82 results about "Ecological index" patented technology

The invention discloses a forest land ecosystem health assessment system, and belongs to the technical field of environmental protection. Comprising the following modules: an intelligent sensing module for realizing omnibearing and high-resolution monitoring of the forest land ecological environment; the feature extraction module automatically extracts, standardizes and dynamically corrects the multi-dimensional features of the ecological system, and constructs a unified ecological index mapping model; the coupling analysis module is used for accurately identifying structural evolution characteristics and key conversion nodes of the ecological system; the health assessment module is used for constructing a dynamic self-adaptive ecological health assessment model, assessing the overall health state of the system and simulating the vulnerability and potential risk of the ecological system under different climate change scenes; the decision and visualization module is used for realizing efficient intelligent interaction and multi-scene decision analysis of ecological big data; and the restoration and regulation and control module intelligently generates a restoration path and an intervention strategy based on ecological risk assessment so as to improve the restoration capability and toughness of the ecological system.

The invention discloses a multi-scale fusion ecological hydrological interaction quantification method, and relates to the technical field of ecological hydrology, and the method comprises the steps: 1, collecting environment driving data and remote sensing data; 2, preprocessing the remote sensing data, and carrying out hydrological process dynamic monitoring and ecological parameter collaborative inversion in combination with environment driving data to respectively obtain hydrological data and ecological indexes; 3, constructing a spatio-temporal data set by using the hydrological data, the ecological indexes and the environment driving data; 4, constructing a bidirectional LSTM neural network model, and performing training optimization by using the spatio-temporal data set to obtain a water volume change predicted value, lag time and corresponding ecological variables; 5, lag effect analysis is carried out according to the water volume change predicted value, the lag time and the corresponding ecological variables, and an interaction quantification network diagram of the ecological hydrological elements is constructed. According to the method, the dynamic coupling relation of the ecological hydrological process of the sand lake basin is quantified, and theoretical support is provided for resource optimization management and ecological restoration engineering.

The invention provides a grassland degradation and recovery condition evaluation method integrating an ecological structure and a service index, and the method comprises the steps: constructing a grassland ecological index data set based on multi-source remote sensing data, soil data and meteorological data, and carrying out the inversion of a single-factor monitoring index comprising an ecological system structure index and an ecological system service index; identifying an ecosystem service beam, and performing ecological partitioning on the research area by using the ecosystem service beam; determining the weight of the single-factor monitoring index of each ecological partition, and calculating a grassland health and service comprehensive index according to the weight of the single-factor monitoring index; and evaluating grassland degradation and recovery grades by using the grassland health and service comprehensive indexes. According to the method, the ecological system structure and the service indexes are comprehensively considered, and the comprehensiveness of the indexes is ensured; meanwhile, the difference of grassland ecosystem functions of different regions is fully considered, the index weight is adjusted according to the region characteristics, and the scientificity and reliability of the evaluation result are remarkably improved.

