15 results about "Land degradation" patented technology

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention discloses a karst region land degradation degree evaluation method. The method comprises the steps of collecting karst multi-source data; constructing a soil quality index, a climate quality index, a vegetation quality index, a terrain quality index, a human activity index and an underground leakage index as a karst region MEDALUS-Karst index system; carrying out standardization and comprehensive score calculation on each index to realize comparability among indexes of different dimensions, and reflecting the land degradation degree of the karst region through a land degradation comprehensive index; the method solves the problems that an MEDALUS model is adopted for land degradation evaluation of the karst region, due to the fact that unique geological structures, hydrological processes and degradation characteristics of the karst region conflict with core assumptions and index systems of the MEDALUS model, key driving factors and spatial distribution characteristics of land degradation are difficult to accurately recognize, and the evaluation accuracy of the land degradation is affected. And thus, the technical problems of lack of pertinence of ecological management measures are solved.

The invention discloses a land reclamation analysis method and system for an industrial and mining area, and relates to the technical field of land reclamation analysis, and the method comprises the steps: collecting the geographic information data of a target industrial and mining area, and analyzing the land degradation attribute and future reclamation behavior prediction result of the target industrial and mining area based on the geographic information data of the target industrial and mining area; generating an external environment influence factor based on the land degradation attribute and future reclamation behavior prediction; the reclamation potential value of the target industrial and mining area is calculated in combination with the ecological restoration parameters and land reclamation standards; and through a comprehensive evaluation decision engine, generating a reclamation feasibility score and outputting a recommended reclamation scheme map. The method has the advantages that environment risk and restoration potential multi-dimensional indexes are integrated, the recommended reclamation scheme map with the remarkably optimized success rate is automatically generated, the feasibility and ecological adaptability of reclamation planning are improved, and the reclamation success rate and land utilization efficiency of the industrial and mining wasteland are effectively improved.

This application relates to an IoT-based intelligent land remediation monitoring method and system. The monitoring method includes: acquiring satellite remote sensing elevation data and historical soil composition data of the land area to be monitored; dividing the land area to be monitored into multiple grid units and assigning grid identifiers; deploying fixed soil sensor nodes and mobile drone nodes to generate a sensor network topology; collecting real-time soil parameters of each grid unit and performing noise filtering and anomaly detection to generate a soil composition dataset; inputting the soil composition dataset and historical soil composition data into a cloud-based prediction model to output a land degradation risk score and remediation strategy; controlling the execution equipment to perform land remediation operations and monitoring changes in soil parameters after execution in real time; updating the parameters of the cloud-based prediction model and optimizing the sensor network topology based on the deviation between the changes in soil parameters after execution and the expected values. This application improves agricultural production efficiency and soil health.

The application discloses a vegetation light protection trigger threshold estimation method and device, the method comprising: obtaining first reflectivity data and second reflectivity data at a first time, the first time being a unit time of a preset time period in a preset area, the first reflectivity data being MODIS 11th band reflectivity data, and the second reflectivity data being MODIS 13th band reflectivity data; determining a first Rchl value according to the first reflectivity data and the second reflectivity data, the first Rchl value being the value of Rchl at the first time; obtaining solar shortwave radiation SWR data corresponding to the first Rchl value at the first time to form an Rchl-SWR corresponding relationship; determining an integrated response curve according to the Rchl-SWR corresponding relationship; and determining the light protection trigger threshold of the vegetation in the preset area according to the integrated response curve. The application can determine the light protection trigger threshold of any type of vegetation in any region in the global range, and provide scientific and technological support for crop management, climate change response and land degradation warning.

The present invention discloses a method for identifying mine ecological problems based on artificial intelligence, which belongs to the technical field of ecological problem identification. It obtains multi-source data by integrating remote sensing technology and Internet of Things sensors, and realizes zoning identification of mines with wide distribution and complex terrain through fusion analysis of remote sensing images, sensor data, and meteorological data. Then, mining area ecological information is obtained for the mining area, and characteristic ecological data related to the corresponding characteristic ecological problem model is extracted based on the mining area ecological information. The characteristic ecological data is substituted into the corresponding characteristic ecological problem model to obtain characteristic ecological risk data for evaluating characteristic risks, thereby identifying the type of ecological problem in the target mine. In addition, ecological restoration information is obtained for land degradation areas, and the ecological restoration hidden dangers in land degradation areas are determined based on this. This not only provides a scientific basis for mine ecological protection and restoration, but also effectively solves the problems of low efficiency and incomplete coverage of traditional manual monitoring.

