86 results about "Ecological assessment" patented technology

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 land utilization type intelligent identification method and system based on remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data; based on a preset land utilization characteristic model, analyzing and processing the multi-source remote sensing data to obtain corresponding land utilization characteristics in the land utilization characteristic model; based on a land utilization characteristic knowledge base, matching and analyzing the land utilization characteristics to obtain a matching result; and according to a matching result, determining a land utilization type corresponding to the matching result, and outputting the land utilization type as an identification result. By adopting the method, multi-dimensional information complementation can be realized, space-time coverage is enhanced, and data reliability is improved; a high-precision classification result is provided, dynamic monitoring is supported, and an application scene is expanded; the defect of a single data source is overcome, manual dependence is reduced, the operation period is shortened, the cost is reduced, the complex scene recognition capability is improved, and space-time continuous dynamic monitoring and fine management support is provided for the fields of ecological assessment and the like.

The invention discloses an ecological protection and restoration planning method and system, and the method comprises the steps: obtaining ecological multi-source data of a target region, and carrying out the spatial registration and format standardization processing through a geographic information system; constructing a three-dimensional ecological background model, and fusing the ecological multi-source data to generate a gridding geographic unit; performing dynamic coupling analysis on the ecological multi-source data by adopting a convolutional neural network, identifying an ecological degradation hot spot region, and outputting a spatial probability distribution diagram; and based on the spatial probability distribution of the ecological degradation hot spot region and the ecological background model, generating a multi-level ecological restoration scheme through a self-organizing feature mapping neural network, and outputting a visual planning map. According to the scheme disclosed by the invention, the accuracy and efficiency of ecological assessment are improved, the economic cost of ecological protection and restoration in the region is reduced, the benefit is improved, a better expected effect can be obtained through tests, and the method has a good popularization prospect.

The invention provides an intraday high-frequency automatic monitoring method and system for lake cyanobacterial bloom based on a GOCI-II satellite, and aims to solve the problems that an existing method is susceptible to interference of thin cloud, low in recognition precision, high in false positive rate, low in efficiency and the like, and the processing flow depends on manpower. The system is based on an improved AFAI index, fine cloud detection, cloud expansion processing and classification correction strategies are combined, misrecognition caused by thin clouds and shadows is effectively restrained, and the extraction accuracy and robustness are improved. The system has the whole-process unattended processing capacity from GOCI-II data automatic downloading, preprocessing, algal bloom recognition, thematic map making to report output, can generate a monitoring report within one hour after satellite imaging, and supports intra-day multi-temporal cyanobacterial bloom dynamic monitoring. The method has been successfully applied to lakes such as Taihu Lake, lakes, Chaohu Lake and Hongze Lake, is particularly suitable for monitoring and early warning cyanobacterial bloom in large and medium lakes, and has wide application prospects in the fields of water environment supervision, water quality risk control, ecological assessment and the like.

The invention discloses a marine ecological protection and restoration project comprehensive evaluation method based on multi-dimensional analysis, and relates to the field of marine ecological analysis, and the method comprises the steps: obtaining multi-dimensional monitoring data through constructing a target region map model and setting a monitoring point, and generating an ecological feature vector; calculating a relative distance and a deviation between the monitoring point and the comparison feature vector to obtain a repair expected goodness of fit; screening monitoring points with low goodness of fit and integrating the ecological data set; training and generating an optimized simulation feature vector based on a GAN model; and analyzing the simulation parameters and evaluating the ecological evolution trend to complete comprehensive evaluation. According to the method, multi-dimensional and high-precision ecological assessment is realized, the accuracy of restoration effect prediction and the scientificity of an assessment result are improved, reliable support is provided for a marine ecological protection restoration decision, and the problems of one-sided monitoring, insufficient prediction and the like of an existing method are solved.

The invention specifically discloses a long-time-sequence high-frequency ecological environment quality space-time differentiation and driving analysis method, and relates to the technical field of remote sensing and ecological environment. The method comprises the following steps: determining an evaluation area, and constructing a macroscopic ecological safety risk evaluation framework; ecological indexes of greenness, humidity, temperature and dryness are calculated, and a time sequence data set is constructed; reconstructing a time sequence data set and constructing a remote sensing ecological index model based on the reconstructed time sequence data set; verifying the precision of the model, and evaluating the reconstruction precision by taking the screened high-quality pixels as true values; analyzing the spatial and temporal change trend and significance output by the model by using slope estimation and trend test, outputting future change continuity by using a Hurst index analysis model, and measuring spatial autocorrelation output by the model by using a Moran index; remote sensing ecological index evolution factors are researched by means of an optimal parameter geographic detector. According to the invention, the precision and timeliness of ecological assessment are improved, and decision support is provided for ecological management.

