104 results about "Species distribution" patented technology

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention discloses an euphausia superba fishery change prediction method based on an ocean circulation-sea ice-species distribution coupling model, and relates to the technical field of ocean science. Euphausia superba fishery historical observation data, satellite remote sensing data and reanalysis data are collected, euphausia superba resources and environmental data are integrated, and the euphausia superba fishery change prediction method based on the ocean circulation-sea ice-species distribution coupling model is obtained. Constructing a polar region resource environment database covering total elements of physics-biology-environment; and based on an FVCOM model, constructing an ocean circulation-sea ice coupling model of a surrounding sea area of the peninsula of the south pole, and simulating and verifying physical environment characteristics of a target sea area. By constructing the ocean circulation-sea ice-species distribution coupling model, integrating multi-source data and analyzing a cooperative driving mechanism of a multi-scale physical process to euphausia superba resource change, compared with a traditional method, the change of an euphausia superba fishery can be simulated and predicted more accurately, and the method has the advantages of being high in practicability and the like. And the problem of inaccurate prediction caused by limited space-time coverage rate of observation data is effectively solved.

The invention provides an ecological system monitoring method and system based on a large model, and the method comprises the steps: carrying out the analysis of ecological data based on a macroscopic ecological model, a plurality of single species distribution models, and a combined species distribution model, and outputting the prediction content; fusing output contents of the three types of models with linkage conditions of environmental data and geographic positions, and processing a fusion result by adopting sequential Bayesian filtering to obtain an estimated value of a species target state in the ecological protection area; the estimated value of the species target state and other multi-source monitoring data are aligned according to timestamps and converted into an input data set, and a target time sequence and key data features included in the input data set are input into a target large model according to a preset prompt template, and predicting the ecological condition in the ecological protection area by using the target large model so as to monitor the ecological protection area. According to the method, multi-dimensional data and a plurality of types of models can be fully utilized to predict the ecological condition of the species, and the reliability and accuracy of prediction are ensured.

The invention provides an urban ecological risk prediction system based on a cellular automaton. The system integrates and obtains remote sensing images, meteorological data and human activity data through a data acquisition module, and extracts green land coverage, land utilization types and species distribution information of cities. Thirdly, a space grid is divided through a space grid division module by means of a dynamic quadtree algorithm, ecological units are generated, and therefore microcosmic space heterogeneity is captured; then, the system utilizes a cellular automaton model to optimize a state transition rule through deep reinforcement learning, and couples a soil erosion model and a hydrological model to simulate the dynamic change of urban ecology. And finally, quantizing parameter uncertainty through a risk assessment module by using a Bayesian belief network and Monte Carlo simulation. According to the system, urban ecological risks can be effectively simulated and predicted, the spatial precision and scientificity of prediction are improved, and the coupling influence of multiple ecological factors is considered at the same time.

The invention discloses a marine fishery resource survey station adaptive optimization method based on a space-time coupling model. According to the method, a space-time continuous species distribution dynamic reference field is constructed by coupling a Bayesian hierarchical species distribution model and a regional ocean model system; performing ecological layering based on time, space and environment dimensions; and when the environment variables change significantly, a habitat suitability index is dynamically reconstructed, and the station layout is optimized again, so that the coverage of a key ecological hot spot area is ensured, and the in-gradient representativeness is maintained. The method has the advantages of fast environmental response, strong spatial representativeness, high adaptability and the like, and is suitable for scenes of resource assessment, ecological protection, biodiversity monitoring and the like.

The invention discloses an aquatic organism diversity rapid evaluation system and method based on GIS and eDNA technologies, and the method comprises the steps: selecting a plurality of sampling points in a target region to collect water samples, and integrating and processing the geographic information data of the sampling points and surrounding regions by adopting a GIS system; extracting eDNA from the collected water sample, performing amplification and sequencing on the eDNA by adopting a high-throughput sequencing technology, obtaining species and relative abundance of the species existing at the sampling point, and forming a species list; importing species distribution information corresponding to the species list into a GIS system, and obtaining a species distribution area and species types; according to the method, a species distribution prediction model is established, aquatic organism diversity change trends of the hot spot area and the potential ecological threat area are monitored in real time, an evaluation report and protection suggestions are formed, and protection measures are updated in time, so that the data acquisition and processing efficiency is improved, and the accuracy of species identification and the comprehensiveness of diversity evaluation are enhanced; and a real-time monitoring and early warning mechanism is provided.

