Using multi-level fusion framework, the space-based data is integrated by time-space fusion algorithm, the feature extraction is carried out to air-based data by LiDAR point cloud analysis and thermal infrared analysis, and the model calculation is carried out to ground-based data by soil water isotope tracing and load calculation of livestock;Air-based data is respectively with space-based data and ground-based data carry out time-space coordination and data fusion, and carry out super-resolution reconstruction and dynamic erosion prediction;Ground-based data and air-based data fusion calculation grazing intensity index and carry out ecological hydrological process real-time inversion;Through space-ground data calibration, air-ground trigger response, multi-module coordination mechanism and model deduction of livestock-ground feedback control carry out grazing early warning and grazing route optimization.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of eco-hydrology and remote sensing monitoring, and more specifically to a multi-scale three-dimensional monitoring method and system for eco-hydrology in agro-pastoral ecotones. Background Technology

[0002] The agro-pastoral ecotone of grasslands and inland river basins is an ecologically sensitive area, which is easily affected by overgrazing and climate change, leading to grassland degradation, soil erosion and hydrological imbalance.

[0003] Currently, existing hydrological monitoring technologies have the following problems:

[0004] (1) Data fragmentation: Traditional methods monitor hydrology (runoff, water level, flow velocity) or vegetation (NDVI, FVC, LAI) separately, lacking analysis of the water-grass-livestock coupling mechanism;

[0005] (2) Scale gap: Satellite data (such as MODIS) has a coarse spatial resolution (>250m) and cannot identify small grazing patches; ground stations are sparse and it is difficult to cover the highly heterogeneous agro-pastoral ecotone.

[0006] (3) Insufficient dynamism: Existing models do not integrate the real-time impact of grazing activities (such as livestock density and species) on soil moisture-vegetation feedback.

[0007] Therefore, how to conduct multi-scale integrated eco-hydrological observations in ecologically fragile areas of agro-pastoral ecotones in grassland and inland river basins, and achieve coordinated monitoring and management of the water-grass-livestock system, overcoming data fragmentation, scale deficiency, and lack of dynamism, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of this, the present invention provides a multi-scale three-dimensional monitoring method and system for eco-hydrology in agro-pastoral ecotones to solve some of the technical problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A multi-scale, three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones includes the following steps:

[0011] S1. Collect space-based data using multi-source satellite sources, collect air-based data using UAV aerial surveying, and collect ground-based data by constructing a three-level "point-line-surface" sensor network;

[0012] S2. Multi-source satellite space-based data are integrated and processed using a multi-level fusion framework and a spatiotemporal fusion algorithm. Features of the space-based data are extracted using LiDAR point cloud analysis and thermal infrared analysis. Model calculations are performed on the ground-based data using soil water isotope tracing and livestock carrying capacity calculation.

[0013] S3. Spatial-based data is spatiotemporally coordinated and fused with space-based and ground-based data respectively, and super-resolution reconstruction and dynamic erosion prediction are performed; ground-based data and spatial-based data are fused to calculate the grazing intensity index and perform real-time inversion of eco-hydrological processes;

[0014] S4. Grazing early warning and grazing route optimization are carried out through a multi-module collaborative mechanism of air-to-ground data calibration, air-to-ground trigger response, and livestock-to-ground feedback control, as well as model inference.

[0015] Preferably, multi-source satellite space-based data includes: high spatial resolution data, high temporal resolution data, active microwave remote sensing data, and gravity satellite data;

[0016] Airborne data includes LiDAR point cloud data, thermal infrared data, and multispectral data acquired by drones;

[0017] Ground data collection includes: soil moisture-temperature profile data, meteorological elements, and groundwater level obtained from fixed monitoring points; and livestock GPS tracks, precipitation, soil, and vegetation δ¹⁸O data obtained from mobile monitoring points. 2 H and δ 18 O.

[0018] Preferably, the specific content of integrating and processing multi-source satellite space-based data using a multi-level fusion framework and a spatiotemporal fusion algorithm is as follows:

[0019] The STARFM algorithm is used to fuse daily MODIS data with Landsat / Sentinel-2 periodic data to generate a fused spectral-spatial composite dataset of 10 m / day. Based on deep learning, missing time periods are filled in to construct a continuous time series and output continuous grassland dynamics and hydrological parameters of 10 m / day.