The invention relates to the technical field of ecological monitoring, and discloses a grassland ecological assessment early warning monitoring system based on artificial intelligence. Collecting vegetation indexes, soil parameters, livestock tracks and topographic data in real time through satellite remote sensing, an unmanned aerial vehicle and Internet of Things equipment; an intelligent algorithm is adopted to process missing values and standardized data, and vegetation phenological characteristics are extracted; the weight is dynamically calculated by fusing the index volatility, the correlation and the ecological coordination rule, and the net influence of the grazing activity on the ecology is accurately quantified based on the contrast area with strict geographical constraints, so that a degradation risk score is generated; high, medium and low risk areas are divided according to scores, and a livestock capacity optimization scheme and targeted measures of a matching policy library are dynamically output. Natural interference and artificial grazing influence are accurately separated, the problem that the risk level is misjudged in a traditional method is solved, and the evaluation adaptability is improved through a dynamic weight model.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a wetland feature extraction system and method based on a multi-source remote sensing image and deep learning, and the system comprises a data acquisition module, a feature fusion module, a CNN model training module and a feedback control module. Dynamically optimizing the feature fusion matrix by adopting a principal component analysis method; constructing a depth feature extraction network, and performing training in combination with a gradient descent algorithm; and feature extraction parameters are fed back and adjusted in real time by using normalized dot product similarity, and feature extraction precision is improved through local consistency optimization. According to the method, the bottlenecks of insufficient precision and limited feature expression ability in wetland feature extraction of a traditional method are broken through, accurate extraction of multi-scale and multi-dimensional wetland features is realized, and high-precision feature data support is provided for applications such as wetland monitoring and ecological evaluation.

The invention relates to the technical field of ecological monitoring, in particular to a resident-free island resource monitoring method based on a spatial three-dimensional model, and the method comprises the following steps: S1, building a remote sensing image interpretation information base; s2, based on the remote sensing image interpretation information base, generating an island space three-dimensional model containing island land terrain and shoreline distribution; s3, generating a total element three-dimensional dynamic map; s4, the interpretation result and the total element three-dimensional dynamic map in the S3 are superposed, and a reef area comprehensive distribution map is generated; s5, calculating the island resource development intensity and the ecological risk level, and outputting an island ecological assessment report; and S6, generating an island resource monitoring report. According to the invention, unmanned aerial vehicle aerial survey, GNSS measurement, depth sounding technology, image interpretation and ecological assessment are combined, a high-precision total-factor three-dimensional dynamic monitoring system is constructed, accurate analysis and visual expression of the current island resource utilization situation are realized, and scientific decision support is provided for island management and ecological protection.

The invention belongs to the technical field of water affair management, and discloses an intelligent water affair dynamic monitoring system and monitoring method based on digital twinning. Comprising the steps of collecting multi-modal water affair data in a preset water affair system and performing data cleaning to obtain standard multi-modal water affair data; adaptive weighted fusion is carried out on the standard multi-modal water affair data, and a multi-modal water affair data matrix is constructed; constructing a digital twin water affair architecture and constructing a simulated water affair management scene; constructing an ecological evaluation model to perform water quality monitoring on the simulated water affair management scene, and outputting water quality evaluation data; constructing a water affair management partition topological structure to perform water resource cross-region scheduling on the simulated water affair management scene, and outputting a water affair partition management strategy; constructing a water affair system assembly operation scene to carry out system fault monitoring, and outputting a system maintenance log; collecting real-time water affair data and combining with a system maintenance log to perform parameter updating on the digital twin water affair architecture to obtain an optimized digital twin water affair architecture; and efficient water affair management is realized.

