75 results about "Ground survey" patented technology

The invention discloses an intelligent planning method of a geological mineral exploration analysis model, and relates to the field of mineral exploration, and the intelligent planning method comprises the following specific steps: S101, obtaining regional geological mineral data through satellite remote sensing, ground survey and historical data collection, and preprocessing the collected geological mineral data; according to the method, powerful prior knowledge support can be provided for simulation learning, exploration planning is more scientific and reliable, mineral resource distribution can be predicted more comprehensively, model generation efficiency and calculation performance are improved, multi-level reasoning and prediction can be carried out on different geological levels, limitation of traditional exploration can be broken through, and the method is suitable for popularization and application. The overall effect of the intelligent planning method is improved; according to the method, global search can be carried out in a large range, local optimum is avoided, meanwhile, the accuracy and reliability of path planning are improved, the diversity and adaptability of path selection are ensured, and a model can more intelligently select a path with high exploration value when facing an unknown area.

The invention discloses an ecological system monitoring method based on an INVEST model, and belongs to the technical field of ecological system monitoring, and the method comprises the steps: S1, building a unified ecological monitoring data frame through satellite remote sensing, an Internet of Things sensor and a ground survey multi-source data collection and cleaning algorithm; s2, dynamically adjusting INVEST model parameters based on regional ecological characteristics and a machine learning algorithm, according to the method, ecological system services are quantitatively evaluated by using an INVEST model core module, key ecological parameters are extracted in combination with a remote sensing inversion technology, the problems that traditional ground survey is high in cost and limited in coverage range are solved, and the method is suitable for large-scale popularization and application. The remote sensing data preprocessing and classification algorithm improves the efficiency and precision of surface feature information extraction, the PLUS model simulates the land utilization change scene and CMIP6 climate scene input, the INVEST model is combined to quantify the future ecosystem service function, and the limitation that the traditional static assessment cannot cope with the dynamic environment change is solved.

The invention relates to the technical field of biomass monitoring, and provides a forest above-ground biomass estimation method and device based on stereo density, and the method comprises the steps: obtaining various canopy height information and quadrat AGB stereo density from ground survey data, and obtaining forest features from airborne laser radar data; carrying out regression analysis on at least one of the various canopy height information and the quadrat AGB stereo density and the forest features to obtain the AGB stereo density of the flight area; stepwise linear regression analysis based on the regional scale is carried out on the AGB stereo density and the target parameters of the flight area to obtain an AGB stereo density estimation result, and a regional scale AGB estimation result is determined according to the AGB stereo density estimation result, the various canopy height information and the corresponding pixel areas. According to the method, planar AGB density calculation is converted into three-dimensional AGB density estimation, and the accuracy of the forest above-ground biomass estimation result in a complex scene is improved.

The invention discloses a natural resource geographic information collection method and system, and belongs to the technical field of geographic information collection, and the method comprises the steps: obtaining natural resource geographic information data, and carrying out the data preprocessing of the multispectral image data in the data; optical abnormal data in the preprocessed multispectral image data are marked, meanwhile, ground abnormal data in ground survey fixed-point measurement data are marked, and geospatial alignment is carried out; identifying a highly overlapped region in the geographic space, and automatically attributing and judging an abnormal mode in the highly overlapped region as being caused by a specific unrecorded environment event; and adding an environmental event influence mark to the attributed natural resource geographic information data, wherein the environmental event influence mark is used for indicating the physical cause of the natural resource geographic information data exception. According to the method and the device, numerical value change relevance caused by the same physical cause in different natural resource geographic data types can be identified, and wrong geographic information interpretation results caused by data inconsistency in subsequent analysis are avoided.