The invention relates to the technical field of image processing, and discloses a mangrove forest ecological information extraction method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring overlooking and side-looking photos through an unmanned aerial vehicle; correcting the overlook picture by using the coordinate information and superposing the overlook picture to the remote sensing image; generating a photographing direction pattern layer based on the side view photo; establishing a mangrove forest species interpretation mark library by using a random forest algorithm; classifying selected samples to generate a species distribution diagram; and finally evaluating the mangrove forest ecological function to form ecological monitoring data. According to the method, multi-angle and multi-level high-precision ecological information extraction of the mangrove forest is realized, so that a more accurate and comprehensive scientific basis is provided for species classification, ecological system function evaluation and protection management of the mangrove forest.

The invention belongs to but is not limited to the technical field of ecological environmental protection, and discloses a river fish habitat connectivity assessment method, which comprises the following steps: collecting historical site data of target river fish and bioclimatic data influencing river fish distribution to establish a variable set; the method comprises the following steps: screening variables without collinearity by utilizing VIF, constructing a species distribution model of fishes in a target river by utilizing a machine learning algorithm, and screening environmental factors with relatively high contribution degree of the model; according to a machine learning sub-model selected by AUC and TSS, establishing a set model and simulating HS of target river fish habitat suitability distribution; dam information of the target evaluation drainage basin is collected, and a tree connectivity index (DCI) is calculated; stacking the HS and the DCI, calculating a habitat connectivity index (FRCI) of the fish in the target river, and evaluating the habitat connectivity. According to the method, the habitat suitability, the structure connectivity and the function connectivity are considered, the credibility and the preciseness of evaluation of the habitat connectivity are improved, and a scientific basis and a practical tool are provided for protection and management of a river ecosystem.

The invention relates to the field of biological recognition and environmental monitoring, and discloses a chironomidae larva recognition system based on deep learning and image recognition, which comprises an image acquisition module, an automatic sample processing device, an image superposition processing module, a deep learning recognition module and an automatic category classification and statistics module, the image acquisition module acquires multi-focal plane chironomidae larva images through an optical microscope and a digital camera, the automatic sample processing device realizes automatic loading operation of a sample, and the image superposition processing module performs adaptive fusion on the multi-focal plane images to generate a full-focal plane image containing multiple pieces of depth information; the deep learning recognition module is used for extracting fine structure features of chironomidae larvae and automatically recognizing and classifying different types of chironomidae larvae, and the automatic type classification and statistics module is used for performing statistical analysis according to classification results and generating a report of type distribution and time sequence change. The method is suitable for automatic monitoring of water ecological environment.

The invention relates to a species suitable habitat simulation method and device under future climate, and the method comprises the steps: obtaining remote sensing data, foundation observation data and species distribution data in a target region, and carrying out the unified preprocessing of the data, and obtaining processed data; constructing environment characteristic variables based on the processed data, and performing screening to obtain a training data set meeting species suitability modeling input conditions; and constructing a species suitability model under the current climate condition by using the training data set, predicting species suitable habitat distribution under a future climate scene by using the species suitability model, and analyzing a spatial change trend according to the species suitable habitat distribution. Therefore, the problems that remote sensing and foundation observation data are isomerism in the aspects of time, space, scale and the like, an effective data fusion and index conversion mechanism is lacked, registration errors and feature inconsistency are easily caused, and the accuracy and reliability of ecological parameter estimation are affected are solved.

The invention provides a key species population spatial distribution quantification method and system in a complex ecological system, and the method comprises the steps: carrying out the feature extraction of biological and environmental data collected in a target ecological region, and carrying out the fusion of the extracted multi-modal features, and generating a comprehensive feature vector; based on the target large model, analyzing the comprehensive feature vector, identifying species in the target ecological region, and obtaining species identification information of each species; based on the species identification information and the comprehensive feature vector corresponding to the target ecological region, quantifying a species population space distribution condition, and generating species population quantification information at least comprising a population distribution diagram, a population density diagram and a population dynamic change diagram; and based on the species population quantitative information and the collected original data, generating and displaying a quantitative report. According to the method, the species can be intelligently, efficiently and accurately identified based on the large model, the distribution condition and dynamic change of the species can be visually displayed, a quantitative report is provided, and a powerful basis is provided for ecological protection and management.

