147 results about "Vegetation type" patented technology

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention discloses a coastal zone carbon sink data acquisition management system and method, and particularly relates to the technical field of carbon sink evaluation, and the method comprises the following steps: dividing carbon flux parameter partitions based on vegetation types, sediment characteristics and historical carbon storage levels, and establishing initial reference values; plant biomass, soil organic carbon content and non-carbon dioxide greenhouse gas flux are regularly collected according to fixed sampling points, and a carbon sink observation data sequence is formed; when data exception triggers an evaluation process, generating a carbon sink credibility index and an ecological interference response index; inputting the two indexes into a prediction model to output an adjustment vector, generating an adjustment strategy in combination with a weight, and dynamically updating an initial carbon flux reference value of each partition; according to the method, regionalized management is realized by constructing carbon flux parameter partitions, a carbon sink observation data sequence is established, an evaluation index is dynamically generated, intelligent updating of a carbon flux reference value is completed in combination with a prediction model, and the precision, responsiveness and adaptive adjustment capability of carbon sink monitoring are improved.

The invention discloses a vegetation gross primary productivity monitoring method based on downscaling and multi-source remote sensing data, and belongs to the field of remote sensing image processing. In order to accurately monitor the GPP change of an engineering scale, the vegetation total primary productivity monitoring method comprises the following steps: acquiring first resolution multisource remote sensing data, downscaling by using CNN, inputting data containing DEM and LUCC in a layer, and dynamically adjusting background weight in a loss function through an LUCC binarization mask (a vegetation region is 1, and a non-vegetation region is 0); a CASA method is used for simulating GPP, a multi-year target area GPP data set is generated, and the maximum light energy utilization rate is determined by the vegetation type; analyzing GPP spatial and temporal change characteristics of the data set by using a Theil-Sen slope estimation method and the like, and extracting spatial distribution and a time change mode; extracting principal components of similar factors by using a principal component analysis method, calculating local correlation coefficients of the principal components and the GPP by using a partial correlation method, identifying dominant influence factors by using a contribution degree decomposition model, and extracting an environmental factor driven map. The method is applied to the field of ecological remote sensing monitoring.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a coastal salt marsh vegetation carbon sink estimation method and system based on multi-temporal phenological characteristics. The method comprises the following steps: acquiring a remote sensing image of a target coastal salt marsh area in a key phenological period; extracting an initial vegetation contour of a vegetation coverage range in the target area from the remote sensing image by adopting an unsupervised classification method; constructing a phenological decision tree model by taking an NDVI threshold method as a core, and integrating a machine learning enhanced node division mechanism and a phenological period slope analysis method to enhance the phenological decision tree model; inputting the initial vegetation contour into a phenology decision tree model, and performing multi-level classification on vegetation types according to the initial vegetation contour through the phenology decision tree model to distinguish vegetation coverage ranges of various types of vegetation in the target coastal salt marsh area; and vegetation carbon density parameters are obtained, and the estimation of the total carbon sink amount of the target coastal salt marsh area is completed through spatial superposition calculation in combination with a vegetation coverage range, so that accurate classification of vegetation and efficient estimation of carbon sink are realized.

The invention discloses an urban traffic three-dimensional model vegetation element automatic generation method and system based on streetscape recognition, a terminal and a storage medium. The method comprises the steps of constructing a vegetation element knowledge graph, defining a vegetation type and typical attributes, directionally collecting a three-dimensional model and a multi-view image, unifying metadata and storing the metadata. Pixel-level semantic segmentation is carried out on the streetscape image, vegetation categories are identified based on transfer learning and an attention mechanism, and ROI and feature vectors are output; screening the candidate set based on ontology mapping, selecting a best matched vegetation model for the local feature vector of the recognized ROI and a pre-stored feature vector in a material library, and recording mapping; based on reference object correction, knowledge graph fusion and automatic discrimination or optimization strategies, reliable scale correction, number or position deduction and batch three-dimensional model import placement meeting ecological constraints are carried out on the vegetation ROI. According to the invention, efficient, intelligent and standardized technical support is provided for traffic simulation and urban visualization.