The invention provides a mining area ecological restoration guidance system based on ecological big data. Relates to the technical field of environmental engineering and big data application, and comprises a data acquisition and fusion module used for acquiring remote sensing images, unmanned aerial vehicle aerial photography, ground sensor and historical monitoring data according to a unified space-time coordinate system and outputting fusion data; the prediction and risk quantification module is used for constructing a space-time deep learning model based on the fused data and outputting an ecological index prediction value and a corresponding risk probability; and the strategy optimization module is used for generating a restoration scheme according to the ecological index prediction value and the corresponding risk probability through a multi-target reinforcement learning model. According to the mining area ecological restoration guidance system based on ecological big data, ecological benefits, economic cost and residual risks can be considered synchronously, and a quantifiable and comparable optimal parameter combination is provided for a mining area restoration scheme.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention relates to the technical field of intelligent mine and environmental protection crossing, and discloses a mine development ecological data fusion and causal mining method based on a double-lineage knowledge graph. The method comprises the steps of constructing a double-pedigree domain ontology model comprising a production activity pedigree and an ecological response pedigree, carrying out multi-modal data space-time alignment and standardization, constructing a dynamic map reflecting the real-time state of a mine, predicting future ecological indexes by using a time sequence diagram neural network introduced with physical constraints, and carrying out real-time monitoring on the real-time state of the mine. And calculating the contribution degree of each production link to abnormity by utilizing anti-fact reasoning so as to lock a disaster-causing source, and generating a production regulation and control instruction according to a causal analysis result, and feeding back and executing the production regulation and control instruction. According to the method and the system, full-process traceability and accurate regulation and control of mine environment problems such as surface deformation and water and soil pollution are realized, so that the problems that data multi-source heterogeneous is difficult to fuse and the causal relationship is difficult to confirm in mine ecological environment monitoring 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 environment monitoring, in particular to a seawall ecologicalization real-time monitoring method based on multi-source data fusion. The method comprises the following steps: acquiring structure monitoring data, ecological monitoring data and water quality monitoring data of a seawall through an Internet of Things sensor, and acquiring remote sensing data of a seawall area in combination with satellite remote sensing and an unmanned aerial vehicle image; performing space-time registration and fusion on different data sources to generate a unified multi-source fusion data set; identifying a red tide abnormal event based on the fused data set, and constructing a red tide influence conduction model for predicting the damage degree of the red tide to the ecological system and the influence of the red tide to the stability of the embankment body; establishing a coupling evaluation model of ecological indexes and structural indexes, calculating a coupling coefficient by using a fuzzy comprehensive evaluation method, dynamically adjusting ecological and structural weights according to a model output result, and calculating a seawall comprehensive health index; graded early warning information is automatically generated according to a preset threshold value, and collaborative monitoring and intelligent early warning of seawall ecology and structure safety are achieved.

The invention relates to the technical field of ecological environment monitoring and resource scheduling, and discloses an ecological scheduling monitoring method and system based on multi-source remote sensing fusion. The method comprises the following steps: performing time-space consistency correction and fusion on multi-source data, and extracting initial ecological parameters; a feature enhancement parameter set and adaptive inversion parameters are obtained through optimization of a convolutional neural network and a genetic algorithm, key ecological indexes are inverted, and real-time ecological state description is generated; based on real-time ecological state adjustment model input, predicting an ecological change trend, and calculating to obtain an optimization decision variable; a water resource distribution scheme is generated, the distribution balance degree is evaluated, and final scheduling output is formed through iterative optimization; a final scheduling result is fed back to a data integration link, and updating and loop optimization of the preliminarily aligned data set are achieved. According to the invention, the integration of ecological monitoring and water resource scheduling under multi-source data fusion can be realized, and the balance of water resource distribution and the real-time performance of ecological system regulation and control are improved.

The invention relates to the technical field of ecological environment monitoring, in particular to a grassland environment resource database construction method, which comprises the following steps of: arranging a multi-modal sensor in a fractal multi-scale grid and synchronously collecting; obtaining a standard frame by adopting quantum random walk compression and space-time registration; extracting a topological bar code through neuromorphic pulse coding and persistent coherent topology analysis, and generating a unified potential space tensor and a missing mask in combination with weighted mutual attention and optimal transmission; constructing an energy function containing carbon and nitrogen conservation and topological deviation, and reconstructing a complete ecological submerged space tensor through Hamiltonian Monte Carlo-diffusion combined sampling; ecological indexes and abnormal events are reasoned in the Shenchang differential graph database, sampling scheduling is driven by prediction errors, closed-loop updating of data, models and sampling is achieved, and therefore the grassland monitoring precision and early warning timeliness are improved.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention discloses a mining area ecological index intelligent evaluation platform, and relates to the technical field of ecological evaluation, and the mining area ecological index intelligent evaluation platform comprises the following steps: obtaining multi-source remote sensing data, and obtaining an initial ecological index; performing ecological factor fusion according to the initial ecological indexes to obtain comprehensive ecological factors; performing principal component dimensionality reduction according to the comprehensive ecological factors to obtain principal component features; performing cross-cycle stability calculation according to the principal component characteristics to obtain a stability index; performing spatial overlay analysis according to the stability index to obtain an ecological distribution pattern; and performing multi-scale coupling analysis according to the ecological distribution pattern to obtain a mining area ecological index. According to the method, the cross-period stability index is calculated based on the principal component characteristics, the change trend of the mining area ecosystem in multiple time periods is comprehensively reflected, the accuracy of dynamic monitoring is improved, the scientificity and interpretability of ecological assessment are improved, and the systematicness and comprehensiveness of ecological assessment are enhanced.