The invention relates to an intelligent fish community stability evaluation method and system based on a network model, and the method comprises the steps: optimizing species distribution data based on the influence of a single species on community biodiversity and the influence of interspecific interaction on a community structure, obtaining species distribution optimization data, and constructing a community association network model; quantifying the functional diversity overlapping degree of the species by adopting a PCoA method and constructing a functional ecological niche network model; constructing a comprehensive network model according to the community association network model and the functional ecological niche network model; and constructing a radar map according to the community association network model, the functional ecological niche network model and the comprehensive network model, and evaluating the network stability based on a radar map area method. According to the method, the deficiency of the interaction relationship between the species caused by incomplete sampling can be better reflected, the network structure is more complex and closer to the real situation, the interaction relationship between the species can be deeply mined, and the accuracy of the evaluation result and the comparability between different biocenosis networks are improved.

The invention relates to the technical field of data processing, and particularly discloses a big data-based marine ecological evaluation and analysis system, which comprises a data acquisition module, a data processing module, a data analysis module and a comprehensive evaluation module. The method can predict and analyze the distribution area of the marine organism species, can analyze and identify potential invasive species in the marine organism species according to the predicted species distribution diagram, can analyze the marine water quality pollution rate, the marine landform change rate and the marine resource change rate, can identify and analyze sea surface image data, and can analyze marine remote sensing data. The marine ecology risk level can be evaluated after the risk evaluation index of the marine ecology is comprehensively analyzed according to various data; according to the method, the condition of the marine ecosystem is more comprehensively known by analyzing various data, so that the risk level of marine ecology is more accurately evaluated.

The invention discloses a method for rapidly evaluating the growth suitability of large-area characteristic crops, and the method comprises the following steps: S1, obtaining the growth distribution point information and key quality data of a multi-production area of the characteristic crops, collecting the environment variables of a research area, and constructing a database; s2, constructing a species distribution model based on the constructed database and a maximum entropy principle; s3, training a species distribution model, and calculating the distribution probability of characteristic crops; collecting a plurality of classification thresholds, and selecting an optimal threshold to obtain a potential distribution area of characteristic crops; s4, obtaining quality classification and origin environment classification results; s5, taking the origin environment classification result as a natural environment gradient, analyzing a response mode of the key quality of the characteristic crops to each environment factor, and establishing suitability classification of the key environment factors on a large scale; and S6, constructing a characteristic crop growth suitability evaluation model. According to the method, the growth suitability evaluation of large-area characteristic agricultural products can be quickly realized at low cost.

The invention relates to a regional climate shelter identification method based on climate change and a species distribution model. The method comprises the following steps: S1, species distribution modeling; step S2, biological velocity calculation; s3, calculating the richness of the species; s4, calculating the habitat quality; step S5, climate stability calculation; step S6, calculating a connectivity index and a reachability index; the method has the advantages that dimensions of three information including space, time and ecology can be effectively captured, species richness, habitat quality, climate stability and biological speed are integrated, the defect that a climate speed index neglects species specific diffusion rate and habitat suitability in shelter recognition is made up, and the method has the advantages of being high in adaptability and high in reliability. And the method is limited by a climate matching threshold value, so that the climate refuge can be identified more accurately.

The invention belongs to the technical field of resources and environment, and discloses an ecological niche theory and machine learning integrated ecological intelligent zoning method, which comprises the steps of environmental factor and phytoplankton community data measurement, sampling point ecological niche width and overlap calculation, key environmental factor identification and ecological environment zoning. According to the method, data such as hydrochemical element phytoplankton communities, ecological niche overlapping and width can be effectively integrated, and a machine learning algorithm is applied to perform analysis, so that factors which have remarkable influence on an ecological system are identified, and scientific and reasonable ecological zoning is performed on a research area according to the key influence factors. The method not only can accurately reflect regional ecological characteristics, but also can provide important data support for ecological protection, species distribution prediction and water resource management. Meanwhile, the method can provide an effective basis for ecological environment monitoring, ecological restoration and regional environment planning in practical application, and has high application value and wide popularization prospect.