The invention relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to territorial space planning, and the method comprises the following steps: based on a successful ecological restoration technical scheme case and a to-be-restored land parcel unit. According to the method, normalization processing is carried out on multi-dimensional attributes between successful ecological restoration technical scheme cases and to-be-restored land parcel units, a source-target land parcel feature fingerprint set is constructed, and specific attributes such as annual average temperature, precipitation, soil types, pH values, altitudes, native vegetation types and labor cost are combined; modeling is realized on data source selection granularity and expression dimension. Furthermore, an importance coefficient is set according to the influence degree of each dimension in a historical repair success case, feature difference weight calculation is guided, and a technical migration adaptation degree score is obtained by combining normalized difference calculation, so that a quantifiable source-place adaptation capability evaluation index is established, and the limitation that judgment depends on experience in the prior art is broken through.

The invention discloses a forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention provides a mining area vegetation reconstruction method based on vegetation classification and division. The method relates to the technical field of ecological environment restoration, and comprises the following steps: S1, acquiring mining area environment data by adopting a mode of combining remote sensing images, unmanned aerial vehicle aerial photography and ground sampling, performing spatial interpolation, normalization and filtering preprocessing on the environment data, and outputting a mining area ecological factor database; s2, on the basis of the ecological factor data, constructing a multi-index evaluation model by combining an analytic hierarchy process with clustering analysis to perform vegetation type division on the mining area, and generating partition templates with different ecological functions; and S3, constructing a plant phenological growth prediction model based on years of phenological observation data and weather forecast information. According to the mining area vegetation reconstruction method based on vegetation classification and division, precise planting and spatial layout are completed, the vegetation survival rate, biomass production and community diversity are remarkably improved, and the strict requirements of mining area ecological reconstruction for diversity and adaptability are met.

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

The invention discloses a road construction carbon emission monitoring method and system, and relates to the technical field of carbon emission monitoring. The method comprises the steps of obtaining earth surface feature information, vegetation types and distribution areas of a construction area, obtaining disturbance areas of different vegetation types, calculating disturbance coefficients, loss coefficients and carbon sink loss amounts of the disturbance areas, calculating the total carbon emission amount of a construction period, constructing an emission evaluation model, and outputting a predicted emission amount of a subsequent construction period in combination with a project scheme. Judging whether the predicted emission exceeds a preset emission threshold value or not, if not, keeping the project scheme unchanged, and if yes, suggesting to modify the project scheme; the carbon sink loss amount caused by vegetation damage can be accurately calculated, so that the total carbon emission amount is more comprehensively and accurately evaluated, and a scientific basis is provided for construction scheme optimization.

The invention provides a desert steppe area mining area gangue discharge field ecological vegetation screening method and system, and the method comprises the steps: determining the classification factors of the site types of topographic regions based on site attribute information, and dividing a plurality of topographic regions into a plurality of different site types; identifying vegetation state information on the terrain area of the target site type to obtain a vegetation mode; calculating an evaluation score of the vegetation mode based on the vegetation type related to the vegetation mode; optimizing the vegetation mode to obtain a target vegetation type on the basis of correlation features among various types of vegetation in the vegetation mode; the method comprises the following steps: determining classification factors of site types according to real area information, dividing a waste rock discharge field into multiple types, determining vegetation modes based on vegetation states of the site types, calculating evaluation scores of the vegetation modes, and further optimizing the vegetation modes to obtain optimal vegetation types of the site types. Thus, vegetation types adapted to various site types are generated to obtain a stable and effective ecosystem.