[0020] By incorporating ESTARFM data and fusing optical Sentinel-2, thermal infrared Landsat TIRS, and microwave Sentinel-1 data, a cloud-independent soil moisture product with a duration of 10 m / 3 days is output.

[0021] The specific details of feature extraction from airborne data using LiDAR point cloud analysis and thermal infrared analysis are as follows:

[0022] LiDAR point cloud data was processed using LAStools to extract grass height, vertical distribution of leaf area, and biomass density; thermal infrared data and multispectral data were fused to calculate the grazing activity index; and DEM data was generated using LiDAR to extract runoff paths, waterlogged areas, and gully development.

[0023] The specific details of model calculation processing of foundation data through soil water isotope tracing and carrying capacity calculation are as follows:

[0024] By combining in-situ pore water samplers with PVC vacuum dialysis and isotope ratio mass spectrometry (IRMS), the water sources of different soil layers were analyzed and the proportion of precipitation recharge was calculated. Sustainable carrying capacity was calculated using grassland area, biomass, and recovery coefficient.

[0025] Preferably, in step S3, the specific content of spatiotemporal coordination and data fusion of space-based data with space-based data and ground-based data includes:

[0026] Collaboration between space-based and air-based data:

[0027] Spatial Calibration: Correcting Sentinel-2 Terrain Shadowing Errors Using UAV LiDAR Data

[0028] Time interpolation: During periods when satellite data is affected by cloud interference, drones collect data at high frequency to generate alternative datasets, and then fill the gaps through spatiotemporal kriging interpolation;

[0029] Linking airborne and ground-based data:

[0030] When the ground-based sensor detects a sudden drop in soil moisture, it triggers an emergency scan by a drone to acquire high spatiotemporal resolution images of the runoff process.

[0031] By matching livestock GPS collar data with UAV thermal infrared images, a grazing trajectory-vegetation response correlation model was constructed to quantify the impact of a single grazing event on soil compaction.

[0032] Preferably, in step S3,

[0033] The specific content of the super-resolution reconstruction is as follows: Based on the generative adversarial network, the UAV RGB imagery and multispectral data are fused to generate a super-resolution 13-band hyperspectral cube for fine vegetation classification.

[0034] The specific content of dynamic erosion prediction is as follows: combining LiDAR micro-topography and historical precipitation data, a particle swarm optimization-support vector machine model is constructed to predict the gully expansion rate in the next 15 days.

[0035] Preferably, in step S3, the ground-based data and airborne data are fused to calculate the grazing intensity index and to perform real-time inversion of eco-hydrological processes:

[0036] The grazing intensity index is calculated by combining UAV thermal infrared imagery with GPS trajectory data.

[0037]

[0038] Where, N livestock For the number of livestock, tstay For the duration of stay, A patch V represents the area of ​​the grassland patch. vegetation For vegetation biomass;

[0039] The real-time inversion of eco-hydrological processes specifically includes:

[0040] Using ground-based eddy covariance tower data to calibrate satellite / UAV-derived ET, soil evaporation E and vegetation transpiration T are separated:

[0041]

[0042] Where, δ ET δ E δ T These are the isotopic characteristics of total evapotranspiration, soil evaporation, and vegetation transpiration, respectively.

[0043] Preferably, in step S4:

[0044] The specific content of the sky-ground data calibration is as follows: Bayesian data assimilation is performed on the ground-based 0-10cm sensor data and Sentinel-1SAR inversion values ​​to generate a 10m resolution daily scale high-precision soil moisture map for soil moisture correction.

[0045] The specific content of the air-to-ground trigger response is as follows: Anomaly warning: When the ground-based sensor detects a sudden drop in soil moisture, the drone is automatically triggered to perform a centimeter-level scan of the target area, and herders are simultaneously notified to adjust their grazing routes; The parameter sensitivity of the hydrological model and the ecological model is optimized based on ground-based isotope data to carry out model iteration;

[0046] The specific content of the livestock-land feedback control is as follows: based on the real-time grazing activity index GII, livestock are dynamically restricted from entering highly vulnerable areas through solar-powered electronic fences.

