Grass original ecology safety dynamic evaluation and early warning system

Abstract

The invention discloses a grassland original ecology safety dynamic evaluation and early warning system, belongs to the field of grassland safety monitoring, and aims to solve the problem that an existing dynamic evaluation and early warning system is difficult to monitor and early warn the self-restoration capacity of a grassland. Through the vegetation monitoring unit, the soil monitoring unit, the livestock monitoring unit and the restoring force monitoring unit, and through a multi-scene simulation platform establishment module of the restoring force monitoring unit, interference scenes of drought and overload grazing are preset and visually displayed based on a vegetation productivity model; a dynamic ecological model is constructed in combination with vegetation, soil and livestock grazing data collected in real time, and deduction of the self-recovery state after grassland degradation is achieved; cA-Markov is utilized to dynamically simulate variation trends of vegetation productivity, grazing intensity and the like, critical threshold values of key indexes such as livestock carrying rate and the like are accurately identified, and the anti-interference capability of an ecological system is quantified; and the decision support module integrates the DPSIR model to generate a grazing forbidding strategy, and forms a whole-process restoration force evaluation chain.

Description

Technical Field

[0001] The present invention relates to the technical field of grassland safety monitoring, and in particular to a grassland ecological safety dynamic assessment and early warning system. Background Art

[0002] Current grassland ecological environment monitoring technology faces significant challenges in real-time dynamic monitoring and effective management. The inherent defects of traditional monitoring systems restrict the accurate grasp of temporal and spatial changes in ecosystems. Static models constructed based on traditional methods of discrete data collection are difficult to effectively depict the complex succession patterns of grassland ecosystems in time series and spatial dimensions. Specifically, the acquisition of ecological data relies heavily on periodic field surveys and manual sampling. This monitoring paradigm results in insufficient real-time data and limited update frequency, making it impossible to capture the immediate status and evolution trends of grassland ecosystems in a timely manner. The lag and low timeliness of monitoring data directly affect the panoramic understanding of the dynamic changes in grassland ecosystems, thereby restricting the formulation and implementation of scientific and refined management decisions.

[0003] In the Chinese patent publication number CN118607970A, a grassland ecological environment monitoring method and system based on digital twins are proposed, including: obtaining initial ecological environment monitoring data of a first grassland target area; establishing a first digital twin model based on the initial ecological environment monitoring data; inputting the same ecological adjustment parameters into the first digital twin model and the first grassland target area respectively to obtain real-time ecological environment monitoring data and first twin ecological environment monitoring data; performing difference processing on the real-time ecological environment monitoring data and the first twin ecological environment monitoring data to obtain the excitation function value of the two; iteratively optimizing the first digital twin model based on the excitation function value and the real-time ecological environment monitoring data to obtain a second digital twin model; and performing real-time ecological adjustment on the first grassland target area based on the second digital twin model. Through the advantages of real-time performance, iterative optimization capability, intelligent management and data integration, the monitoring, analysis and management effects of grassland ecological environment are significantly improved, and it has higher application value and practicality compared with traditional technologies. While existing monitoring systems for grassland ecological security already cover management dimensions such as vegetation cover, habitat quality, and livestock carrying capacity, they generally pay insufficient attention to the resilience of grassland ecosystems themselves. In fact, long-term natural stresses (such as drought and extreme weather), biological disasters (such as rodent and insect pests and invasive weeds), and unreasonable human interference (such as overgrazing and unregulated development) are continuously weakening grassland ecosystems' self-repair capabilities. The lack of monitoring and early warning of this key ecological attribute makes it difficult for existing prevention and control systems to comprehensively assess and warn of ecosystem health thresholds and degradation risks.