The invention discloses an under-forest plant growth monitoring method and system based on image recognition, and belongs to the field of plant growth monitoring, and the monitoring method specifically comprises the following steps: I, selecting an under-forest plant growth region, and carrying out the region division according to the factors of terrain, vegetation type and ecological sensitivity; iI, acquiring various environmental data such as soil parameters and meteorological data of a plant growth area in real time, capturing plant image data at regular time, and constructing data pairs accurately matched with timestamps; iII, uploading the collected data to a cloud platform, carrying out unified storage and classified arrangement on the environment and image data, and preprocessing the image data at the same time; according to the invention, simultaneous acquisition, transmission and storage of various data of environmental soil and plant images can be realized, the accuracy of state identification is improved, the ability to understand and predict a complex growth process is enhanced, automatic identification and instant early warning of an abnormal state are realized, and the management response efficiency of the system is improved.

The invention discloses a mountain vegetation classification method based on a remote sensing image and a vegetation index, and relates to the technical field of mountain vegetation classification, and the method comprises the steps: obtaining remote sensing image data of a research region in a set time span, and dividing the remote sensing image data into a plurality of sub-regions; and analyzing and extracting vegetation index time sequence characteristics and seasonal growth characteristics of each sub-region in a set time span, preliminarily determining the vegetation type of each sub-region as an initial label, and using the obtained initial label, vegetation index time sequence characteristics and seasonal growth characteristics of each sub-region to train a classification model. According to the method, the change of the vegetation index of each sub-region in the time sequence is calculated, and the change of the seasonal vegetation index is combined, so that the static vegetation type is considered, the growth cycle of the vegetation and the change characteristics of the vegetation in different seasons are dynamically captured, and the real change condition of the mountain vegetation can be reflected more accurately.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

The invention discloses an ancient and famous tree identification method based on multi-source remote sensing data. According to the method, an ancient tree ecological niche model is introduced, the association degree of the actual habitat parameters of the regional ancient trees and the remote sensing features is quantified as the weight, and the feature selection process of the random forest is fused, so that the screened features have the capability of distinguishing different vegetation and meet the real living environment requirements of the ancient trees. According to the method, the problem of high distinction degree but irrelevance caused by the fact that a traditional method only depends on feature distinction degree screening is avoided, the key features directly related to the ancient tree can be more accurately captured, and the recognition pertinence of a subsequent model to the target tree species is improved; the ecological niche constrained feature screening mechanism ensures the strong relevance between the features of the input model and the ancient tree habitat from the source, the multi-modal feature fusion network is combined to comprehensively extract space, spectrum and structure information, the conditions of missing detection and false detection caused by habitat changes or new ancient trees are reduced, and the recognition effect is more stable and reliable in long-term application.

The present application provides a kind of wind erosion sand transport rate vegetation correction factor dynamic optimization method, introduce the inherent cementation index of soil inorganic / organic carbon ratio, by quantifying carbon ratio influence coefficient, the inherent influence of soil carbon component structure on soil aggregate stability, sand critical condition is integrated into vegetation correction system, so that vegetation correction process is no longer separated from soil base attribute, substantially improve the physical mechanism rationality of wind erosion model. Meanwhile, through remote sensing per-pixel modeling and multi-factor spatial overlay operation, realize the dynamic adjustment of each pixel vegetation correction factor with the difference of soil carbon structure, vegetation coverage, vegetation type, solve the distortion problem caused by the parameter uniformization and staticization of traditional model.

The present application relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to land space planning, comprising the following steps: based on successful ecological restoration technical scheme cases and land units to be restored.The present application realizes modeling in data source selection granularity and expression dimension by normalizing the multi-dimensional attributes between successful ecological restoration technical scheme cases and land units to be restored, constructing source-target land feature fingerprint sets, and combining annual mean temperature, precipitation, soil type, pH value, altitude, native vegetation type and labor cost and other specific attributes.Further, by setting importance coefficients according to the influence degree of each dimension in historical restoration success cases, the present application guides feature difference weight calculation, and obtains technical migration adaptation score by combining normalized difference value calculation, thereby establishing a quantifiable source land adaptation capability evaluation index, and breaking the original limitations of relying on experience judgment.