[0047] A multi-scale three-dimensional monitoring system for eco-hydrology in agro-pastoral ecotones, based on the aforementioned multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones, includes: a space-based module, an air-based module, a ground-based module, a data processing module, a data fusion module, and a multi-module collaborative decision-making module;

[0048] The space-based module is used to collect space-based data using multi-source satellites; the air-based module is used to collect air-based data using UAV aerial surveying; and the ground-based module is used to construct a three-level "point-line-surface" sensor network to collect ground-based data.

[0049] The data processing module is used to integrate and process multi-source satellite space-based data using a multi-level fusion framework and spatiotemporal fusion algorithm, extract features from space-based data through LiDAR point cloud analysis and thermal infrared analysis, and perform model calculations on ground-based data through soil water isotope tracing and livestock carrying capacity calculation.

[0050] The data fusion module is used to perform spatiotemporal coordination and data fusion of airborne data with space-based and ground-based data, and to perform super-resolution reconstruction and dynamic erosion prediction; the fusion of ground-based and airborne data is used to calculate the grazing intensity index and to perform real-time inversion of eco-hydrological processes.

[0051] The multi-module collaborative decision-making module is used to conduct grazing early warning and grazing route optimization through a multi-module collaborative mechanism and model inference, which includes air-to-ground data calibration, air-to-ground trigger response, and livestock-to-ground feedback control.

[0052] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones.

[0053] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones.

[0054] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a multi-scale three-dimensional monitoring method and system for eco-hydrology in agro-pastoral ecotones. By integrating satellite remote sensing, UAV aerial surveying, ground IoT sensing and dynamic eco-hydrological process models, it realizes the coordinated monitoring and dynamic assessment of the water-grass-livestock system, serving ecological protection and sustainable livestock management. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0056] Figure 1 A schematic diagram of a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones provided by the present invention;

[0057] Figure 2 A schematic diagram of a multi-scale three-dimensional monitoring system for eco-hydrology in an agro-pastoral ecotone provided by the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] This invention discloses a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones, such as... Figure 1 This includes the following steps:

[0060] S1. Collect space-based data using multi-source satellite sources, collect air-based data using UAV aerial surveying, and collect ground-based data by constructing a three-level "point-line-surface" sensor network;

[0061] S2. Multi-source satellite space-based data are integrated and processed using a multi-level fusion framework and a spatiotemporal fusion algorithm. Features of the space-based data are extracted using LiDAR point cloud analysis and thermal infrared analysis. Model calculations are performed on the ground-based data using soil water isotope tracing and livestock carrying capacity calculation.

[0062] S3. Spatial-based data is spatiotemporally coordinated and fused with space-based and ground-based data respectively, and super-resolution reconstruction and dynamic erosion prediction are performed; ground-based data and spatial-based data are fused to calculate the grazing intensity index and perform real-time inversion of eco-hydrological processes;

[0063] S4. Grazing early warning and grazing route optimization are carried out through a multi-module collaborative mechanism of air-to-ground data calibration, air-to-ground trigger response, and livestock-to-ground feedback control, as well as model inference.

[0064] To further implement the above technical solutions, multi-source satellite space-based data includes: high spatial resolution data, high temporal resolution data, active microwave remote sensing data, and gravity satellite data; specifically including: grassland cover FVC, normalized burn index NBR, normalized vegetation index NDVI, land surface temperature LST, soil moisture, soil exposure rate, and changes in groundwater storage.

[0065] In this embodiment, the high spatial resolution data are: Sentinel-2 satellite multispectral imager, 0-20m resolution, revisited every 5 days, used to retrieve grassland cover FVC, normalized burn index NBR and soil bareness; Landsat-8 / 9OLI: 15-30m resolution, revisited every 16 days, to supplement historical baseline data.