The invention discloses a coastal zone planning method and system based on MaXent and hotspot analysis, and relates to the technical field of coastal zone planning. Comprising the following steps: S1, collecting environment variable data, species distribution data and development intensity data, constructing a unified space-time grid system, and realizing time sequence expression of the data through data preprocessing; s2, constructing a quarterly time sequence maximum entropy model, and performing time sequence evaluation on each grid habitat state to form a habitat evolution type; s3, identifying time sequence significant hot spots and hot spot persistence and intensity features, constructing ecological development relationship comprehensive representation, and identifying ecological development conflict types; and S4, spatially superposing the habitat evolution type and the ecological development conflict type to form a self-adaptive access rule, and generating differentiated planning suggestions according to regions. The problem that planning lacks dynamic adaptability due to the fact that coastal zone habitat suitability and development intensity depend on static time point data for a long time and a time sequence evolution rule cannot be recognized is solved.

The invention discloses a regional ecological network construction method coupled with a land-species simulation technology, and the method comprises the following steps: S1, determining an ecological source land through morphological landscape analysis, screening resistance surface indexes through land utilization data, and carrying out the weighted summation to construct a comprehensive resistance surface; s2, determining a species distribution probability according to the species point location data and the environmental data, and correcting the ecological source land according to the species distribution probability; s3, correcting the comprehensive resistance surface according to the reciprocal of the species distribution probability; s4, identifying an ecological corridor and an ecological pinch point through the minimum cost path and the interaction force by adopting a circuit theory; and S5, carrying out importance analysis on the ecological source land and the ecological corridor by utilizing centrality analysis and a gravity model. According to the constructed land-species ecological network, the ecological system function is comprehensively considered, and efficient realization of the ecological flow value in the regional ecological network coupled with land-species is facilitated.

The invention discloses a species information collection method and system based on an unmanned aerial vehicle, electronic equipment and a medium, and relates to the technical field of unmanned aerial vehicle control. The method comprises the steps that forest space information of a target area is acquired, and a forest layer distribution diagram of the target area is generated based on forest layer height data and interlayer distance data in the forest space information; according to the forest layer distribution map, determining a first hovering position of the master control unmanned aerial vehicle and second hovering positions of the sub unmanned aerial vehicles corresponding to the plurality of forest layers, and receiving initial species coordinate information, collected by the master control unmanned aerial vehicle, of each forest layer; determining a passing path of the sub unmanned aerial vehicle corresponding to each forest layer by combining the initial species coordinate information and the second hovering position of each forest layer; and according to each passing path, controlling the corresponding sub unmanned aerial vehicle to perform species collection, obtaining a species image and a species distribution position corresponding to each forest layer, and generating a species distribution report of the target area. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of species information collection is achieved.

The invention relates to the technical field of species ecological niche modeling, and provides a coastal wetland waterfowl ecological corridor construction method and system, and the method comprises the steps: collecting target waterfowl species distribution point data, and obtaining a waterfowl species distribution data set; performing habitat suitability modeling on environmental factor data and the waterfowl species distribution data set through a species distribution model to obtain a coastal wetland ecological source land; habitat suitability prediction modeling is carried out on the environment variables through a random forest algorithm, resistance value assignment is carried out on suitability indexes, and a coastal wetland comprehensive resistance surface is obtained; and carrying out multi-scene corridor path identification on the coastal wetland comprehensive resistance surface and the coastal wetland ecological source land to obtain the coastal wetland waterfowl ecological corridor. According to the method, the functionality of the ecological corridor under different ecological backgrounds can be comprehensively reflected, so that the ecological suitability of the constructed corridor is improved.