The application provides a vegetation carbon storage prediction method and device and a storage medium, wherein the method comprises the following steps: extracting a feature set of a to-be-measured region according to a remote sensing image, an elevation model and historical vegetation index information of the to-be-measured region; inputting the feature set into a random forest vegetation classification model for prediction to obtain at least one vegetation type of the to-be-measured region and regional information of each vegetation type in the remote sensing image; performing segmentation processing on the remote sensing image of the to-be-measured region based on a pre-trained vegetation segmentation model to obtain at least one vegetation proportion information of the to-be-measured region and corresponding regional information of each vegetation proportion information in the remote sensing image; and determining the carbon storage of the to-be-measured region according to the vegetation type of the to-be-measured region, the regional information of each vegetation type in the remote sensing image, the vegetation proportion information and the corresponding regional information of each vegetation proportion information in the remote sensing image. The application effectively improves the accuracy of forest carbon storage estimation.

This invention belongs to the field of ecological environment monitoring, specifically a dynamic monitoring method for carbon flux in wetland ecosystems that integrates eddy covariance (ECD) and satellite remote sensing. The method includes the following steps: S1: Monitoring station establishment: Based on the ECD method, flux monitoring stations are established in typical areas of lake wetlands. The selected typical areas should represent the main ecological characteristics and vegetation types of the wetlands to ensure the representativeness and reliability of the monitoring data. This invention combines ECD observation with satellite remote sensing technology to achieve real-time visual monitoring of carbon flux. It provides both data accumulation and visualization, improving the accuracy and reliability of wetland carbon source / sink assessment results. Simultaneously, the visualized spatial distribution of carbon sources / sinks facilitates the detection of changes in carbon sources / sinks in wetland areas, enabling timely detection and response to changes in wetland ecosystems, and scientifically increasing wetland carbon sinks through artificial regulation.

The invention discloses a method for calculating a comprehensive roughness coefficient of a riverway of a vegetation-covered beach based on dynamic partition. The method comprises the following steps: dividing the riverway into a river channel and a vegetation beach land according to actually measured topographic data of the riverway; obtaining a partition roughness coefficient of the vegetation beach land, a wetted weekly parameter of a river channel, a wetted weekly parameter of the vegetation beach land, a full wetted weekly parameter of a river channel and a roughness coefficient of the river channel under the overtopping beach scene, and calculating a comprehensive roughness coefficient of the river channel under the overtopping beach scene; when the vegetation type or the vegetation submerging degree changes, the partition roughness coefficient of the vegetation beach land changes to cause the change of the comprehensive roughness coefficient of the river channel, and the comprehensive roughness coefficient of the river channel is calculated by combining the comprehensive roughness coefficient of the river channel under the beach overtopping situation. According to the method, the calculation difficulty of the comprehensive roughness coefficient of the river channel is reduced, the problem of instantaneous change of the comprehensive roughness coefficient of the river channel under the condition that the vegetation submerging degree of the vegetation bottomland of the river mouth river channel region changes drastically is solved, and rapid inversion of the on-way resistance parameter of the river channel is realized.

The present application relates to a kind of grassland vegetation coverage measuring device and measuring method, device includes connecting column, lifting mechanism, host computer and camera, host computer is connected with lifting mechanism and camera communication;Connecting column includes vertical connection column and cross bar, column one end is fixed on ground, host computer is installed on column;Lifting mechanism includes lifting block and telescopic machine, the output shaft of telescopic machine connects lifting block, the top of lifting block is equipped with guide shaft, the one end of cross bar is equipped with with guide shaft cooperation installation guide hole, camera is connected with lifting block;Camera changes observation height according to vegetation type.Compared with prior art, the height of camera is controlled by lifting mechanism in the present application, when using, camera hangs under cross bar to collect information, when height needs to be adjusted, telescopic machine can be started by host computer or remote signal control, drive camera to rise or fall, so as to change the effect of camera height control collection range, and the measuring effect is better.