[0066] High temporal resolution data include: MODIS medium resolution imaging spectrometer, 250–500 m resolution, daily coverage, monitoring surface temperature (LST), evapotranspiration (ET), and snow cover dynamics; and GOES-R geostationary environmental satellite, 5-minute time-series data (1–4 km resolution), capturing the impact of short-duration heavy precipitation events on soil erosion.

[0067] Active microwave remote sensing includes: Sentinel-1SAR, C-band, 12-day revisit, 10m resolution, penetrating cloud layers to monitor soil moisture (SM) and surface deformation (soil compaction caused by overgrazing); and SMAP, 36km resolution, 3-day revisit, L-band microwave inversion of deep soil moisture (5–100cm).

[0068] The gravity satellite data is GRACE-FO, which acquires monthly groundwater storage anomalies (ΔGWSA) and changes in total water storage in the basin.

[0069] Airborne data includes LiDAR point cloud data, thermal infrared data, and multispectral data acquired by drones;

[0070] Ground data collection includes: soil moisture-temperature profile data, meteorological elements, and groundwater level obtained from fixed monitoring points; and livestock GPS tracks, precipitation, soil, and vegetation δ¹⁸O data obtained from mobile monitoring points. 2 H and δ 18 O.

[0071] In this embodiment, the space-based data acquisition method is as follows:

[0072] The configuration of the UAV platform and sensors is as follows: Specifically, the airborne module adopts a mixed formation of multi-rotor (providing detailed information) and fixed-wing (wide coverage) drones, optimized for deployment according to different monitoring targets. The multi-rotor UAV (DJI Matrice300RTK) carries a high-precision multispectral camera (Parrot Sequoia+, 5 bands, 10nm bandwidth), a miniature LiDAR (Velodyne Puck Lite, ±3cm accuracy), and a thermal infrared sensor (FLIRVue Pro R, 640×512 resolution) for fixed-point hovering observation (e.g., livestock gathering areas, erosion gullies) and centimeter-level resolution data acquisition (flight altitude 50m, ground resolution 0.5~2cm). The fixed-wing UAV (SenseFly eBee X) carries a multispectral imager (RedEdge-MX, 10 bands) and a high-resolution RGB camera (42MP) for large-area rapid patrols (single-time coverage 50km). 2 (Flight altitude 150m, resolution 5cm), suitable for monthly grassland biomass surveys;

[0073] Flight planning: If there are areas with abnormal output values ​​in the space-based module, the UAV aerial survey mission is dynamically generated; the terrain following algorithm is adopted to adapt to the hilly terrain of grassland, inland river basin (elevation difference <200m) and maintain constant ground resolution.

[0074] The three-level sensor network of "point-line-surface" is as follows:

[0075] Point-based monitoring nodes include a soil moisture-temperature profiler (5 layers: 0-10cm, 10-20cm, 20-30cm, 30-50cm, 50-100cm), measuring at a frequency of 15 minutes with an accuracy of ±2% (e.g., Decagon EC-5); a meteorological six-element station for measuring air temperature, air humidity, 2m wind speed, 2m wind direction, precipitation, air pressure, and radiation (net radiometer), supporting minute-level data transmission; and a groundwater level gauge (pressure type, range 0-50m, accuracy ±1cm) for monitoring shallow groundwater dynamics.

[0076] The point monitoring node deployment strategy is to deploy one comprehensive station every 2km along the hydrological gradient (riverbank zone → hilltop), covering the main ecological units (grassland, sandy land, wetland, and mountainous area);

[0077] The linear transmission network, including LoRaWAN IoT, is a low-power wide-area network (transmission radius 10km). Node data is aggregated to the gateway and transmitted back to the cloud platform via 4G / BeiDou dual-mode. Frequency hopping technology (FHSS) is used for anti-interference design to avoid interference from livestock activities in grazing areas.

[0078] The areal motion monitoring included the use of a handheld hyperspectral imager (ASD FieldSpec4) for constructing a grassland community spectral library (350–2500 nm) with a resolution of 3 nm, and a mobile isotope sampling vehicle equipped with a laser water isotope analyzer (Picarro L2130-i) for real-time measurement of precipitation, soil water, and vegetation water δ¹⁸O. 2 H and δ 1 8O, accuracy <0.5‰;

[0079] It also includes a collar sensor that uses GPS / BeiDou dual-mode positioning (accuracy ±1m) to record the location every 5 minutes and uses a triaxial accelerometer to identify feeding (head-down frequency 0.5~2Hz), walking (periodic vibration) and resting (low acceleration variance) behaviors.