The invention relates to the technical field of marine ecological environment monitoring, in particular to a marine ecological environment assessment early warning system based on big data and AI, which comprises a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological index generation module and a coupling early warning generation module. Wherein the multi-source data fusion module is used for collecting multi-source data in real time; the dynamic feature extraction module is used for extracting 72-hour change gradients of the three types of core parameters; the meteorological disturbance coefficient calculation module is used for calculating and outputting a meteorological disturbance coefficient; the ecological index generation module is used for generating a water quality deterioration index and a biological stress index; and the coupling early warning generation module is used for outputting an ecological risk level signal. According to the method, meteorological interference is reduced in real time, water quality deterioration and biological stress indexes are subjected to weighted fusion, the marine ecological environment risk is accurately evaluated, and scientific support is provided for ecological protection and pollution early warning.

The invention discloses a fruit tree ecological index assessment method based on an orthoimage and three-dimensional point cloud data, and the method comprises the following steps: obtaining an orthoimage map and three-dimensional point cloud data of a fruit tree region, carrying out the individual tree segmentation of the orthoimage map, and determining the individual tree region of each fruit tree; constructing a three-dimensional point cloud data set on the single tree area, performing point cloud denoising and layering, and outputting an independent layered three-dimensional point cloud of a single fruit tree; and calculating the core ecological index of the fruit tree by using the independent layered three-dimensional point cloud, so that the scheme is suitable for efficient monitoring and evaluation of a large-scale orchard, and the evaluation precision and efficiency are remarkably improved.

The invention discloses a mangrove forest protection effect intelligent evaluation and scene prediction system, and relates to the field of ecological environment information intelligence, and the system comprises a key driving factor recognition module which is used for determining a core variable influencing the protection effect and a dynamic response interval of the core variable based on a statistical analysis and nonlinear fitting method; the protection effect quantitative evaluation module is used for calculating a net improvement effect of protection intervention by comparing ecological index differences inside and outside the protection area; the causal effect analysis module is used for separating independent contributions of natural factors and artificial protection by adopting a dual machine learning framework; the space-time dynamic modeling module is used for performing uncertainty modeling on the space-time evolution of the ecological indexes by using a Bayesian hierarchical structure; and the multi-scene prediction module is used for realizing ecological response prediction under multi-factor driving and supporting input and simulation of user-defined scenes. According to the scheme, multi-dimensional and high-precision evaluation and future trend reliable prediction of mangrove forest protection effects can be realized.

The invention provides a mining area ecological restoration engineering evaluation system based on multi-source data fusion. The system comprises a data acquisition module which is used for acquiring multi-source heterogeneous original data related to a mining area environment; and the data preprocessing and fusion module is used for carrying out space-time alignment, format standardization and feature extraction on the multi-source original data and generating a unified ecological index data set based on a multi-scale deep learning fusion framework, and the preprocessing and fusion module comprises a dynamic credibility evaluation sub-module, a weighted fusion sub-module and an intelligent analysis module. The mining area ecological restoration project evaluation system based on multi-source data fusion can automatically explore an optimal restoration parameter combination in ecological health index amplification and cost-benefit tradeoff, realizes online adjustment and dynamic feedback of a scheme, and greatly improves project decision efficiency and restoration effect.