The invention relates to a method for determining a spatial range of an urban ecological corridor based on habits of birds under forest, and relates to the technical field of landscape ecology and urban ecological planning. The method comprises the following steps: acquiring a target environment variable of a preselected under-forest bird species in a target research area; inputting the target environment variable into a pre-trained species distribution model, simulating a potential suitable-for-growth area of the pre-selected under-forest bird species, and outputting a habitat suitability index; determining an ecological source space distribution range based on the potential suitable growth area; constructing a landscape transfer resistance surface based on the habitat suitability index; inputting the data of the ecological source space distribution range and the landscape transfer resistance surface into a minimum cumulative resistance model to generate a path of the urban ecological corridor; and based on the circuit theory model, determining the function connectivity in the transfer process of the preselected under-forest bird species in the urban ecological corridor path, and determining the inflection point of the change of the function connectivity as the width of the urban ecological corridor. By adopting the method, the spatial range of the urban ecological corridor can be determined.

The invention relates to the technical field of species distribution analysis, and discloses a dove tree population natural update limitation-based species distribution analysis system, which comprises a habitat factor acquisition module, an update limitation evaluation module, a distribution prediction module, a spatial constraint correction module and an adaptability atlas optimization module. The habitat factor acquisition module acquires data in real time and constructs a multi-layer environment factor topology; the update limit evaluation module evaluates update limit in combination with genetic diversity and seed diffusion constraint; the distribution prediction module generates a distribution strategy through spatial modeling and ecological niche optimization; the spatial constraint correction module corrects a diffusion range and genetic parameters based on terrain and climate factors; and the adaptability graph optimization module feeds back an analysis result to carry out closed-loop optimization on the strategy. The system is provided with a multi-stage obstacle avoidance mechanism and a redundant link, so that the habitat change response capability is improved. According to the method, accurate analysis and dynamic optimization of davidia involucrata distribution are realized, and technical support is provided for species protection.

This invention discloses a method for predicting changes in Antarctic krill fishery grounds based on a coupled ocean circulation-sea ice-species distribution model. This method, which relates to the field of marine science and technology, collects historical observational data on the Antarctic krill fishery, satellite remote sensing data, and reanalysis data, integrates Antarctic krill resource and environmental data, and constructs a polar resource and environmental database covering all physical, biological, and environmental factors. Based on the FVCOM model, a coupled ocean circulation-sea ice model is constructed for the waters surrounding the Antarctic Peninsula to simulate and verify the physical environmental characteristics of the target sea area. By constructing a coupled ocean circulation-sea ice-species distribution model, integrating multi-source data, and analyzing the synergistic driving mechanisms of krill resource changes through multi-scale physical processes, this method can more accurately simulate and predict changes in Antarctic krill fishery grounds than traditional methods, effectively addressing the problem of inaccurate predictions caused by the limited spatiotemporal coverage of observational data.

The invention relates to the technical field of ecological environment protection, in particular to a multi-dimensional evaluation method for a connectivity function of a land directed ecological network, which comprises the following steps: collecting species distribution data and environment variable data of a target area to measure land inhabitation suitability; screening land habitat plaques of the target area according to the land habitat suitability; identifying a species migration corridor according to the land region species migration resistance surface and the land region habitat plaque; establishing a land directed ecological network according to the land habitat plaques and the species migration galleries; an improved multi-centrality algorithm is adopted to measure the directed degree centrality, the directed adjacent centrality and the directed intermediary centrality of the directed ecological network, and then the ecological node comprehensive centrality and the ecological connection edge comprehensive centrality of the land area directed ecological network are evaluated. Therefore, the problem that the importance of each component of the ecological network has an omission risk due to the fact that the existing connectivity assessment based on the undirected ecological network does not consider the interaction demand of the ecological flow is solved.

The invention discloses an intelligent forest wild animal monitoring method and system based on multi-source data fusion, and relates to the field of wild animal monitoring and ecological protection, and the method constructs an integrated monitoring system by arranging a ground infrared camera network and an unmanned aerial vehicle multi-sensor inspection platform and accessing satellite remote sensing data. And the system analyzes infrared data in real time by using the edge computing node and triggers emergency response of the unmanned aerial vehicle, so that quick tracking of key events is realized. Pixel-level space-time alignment and cross-modal feature fusion are performed on multi-source image data, and a target detection model is input, so that species and behaviors can be accurately identified. Meanwhile, after the multispectral data and the geographical environment data are subjected to gridding alignment, the data are input into a habitat analysis model, and a habitat suitability probability graph is generated. And automatically generating protection decision support information through comprehensive correlation analysis of species distribution and habitat quality. According to the invention, all-weather, full-space and intelligent wild animal monitoring and protection response are realized.