[0080] To further implement the above technical solution, the specific details of integrating and processing multi-source satellite space-based data using a multi-level fusion framework and spatiotemporal fusion algorithm are as follows:

[0081] The STARFM algorithm is used to fuse daily MODIS data (250m) with Landsat / Sentinel-2 periodic data (30m / 10m) to generate a fused spectral-spatial composite dataset of 10m / day. Based on deep learning, missing time periods are filled in to construct a continuous time series. The input is the fused spectral-spatial composite dataset of 10m / day, and the output is continuous grassland dynamics and hydrological parameters of 10m / day, with an accuracy equal to the correlation coefficient R with ground-measured data. 2 >0.89;

[0082] The method for generating synthetic datasets is as follows:

[0083]

[0084] Where, L(x) i ,y i ,t k ) represents the fused image to be generated, i.e., at the target time t. k Location (x) i ,y i High spatiotemporal resolution synthetic image data; H(x) i ,y i ,t o ) represents a high spatial resolution image, i.e., at reference time t o Acquired high-resolution image data; L'(x i ,y i ,t k ) represents a low spatial resolution image, i.e., at the target time t k Acquired low-resolution image data; H'(x i ,y i ,t o ) represents a downscaled version of the low spatial resolution image, which is the image data after resampling the high spatial resolution image H to a low-resolution grid, used to calculate the differences; w m This is the spatiotemporal similarity weight, used to measure the similarity between candidate pixel m and target pixel (x). i ,y i The similarity in spectral and spatial dimensions is weighted to 1; n represents the candidate pixel data, i.e., the total number of similar pixels participating in the fusion.

[0085] By introducing ESTARFM (Enhanced STARFM) and fusing optical Sentinel-2, thermal infrared Landsat TIRS, and microwave Sentinel-1 data, cloud-independent soil moisture products for 10m / 3 days are output.

[0086] In this embodiment, the ESTARFM fusion algorithm is introduced to address cloud cover interference. Microwave data provides sub-cloud soil moisture information, while thermal infrared data constrains the surface energy balance, outputting a 10m / 3-day cloud-independent soil moisture product (RMSE < 0.04m). 3 / m 3 );

[0087] The specific details of feature extraction from airborne data using LiDAR point cloud analysis and thermal infrared analysis are as follows:

[0088] LiDAR point cloud data was processed using LAStools to extract grass height, vertical distribution of leaf area, and biomass density; thermal infrared data and multispectral data were fused to calculate the grazing activity index; and DEM data was generated using LiDAR to extract runoff paths, waterlogged areas, and gully development.

[0089] Biomass density BD is:

[0090] BD = 0.21 × GH 1.7 ×LAI(z) avg

[0091] Where GH is the grass height and LAI(z) is the vertical distribution of leaf area;

[0092] The Grazing Activity Index (GAI) is:

[0093]

[0094] When GAI > 2.5, it is identified as an overgrazing hotspot;

[0095] Among them, T surface T represents the surface temperature (°C), which is the surface radiation temperature directly measured by a thermal infrared sensor; ambient NDVI represents the ambient background temperature (°C), which is the average temperature of undisturbed natural vegetation areas or shaded areas within the study area; NDVI represents the normalized vegetation index, which reflects the vegetation cover and vitality; 0.1 is an empirical constant.

[0096] LiDAR generates a 5cm resolution DEM to extract runoff paths, water accumulation areas, and gully development (GD) with an accuracy of ±5cm.

[0097] The specific details of model calculation processing of foundation data through soil water isotope tracing and carrying capacity calculation are as follows:

[0098] By combining in-situ pore water samplers with PVC vacuum dialysis and isotope ratio mass spectrometry (IRMS), the water sources of different soil layers were analyzed and the proportion of precipitation recharge was calculated. Sustainable carrying capacity was calculated using grassland area, biomass, and recovery coefficient.