The invention provides a multi-tree forest aboveground biomass remote sensing estimation method and system and a storage medium, and the method comprises the steps: obtaining multi-source remote sensing data based on a target region, including airborne LiDAR point cloud, ground survey data and DEM data; constructing a terrain heterogeneity index DGTHI in combination with DEM data, and quantifying terrain complexity through weight fusion of standardized terrain factors; extracting forest characteristic parameters and calculating the overground biomass of the single tree; performing classification modeling on the sampling region based on DGTHI, respectively establishing multiple linear regression models for different tree species, and optimizing feature parameter selection; and evaluating the precision of the model through cross validation, and popularizing the model to a county scale to perform overground biomass space inversion and mapping. According to the method, terrain heterogeneity classification and tree species specificity modeling are fused, so that the estimation precision of the forest biomass in the complex terrain region is remarkably improved, and a technical support is provided for regional carbon sink monitoring and sustainable forestry management.

The invention provides a mangrove forest degradation type and reason identification method and system based on remote sensing and a multi-factor index system. The method comprises the following steps: firstly, integrating a multi-temporal remote sensing image, ecological monitoring data and ground survey data, and constructing a systematic degradation factor index system covering natural, climate, biological and human factors; secondly, extracting typical degradation characteristics such as vegetation coverage decline and species composition change by using a remote sensing change detection technology and a principal component analysis method; then, through K-means clustering, an ecological classification algorithm based on rules and an ecological threshold standard, mangrove forest degradation types are identified and classified, and the types comprise a man-made interference type, a biological erosion type, a natural succession type and a multi-factor coupling type; then, adopting a supervised machine learning model and combining a feature importance evaluation method such as a Shapley value to identify a degradation cause and a weight thereof; and finally, verifying the precision of the model through an on-site monitoring quadrat and an expert interpretation result, and outputting visual results such as a degradation type distribution diagram, a factor contribution distribution diagram and the like.

The invention relates to the technical field of garbage transportation channel ecological restoration, in particular to an urban core mountain garbage transportation channel ecological restoration method which comprises the steps that firstly, aerial photography surveying and mapping are conducted on a transportation channel and a surrounding site to determine an ecological restoration range; geological exploration is carried out, road, soil, runoff and air pollution information is collected and evaluated, and a road lifting and ecological restoration mode is determined; treating garbage and pollution sources of surrounding polluted land parcels according to an environmental assessment conclusion, replacing and filling planting soil, and selecting an ecological restoration variety to form a restoration space; finally, a leisure footpath base is repaired according to geological exploration, a footpath is constructed, and an ecological fitness footpath system is formed by means of a mountain landscape structure. Multiple measures are integrated, the ecological environment is effectively improved, resources are reasonably utilized, social and economic benefits are achieved, the urban image and the resident life quality are improved, and the method has important significance in the field of ecological restoration of the garbage transportation channel.

The invention relates to the field of mineral resource exploration, and discloses a mineral prediction method based on multivariate geoscience information superposition recognition, which comprises the following steps: collecting multivariate three-dimensional geoscience information through multiple modes such as unmanned aerial vehicle remote sensing and ground survey, classifying and sorting, and carrying out deep preprocessing and feature extraction such as denoising and correction to obtain a multi-element three-dimensional geoscience information model; a fusion layer is generated by adopting an adaptive fusion algorithm, a mineralization prediction model is constructed in combination with machine learning, and after training optimization, prediction regions of different grades are delineated and the resource quantity is estimated. And a verification feedback mechanism is also set, and each link is optimized and improved according to a field exploration result. The method improves the mineral product prediction accuracy and adaptability, improves the exploration efficiency, reduces the cost, promotes the technical development of the mineral exploration field, and has an important application value.

The invention belongs to the field of remote sensing, and provides a straw coverage rate estimation method based on soil type zoning, which comprises the following steps: determining the soil type in a research area, and performing soil zoning; aiming at each subarea of different soil types in the research area, acquiring an unmanned aerial vehicle image, and calculating a straw coverage rate; acquiring an optical satellite remote sensing image at the same time as the ground survey of the research area, and calculating features; for each soil type partition, performing correlation analysis on the features, selecting the features related to the straw coverage rate as input, and combining the features of different types to form a feature combination; and for each soil type partition, taking the sample and feature combination as input, establishing a straw coverage rate estimation model by using a random forest regression algorithm, and estimating the straw coverage rate of a non-sample point area. According to the method, the collection efficiency and reliability of ground straw coverage rate samples are remarkably improved, and meanwhile, the precision of straw coverage rate estimation under the background of various soil types is greatly improved.