The invention discloses a farmland tree remote sensing image optimization method, relates to the technical field of remote sensing image processing, and solves the problems that in an existing optical image processing technology, a data processing flow is too complex in an optical image processing process, and wrong division and missing division are likely to occur, so that farmland tree images in optical images are not clear, and the image quality is poor. Therefore, the application of optical images in agricultural operation activities is restricted. A farmland tree remote sensing image optimization method comprises a data acquisition stage, a vegetation and non-vegetation distinguishing stage, a vegetation type distinguishing stage, a farmland tree and non-farmland tree distinguishing stage and a farmland tree remote sensing image optimization stage. The method is suitable for the fields of farmland tree remote sensing monitoring, ecology, remote sensing technology, geographic information systems and the like.

The invention discloses a vegetation species identification method for enhancing AI computing power and time sequence tracing, and the method comprises the steps: collecting and preprocessing multi-source remote sensing data, and constructing a vegetation classification data set with time-space alignment and uniform resolution; generating a vegetation mask based on the vegetation classification data set, obtaining a standardized sample slice, and constructing a training data set in combination with spectral features; performing time-phase-sharing processing on the training data set based on vegetation phenological characteristics, and outputting a preliminary vegetation classification result; performing object-level optimization on the preliminary vegetation classification result to obtain an optimized vegetation classification result; and correcting an optimized vegetation classification result in combination with topographic data, generating a vegetation species classification map of a target year, and realizing vegetation dynamic change inversion in a specified time period through transfer learning. Therefore, traditional resolution limitation can be broken through, the classification error problem caused by independent use of multi-source data is solved, the distinction degree of complex vegetation types is remarkably improved, and historical vegetation dynamic backtracking analysis is supported.

The invention discloses a resident-free island vegetation classification method. The method comprises the steps of S1, data acquisition and preprocessing; s2, constructing a random forest model; s3, feature importance evaluation; s4, evaluating feature correlation; s5, feature optimization; s6, precision evaluation; according to the method, the problem that high-precision classification and time sequence dynamic monitoring of non-resident island coastal blue carbon resources in a complex environment are difficult to realize is solved, a remote sensing fine monitoring method is provided, and only satellite remote sensing and ground monitoring data are used; and fine classification and time sequence change detection of resident-free island vegetation types are realized.

A cascade vegetation surface moisture content detection method based on a visual image is characterized by comprising the following steps: step 1, constructing a cascade vegetation surface moisture content detection system based on the visual image; 2, the image acquisition module acquires a vegetation image of a target area; 3, the surface state recognition module quickly recognizes attachments on the surface of the vegetation in the vegetation image, and classifies the vegetation surface state into a saturated water-containing state and a surface dry state; step 4, the vegetation type identification module carries out vegetation type identification on the vegetation image I in the surface dry state, and identifies the biological type of each vegetation in the vegetation image I; and 5, according to the biological category of each vegetation, the moisture content prediction module calls the exclusive moisture content regression prediction model of the corresponding category from the model library to predict the moisture content of the vegetation of the biological category, and outputs a moisture content prediction result of each vegetation.

The application relates to the technical field of water environment remote sensing monitoring, and provides a water vegetation type monitoring method and device based on optical and SAR satellite images. The method comprises the following steps: acquiring optical remote sensing image data and radar image data of the same water area after spatiotemporal registration; acquiring optical image band data, near-infrared reflectivity and red light band reflectivity of each pixel point from the optical remote sensing image data; selecting sub-channel features corresponding to the pixel points from cross-polarization channel feature data; obtaining normalized vegetation indexes of each pixel point based on the near-infrared reflectivity and the red light band reflectivity; obtaining classification results corresponding to each pixel point based on the normalized vegetation indexes, the optical image band data and the sub-channel features, so as to determine all water vegetation types in the water area. The method realizes large-range, high-frequency and high-precision monitoring of water vegetation types in large shallow lakes.