[0099] To further implement the above technical solution, step S3, which involves spatiotemporal coordination and data fusion of space-based data with both space-based and ground-based data, includes the following:

[0100] Collaboration between space-based and air-based data:

[0101] Spatial calibration: UAV LiDAR data correction for Sentinel-2 terrain shadowing error, improving the accuracy of surface reflectance inversion (RMSE reduced by 12%);

[0102] Time interpolation: During periods when satellite data is affected by cloud interference, drones collect data at high frequency to generate alternative datasets, and then fill the gaps through spatiotemporal kriging interpolation;

[0103] Linking airborne and ground-based data:

[0104] When a ground-based sensor (such as a soil moisture meter) detects a sudden drop in soil moisture (ΔSM>30% / 24h), it triggers an emergency scan by a drone to acquire high spatiotemporal resolution images of the runoff process.

[0105] By matching livestock GPS collar data with UAV thermal infrared images, a grazing trajectory-vegetation response correlation model was constructed to quantify the impact of a single grazing event on soil compaction.

[0106] To further implement the above technical solution, in step S3,

[0107] The specific content of the super-resolution reconstruction is as follows: Based on the generative adversarial network, the UAV RGB imagery and multispectral data are fused to generate a super-resolution 13-band hyperspectral cube for fine vegetation classification.

[0108] The specific content of dynamic erosion prediction is as follows: combining LiDAR micro-topography and historical precipitation data, a particle swarm optimization-support vector machine (PSO-SVM) model is constructed to predict the gully expansion rate in the next 15 days.

[0109] To further implement the above technical solution, in step S3, the ground-based data and airborne data are fused to calculate the grazing intensity index and to perform real-time inversion of eco-hydrological processes:

[0110] The grazing intensity index is calculated by combining UAV thermal infrared imagery (livestock congregation hotspots) with GPS trajectories:

[0111]

[0112] Where, N livestock For the number of livestock, t stay For the duration of stay, A patch V represents the area of ​​the grassland patch. vegetation For vegetation biomass;

[0113] The real-time inversion of eco-hydrological processes specifically includes:

[0114] Using ground-based eddy covariance data to calibrate satellite / UAV-derived ET, soil evaporation E and vegetation transpiration T are separated:

[0115]

[0116] Where, δ ET δ E δ T These are the isotopic characteristics of total evapotranspiration, soil evaporation, and vegetation transpiration, respectively.

[0117] To further implement the above technical solution, in step S4:

[0118] The specific steps of the space-to-ground data calibration are as follows: Bayesian data assimilation is performed on the 0-10cm ground-based sensor data (±2%) and the Sentinel-1 SAR inversion values ​​(10m resolution) to generate a high-precision daily soil moisture map with a 10m resolution for soil moisture correction (RMSE < 0.03m). 3 / m 3 );

[0119] The specific content of the air-to-ground trigger response is as follows: Anomaly warning: When the ground sensor detects a sudden drop in soil moisture (ΔSM>25% / 6h), the drone is automatically triggered to perform a centimeter-level scan of the target area, and herders are simultaneously notified to adjust their grazing routes; The parameter sensitivity of the hydrological model (SWAT-HS) and the ecological model (CENTURY) is optimized based on ground isotope data (such as precipitation-soil water-vegetation water conversion rate) to perform model iteration;

[0120] The specific content of the livestock-land feedback control is as follows: based on the real-time grazing activity index GII, livestock are dynamically restricted from entering highly vulnerable areas (such as desertified grasslands with soil moisture content <12%) through solar-powered electronic fences (RFID control).

[0121] A multi-scale three-dimensional monitoring system for eco-hydrology in agro-pastoral ecotones, based on a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones, such as... Figure 2 It includes: space-based module, air-based module, ground-based module, data processing module, data fusion module, and multi-module collaborative decision-making module;

[0122] The space-based module is used to collect space-based data using multi-source satellites; the air-based module is used to collect air-based data using UAV aerial surveying; and the ground-based module is used to construct a three-level "point-line-surface" sensor network to collect ground-based data.

