2197 results about "Vegetation" patented technology

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

The invention provides a forest area monitoring method and system based on unmanned aerial vehicle routing inspection, and the method comprises the steps: firstly obtaining a historical routing inspection data set containing a geographic position identifier and a topographic feature parameter in a target forest area, and generating an initial routing inspection route indicating a flight path and an image collection node according to the historical routing inspection data set; then, an unmanned aerial vehicle carrying a multispectral sensor is called to carry out dynamic routing inspection according to an air route, a real-time monitoring image set composed of vegetation coverage images of a plurality of monitoring areas under different timestamps is obtained, and then feature extraction and anomaly detection are carried out on the real-time monitoring image set; according to the method, an image anomaly feature set containing vegetation and surface structure anomaly indexes is determined, and finally, a forest region monitoring optimization strategy is generated based on the image anomaly feature set, so that the unmanned aerial vehicle inspection frequency and image acquisition node space distribution are adjusted, and more efficient and accurate monitoring of a forest region is realized.

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention discloses a forest carbon sink function monitoring method based on multi-source data. The method comprises the following steps: collecting data such as vegetation, soil, weather, landform and interference factors; the collected data are preprocessed; the preprocessed data are input into a Biome-BGC model for processing, and ecological system carbon flux parameters are obtained; determining a vegetation carbon sink amount and a soil carbon sink amount based on an ecological system carbon flux parameter, and obtaining a comprehensive carbon sink amount by combining artificial and natural interference carbon emission amounts; determining the forest carbon sink potential through a constructed forest carbon sink potential calculation formula based on the calculated comprehensive carbon sink amount; based on the calculated comprehensive carbon sink amount, time stability, space stability and anti-interference stability are determined, weighted summation is carried out on the time stability, the space stability and the anti-interference stability, and the carbon sink stability is determined; according to the method, the forest carbon sink function is monitored through multi-source data, the monitoring accuracy is improved, and the forest carbon sink potential, the carbon sink stability and the investment value are accurately quantified.

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention relates to the technical field of hydrological process simulation, in particular to an ecological hydrological process simulation method and system based on dynamic vegetation and a medium. The method comprises the following steps: acquiring a vegetation physiological monitoring parameter set and a root system water absorption dynamic data set; performing time-space unified registration on the vegetation physiological monitoring parameter set and the root system water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set; performing soil dry and wet process division on the target area based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; dividing the vegetation physiological dynamic parameter set according to the segmented soil moisture state sequence to obtain a state classification vegetation physiological data set; and constructing a vegetation water stress adaptation strategy model based on the state classification vegetation physiological data set. According to the method, the simulation precision of vegetation coverage and soil moisture dynamic balance is improved, and the problem of simulation errors caused by vegetation parameter static processing in the prior art is solved.

The invention relates to the technical field of resource management, in particular to a seawall ecological protection-oriented vegetation water demand prediction method and system, and the method comprises the steps: collecting the environment monitoring data of a seawall region; establishing a salinity stress factor model based on the soil data, calculating a comprehensive salinity stress coefficient of the monitoring point, and performing multi-factor dynamic correction on the vegetation water demand to obtain a vegetation basic water demand; establishing a multi-dimensional time sequence model of a tidal cycle and a soil moisture supplement relationship considering geologic structure differences, and obtaining resource scheduling weights under different tidal stages and environmental conditions; according to the vegetation data, the underground data and the environmental data of the different seawall regions, based on a multi-region collaborative water resource allocation network, obtaining a deployment optimization scheme based on regional characteristic parameters; and generating a vegetation water demand prediction result and a graded resource scheduling scheme by integrating the vegetation basic water demand, the resource scheduling weight and the allocation optimization scheme, and performing irrigation.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention discloses a monitoring system for forest ecological carbon sink calculation, and belongs to the field of ecological environment monitoring and carbon sink evaluation. Comprising a foundation optical remote sensing module, a crown biomass calculation module, a soil carbon component identification module, a vertical carbon flux detection module, a regional microclimate analysis module, a time sequence big data coupling module, a carbon source and carbon sink model fusion module and a result correction feedback module. The method comprises the following steps: collecting vegetation three-dimensional structure data through a multi-frequency laser radar, calculating unit-area canopy carbon storage acceleration in combination with seasonal climate interference parameters, and fusing turbulent carbon exchange and soil carbon change data to construct a regional carbon flux coupling model; the system collects cross-rosa chinensis carbon change data to construct a multi-dimensional feature tensor diagram, dynamically updates a weight and a fitting function, realizes carbon sink dynamic estimation and feedback optimization based on a biology-climate-time three-dimensional cooperation mechanism, and improves forest carbon sink monitoring precision and stability. The beneficial effect is that scientificity and accuracy of ecological environment monitoring and carbon sink evaluation are improved.