[0004] To this end, we proposed a dynamic assessment and early warning system for grassland ecological security. Summary of the Invention

[0005] The purpose of the present invention is to provide a grassland ecological security dynamic assessment and early warning system, which solves the problem in the background technology that the current dynamic assessment and early warning system is difficult to monitor and warn the grassland's own restoration ability.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a grassland ecological security dynamic assessment and early warning system, comprising: Vegetation monitoring unit for: Dynamic monitoring and early warning of vegetation health status can be achieved through the fusion of multi-source data from space, air and ground; Soil monitoring unit for: Combine microenvironmental sensing with carbon cycle models to achieve quantitative analysis of soil ecological functions; Livestock monitoring units for: assessing grazing pressure through multimodal data collection; Resilience monitoring unit for: Based on multi-scenario simulation and decision-making models, an ecosystem anti-interference capacity assessment system is constructed; the system realizes the full process intelligence from data collection to decision support by establishing a grassland ecological security threshold indicator system.

[0007] Furthermore, the vegetation monitoring unit includes a satellite access module, a drone deployment module, and an Internet of Things sensor monitoring module; The satellite access module is used to access Sentinel-2 and Landsat-9 multispectral satellites to obtain 10-30m resolution vegetation spectral data including red and near-infrared bands; The drone deployment module is used to deploy drones equipped with Parrot Sequoia multispectral cameras and Riegl LiDAR to obtain 0.1m resolution 3D point cloud data of vegetation canopies; The IoT sensor monitoring module is used to deploy vegetation fluorescence sensors and hyperspectral instruments to collect photosynthetic efficiency and species spectral characteristics in real time.

[0008] Furthermore, the vegetation monitoring unit also includes a multi-source data processing module, a dynamic prediction module and a vegetation early warning module; The multi-source data processing module is used to perform radiometric calibration on satellite and UAV data; Dynamic prediction module, used to build a vegetation cover prediction model based on LSTM neural network and estimate net primary productivity by combining EC-LUE model and CASA model; The vegetation early warning module is used to set thresholds for a year-on-year decrease in vegetation coverage of >15% or a year-on-year decrease in NPP of >20%, and generate early warning signals.

[0009] Furthermore, the soil monitoring unit includes a microenvironment monitoring module and a carbon cycle detection module; The microenvironment monitoring module is used to deploy a microelectrode array to monitor microbial biomass carbon in real time; The carbon cycle detection module is used to invert surface temperature through UAV thermal infrared images and construct a carbon flux spatial distribution map in combination with the soil respiration chamber.

[0010] Furthermore, the soil monitoring unit also includes a DNDC model building module and a soil early warning module; The DNDC model construction module is used to establish a distributed carbon and nitrogen cycle model, input vegetation parameters, simulate the dynamic changes of soil carbon storage, dynamically simulate the impact of climate change on soil carbon pools, and output emission flux; The soil early warning module is used to set a degradation threshold of soil organic carbon of less than 10g / kg, and trigger a functional decline warning when the monthly carbon storage decrease is greater than 2%. This identifies early signs of soil functional decline and provides a decision-making basis for improvement measures.

[0011] Furthermore, the livestock monitoring unit includes a multimodal monitoring module, an auxiliary data acquisition module and a grazing pressure identification module; The multimodal monitoring module is used to deploy infrared cameras to collect livestock activity tracks; Auxiliary data collection module, used to connect GPS collars and pastoral management systems to obtain animal location data and stocking rates; The grazing pressure identification module is used to quantify grazing pressure based on multi-source data and identify areas at risk of overgrazing.

[0012] Furthermore, the livestock monitoring unit also includes a livestock early warning module, which implements graded early warning based on grazing pressure and livestock status data.

[0013] Furthermore, the resilience monitoring unit includes a multi-scenario simulation platform establishment module, a dynamic assessment and optimization module, and a decision support module; The multi-scenario simulation platform establishment module is used to preset and visualize disturbance scenarios such as drought and overgrazing based on vegetation productivity models. It also establishes a real-time grassland ecological model based on data collected by vegetation, soil, and wildlife monitoring units. This model is arranged according to realistic disturbance scenarios, and the development status of grasslands and their self-recovery after degradation are simulated. Dynamic evaluation and optimization module for identifying the critical threshold of stocking rate > 1.2 sheep units / hectare through CA-Markov and Monte Carlo; The decision support module is used to integrate the DPSIR model to calculate the social-ecological pressure index and generate a dynamic demarcation strategy for grazing ban areas.