[0123] The data processing module is used to integrate and process multi-source satellite space-based data using a multi-level fusion framework and spatiotemporal fusion algorithm, extract features from space-based data through LiDAR point cloud analysis and thermal infrared analysis, and perform model calculations on ground-based data through soil water isotope tracing and livestock carrying capacity calculation.

[0124] The data fusion module is used to perform spatiotemporal coordination and data fusion of airborne data with space-based and ground-based data, and to perform super-resolution reconstruction and dynamic erosion prediction; the fusion of ground-based and airborne data is used to calculate the grazing intensity index and to perform real-time inversion of eco-hydrological processes.

[0125] The multi-module collaborative decision-making module is used to conduct grazing early warning and grazing route optimization through a multi-module collaborative mechanism and model inference, which includes air-to-ground data calibration, air-to-ground trigger response, and livestock-to-ground feedback control.

[0126] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones.

[0127] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones.

[0128] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0129] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones, characterized in that, Includes the following steps: S1. Collect space-based data using multi-source satellite sources, collect air-based data using UAV aerial surveying, and collect ground-based data by constructing a three-level "point-line-surface" sensor network; S2. Multi-source satellite space-based data are integrated and processed using a multi-level fusion framework and a spatiotemporal fusion algorithm. Features of the space-based data are extracted using LiDAR point cloud analysis and thermal infrared analysis. Model calculations are performed on the ground-based data using soil water isotope tracing and livestock carrying capacity calculation. S3. Spatial-based data is spatiotemporally coordinated and fused with space-based and ground-based data respectively, and super-resolution reconstruction and dynamic erosion prediction are performed; ground-based data and spatial-based data are fused to calculate the grazing intensity index and perform real-time inversion of eco-hydrological processes; S4. Grazing early warning and grazing route optimization are carried out through a multi-module collaborative mechanism of air-to-ground data calibration, air-to-ground trigger response, and livestock-to-ground feedback control, as well as model inference.

2. The multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to claim 1, characterized in that, Multi-source satellite space-based data includes: high spatial resolution data, high temporal resolution data, active microwave remote sensing data, and gravity satellite data; Airborne data includes LiDAR point cloud data, thermal infrared data, and multispectral data acquired by drones; Ground data collection includes: soil moisture-temperature profile data, meteorological elements, and groundwater level obtained from fixed monitoring points; and livestock GPS tracks, precipitation, soil, and vegetation δ²H and δ¹ values ​​obtained from mobile monitoring points. 8 O.

3. The multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to claim 2, characterized in that, The specific content of integrating and processing multi-source satellite space-based data using a multi-level fusion framework and spatiotemporal fusion algorithm is as follows: The STARFM algorithm is used to fuse daily MODIS data with Landsat / Sentinel-2 periodic data to generate a fused spectral-spatial composite dataset of 10 m / day. Based on deep learning, missing time periods are filled in to construct a continuous time series and output continuous grassland dynamics and hydrological parameters of 10 m / day. By incorporating ESTARFM data and fusing optical Sentinel-2, thermal infrared Landsat TIRS, and microwave Sentinel-1 data, a cloud-independent soil moisture product with a duration of 10 m / 3 days is output. The specific details of feature extraction from airborne data using LiDAR point cloud analysis and thermal infrared analysis are as follows: LiDAR point cloud data was processed using LAStools to extract grass height, vertical distribution of leaf area, and biomass density; thermal infrared data and multispectral data were fused to calculate the grazing activity index; and DEM data was generated using LiDAR to extract runoff paths, waterlogged areas, and gully development. The specific details of model calculation processing of foundation data through soil water isotope tracing and carrying capacity calculation are as follows: By combining in-situ pore water samplers with PVC vacuum dialysis and isotope ratio mass spectrometry (IRMS), the water sources of different soil layers were analyzed and the proportion of precipitation recharge was calculated. Sustainable carrying capacity was calculated using grassland area, biomass, and recovery coefficient.