The invention discloses a method and a system for estimating plant diversity based on a remote sensing scale effect, which belong to the field of plant diversity monitoring, and are characterized in that a high-resolution remote sensing image of a to-be-monitored area is acquired, and the pixel size is approximately equal to the size of a single plant; extracting a plurality of investigation quadrats in the monitoring area, and obtaining plant diversity true values of the quadrats; preprocessing the remote sensing image to obtain surface reflectance data, and upscaling the high-resolution image by adopting a pixel averaging method to obtain a low-resolution remote sensing image; calculating a nonlinear vegetation index based on the remote sensing images, and calculating a scale deviation of the vegetation index between the remote sensing images with two resolutions; and constructing a mathematical relationship model between the scale deviation and the plant diversity truth value, and applying the mathematical relationship model to the whole remote sensing image so as to estimate the plant diversity of the whole area. According to the method, the plant diversity in a large range can be quickly and accurately estimated, heavy ground survey work is reduced, and manpower, time and economic cost are saved.

The invention belongs to the field of degraded grassland restoration evaluation research, and discloses a degraded grassland restoration evaluation research method based on multivariate remote sensing data, and the method comprises the steps: carrying out the observation through employing a FragMap unmanned aerial vehicle aerial photography analysis system, and carrying out the preprocessing through employing Pixel Base Management Classifier software; collecting grassland above-ground biomass data; acquiring and preprocessing remote sensing data; constructing a grassland coverage and aboveground biomass remote sensing estimation model, and performing precision verification; analyzing grassland coverage and biomass space difference before and after restoration of the degraded grassland; and analyzing space-time dynamic changes of grassland coverage and biomass. According to the method, a high-temporal-spatial-resolution NDVI data set of a research area is constructed by combining a high-temporal-spatial-resolution data fusion algorithm, a grassland coverage and aboveground biomass remote sensing estimation model is constructed by combining ground survey and unmanned aerial vehicle aerial photography, and the grassland coverage and aboveground biomass change conditions before and after a degraded grassland restoration measure is implemented are systematically evaluated. And the restoration effects of different restoration measures on the degraded grassland are evaluated.

The invention relates to the technical field of wetland data monitoring, and discloses a coastal wetland carbon reserve dynamic monitoring method based on time sequence remote sensing, which comprises the following steps: collecting the vegetation condition of a coastal wetland, speculating the carbon reserve of the coastal wetland, judging whether the carbon reserve is abnormal or not, and correspondingly adjusting a detection scheme. According to the coastal wetland carbon reserve dynamic monitoring method based on time sequence remote sensing, multiple methods such as remote sensing monitoring, ground survey and laboratory analysis are comprehensively considered, the comprehensiveness and accuracy of carbon reserve monitoring are ensured, the comparison threshold is dynamically adjusted through two-time judgment in combination with historical data and a verification conclusion, and the carbon reserve monitoring accuracy is improved. The method adapts to carbon reserve change conditions of different sub-regions, forms a systematized monitoring process from region division to overall analysis, can effectively identify abnormal conditions in a target region, improves the reliability of carbon reserve assessment through comparison verification and collection verification, and improves the accuracy of carbon reserve assessment. And reliable data support is provided for subsequent monitoring and management.