[0014] Furthermore, the invention also includes a comprehensive early warning unit, which includes a first-level weight calculation module, a second-level weight calculation module and a third-level weight calculation module; The vegetation warning value calculated by the first-level weight calculation module accounts for 60% of the total weight value, the soil warning value calculated by the second-level weight calculation module accounts for 20% of the total weight value, and the livestock warning value calculated by the third-level weight calculation module accounts for 20% of the total weight value; In the secondary weight calculation module, the vegetation warning index includes two parts, namely, the year-on-year decline rate of coverage accounts for 40%, and the year-on-year decline in productivity accounts for 60%. The soil warning index includes two parts, namely, the organic carbon decline rate accounts for 60%, and the carbon storage decline rate accounts for 40%. The livestock warning index specifically includes grazing pressure accounts for 100%.

[0015] Furthermore, in the three-level weight calculation module, the weights of the vegetation warning module, the soil warning module, and the wildlife warning module are adjusted according to the dynamic adjustment factor; Vegetation warning score: V = 40% * standardized value of coverage decline rate + 60% * standardized value of productivity decline rate; Soil warning score: S=60%*normalized value of organic carbon decline+40%*normalized value of carbon storage decline; Livestock warning score: W=100%*grazing pressure intensity; The above standardization method is: convert the actual value into the 0-1 interval, the threshold corresponds to 1, and the normal state corresponds to 0; Comprehensive Early Warning Index: CEI= ,in It is a first-level weight. After adjusting with dynamic factors, the final warning index ranges from 0 to 1. The higher the value, the higher the risk level.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a dynamic assessment and early warning system for grassland ecological security. In the existing technology, dynamic assessment and early warning systems have difficulty monitoring and issuing early warnings on the grassland's own restoration capacity. However, the present invention establishes a module through a multi-scenario simulation platform of a vegetation monitoring unit, a soil monitoring unit, a wildlife monitoring unit, and a restoration capacity monitoring unit. Based on the vegetation productivity model, disturbance scenarios such as drought and overgrazing are preset and visualized. In combination with real-time vegetation, soil, and livestock data, a dynamic ecological model is constructed to deduce the self-recovery state of the grassland after degradation. The dynamic assessment and optimization module uses CA-Markov and Monte Carlo methods to accurately identify the critical thresholds of key indicators such as stocking rate and quantify the ecosystem's anti-interference ability. The decision support module integrates the DPSIR model to generate grazing ban strategies, forming a full-process restoration capacity assessment chain, filling the gaps in traditional systems in monitoring ecological restoration potential, and enabling the early warning system to simultaneously reflect the current status of grassland ecological security and the dynamics of its self-repair capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the overall program flowchart of the grassland ecological security dynamic assessment and early warning system of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0019] In order to solve the technical problem of how to monitor and provide early warning of grassland self-repair capacity, such as Figure 1 As shown, the following preferred technical solutions are provided: A grassland ecological security dynamic assessment and early warning system, comprising: Vegetation monitoring unit for: Dynamic monitoring and early warning of vegetation health status can be achieved through the fusion of multi-source data from space, air and ground; Soil monitoring unit for: Combine microenvironmental sensing with carbon cycle models to achieve quantitative analysis of soil ecological functions; Livestock monitoring units for: Collect and evaluate livestock pressure intensity through multimodal data; Resilience monitoring unit for: Based on multi-scenario simulation and decision-making models, an ecosystem anti-interference capacity assessment system is constructed; the system realizes the full process intelligence from data collection to decision support by establishing a grassland ecological security threshold indicator system.

[0020] The vegetation monitoring unit includes a satellite access module, a drone deployment module, and an IoT sensor monitoring module; The satellite access module is used to access Sentinel-2 and Landsat-9 multispectral satellites to obtain 10-30m resolution vegetation spectral data including red and near-infrared bands, realize high-frequency collection of macro-scale vegetation spectral data, and construct basic data sets of regional vegetation coverage and vegetation productivity.

[0021] The drone deployment module is used to deploy drones equipped with Parrot Sequoia multispectral cameras and Riegl LiDAR to obtain 0.1m resolution three-dimensional point cloud data of vegetation canopies and capture the vertical structure and micro-patch characteristics of vegetation canopies.