The invention discloses a goaf ecological restoration method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source remote sensing data and geological survey data of a goaf, and generating a three-dimensional geological damage model of the goaf through a space-time fusion technology; performing multi-scale feature extraction on the three-dimensional geological damage model by using a deep convolutional neural network to obtain a multi-dimensional ecological feature data set, identifying an ecologically fragile region and an instability risk region, and outputting an ecological risk level map; performing parallel processing on the multi-dimensional ecological characteristic data set to generate an ecological assessment report containing health degree scores of each ecological subsystem of the goaf; and based on the ecological risk grade map and the ecological assessment report, a multi-objective optimization algorithm is adopted to generate a vegetation-soil / rock mass-water body collaborative restoration strategy. By means of the goaf ecological restoration method and device, accurate assessment and collaborative strategy generation of goaf ecological restoration can be achieved, and scientificity, efficiency and overall ecological benefits of restoration engineering are improved.

The invention provides an ecological environment restoration evaluation method based on deep learning, and relates to the technical field of intelligent ecological environment restoration evaluation. Comprising the following steps: acquiring multi-source data as a time sequence, including remote sensing image data, environment quality data and biodiversity data; constructing an ecological environment knowledge graph, and performing entity relationship reconstruction and time sequence prediction on the knowledge graph through a graph neural network; performing multi-class segmentation on the remote sensing image data through the multi-class segmentation model to obtain a semantic class response graph; and obtaining a classification result according to the semantic category response graph, the difference of the semantic category response graphs of the multiple time points, final layer output of the graph neural network, and the difference of the regional environment quality data and the biodiversity data of the multiple time points. Through the deep learning technology, fusion and analysis of multi-source data are realized, the ecological environment change trend is predicted, the ecological environment restoration degree is assessed, the scientificity and reliability of ecological assessment are improved, and intelligent support is provided for ecological protection and restoration.

The invention relates to the technical field of grassland ecological evaluation, in particular to a dynamic reference index calculation method and system for grassland ecological quality evaluation. The method comprises the following steps: acquiring historical reference data of a non-degraded sample plot covered with different interference degrees in a target area, and constructing a reference system time sequence prediction model based on the historical reference data; based on pre-acquired historical monitoring data of a to-be-evaluated grassland, utilizing the reference system time sequence prediction model to obtain a dynamic reference index potential value; extracting an interference factor from the historical reference data, and setting an interference weight for the interference factor; and correcting the potential value of the dynamic reference index by using the interference weight based on the historical monitoring data and the historical reference data to obtain the dynamic reference index. The problem that in the prior art, a fixed reference index is difficult to dynamically respond to the change of the environment, so that the evaluation of the ecological condition of the grassland is inaccurate is solved, and the scientificity of reference index calculation is greatly improved.

The invention provides an urban green land carbon storage and sink quantification method based on remote sensing and field investigation, and relates to the technical field of environmental science and engineering, remote sensing technology and ecological assessment, and the method comprises the steps: obtaining a high-definition remote sensing image, combining a green land planning file, determining the distribution of various types of green lands, calculating an NDVI, and extracting an effective green land space; and generating a sampling point scheme according to the area proportion. Growing parameters of arbors and shrubs in the quadrat are investigated on site, biomass is calculated and converted into carbon reserves, an NDVI and carbon density model is established, and a carbon density spatial distribution diagram is generated. And constructing a carbon sink model based on the relationship between the age of the arbor and the carbon reserves, calculating the annual carbon sink, establishing a relationship model between the NDVI and the carbon sink, and generating a carbon sink spatial distribution diagram. And integrating the two diagrams to output carbon storage and sink capability evaluation results of different green land types, thereby providing a scientific basis for urban ecological planning.

The invention relates to the technical field of ecological assessment, in particular to a multi-factor coupled ecological drought assessment method and assessment system. The method comprises the following steps: acquiring geographical location information data; performing ecological mode matching on the standard geographic position information data and a preset geographic database to generate regional ecological mode matching data; performing drought evaluation multi-factor construction on the geographic position information data to generate regional drought evaluation factors; performing factor feature data extraction on the regional drought evaluation factors through the regional ecological mode matching data to obtain drought evaluation factor feature attribute data; and performing response relationship qualitative analysis on the regional drought evaluation factors according to the drought evaluation factor characteristic attribute data to generate qualitative analysis data of a drought-ecosystem. According to the method, through multi-factor coupling, ecological response analysis, recessive index introduction, standardization of an evaluation framework and result visualization, the accuracy of ecological drought evaluation is improved.