The application discloses a land reclamation analysis method and system for industrial and mining areas, relates to the technical field of land reclamation analysis, and comprises the following steps: collecting geographic information data of a target industrial and mining area, and analyzing land degradation attributes and future reclamation behavior prediction results of the target industrial and mining area based on the geographic information data of the target industrial and mining area; generating external environmental influence factors based on the land degradation attributes and future reclamation behavior prediction; combining ecological restoration parameters and land reclamation standards to calculate reclamation potential values of the target industrial and mining area; and generating a reclamation feasibility score and outputting a recommended reclamation scheme atlas through a comprehensive evaluation decision engine. The application has the advantages that: multi-dimensional indexes of environmental risks and restoration potentials are integrated, and a recommended reclamation scheme atlas with a significantly optimized rate is automatically generated, so that the feasibility and ecological adaptability of reclamation planning are improved, and the success rate of reclamation of industrial and mining wastelands and land utilization efficiency are effectively improved.

This invention relates to the field of soil permeability testing, specifically a method for testing surface soil moisture permeability in degraded forest land. The invention involves selecting several test points in the degraded forest land area to be measured, acquiring surface images of each test point before and after the installation of a dual-structure infiltration ring, identifying man-made crack anomalies and their predicted directions, and reselecting test points for infiltration ring installation based on the predicted crack directions. At intervals, negative pressure points are selected among all test points and negative pressure is applied. An initial infiltration measurement is performed at each test point using a Marshall bottle. A second infiltration measurement is performed after removing the topsoil at each test point. The steady-state infiltration rate obtained from the two infiltration measurements is combined to calculate the crust contribution rate. The preferential flow contribution rate is analyzed based on the second infiltration measurement results at negative and non-negative pressure points. Finally, the causes of forest land degradation are assessed by combining the crust contribution rate and the second steady-state infiltration rate at the negative pressure points, providing a scientific basis for restoration decisions.

Disclosed are a preparation process for a seed ball used for ecological restoration, and a sowing method. The preparation process comprises: preparing a wrapping material, preparing a wrapping slurry, and forming a seed ball. In preparing the wrapping material, a protective shell substance and a soil binder are mixed uniformly to obtain the wrapping material. In preparing the wrapping slurry, water is added to the wrapping material and mixed uniformly to obtain the wrapping slurry. In forming the seed ball, the wrapping slurry and plant seeds are processed into a ball to obtain the seed ball. A seed ball prepared by the present invention maintains a high germination success rate and survival rate even in degraded land environments in arid and semi-arid regions.

The application discloses a black land degradation intelligent evaluation method and system based on multi-source remote sensing images, relates to the technical field of land degradation intelligent evaluation, and realizes intelligent evaluation through multi-source remote sensing data fusion and a double-branch attention deep convolutional neural network; the method first carries out pretreatment such as radiation correction and geometric registration on optical, thermal infrared and radar remote sensing images, and constructs a multi-dimensional data set containing time-series spectra, textures and ground surface temperatures; then key features are extracted in parallel through a spatial branch and a time-series branch, and adaptive weighted fusion is carried out, and finally, a pixel-level degradation grade map and a change trend report are output; the system can automatically, widely and at low cost complete dynamic monitoring of black land degradation, has high evaluation precision and strong timeliness, and provides reliable technical support for accurate protection of black land and improvement of farmland quality.

The present invention discloses a method and system for assessing land degradation, which relates to the field of image processing technology. The method involves acquiring land images using multi-source remote sensing, classifying land organisms through multi-level extraction of remote sensing spatial features, constructing a spatiotemporal coupled dynamic particle evolution model based on land organism distribution maps, and predicting the dynamic particle evolution of land organisms. A model for quantifying abiotic value is established, which dynamically calculates a multidimensional value indicator system based on weights assigned according to historical data and machine learning algorithms. Land degradation is assessed based on the historical values of abiotic land values and the dynamic particle evolution predictions of land organisms. The method combines deformable convolution with transfer learning for adaptive feature extraction, improving classification accuracy in complex terrain. Land degradation is assessed by constructing a particle evolution model to quantify the spatiotemporal response of biomes and simultaneously constructing a multidimensional abiotic value indicator system, thereby enhancing the credibility of blockchain technology.