[0022] The IoT sensor monitoring module is used to deploy vegetation fluorescence sensors and hyperspectral instruments to collect photosynthetic efficiency and species spectral characteristics in real time, as well as vegetation physiological indicators and morphological characteristics, to build an "air-ground" data verification system.

[0023] The vegetation monitoring unit also includes a multi-source data processing module, a dynamic prediction module, and a vegetation early warning module; The multi-source data processing module is used to perform radiometric calibration on satellite and UAV data; Dynamic prediction module, used to build a vegetation cover prediction model based on LSTM neural network and estimate net primary productivity by combining EC-LUE model and CASA model; The vegetation early warning module is used to set thresholds for a year-on-year decrease in coverage of >15% or a year-on-year decrease in NPP of >20%, generate early warning signals, extract vegetation indices and structural characteristics through the multi-source data processing module, and use the dynamic prediction module to predict changes in vegetation coverage and vegetation productivity in the next three months, estimate regional carbon sequestration capacity, and use the vegetation early warning module to identify vegetation degradation risks in real time and output graded early warning signals.

[0024] The soil monitoring unit includes a microenvironment monitoring module and a carbon cycle detection module; The microenvironment monitoring module is used to deploy microelectrode arrays to monitor organic carbon in real time; The carbon cycle detection module is used to invert surface temperature through UAV thermal infrared images and construct a carbon flux spatial distribution map in combination with the soil respiration chamber.

[0025] The soil monitoring unit also includes a DNDC model building module and a soil early warning module; The DNDC model construction module is used to establish a distributed carbon and nitrogen cycle model, input vegetation parameters, simulate the dynamic changes of soil carbon storage, dynamically simulate the impact of climate change on soil carbon pools, and output emission flux; The soil early warning module is used to set a degradation threshold of soil organic carbon of less than 10g / kg, and trigger a functional decline warning when the monthly carbon storage decrease is greater than 2%. This identifies early signs of soil functional decline and provides a decision-making basis for improvement measures.

[0026] The livestock monitoring unit includes a multimodal monitoring module, an auxiliary data acquisition module, and a grazing pressure identification module; The multimodal monitoring module is used to deploy infrared cameras to collect livestock activity tracks.

[0027] The auxiliary data collection module is used to connect GPS collars to pastoral management systems to obtain livestock location data and stocking rates. It integrates GPS collar data with socioeconomic parameters to construct an "ecological-social" dataset and quantify the impact of human activities (such as stocking rates) on ecological security.

[0028] The grazing pressure identification module is used to quantify grazing pressure based on multi-source data and identify overgrazing risk areas. The livestock monitoring unit also includes a livestock early warning module, which implements graded early warning based on grazing pressure and livestock status data.

[0029] The resilience monitoring unit includes a multi-scenario simulation platform establishment module, a dynamic assessment and optimization module, and a decision support module; The multi-scenario simulation platform establishment module is used to preset disturbance scenarios such as drought and overgrazing based on the vegetation productivity model and visualize them. At the same time, based on the data collected by the vegetation monitoring unit, soil monitoring unit, and livestock monitoring unit, a real-time grassland ecological model is established and arranged according to the actual disturbance scenarios to deduce the grassland development status and grassland degradation recovery status. Dynamic evaluation and optimization module for identifying the critical threshold of stocking rate > 1.2 sheep units / hectare through CA-Markov and Monte Carlo; The decision support module is used to integrate the DPSIR model to calculate the social-ecological pressure index and generate a dynamic demarcation strategy for grazing ban areas.

[0030] A grassland ecological security dynamic assessment and early warning system also includes a comprehensive early warning unit, which includes a first-level weight calculation module, a second-level weight calculation module and a third-level weight calculation module; The vegetation warning value calculated by the first-level weight calculation module accounts for 60% of the total weight value, the soil warning value calculated by the second-level weight calculation module accounts for 20% of the total weight value, and the livestock warning value calculated by the third-level weight calculation module accounts for 20% of the total weight value; In the secondary weight calculation module, the vegetation early warning indicator consists of two parts: the year-on-year decline in coverage accounts for 40%, and the year-on-year decline in productivity accounts for 60%. The soil early warning indicator consists of two parts: the organic carbon decline rate accounts for 60%, and the carbon storage decline rate accounts for 40%. The livestock early warning indicator specifically includes grazing pressure, which accounts for 100%, as shown in the figure below.