The invention provides a road network ecological bearing capacity estimation method and system based on multi-source remote sensing data, and relates to the technical field of remote sensing technology and ecological evaluation. The method comprises the following steps: acquiring a multispectral remote sensing image and ecological system basic data for preprocessing; supervising and classifying the remote sensing image to extract linear ground feature pattern spots and construct a topological relation to obtain road network topological structure data; associating the historical traffic flow statistical data with the road network space and performing interpolation to generate day and night noise spatial and temporal distribution data; extracting habitat distribution data based on the habitat type thematic map and the historical field survey point of the species, and obtaining acoustic sensitive threshold data of the species in combination with behavior experiment records; according to the method, noise spatial-temporal distribution data and habitat distribution data are superposed, noise data in a habitat range are screened out, then the noise data are compared with an acoustic sensitive threshold pixel by pixel, the total area of pixels exceeding the threshold is counted to serve as a road network ecological interference index, and accurate estimation of road network ecological interference is achieved.

The utility model relates to the field of water quality ecological evaluation, and discloses a portable biological early warning instrument for water quality ecological evaluation, which comprises a water storage barrel, a barrel cover is fixedly connected in the water storage barrel, a handle is fixedly connected on the outer wall of the water storage barrel, an L-shaped block is fixedly connected on the upper surface of the barrel cover, and a water inlet is formed in the upper surface of the L-shaped block. And a first motor is fixedly connected to the interior of the L-shaped block, a connecting column is fixedly connected to the output end of the first motor, the outer wall of the connecting column is rotationally connected to the interior of the barrel cover, a first gear is fixedly connected to the outer wall of the connecting column, and a scraping plate is fixedly connected to the bottom end of the first gear. According to the device, the first motor drives the first gear and the scraping plate to rotate, the scraping plate plays a role in stirring and preventing microorganisms from being attached to the inner wall of the water storage barrel at the same time, and the first gear drives the second gear to rotate on the gear ring and drives the stirring rod to rotate at the same time, so that the effects of fully stirring and scraping and washing the inner wall are achieved; and the detection accuracy and reliability are improved.

The invention relates to the technical field of ecological assessment, and discloses a coastal wetland ecosystem condition assessment method and device, a medium and a product, different smooth regression functions are obtained through double generalized additive model processing, and the influence of an environmental single factor on vegetation and the cooperative influence of the environment and the vegetation on benthic organisms are accurately quantified. Furthermore, the index value is determined by taking historical data as a reference and combining a nonlinear rule of a smooth function, so that subjective weighting deviation is avoided. Furthermore, the weight is calculated based on the association between the real-time index value and the biological index, so that human subjective intervention is avoided, and the weight is ensured to reflect the actual contribution of the index to the ecological system. Meanwhile, the influence of single data deviation is reduced by using the multi-tree integration characteristic of the random forest model, so that the weight result is more stable. Furthermore, the complex ecosystem condition of the coastal wetland can be comprehensively evaluated in combination with the index value and the weight, the biodiversity is protected, and the stability of the ecosystem is maintained.

The invention discloses a WSRS framework-based ecological safety assessment method for a drainage basin in an arid region, relates to the technical field of ecological assessment, and solves the technical problems that the prior art is not adaptive to a coupling process of water, salt, evapotranspiration and underground water and lacks key factors, and an assessment method lacks weight uncertainty description and robustness analysis. The method comprises the steps of S1, acquiring multi-source data of a target drainage basin, and performing data preprocessing; s2, constructing a WSRS evaluation index system, and carrying out standardization processing on indexes; s3, based on the standardized indexes, determining the comprehensive weight of each index by adopting a subjective and objective combination weighting method; s4, performing comprehensive evaluation based on the comprehensive weight, and quantifying uncertainty of an evaluation result by adopting a cloud model to obtain an ecological safety index; s5, constructing a composite stress model and correcting an ecological safety index; and S6, carrying out risk grade division and result visualization. The method has the advantages that the arid region process adaptability is high, the evaluation result is stable and explainable, and the like.

This invention discloses a method for assessing the impact of photovoltaic (PV) installations on grassland ecosystems based on multi-source remote sensing data. The method includes the following steps: constructing a multi-source remote sensing dataset of the PV installation area and surrounding grasslands; acquiring time series data of multi-dimensional ecological indicators such as vegetation, water, and energy; establishing multi-ring buffer zones based on the PV boundary, and extracting the variation characteristics of each ecological indicator within and outside the PV area and at different distances; and quantitatively assessing the intensity and spatial decay characteristics of the impact of PV installations on the grassland ecosystem through spatial difference analysis and comprehensive ecological impact indicators. This method enables long-term dynamic monitoring of the ecological effects of PV construction without ground-based observation, and has the advantages of high automation, strong scalability, and wide application range, providing scientific support for the ecological assessment and management of grassland renewable energy development.