The invention discloses a water ecological index data processing method and system combining the Internet of Things and artificial intelligence, and belongs to the technical field of water body monitoring and analysis, and the method comprises the steps: grouping sensors, synchronizing a clock, and forming a space grouping data flow; comparing the grouped data with the steady-state baseline in real time, triggering high-density event acquisition, and outputting context data pulses; obtaining and standardizing mapping environment data, and generating a multi-dimensional spatio-temporal feature cube; dynamically distributing weights to calculate a water ecological index, quantifying credibility, and outputting a dynamic water ecological index; and when the credibility is lower than a threshold value, analyzing parameter contributions and tracing abnormities, generating and issuing a calibration strategy, and realizing grouping self-calibration and sampling adjustment. According to the method, multi-source collaborative fusion, dynamic weight distribution and an intelligent self-correction mechanism are adopted, the real-time performance, intelligence and evaluation accuracy of water ecology monitoring can be improved, and efficient and reliable data support and early warning capacity are provided for complex water environment changes.

The invention relates to the technical field of intelligent environmental protection monitoring, in particular to a method for intelligently monitoring and analyzing land utilization change in an ecological protection red line, which comprises the following steps of: acquiring a historical remote sensing image, extracting pixel-level vegetation and artificial indexes, constructing an ecological index time sequence data set, and constructing an ecological index time sequence data set based on the data set; a dynamic baseline prediction model fusing a long-term trend term, a seasonal periodic term and a standard deviation is established for each pixel, personalized prediction of a theoretical state of the pixel is realized, abnormality diagnosis is performed through comparison with a confidence interval output by the model, and an abnormal pixel is marked as a collaborative abnormal pixel, so that comprehensive risk assessment is carried out, and the risk assessment efficiency is improved. The method comprises the steps of calculating risk scores of all indexes and carrying out weighted fusion to obtain a comprehensive risk index, then dividing risk levels, outputting an anomaly diagnosis report, finally, carrying out reverse calibration on model parameters based on confirmed anomaly cases, and mining risk modes from case characteristics through unsupervised clustering.

The invention discloses a coastal zone ecological safety early warning method and system based on big data, and relates to the technical field of big data analysis, and the method comprises the steps: determining a coastal type through remote sensing and geological map spots, carrying out the ecological multi-source data collection according to the coastal type, and carrying out the data preprocessing; based on preprocessed data, a sparse attention mechanism is combined with an LSTM network, key ecological factors are dynamically recognized, and ecological index trends are predicted, the time causal relationship between ecological variables is recognized through a Granger causal test method, the root cause traceability of early warning is improved, information granulation modeling is combined, and the early warning efficiency is improved. According to the method, the processing capacity of the system on nonlinear and fuzzy information is enhanced, the stability and adaptability of an early warning model are improved, through conditional transfer entropy analysis, intermediary interference is effectively eliminated, the accuracy of causal identification is ensured, and finally accurate identification, interpretable early warning and scientific intervention on coastal zone ecological safety problems are achieved.

The invention discloses an ecological product value analysis method and system based on multi-source data fusion, and relates to the technical field of ecological product value analysis. Original ecological data are divided into two-dimensional space grid units according to a fixed scale through a space grid construction and index unit; according to the method, automatic mapping from an actual geographic coordinate system to an analysis grid unit is realized, so that multi-source data has a unified projection basis in space, the problems that multiple data source space references are inconsistent and results are difficult to transversely compare in original ecological evaluation are solved, and a strict space organization structure is provided for subsequent ecological supply modeling. In the process of mapping the ecological indexes to the grid units, the system designs a neighborhood interpolation-based mechanism for averaging and filling multiple observation points and invalid units, so that the problem of local deficiency caused by non-uniform distribution of the observation points or influence of shielding on remote sensing data in field sampling is effectively relieved, and the integrity and analysis reliability of a spatial expression matrix are improved.

The invention discloses a tea garden ecological condition assessment method based on a laser radar point cloud, and belongs to the technical field of agricultural ecological monitoring, and the method comprises the following steps: S1, collecting tea garden point cloud data through an unmanned aerial vehicle-mounted laser radar system; s2, extracting key ecological parameters including canopy density, leaf area index, three-dimensional green quantity, gradient and spatial heterogeneity; s3, analyzing and determining a key threshold value of each parameter based on an empirical cumulative distribution function; s4, respectively adopting a Logistic function, an inverted S-shaped curve and a piecewise linear scoring method to construct a scoring model of each parameter; s5, the final weight of each scoring model is determined through grey correlation degree analysis in combination with expert scoring; s6, performing weighted calculation to obtain a comprehensive ecological index so as to evaluate the ecological condition of the tea garden; and S7, dynamically updating model parameters. According to the method, accurate and objective evaluation of the ecological condition of the tea garden is realized by means of laser radar point cloud data, the evaluation model can be optimized by a dynamic updating mechanism, and scientific guidance is provided for sustainable development of the tea garden.