[0031]

[0032] In the three-level weight calculation module, the weights of the vegetation warning module, soil warning module, and livestock warning module are adjusted according to the dynamic adjustment factor, as shown in the figure below.

[0033] Vegetation warning score: V = 60% * productivity year-on-year decline rate + 40% * normalized value of coverage decline rate; Soil warning score: S=60%*organic carbon decline rate+40%*+carbon storage decline normalized value; Livestock warning score: W = normalized value of grazing pressure intensity; The above standardization method is: convert the actual value into the 0-1 interval, the threshold corresponds to 1, and the normal state corresponds to 0; Comprehensive Early Warning Index: CEI= ,in It is a first-level weight. After adjusting with dynamic factors, the final warning index ranges from 0 to 1. The higher the value, the higher the risk level.

[0034]

[0035] For example, in July 2023 (growing season + drought period) of a grassland in Inner Mongolia, after weight adjustment, the vegetation module is 60%, the soil module is 10% (-10%), and the livestock module is 30% (+10%). Monitoring data: vegetation coverage decreased by 18% (standardized value 0.6), vegetation productivity decreased by 30% (standardized value 0.8), soil organic carbon was 10 mg / kg (standardized value 0.9), carbon storage decreased by 3% per month (standardized value 0.7), grazing pressure intensity increased by 12% (standardized value 0.7), single module score: V=0.6×0.8+0.4×0.6=0.72, S=0.6×0.9+0.4×0.7=0.82, W=0.7, comprehensive early warning index: CEI=0.60×0.72+0.10×0.82+0.3×0.70=0.724, warning level: red (CEI=0.724≥0.7), triggering the emergency repair plan for the entire region.

[0036] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A grassland ecological security dynamic assessment and early warning system, characterized by: include: Vegetation monitoring unit for: Dynamic monitoring and early warning of vegetation health status can be achieved through the fusion of multi-source data from space, air and ground; Soil monitoring unit for: Combine microenvironmental sensing with carbon cycle models to achieve quantitative analysis of soil ecological functions; Livestock monitoring units for: assessing grazing pressure through multimodal data collection; Resilience monitoring unit for: Based on multi-scenario simulation and decision-making models, an ecosystem anti-interference capacity assessment system is constructed; the system realizes the full process intelligence from data collection to decision support by establishing a grassland ecological security threshold indicator system.

2. A grassland ecological security dynamic assessment and early warning system according to claim 1, characterized in that: The vegetation monitoring unit includes a satellite access module, a drone deployment module and an Internet of Things sensor monitoring module; The satellite access module is used to access Sentinel-2 and Landsat-9 multispectral satellites to obtain 10-30m resolution vegetation spectral data including red and near-infrared bands; The drone deployment module is used to deploy drones equipped with Parrot Sequoia multispectral cameras and Riegl LiDAR to obtain 0.1m resolution 3D point cloud data of vegetation canopies; The IoT sensor monitoring module is used to deploy vegetation fluorescence sensors and hyperspectral instruments to collect photosynthetic efficiency and species spectral characteristics in real time.

3. A grassland ecological security dynamic assessment and early warning system according to claim 2, characterized in that: The vegetation monitoring unit also includes a multi-source data processing module, a dynamic prediction module and a vegetation early warning module; The multi-source data processing module is used to perform radiometric calibration on satellite and UAV data; Dynamic prediction module, used to build a vegetation cover prediction model based on LSTM neural network and estimate net primary productivity by combining EC-LUE model and CASA model; The vegetation early warning module is used to set thresholds for a year-on-year decrease in vegetation coverage of >15% or a year-on-year decrease in NPP of >20%, and generate early warning signals.

4. The grassland ecological security dynamic assessment and early warning system according to claim 1, characterized in that: The soil monitoring unit includes a microenvironment monitoring module and a carbon cycle detection module; The microenvironment monitoring module is used to deploy a microelectrode array to monitor microbial biomass carbon in real time; The carbon cycle detection module is used to invert surface temperature through UAV thermal infrared images and construct a carbon flux spatial distribution map in combination with the soil respiration chamber.