The invention discloses a data-driven actual evapotranspiration estimation method fusing vegetation biophysical characteristics, which comprises the following steps: firstly, collecting actually measured meteorological and remote sensing data of a flux tower station, and carrying out quality control and energy balance correction to obtain an actual evapotranspiration reference value; then, a PMLV2 model is constructed station by station based on the processed data, and vegetation biophysical characteristic variables such as stomatal conductance are obtained through simulation; summarizing the features with meteorological and remote sensing data to form an input factor set, and summarizing training sets and test sets of all stations according to vegetation types; and finally, taking the factor set as input, constructing an LSTM actual evapotranspiration estimation model by using the training set, and evaluating the precision of the model through the test set. According to the method, vegetation biophysical characteristic variables simulated by the PMLV2 model are fused, so that the adaptability and generalization ability of the data driving model when environmental conditions change are enhanced, and the problems of overfitting and performance reduction when samples are scarce or representativeness is insufficient in a traditional data driving method are effectively relieved.

The utility model discloses a reservoir area hydro-fluctuation belt ecological slope protection structure, and relates to the field of ecological slope protection structures, the reservoir area hydro-fluctuation belt ecological slope protection structure comprises a soil base and a concrete connecting seat, underwater gabion protection feet are attached to the bottom of the side of the soil base at equal intervals, and a gravel permeation area is laid on the outer surface of the soil base; and a vegetation type Reynolds protection pad is fixedly arranged on the outer surface of the gravel permeation area. According to the ecological slope protection structure for the reservoir area hydro-fluctuation belt, through the arrangement of the vegetation type Reynolds protection pad, when the vegetation type Reynolds protection pad covers the outer surface of the gravel permeation area, it can be effectively ensured that the gravel permeation area does not slip off when being impacted by water flow; according to the ecological slope protection structure, the plant seeds can have an excellent soil fixation function during germination and growth, so that the ecological slope protection structure is ensured not to have a large-area soil loss phenomenon during a water swelling period, and the ecological slope protection structure can be ensured to be normally used after being used for a long time.

The present invention relates to a modular grow house system designed for optimizing the cultivation of various vegetation types through advanced environmental control. The system comprises multiple grow chambers, each equipped with sensors to monitor temperature, humidity, light, and soil moisture, and actuators to regulate these conditions. A central control unit, including receiving, monitoring, processing, and regulating units, manages the environmental conditions based on plant profiles stored in digital formats like JSON, XML, or CSV. The modular design features interchangeable panels, standardized sensor and actuator mounts, and quick-connect fittings, allowing flexible configuration and expansion. Safety features, such as temperature and humidity alarms, and a user interface for manual control, enhance the system's functionality. Additionally, the control unit supports remote updates via a cloud platform, ensuring up-to-date plant profiles and control algorithms. This invention provides a scalable, automated solution for controlled environment agriculture, improving productivity and sustainability in various settings.

The application provides a kind of geological disaster post-vegetation recovery monitoring method based on high-resolution remote sensing image, comprising: constructing fusion type "spectrum-vegetation index-texture" feature set;In sunny weather conditions, more evenly select n typical vegetation samples in the target area;Construct bagging type ensemble learning model;Analysis of the obvious differences of arbor, shrub and herbaceous vegetation in vegetation height, single plant vegetation horizontal projection coverage area, root depth and root extension range;By analyzing the vegetation change rate index and vegetation type change rate index of several continuous time phase remote sensing images according to the time phase change information, the vegetation recovery of the target area is quantitatively analyzed, so as to effectively improve the accuracy of pixel classification method, greatly improve the accuracy of quantitative analysis of vegetation in the target area, and greatly reduce the cost of long-term accurate monitoring.