The present invention discloses a method for identifying forest fire source types by considering environmental characteristics of fire ignition points. The method comprises the following steps: obtaining MODIS burned area products, land cover products and ground forest fire survey data, performing preprocessing thereon, extracting forest fire footprints, screening out forest fire footprints that match the ground survey data; deeply mining the information of each footprint; extracting variable factors by utilizing the spatiotemporal characteristics of the fire ignition point and the center point, and dividing the variable factors into a modeling data set and a verification data set; screening out an optimal variable combination from a forest fire event sample data set, debugging the optimal model training parameters, establishing a fire source identification model, inputting verification data, evaluating the accuracy of the fire source identification result of the model and the fire source information provided in the survey report, and obtaining a fire point-model-variable combination that can identify the fire source of the forest fire event. The method improves the accuracy of identifying the fire source type of the forest fire event, thereby improving the accuracy and effectiveness of fire risk assessment and forest fire management, and can more enrich and accurately improve forest fire event information.

The invention discloses a coastal zone nitrogen and phosphorus reduction effect evaluation method, which comprises the following steps: acquiring multispectral image data and a digital surface model, and acquiring actual gradient, altitude and long-term hydrological dynamic data through ground actual measurement, sampling and investigation; extracting core parameters, correcting a digital surface model by utilizing ground actual measurement altitude data, constructing a target coastal zone precise digital elevation model, determining a gradient and dividing a gradient interval, and identifying a target coastal zone type and a planting / slope protection combined structure by combining multispectral image data, ground survey data and the digital surface model; obtaining a nitrogen and phosphorus reduction reference value according to the core parameters, extracting a rainfall intensity correction coefficient and a water level amplitude correction coefficient in combination with hydrological dynamic data, and calculating a final nitrogen and phosphorus reduction proportion; and spatial visualization is carried out, so that efficient, accurate and standardized evaluation of the nitrogen and phosphorus reduction effect of the coastal zone is realized, and a scientific basis is provided for design, optimization and effect inspection of ecological restoration engineering of the coastal zone.

The invention relates to the technical field of forestry prevention and control, in particular to a pine wood nematode disease autumn general survey ground survey method which comprises the following steps in specific use: recording survey site data; recording survey image data; drawing a survey site diagram; downloading survey image data; writing a survey report; the survey site can be quickly and accurately searched, the survey site and image data are recorded and transmitted, a survey table is filled and reported, and a survey report is formed; in this way, the technical problems that in the prior art, a ground checking method is low in checking speed, difficult in data transmission and low in working efficiency are solved; according to the method, the Ovui map is used, so that the survey site can be quickly and accurately found, and survey site data is recorded; according to the method, a today watermark camera is used, survey image data can be rapidly and accurately recorded and transmitted, and autumn general survey work can be accurately evaluated.

To provide a site information management system capable of accurately grasping the construction status at a site by a work machine for carrying out ground survey, ground work, foundation work, etc., even from a remote location without substantial time delay. [Solution] The system includes a construction management device (20), a gateway (40), a cloud server (10) that stores ground information for the site, and a viewing terminal (60) connected to the cloud server (10). The construction management device (20) sequentially generates text-format transmission data related to the operating status of a work machine (200), sequentially generates on-site construction status data based on the ground information for the site acquired from the cloud server (10) and the transmission data, and displays the construction status at the site. The cloud server (10) sequentially generates construction status data for viewing based on the ground information for the site and the transmission data acquired from the construction management device (20) via the gateway (40). The viewing terminal (60) displays the construction status at the site based on the construction status data for viewing.

The present application provides a route layout method and system based on remote sensing satellite data, which relates to the field of route planning technology, including: identifying obstacles based on acquired remote sensing satellite data; calculating the corrected height of obstacles based on satellite attitude parameters and solar incidence angle; combining DEM data with the corrected height of obstacles to construct a digital surface model containing obstacles, and obtaining an obstacle avoidance route layout strategy through the digital surface model according to the requirements of aerial gravity and magnetic measurements. The present application can solve the technical problem in the prior art that it is difficult to fully cover obstacles on the route due to the traditional method relying on ground surveys and manual experience, resulting in potential safety hazards in route planning. By accurately identifying and avoiding aerial obstacles on the route, remote sensing satellite data and digital surface models are used to automatically layout routes that avoid obstacles, thereby improving the safety of route planning.