The invention relates to the technical field of disease and insect pest detection, and further relates to a disease and insect pest image multispectral imaging and deep learning intelligent detection system, which comprises a multispectral radiance calibration unit, an image analysis and processing unit and a disease and insect pest probability detection unit, the multispectral radiance calibration unit is used for carrying out multiband imaging on vegetation leaf surfaces in the forest region, deducting dark field digital counts from original digital counts and then converting the original digital counts into radiance of corresponding bands; the image analyzing and processing unit is used for converting the radiance into surface feature apparent reflectivity; multiplying the scab spectrum gradient contrast index by the spectrum heterogeneity index to obtain coupled pest and disease damage characteristics; and the pest and disease damage probability detection unit is used for mapping the convolution output of each layer into a pest and disease damage probability value based on a generalized error function. The accuracy and practicability of disease and pest detection are remarkably improved.

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention relates to a water and fertilizer management control method based on image data processing, and the method comprises the steps: collecting a visible light image, a multispectral image and a thermal infrared image of a crop, carrying out the feature extraction, calculating the plant morphological parameters, vegetation indexes and vegetation canopy temperature distribution of the crop, creating a three-dimensional physiological feature matrix, creating an image segmentation network model, and carrying out the feature extraction. Inputting the visible light image, the multispectral image, the thermal infrared image and the three-dimensional physiological feature matrix into an image segmentation network model to segment plant organs, quantifying organ-level phenotypic parameters, and establishing a water and fertilizer decision model based on reinforcement learning; and taking real-time data of the three-dimensional physiological feature matrix, the organ-level phenotypic parameters and the soil specified parameters as state input, taking a water and fertilizer proportioning scheme as an action space, and implementing a water and fertilizer management scheme through a dynamic optimization control strategy. The technical problems that data integration of a single sensor is insufficient, phenotype analysis precision is limited, and adaptability of a decision model is poor in a traditional water and fertilizer regulation scheme are solved.

The invention discloses a water and soil loss dynamic risk assessment system based on a remote sensing image, and relates to the field of water and soil loss risk assessment. The invention discloses a water and soil loss dynamic risk assessment system based on remote sensing images. The system comprises a data acquisition and preprocessing module, a dynamic feature extraction and analysis module, a hierarchical risk assessment model and a risk grading and modeling module, the method further comprises the following steps: acquiring a remote sensing image and historical soil erosion map data of a specific administrative region in a specific period through a data acquisition and preprocessing module; the dynamic feature extraction and analysis module is used for extracting land utilization types, vegetation coverage and quarterly change features of topographic factors through the acquired data; according to the method, a three-layer model architecture is arranged, static historical data, human activity disturbance and social exposure are decoupled and quantified, and the evaluation dimension is improved, so that the accuracy of an evaluation conclusion is improved.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

The invention relates to the technical field of earthquakes, in particular to an optical remote sensing image feature landslide information extraction method. A double-time-phase NDVI time sequence analysis strategy is adopted, and landslide area recognition is achieved by constructing a vegetation index difference chart before and after an earthquake. A landslide mass area before an earthquake presents a high NDVI value due to complete vegetation coverage, and a vegetation index of the area in an image after a disaster is significantly attenuated due to surface disturbance, so that an obvious change response is formed. If the normalized vegetation index does not change, a non-landslide area can be determined, and the greater the change of the normalized vegetation index is, the greater the possibility of landslide occurrence is, and a landslide preselection area is determined; then Otsu threshold segmentation is carried out by combining cloud layer features and water body features, cloud layer and water body change parts are effectively eliminated, object-oriented geometric shape rule fine recognition is carried out on a preselected area, a DEM is adopted to calculate a slope value to constrain low-lying or flat earth surface changes, and efficient and accurate landslide information extraction is achieved.