The present application relates to the technical field of marine ecological environment monitoring, and particularly relates to a marine ecological environment assessment and early warning system based on big data and AI, comprising a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological index generation module and a coupling early warning generation module; wherein: the multi-source data fusion module is used for real-time collection of multi-source data; the dynamic feature extraction module is used for extraction of 72-hour change gradients of three types of core parameters; the meteorological disturbance coefficient calculation module is used for calculation and output of meteorological disturbance coefficients; the ecological index generation module is used for generation of water quality deterioration indexes and biological stress indexes; and the coupling early warning generation module is used for output of ecological risk grade signals. Through real-time reduction of meteorological interference and weighted fusion of water quality deterioration and biological stress indexes, the present application can accurately assess marine ecological environment risks and provide scientific support for ecological protection and pollution early warning.

The application relates to the technical field of ecological safety optimization, and discloses an ecological safety pattern intelligent simulation optimization system based on reinforcement learning. An experience pool forming module of the system divides historical optimization strategies into multiple experience layers to form a multilayer experience pool based on the gap between ecological indexes and strategy confidence of the historical optimization strategies. A strategy determining module determines intelligent optimization strategies at each stage based on a set ecological safety target state, and screens the multilayer experience pool to obtain experience optimization strategies in combination with strategy confidence and target matching degree. A strategy adjusting module adjusts the two strategies by using a dynamic mechanism to cope with real-time ecological changes, and determines a target optimization strategy. A simulation optimization module simulates and optimizes the ecological safety pattern based on the target optimization strategy. A state acquisition module acquires an actual state. An experience pool updating module updates the content of the experience layers based on the difference factors between the actual state and the target state, and perfects the multilayer experience pool data. The system can improve the dynamics and adaptability of ecological safety pattern optimization.

The invention discloses a marine disaster key hidden danger identification method for marine space planning monitoring and early warning, and relates to the technical field of marine space hidden danger identification, and the method comprises the following steps: obtaining ecological indexes and disaster parameters of a fishery resource area; analyzing the incidence relation between the ecological indexes and different disaster parameters to obtain influence parameters; analyzing historical influence parameters to obtain a comprehensive influence graph of the influence parameters on the ecological indexes; predicting the trend of the influence parameter to obtain a prediction parameter, and predicting the change of the ecological index based on the comprehensive influence graph and the prediction parameter to obtain an ecological prediction index; judging whether the fishery resource area has hidden dangers or not according to the ecological prediction index; the method is used for solving the problems that when an existing marine space hidden danger recognition technology is applied to a fishery resource area, the hidden danger recognition mode is not reasonable, influence factors are not comprehensively considered, and fishery resources are reduced.

The application provides a kind of improved remote sensing ecological index calculation method based on space-time data fusion, the method comprises the following steps: collecting remote sensing data of the study area;The collected remote sensing data is pretreated;The water in the processed remote sensing data is masked and treated;The remote sensing data after pretreatment is spatiotemporally fused;According to the remote sensing data after fusion processing, the index data for representing the improved remote sensing ecological index is calculated respectively;Get aerosol optical depth to represent air quality and nighttime light data to represent human activity intensity on the gee platform, and resample the two index data;The index data is parameter standardized, and the standardized data is obtained;Principal component analysis is used, and principal component extraction is carried out according to the standardized data, to obtain the evaluation index result.The application can improve the spatiotemporal resolution of data through space-time data fusion technology, and solve the problem of lack of comprehensiveness in index selection of remote sensing ecological index.