The present invention discloses an integrated sky-ground monitoring method for monitoring pine wilt disease, including: S1. First, extract diseased dead trees through UAV images, extract patches of diseased dead trees through satellite images, and conduct on-site marking, positioning and measurement of dead trees through ground surveys; S2. Obtain high-spatial-resolution satellite remote sensing data and UAV remote sensing data as data sources respectively, and combine ground survey data with forest resource second-class survey data to jointly form a data set of the monitoring area; S3. Establish samples of diseased wood through the above data, and the samples include spectral characteristics, texture characteristics and geometric characteristics of pine wilt disease dead trees; S4. According to the samples, establish the spatial distribution information and location information of pine wilt disease dead trees. The present invention has high efficiency in screening epidemic areas, can achieve large-area and high-efficiency monitoring, convenient acquisition of UAV remote sensing data, high recognition accuracy, and guaranteed accuracy verification.

The invention relates to the field of farmland erosion degree rapid monitoring methods, and discloses a black soil area farmland soil erosion degree remote sensing extraction method, which comprises the following steps: S1, acquiring farmland range data, and collecting vector data of a farmland range of a research area; s2, selecting a remote sensing image, and performing image preprocessing such as radiation correction, atmospheric correction, geographical registration and cultivated land range mask extraction; s3, a soil erosion degree index is calculated, and remote sensing image related wavebands are calculated through professional software; step S4, soil erosion degree grading: according to ground survey data, soil erosion degree index values are graded into non-erosion, slight erosion, medium erosion and serious erosion, the method is simple in input quantity, high in adaptability and suitable for dynamically monitoring the cultivated land erosion degree in a large range, and a scientific basis is provided for land resource management and soil protection.

The utility model relates to an anti-dust-deposition elevator landing door sill shielding plate which comprises an elevator sill and an elevator door, and the elevator door is arranged in the elevator sill in a sliding mode. The utility model relates to the technical field of an elevator landing door geological exploration baffle. According to the anti-dust-deposition elevator landing door sill shielding plate, when elevator doors are opened, the two elevator doors are located on the two sides of the elevator sill correspondingly, and when an elevator is located in the elevator sill, the elevator doors press the air bags, so that air of the air bags moves to the positions not pressed by the elevator doors, and the air bags are prevented from being pressed by the elevator doors; when the elevator door is opened towards the two sides in the linear direction of the elevator sill, air in the air bag can be squeezed to the middle of the air bag, the middle of the air bag is expanded and swelled, the shielding strip arranged at the top end of the air bag is jacked up, and the purpose of sealing the top end of the elevator sill is achieved. And dust and foreign matters can be effectively prevented from being brought into the elevator sill by passengers, so that the elevator sill can be effectively protected.

The invention relates to the technical field of coal mine water prevention and control, and particularly discloses an intelligent monitoring and control method for ground measurement water prevention and control for a coal mine. The hydrological disaster related information of the underground coal mine roadway is monitored, the position of the hydrological disaster source in the underground coal mine roadway is intelligently recognized, the drainage device near the hydrological disaster source is started, loss caused by hydrological disasters is avoided or reduced to a certain extent, and the safety of the underground coal mine roadway is improved. By monitoring the severity of each hydrological disaster source of the underground coal mine roadway, the reference drainage parameters of the operation drainage device of each hydrological disaster source are analyzed, and corresponding control is performed based on the reference drainage parameters, so that the pertinence and effectiveness of water prevention and control work of the underground coal mine roadway are improved to a certain extent; the drainage effects of the operation drainage devices on the two sides of the hydrological disaster source are monitored, the auxiliary operation drainage device is started, regulated and controlled, reference drainage parameters of the auxiliary operation drainage device are analyzed and regulated and controlled according to actual conditions, and it is ensured that drainage operation can be effectively conducted under various complex hydrological disaster conditions.