5. A grassland ecological security dynamic assessment and early warning system according to claim 4, characterized in that: The soil monitoring unit also includes a DNDC model building module and a soil early warning module; The DNDC model construction module is used to establish a distributed carbon and nitrogen cycle model, input vegetation parameters, simulate the dynamic changes of soil carbon storage, dynamically simulate the impact of climate change on soil carbon pools, and output emission flux; The soil early warning module is used to set a degradation threshold of soil organic carbon of less than 10g / kg, and trigger a functional decline warning when the monthly carbon storage decrease is greater than 2%. This identifies early signs of soil functional decline and provides a decision-making basis for improvement measures.

6. The grassland ecological security dynamic assessment and early warning system according to claim 1, characterized in that: The livestock monitoring unit includes a multimodal monitoring module, an auxiliary data acquisition module and a grazing pressure identification module; The multimodal monitoring module is used to deploy infrared cameras to collect livestock activity tracks; Auxiliary data collection module, used to connect GPS collars and pastoral management systems to obtain animal location data and stocking rates; The grazing pressure identification module is used to quantify grazing pressure based on multi-source data and identify areas at risk of overgrazing.

7. A grassland ecological security dynamic assessment and early warning system according to claim 6, characterized in that: The livestock monitoring unit also includes a livestock early warning module, which implements graded early warning based on grazing pressure and livestock status data.

8. The grassland ecological security dynamic assessment and early warning system according to claim 1, characterized in that: The resilience monitoring unit includes a multi-scenario simulation platform establishment module, a dynamic assessment and optimization module, and a decision support module; The multi-scenario simulation platform establishment module is used to preset and visualize disturbance scenarios such as drought and overgrazing based on vegetation productivity models. It also establishes a real-time grassland ecological model based on data collected by vegetation, soil, and wildlife monitoring units. This model is arranged according to realistic disturbance scenarios, and the development status of grasslands and their self-recovery after degradation are simulated. Dynamic evaluation and optimization module for identifying the critical threshold of stocking rate > 1.2 sheep units / hectare through CA-Markov and Monte Carlo; The decision support module is used to integrate the DPSIR model to calculate the social-ecological pressure index and generate a dynamic demarcation strategy for grazing ban areas.

9. The grassland ecological security dynamic assessment and early warning system according to claim 8, characterized in that: The invention also includes a comprehensive early warning unit, which includes a first-level weight calculation module, a second-level weight calculation module and a third-level weight calculation module; The vegetation warning value calculated by the first-level weight calculation module accounts for 60% of the total weight value, the soil warning value calculated by the second-level weight calculation module accounts for 20% of the total weight value, and the livestock warning value calculated by the third-level weight calculation module accounts for 20% of the total weight value; In the secondary weight calculation module, the vegetation warning index includes two parts, namely, the year-on-year decline rate of coverage accounts for 40%, and the year-on-year decline in productivity accounts for 60%. The soil warning index includes two parts, namely, the organic carbon decline rate accounts for 60%, and the carbon storage decline rate accounts for 40%. The livestock warning index specifically includes grazing pressure accounts for 100%.

10. The grassland ecological security dynamic assessment and early warning system according to claim 9, characterized in that: In the three-level weight calculation module, the weight of the vegetation warning module, the weight of the soil warning module and the weight of the wildlife warning module are adjusted according to the dynamic adjustment factor; Vegetation warning score: V = 40% * standardized value of coverage decline rate + 60% * standardized value of productivity decline rate; Soil warning score: S=60%*normalized value of organic carbon decline+40%*normalized value of carbon storage decline; Livestock warning score: W=100%*grazing pressure intensity; The above standardization method is: convert the actual value into the 0-1 interval, the threshold corresponds to 1, and the normal state corresponds to 0; Comprehensive Early Warning Index: CEI= ,in It is a first-level weight. After adjusting with dynamic factors, the final warning index ranges from 0 to 1. The higher the value, the higher the risk level.

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