4. The multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to claim 1, characterized in that, Step S3, which involves spatiotemporal coordination and data fusion of space-based, space-based, and ground-based data, includes the following: Collaboration between space-based and air-based data: Spatial calibration: Correcting Sentinel-2 terrain shading errors using UAV LiDAR data; Time interpolation: During periods when satellite data is affected by cloud interference, drones collect data at high frequency to generate alternative datasets, and then fill the gaps through spatiotemporal kriging interpolation; Linking airborne and ground-based data: When the ground-based sensor detects a sudden drop in soil moisture, it triggers an emergency scan by a drone to acquire high spatiotemporal resolution images of the runoff process. By matching livestock GPS collar data with UAV thermal infrared images, a grazing trajectory-vegetation response correlation model was constructed to quantify the impact of a single grazing event on soil compaction.

5. The multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to claim 1, characterized in that, In step S3, The specific content of the super-resolution reconstruction is as follows: Based on the generative adversarial network, the UAV RGB imagery and multispectral data are fused to generate a super-resolution 13-band hyperspectral cube for fine vegetation classification. The specific content of dynamic erosion prediction is as follows: combining LiDAR micro-topography and historical precipitation data, a particle swarm optimization-support vector machine model is constructed to predict the gully expansion rate in the next 15 days.

6. The multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to claim 1, characterized in that, In step S3, ground-based data and airborne data are fused to calculate the grazing intensity index and perform real-time inversion of eco-hydrological processes: The grazing intensity index is calculated by combining UAV thermal infrared imagery with GPS trajectory data. in, For the number of livestock, For the duration of stay, The area of ​​the grassland patch. For vegetation biomass; The real-time inversion of eco-hydrological processes specifically includes: Using ground-based eddy covariance tower data to calibrate satellite / UAV-derived ET, soil evaporation E and vegetation transpiration T are separated: in, , , These are the isotopic characteristics of total evapotranspiration, soil evaporation, and vegetation transpiration, respectively.

7. The multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to claim 1, characterized in that, In step S4: The specific content of the sky-to-ground data calibration is assimilation: Bayesian data assimilation is performed on the ground-based 0-10cm sensor data and Sentinel-1 SAR inversion values ​​to generate a 10m resolution daily-scale high-precision soil moisture map for soil moisture correction. The specific content of the air-to-ground trigger response is as follows: abnormal early warning, when the ground-based sensor detects a sudden drop in soil moisture, the drone is automatically triggered to scan the target area at the centimeter level, and herders are simultaneously notified to adjust their grazing routes; the parameter sensitivity of the hydrological model and the ecological model is optimized based on ground-based isotope data to carry out model iteration; The specific content of the livestock-land feedback control is as follows: based on the real-time grazing intensity index GII, livestock are dynamically restricted from entering highly vulnerable areas through solar-powered electronic fences.

8. A multi-scale three-dimensional monitoring system for eco-hydrology in agro-pastoral ecotones, characterized in that, A multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones according to any one of claims 1-7 includes: a space-based module, an air-based module, a ground-based module, a data processing module, a data fusion module, and a multi-module collaborative decision-making module; The space-based module is used to collect space-based data using multi-source satellites; the air-based module is used to collect air-based data using UAV aerial surveying; and the ground-based module is used to construct a three-level "point-line-surface" sensor network to collect ground-based data. The data processing module is used to integrate and process multi-source satellite space-based data using a multi-level fusion framework and spatiotemporal fusion algorithm, extract features from space-based data through LiDAR point cloud analysis and thermal infrared analysis, and perform model calculations on ground-based data through soil water isotope tracing and livestock carrying capacity calculation. The data fusion module is used to perform spatiotemporal coordination and data fusion of airborne data with space-based and ground-based data, and to perform super-resolution reconstruction and dynamic erosion prediction; the fusion of ground-based and airborne data is used to calculate the grazing intensity index and to perform real-time inversion of eco-hydrological processes. The multi-module collaborative decision-making module is used to conduct grazing early warning and grazing route optimization through a multi-module collaborative mechanism and model inference, which includes air-to-ground data calibration, air-to-ground trigger response, and livestock-to-ground feedback control.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones as described in any one of claims 1-7.

10. A processing terminal, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements a multi-scale three-dimensional monitoring method for eco-hydrology in agro-pastoral ecotones as described in any one of claims 1-7.

Images