The invention discloses a high-position landslide identification method based on cooperation of multi-source remote sensing and ground survey, and belongs to the technical field of geological disaster monitoring and identification. Comprising the following steps: constructing a multi-source information basic data set; generating deformation characteristic data representing a deformation activity state; environment characteristic data representing landslide forming conditions are constructed; screening landslide candidate areas which simultaneously meet the deformation activity criterion and the landslide formation condition criterion; performing comprehensive feature analysis and type discrimination on the landslide candidate region to obtain a landslide identification conclusion and corresponding uncertainty information; determining a ground survey checking scheme according to the uncertainty information and executing the ground survey checking scheme; if yes, taking the landslide identification conclusion as a final result, and outputting a corresponding landslide type and uncertainty information; and if not, correcting the landslide identification conclusion according to the check result, updating the uncertainty information, taking the corrected conclusion as a final result, and outputting the corresponding landslide type and the updated uncertainty information.

The invention provides a natural ecosystem investigation method. The natural ecosystem investigation method comprises the following steps: S1, acquiring a high-resolution remote sensing image and DEM data, and dividing ecosystem types; s2, typical ecological problems of the ecological system are determined, sample plot design and sample plot dynamic arrangement are carried out, investigation points are arranged in a sample plot, and sampling analysis is carried out; and S3, performing data analysis according to the plaque partition map, the sample plot location map and the remote sensing image map. According to the dynamic survey method combining quadrat / transect and sampling point actual ground survey, the mutual verification of ground, remote sensing and model data is realized by combining a satellite remote sensing and model simulation technology with multiple data sources, typical / important ecological problem partitions in a watershed ecosystem range are found out and verified, and a watershed ecological system is established. Support is provided for design and implementation of the ecological engineering project, and a data index basis is provided for acceptance of the ecological engineering project.

The present invention relates to the technical field of land use and quality assessment, and particularly relates to a method for evaluating the natural quality of cultivated land driven by all-remote sensing data. The present invention combines remote sensing monitoring technology for cultivated land quality evaluation, which can frequently and persistently provide surface information, and can monitor the dynamic changes of cultivated land quality on a long time scale. Considering that unsupervised machine learning has strong objectivity and does not require human intervention, it automatically finds the internal laws of cultivated land quality within the evaluation unit and the complex non-linear relationships with various evaluation indicators, and realizes a method for evaluating cultivated land quality driven by remote sensing indicators based on a competitive neural network. The present invention overcomes the limitations of traditional cultivated land quality evaluation relying on expert experience and the acquisition of evaluation indicators relying on ground surveys, greatly reduces the huge consumption of human and material resources in traditional cultivated land quality evaluation, and with its long-time multi-scale monitoring advantage, realizes the timely and efficient supervision of cultivated land resources.

The application relates to the technical field of G01C11 / 00, in particular to a surveying information data collection method and a storage medium. The surveying information data collection method comprises the following steps: S1. using a flight device to carry a position module and a camera module, acquiring ground surveying image information by shooting through the camera module; and S2. editing and storing region range position information through a setting module, transmitting the region range position information to the flight device through an information transmission module, and starting the camera module to shoot images in the region range. Through the surveying information data collection method and system, the position data information of specific surveying can be fed back to the flight device, and surveying images of specific position regions can be acquired.

The invention discloses a seasonal analysis method for evaluating the carbon emission effect of urban greening layout, belongs to the technical field of urban greening layout, and aims to solve the problem that some studies in the prior art pay attention to the carbon sink function of urban greening, but most studies cannot fully consider the influence of seasonal variation on the carbon emission effect. Comprising the following steps: S1, performing field investigation and field acquisition on greening layout data of different regions and different seasons of a city; s2, combining a remote sensing technology and ground survey; and S3, establishing carbon emission including plant growth, maintenance management and material production including seedling cultivation, greening facility manufacturing and daily use in different seasons by using a life cycle evaluation method. According to the seasonal analysis method for evaluating the carbon emission effect of the urban landscaping layout, through seasonal analysis, the carbon sink function and carbon emission characteristics of urban landscaping in different seasons can be determined, a scientific basis is provided for urban landscaping planning, and optimization and adjustment of the landscaping layout are guided.