The invention discloses a multi-element cooperative vegetation restoration and reconstruction method and application, and relates to the technical field of ecological restoration, the method comprises the following steps: S1, sand fixation: spraying a sand fixation agent slurry to a target planting area, uniformly mixing the sand fixation agent slurry with sandy soil to form a sand fixation agent layer, arranging a grass checkered sand barrier on a windward slope, and fixing the sand fixation agent layer; s2, water retention: mixing a water-retaining agent with water to obtain a water-retaining agent mixed solution; spraying a water-retaining agent mixed solution on the sand stabilization layer to form a water-retaining agent layer; s3, fertilizer increasing: applying a compound organic fertilizer to the surface of the water-retaining agent layer to improve the organic matter content and nutrient supply capability of the soil; and S4, planting: selecting proper plants for planting, regularly checking vegetation growth conditions and soil humidity after planting, and timely supplementing a water-retaining agent and a compound organic fertilizer according to needs. According to the method for restoring and reconstructing the vegetation, the survival rate of sand land plant planting can be effectively increased, and the land degradation phenomenon is improved.

The application provides a kind of near real-time land degradation monitoring method and system, the method comprises: obtaining the historical and near real-time multi-source remote sensing data of target area and carrying out space-time sequence reconstruction, obtain the spectral feature time series of each pixel point in target area;Obtain the topographic features and climate characteristics of target area and fuse with spectral feature time series, obtain the multi-dimensional feature vector of each pixel point;Obtain the historical land cover classification of each pixel point in the first historical reference period, calculate the near real-time land cover classification of each pixel point;According to the multi-dimensional feature vector of each pixel point, and / or historical land cover classification and near real-time land cover classification, calculate the land degradation evaluation index of each pixel point, and evaluate the land degradation of pixel point. Realize the quick prediction and use of near real-time data land cover classification, ensure the real-time of monitoring result, and detect land degradation from multiple aspects, form a complete monitoring system.

The application provides a land degradation degree classification method based on a convolutional neural network and multi-source data, and comprises the following steps: S1, determining a land degradation degree standard; S2, acquiring land degradation degree sample point data and classifying the sample points according to the land degradation degree standard; S3, acquiring environmental variable data and performing preprocessing; S4, uniformly processing the classified land degradation degree sample point data and the preprocessed environmental variable data to obtain sample data; S5, constructing a land degradation degree classification model and training the model; and S6, acquiring a land degradation degree classification result by using the trained land degradation degree classification model. The method can solve the problems in the prior art, such as only focusing on a single environmental index, ignoring other multi-source environmental factors, neglecting complex nonlinear relationships existing in actual processes, resulting in a large deviation of results, and having great subjectivity.

The invention relates to the technical field of land degradation restoration, and discloses a land degradation area vegetation restoration planning method based on AI image recognition, which integrates multi-source data of satellite remote sensing, an unmanned aerial vehicle, a meteorological station, a soil sensor and the like, generates a composite environment grid with a unified space-time reference through a space-time diagram convolutional network, extracts drought and rainstorm precursor characteristics, and carries out vegetation restoration on the land degradation area. The disaster probability is predicted by using a space-time Transform model; dividing degraded and non-degraded areas by using UNet, embedding a 3-PG vegetation growth model in the UNet, constructing a vegetation type library in combination with meteorological partitions, outputting planting parameters, and performing regression optimization and verification through a Gaussian process; integrating the multi-source data and the phenological data to construct a vegetation knowledge graph, and generating a restoration scheme through a graph neural network; a digital twinborn body is constructed based on a Unity engine, the feasibility of a vegetation growth verification scheme is simulated, and model parameters are updated by combining ensemble Kalman filtering with unmanned aerial vehicle data. According to the method, precision and dynamic optimization of repair planning are realized through an AI technology.