This application discloses a method for determining the threshold of watershed ecological environment quality based on the PSR model, belonging to the field of watershed ecological assessment technology; it includes: determining the evaluation index items of the target watershed; arranging and combining all possible score values ​​of each index to obtain all score combinations; calculating the comprehensive score of each combination to generate a full sample space; performing fuzzy clustering on the full sample space, iteratively determining the membership degree and cluster center value of each sample to multiple preset cluster categories; sorting the cluster center values ​​of each cluster category in ascending order to form multiple health levels; classifying each sample to the cluster category with the largest membership degree based on the principle of maximum membership degree to obtain the sample set corresponding to each health level; fitting continuous distribution density curves to the sample sets of each health level, and determining the intersection of the distribution density curves of adjacent health levels as the critical threshold between adjacent health levels; and determining the health level of the target watershed based on each critical threshold.

The invention relates to the technical field of remote sensing monitoring and ecological evaluation, in particular to a karst rocky desertification evolution identification method fusing an ecological process. The method comprises the following steps: acquiring a suitable detection height of a detection area according to a proportion condition of pure pixels in an image acquired by remote sensing at different heights and an overall pixel purity index; the method comprises the following steps: determining a rocky desertification comprehensive evolution index based on remote sensing data, analyzing a rocky desertification change degree, establishing a prediction model, and judging the influence degree of different driving factors on the current rocky desertification evolution process according to the difference between a prediction result and an actual result and the influence caused by the change of the different driving factors. And finally, generating a stony desertification evolution diagram and a stony desertification risk zoning diagram. According to the method, stony desertification static monitoring in the past is converted into dynamic evolution analysis, and deep understanding of stony desertification evolution trend from representation to mechanism is realized.

The invention relates to a vegetation damage identification and ecological evaluation method for a power transmission and transformation construction area. The method comprises the following steps: data preparation and preprocessing; identifying a vegetation damaged area; optimizing and verifying a result; ecological system service value loss evaluation; and ecological restoration suggestion generation: fusing the vegetation damage degree and the spatial distribution information of the ecological system service value loss, carrying out restoration priority partitioning, providing differentiated ecological restoration measures for different partitions, and outputting a partition ecological restoration planning map and an evaluation report. Through ecological system service value evaluation, quantification of ecological economic loss is realized, and a scientific basis is provided for power transmission and transformation project ecological supervision and restoration.

The invention discloses a vegetation classification method based on space-time multi-modal deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining a multi-temporal optical image, a radar image and digital elevation model data of a to-be-classified region, and forming multi-modal data; extracting spectral features, microwave features, topographic features and texture features to form fusion features; calculating graph node features, and updating the graph node features to form graph features; and fusing the fusion features and the graph features to obtain pixel-level coarse classification logs, carrying out feature extraction and fusion on the fusion features to form region-level coarse classification logs, carrying out fusion to obtain a coarse classification probability, and carrying out fine classification on coarse basic features to obtain a final classification result. The method provided by the invention can adapt to a mountainous area environment with multiple clouds, multiple shadows and large topographic relief, improves the stability and classification fineness of vegetation type identification, and is suitable for wide-range vegetation monitoring and ecological assessment scenes.

The invention relates to the technical field of photovoltaic power generation ecological assessment, and discloses an unmanned aerial vehicle collaborative data acquisition method based on photovoltaic power generation project ecological restoration assessment. The method comprises the following steps: acquiring multispectral remote sensing image data and terrain elevation data of a target area, and extracting vegetation coverage features and soil degradation features in the multispectral remote sensing image data and the terrain elevation data; calling the trained three-dimensional point cloud segmentation network to carry out landform unit division on the landform elevation data, and generating an ecological unit partition set containing a mapping relationship between unit type identifiers and geographic boundary coordinates; and based on the set, calling a vegetation index analysis model to carry out multi-source feature analysis on vegetation coverage features, and generating a feature fusion result containing spectral reflectivity features and biomass estimation features. According to the method, the comprehensiveness and accuracy of ecological data acquisition are improved through multi-source data fusion and multi-model collaboration, and powerful data support is provided for ecological restoration evaluation of a photovoltaic power generation project.