The invention relates to the field of data analysis, in particular to a plough layer salt and alkali conditioning optimization method based on soybean response, and the method comprises the steps: obtaining a sample point original prediction error sequence through calculating the original prediction error of a sparse ground exploration sample point; the method comprises the following steps of: performing joint weighting on geographic space proximity and surface feature similarity to obtain a spatial residual propagation factor; performing principal component analysis on multi-dimensional surface soil attributes of a grid neighborhood to obtain grid unit local feature heterogeneity assessment; carrying out correlation analysis on a sample point error absolute value and local feature heterogeneity and modulating a spatial residual propagation factor to obtain a yield prediction value of a grid unit; position correction and response enumeration evaluation are carried out on the candidate conditioning doses to obtain the optimal conditioning doses of the grid units, so that the problems of yield prediction distortion and variable prescription mismatching caused by the fact that high-density surface remote sensing and sparse deep sampling points are difficult to fuse are solved.

The invention belongs to the field of industrial data processing, and particularly relates to a multi-scale geographic information fusion method for territorial space surveying and mapping. Firstly, multi-source data acquisition including satellite image, unmanned aerial vehicle surveying and mapping and ground measurement is carried out; performing format conversion, noise removal and integrity verification preprocessing on the original data; according to the information source and the scale feature classification data, generating a first fusion data set through timestamp synchronization and spatial registration, and fusing to form a second fusion data set; performing multi-dimensional quantitative scoring by constructing a comprehensive scoring index, and performing secondary scoring screening on data in a scoring result; and finally, combining the screened data into a final fusion data set. Compared with the prior art, the advantages of multi-source data can be fully integrated, the problem of data difference is solved, comprehensive, accurate and multi-scale geographic information is provided, and the territorial space surveying and mapping and planning requirements are met.

The invention provides a dynamic evaluation method and device for fruit tree growth vigor, and the method comprises the steps: obtaining unmanned plane remote sensing data and ground survey data of a target region in the whole annual growth period, and obtaining three-dimensional point cloud data and multispectral orthoimage data based on the unmanned plane remote sensing data; extracting fruit tree phenotype index data in the three-dimensional point cloud data and fruit tree greenness index data in the multispectral orthoimage data; carrying out fusion analysis on the ground survey data, the fruit tree phenotype index data and the fruit tree greenness index data, and constructing a time sequence fruit tree growth comprehensive evaluation model; and inputting the obtained remote sensing data of the to-be-tested unmanned aerial vehicle in the target area into the time sequence fruit tree growth comprehensive evaluation model to obtain a fruit tree growth evaluation result. By combining unmanned aerial vehicle remote sensing data and ground survey data, fruit trees are monitored from two dimensions of spatial structure and spectral information. According to the invention, multi-dimensional evaluation of the growth condition of the fruit tree is realized, and the evaluation accuracy is improved.

The utility model belongs to the technical field of building ground measurement, and particularly relates to a large-area ground flatness measuring device which comprises a main body, and a mounting seat is fixedly mounted at the bottom of the main body; a measuring mechanism is fixedly mounted at the bottom of the mounting seat; wherein the measuring mechanism comprises a measuring assembly, and one side of the measuring assembly is fixedly provided with a positioning assembly; the measuring assembly comprises a fixing seat and a connecting arm, a support is fixedly installed at the bottom of the fixing seat, the connecting arm drives the detection wheel to be hinged to the support, a connecting rod is hinged to one end of the connecting arm, and the top of the connecting rod abuts against the pressure sensor. According to the utility model, the mounting seat is mounted at the bottom of the main body, and the connecting flanges are arranged at the two sides of the mounting seat, so that extension splicing can be carried out, the measuring device is suitable for large-area ground flatness detection, and meanwhile, the positioning mechanism is arranged to spray paint and position a position with a measurement problem, so that subsequent finishing is facilitated.