The invention provides a marine ecological evaluation system and method based on big data analysis, and the method comprises the steps: obtaining marine biological diversity data and image data of a marine monitoring region, the marine biological diversity data comprising historical species distribution data and ecological niche indexes; performing streaming processing on the image data by using an edge computing architecture to obtain a biological activity feature map; according to the spatial resolution of the biological activity feature map and historical species distribution data, grid division is performed on the marine monitoring area, a reference species distribution density value of each grid unit is generated, and meanwhile, a species distribution density value of each grid unit is calculated; the negative deviation between the species distribution density value of each grid unit and the corresponding reference species distribution density value is calculated, and when the negative deviation is larger than a preset extinction threshold value, the corresponding grid unit is determined as a species distribution density attenuation area; according to the technical scheme provided by the invention, dynamic evaluation and early warning of the health condition of the marine ecosystem are realized.

The application discloses a coastal zone planning method and system based on MaXent and hotspot analysis, and relates to the technical field of coastal zone planning. The method comprises the following steps: S1, collecting environmental variable data, species distribution data and development intensity data, constructing a unified space-time grid system, and realizing time series expression of the data through data preprocessing; S2, constructing a quarterly time series maximum entropy model, time series evaluating habitat states of each grid, and forming habitat evolution types; S3, identifying time series significant hotspots and hotspot persistence and intensity characteristics, constructing a comprehensive representation of ecological development relationship, and identifying ecological development conflict types; and S4, spatially superimposing the habitat evolution types and the ecological development conflict types, forming adaptive access rules, and generating differentiated planning suggestions according to regions. The problems that the long-term dependence of coastal zone habitat suitability and development intensity on static time point data and the inability to identify time series evolution rules result in the lack of dynamic adaptability of planning are solved.

This invention provides a method and system for assessing the impact of construction disturbance on biodiversity in nature reserves, relating to the field of computer technology. The method includes: acquiring species distribution data, construction disturbance data, and environmental data within the nature reserve; constructing an ecological association network based on the species distribution data to obtain a species interaction network; extracting network structure features from the species interaction network to obtain a set of key species; modeling the disturbance propagation dynamics based on the set of key species and the construction disturbance data to obtain an impact propagation path map; predicting biodiversity responses based on the impact propagation path map and environmental data to obtain trends in changes in biological community composition; and conducting a comprehensive impact assessment based on these trends, outputting an impact level index by comparing the degree of deviation from historical baseline states. This invention effectively improves the overall accuracy and predictive precision of the assessment of the impact of construction disturbance on biodiversity in nature reserves.

The application discloses a kind of urban bird species distribution prediction method and system based on deep learning, it is related to the technical field of ecology and landscape architecture, by realizing the unified modeling of environmental characteristics, species co-occurrence relationship and spatial proximity information;Through environmental characteristic intelligent weighting and enhancement mechanism and multi-task joint learning strategy, the expression ability of model to ecological correlation structure is improved;Combined with species richness stratified evaluation and calibration mechanism, the reliability of the prediction result of uninvestigated area is enhanced;Form the automation technical process from data processing, model training to regional species richness output, can provide efficient, systematic technical support for urban biodiversity assessment and ecological planning.

The present invention belongs to the technical field of field species monitoring, and specifically relates to a field monitoring system for studying the impact of climate change on species distribution, including at least one monitoring station and a cloud platform; the monitoring station includes a first observation terminal and a second observation terminal connected in communication; wherein, when the climate data monitored in real time by the first observation terminal meets a pre-set first interval, a trigger sends a first switching signal; the second observation terminal receives and responds to the first switching signal, and then switches the observation mode and adjusts the observation height to capture the distribution change data of the species; the cloud platform is used to receive meteorological data and species distribution change data from each monitoring station, and analyze the impact of climate change on species distribution. The present invention not only achieves the goal of high efficiency and energy saving through the precise synchronization of the switching signal and the close cooperation of the observation terminal, but also greatly simplifies the operation process, so as to drive away birds, clean up and adjust the observation height of species distribution data, etc., and improve the overall work efficiency.