This application discloses a vegetation classification method based on spatiotemporal multimodal deep learning, relating to the field of image processing technology. The method includes: acquiring multi-temporal optical images, radar images, and digital elevation model data of the region to be classified, forming multimodal data; extracting spectral features, microwave features, topographic features, and texture features to form fused features; calculating graph node features and updating the graph node features to form graph features; fusing the fused features and graph features to obtain pixel-level coarse classification logits; extracting and fusing the fused features to form region-level coarse classification logits; fusing to obtain coarse classification probabilities; and performing fine classification on the coarse class basic features to obtain the final classification result. The method of this application can adapt to mountainous environments with frequent cloud cover, shadows, and large topographic relief, improving the stability and classification precision of vegetation type identification, and is suitable for large-scale vegetation monitoring and ecological assessment scenarios.

The invention discloses an urban ecological evaluation method based on dynamic evolution and spatial correlation, and belongs to the technical field of urban ecological performance. The method comprises the following steps: establishing an urban ecological performance index system through a Markov chain and a kernel density estimation method, and dynamically evaluating the urban ecological performance; weighting the urban ecological performance index system through an entropy method; analyzing spatial correlation of urban ecological performance by adopting a global Moran index and Datonian coefficient decomposition method; and combining convergence and a convergence model to evaluate the long-term development trajectory of urban ecological performance. The space-time evolution rule and the space correlation characteristics of the urban ecological performance are disclosed from time and space dimensions, so that the dynamic change state of the city and different cities are linked, the comprehensive performance of the city in the aspects of ecological resource utilization, environmental protection and carbon emission control is comprehensively reflected, the defects of an existing evaluation system are overcome, and the evaluation system is suitable for the urban ecological environment. And the accuracy, comprehensiveness and scientificity of evaluation are improved.

The invention relates to the technical field of ecological assessment, in particular to a regional ecological restoration potential assessment method based on multi-dimensional data, which comprises the following steps: collecting pH value, moisture and nutrients; calculating microcosmic abnormal indexes and types, and determining microcosmic sub-regions; collecting the vegetation index and the coverage degree of the microscopic sub-region; calculating a mesoscopic anomaly index, and determining a mesoscopic sub-region; collecting rainfall and temperature of the mesoscopic sub-region; calculating a macroscopic adaptation index, and determining an index mode; calculating a potential index, and determining a macroscopic abnormal sub-region; calculating the aggregation degree of the macroscopic sub-regions, and adjusting the weight; and generating a recovery potential evaluation report. According to the invention, through step-by-step acquisition and analysis of multi-dimensional data, microscopic environment indexes, vegetation conditions and meteorological conditions are effectively integrated, a hierarchical evaluation system from microcosmic to macroscopic is formed, and the problem of low accuracy of evaluation of regional vegetation recovery potential caused by dependence on a single static index and a rough model is effectively solved.

The application discloses an ecological protection and restoration planning method and system, and the method comprises the following steps: acquiring ecological multi-source data of a target region, performing spatial registration and format standardization processing through a geographic information system; constructing a three-dimensional ecological background model, and generating a gridded geographic unit by fusing the ecological multi-source data; performing dynamic coupling analysis on the ecological multi-source data by using a convolutional neural network, identifying an ecological degradation hotspot region, and outputting a spatial probability distribution map; based on the spatial probability distribution of the ecological degradation hotspot region and the ecological background model, generating a multi-level ecological restoration scheme through a self-organizing feature mapping neural network, and outputting a visual planning atlas. The scheme disclosed by the application improves the accuracy and efficiency of ecological assessment, reduces the economic cost of regional ecological protection and restoration, improves the efficiency, and can achieve good expected effects through tests, and has a good popularization prospect.

This invention discloses a remote sensing identification and stand age estimation method for multi-mode rubber plantations. Utilizing multi-source remote sensing data and advanced image processing and analysis methods, it accurately monitors and estimates the distribution and stand age of rubber plantations with different modes, such as large-scale and small-scale farming. First, through data preparation and integration, combined with unsupervised image segmentation and classification optimization using Gaussian mixture models, a high-precision rubber plantation distribution map is generated. Second, based on time-series vegetation-moisture index (e.g., NDWI) data, phenological features are extracted to identify the planting year of rubber trees and estimate stand age, ultimately generating a rubber stand age distribution map, providing scientific support for plantation management and ecological assessment.