The invention belongs to the technical field of environment monitoring and intelligent analysis, and discloses an intelligent microscopic imaging method and system for water ecology investigation, and the method comprises the steps: carrying out the preprocessing of the microscopic image data of a water ecology sample, and obtaining the microscopic image feature data of the water ecology sample; positioning and classifying targets in the microscopic image feature data of the water ecological sample by using a dual-stage recognition mechanism to obtain key features of each target; and performing ecological health analysis on the key features of each target, evaluating ecological indexes of the target, generating an identification report in real time, uploading the identification report to a cloud for data storage and sharing, constructing a regional species database, and assisting ecological early warning. According to the method, through a two-stage identification mechanism combining the YOLO algorithm and the visual converter, rapid and accurate target positioning and classification are realized in microscopic image analysis of the water ecological sample, so that the problem of individual separation in a high-density sample is effectively solved, and accurate classification of aquatic species is ensured.

The invention relates to the technical field of water conservancy projects, and discloses a high seismic intensity area and reservoir area bridge scheme comparison and selection method and system, and the method comprises the steps: building a multi-field coupling model based on the earthquake, landslide and large water level rise and fall, and obtaining a safety index, a function index and an ecological index in SPI values of a comprehensive performance index SPI based on the multi-field coupling model; establishing a dynamic full-life-cycle dynamic LCC model; an improved NSGA-III algorithm is adopted, optimization is carried out to generate a plurality of initial candidate schemes by taking minimization of the LCC value of the dynamic full-life-cycle dynamic LCC model, maximization of the comprehensive performance index SPI and minimization of carbon emission as multiple objectives, and an optimal candidate scheme is screened from the initial candidate schemes in combination with TOPSIS multi-attribute decision, the comprehensive performance index SPI comprises a safety index, a functional index, an ecological index and a cultural index. According to the method, the slope instability probability is reduced by arranging the multi-field coupling model based on earthquake, landslide and great water level rising and falling.

This invention belongs to the field of remote sensing technology and discloses a method for optimizing habitat quality parameters in the InVEST model based on Remote Sensing Ecological Indices (RSEI). This invention overcomes the technical bottleneck of traditional InVEST models, which rely on manual experience to set parameters and are difficult to update dynamically over time. By constructing a multi-source remote sensing data-driven RSEI, and utilizing its statistical characteristics and spatial distribution patterns across different land use types, it achieves objective quantification and dynamic updating of four key parameters: habitat suitability, threat weight, threat attenuation distance, and habitat sensitivity. Without requiring data from ecological monitoring stations, it directly incorporates continuous changes in ecological environment quality into the InVEST parameter system, resulting in higher objectivity, temporal consistency, and spatial autocorrelation in habitat quality calculations. This provides a novel technical approach for the automated and time-series assessment of regional-scale habitat quality.

The application provides a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index, and belongs to the technical field of toxic element pollution ecological risk evaluation; a technical problem to be solved is to provide a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index; a technical scheme is that sampling is carried out on soils in a region polluted by toxic elements in different degrees in multiple time periods and multiple sites, and the sampled soils are respectively used for soil potential toxic element content detection, soil physical and chemical property detection and soil nematode community structure analysis; characteristic indexes of potential toxic element pollution in the soils are respectively calculated, including a pollution factor, a Nemerow index, a pollution load index and a potential ecological risk index; soil nematode community structure is analyzed, and evaluation indexes of the soil nematode community structure are respectively calculated; and the application is applied to toxic element pollution ecological risk evaluation.

The application relates to a remote sensing ecological index monitoring method and device suitable for long time sequences. The method and device perform data synthesis on acquired long time sequence Landsat remote sensing data; calculate time sequence remote sensing indexes of the Landsat remote sensing data; detect invariable regions of the time sequence remote sensing indexes of the Landsat remote sensing data; normalize the time sequence remote sensing indexes in the probability space of the invariable regions; uniformly perform principal component analysis on normalized indexes of multiple time phases to acquire unified principal component load coefficients. The application is based on medium spatial resolution Landsat remote sensing images, utilizes time sequence data synthesis, time sequence invariable region detection, time sequence principal component analysis and other methods to design a remote sensing ecological index suitable for long time sequence dramatic change regions, and finally accurately reflects the distribution and evolution conditions of ecological environment conditions in a large area range and a long time sequence.