The invention relates to the technical field of garden monitoring, in particular to a landscaping monitoring system and method, and the system comprises a collection module which is used for obtaining multi-angle images of a garden in stages, and selectively collecting the images according to a correlation judgment result; the analysis module is used for extracting vegetation indexes, disease targets, leaf health features and falling object state features; the identification selection module is used for generating a dynamic acquisition strategy according to the relevance abnormal index and triggering the acquisition module to directionally acquire the second-level image; the weighted fusion module is used for performing adaptive weighted fusion on the multi-source features and calculating a comprehensive wilt risk level; the early-warning module is used for generating early-warning signals and early-warning grades, the view limitation of traditional single-view-angle monitoring is broken through, large-range vegetation vitality abnormal areas can be rapidly screened, detail features of trunk diseases, low leaf withering and microscopic disease spots can be accurately captured, and the comprehensiveness and reliability of early withering recognition are remarkably improved.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention relates to the technical field of desertification control, in particular to a sand barrier aerial seeding collaborative layout optimization system and method based on an unmanned aerial vehicle cluster game, which constructs a microenvironment digital model of terrain, wind field, humidity and temperature through a multi-mode desert microenvironment sensing module and combines with a sand barrier-vegetation coupling dynamic model. Space-time correlation between sand barrier configuration and vegetation growth is achieved, the self-adaptive sand barrier parameter optimization module and the precise seed aerial seeding control module work cooperatively, seed putting is optimized, the sand stabilization efficiency and the vegetation survival rate are remarkably improved to 60%-75%, the treatment period is shortened to 1.5-2.5 years, and the control cost is reduced. The closed-loop feedback and dynamic optimization module further ensures continuous improvement of the treatment effect, a traditional'sand blocking 'strategy is converted into'ecological microenvironment engineering', a complete microenvironment regulation and control mechanism is constructed, the treatment efficiency is improved by 35%-50%, and fundamental change of the treatment concept is achieved.

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention provides an ecological restoration and rapid vegetation planting method suitable for an open grassland mining area, and provides a novel grass seed assembly structure, and according to regional climate characteristics and geographical general situations, in combination with actual planting conditions of a vegetation restoration area, and according to planting principles of local nature, species diversity, ecological complementation and the like. Based on the main biological ecology characteristics of different plant species and the collaborative succession and growth promotion co-fusion development characteristics of the suitable growth environment, multi-system, multi-dimension and multi-species technical assembly is carried out according to local conditions, and a cost-saving, efficient, function-harmonious and landscaping rapid vegetation recovery technical method is created.

The invention belongs to the technical field of drip irrigation equipment, and discloses a water-saving vegetation drip irrigation device for desertification control, which is technically characterized by comprising a bottom ring, a plurality of groups of annularly distributed drip irrigation pipes are fixedly mounted on the bottom wall of the bottom ring, and a plurality of groups of permeable holes are respectively formed in the surfaces of the drip irrigation pipes along different heights. The drip irrigation pipe is internally provided with a layered drip irrigation mechanism matched with the water permeable holes, the layered drip irrigation mechanism comprises a sealing assembly and a fixed-height drainage assembly, and the sealing assembly is matched with the fixed-height drainage assembly, so that the water permeable holes at different heights in the surface of the drip irrigation pipe can be gradually switched to a through state from top to bottom; the irrigation depth can be gradually increased through the water permeable holes in the surface of the drip irrigation pipe, the vegetation root system can be guided to extend towards the deep soil during irrigation, and the vegetation root system is promoted to autonomously absorb water contained in the deep soil.

The invention discloses a power transmission line tower footing bare soil vegetation restoration capability comprehensive evaluation method, and relates to the technical field of vegetation restoration capability evaluation, and the method comprises the steps: building an evaluation index system, building a quantitative model, carrying out data collection, and carrying out calculation and grading. According to the method, a comprehensive evaluation index system is constructed, electromagnetic radiation related indexes and microtopography related indexes are covered, the actual vegetation restoration situation can be reflected more accurately, the influence coefficient is measured and calculated in detail, the influence of electromagnetic radiation and microtopography changes on vegetation is fully considered, a basis is provided for accurate evaluation, and the vegetation restoration efficiency is improved. The problems of inaccurate and incomplete evaluation are effectively solved, quantitative model construction and optimization are performed, time factors are introduced to dynamically evaluate the vegetation restoration capability, problems can be found in time, a restoration strategy can be adjusted, accurate grade division is performed, reasonable selection of vegetation varieties is facilitated, proper restoration measures are adopted, the vegetation restoration success rate is increased, and the vegetation restoration efficiency is improved. The ecological stability of the tower footing area is enhanced.