496 results about "Land use" patented technology

The invention relates to the technical field of space planning, and discloses a territorial space planning multi-source data fusion processing method and system, and the method comprises the steps: carrying out the format analysis and semantic annotation of multi-source territorial space data, and obtaining the standard territorial space data of territorial planning; performing hierarchical feature extraction on the standard territorial spatial data to obtain spatial features and attribute features of territorial planning; performing adaptive feature fusion on the spatial features and the attribute features to obtain a multi-semantic fusion feature map of territorial planning; constructing a spatial constraint rule base of territorial planning based on a use control rule and an ecological protection red line of territorial planning; based on a spatial constraint rule base, resolving conflict pattern spots in the multi-semantic fusion feature map, and performing semantic enhancement on the resolved multi-semantic fusion feature map to obtain an optimized fusion result; performing visual rendering on an optimization fusion result to obtain an auxiliary decision graph; according to the invention, the efficiency of data fusion processing in territorial space can be improved.

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention discloses a flood disaster scene-oriented urban power distribution network toughness evaluation method and system, and belongs to the technical field of power distribution network toughness evaluation, and the method comprises the steps: obtaining rainfall data, land utilization data, elevation data and the like, and inputting the data into a waterlogging simulation module; the waterlogging simulation module simulates rainfall runoff production and runoff convergence to generate ponding distribution; the disaster judgment module obtains the fault probability of the power distribution equipment and the line in combination with the power distribution equipment and line disaster fault model, and generates a system disaster scene under the flood disaster; the load reduction optimization module constructs a power distribution system load reduction optimization model, and takes the minimization of the total load reduction amount of the power distribution system as a target; and the system toughness evaluation module measures the overall toughness of the power distribution system in the flood disaster scene. According to the method, the overall toughness level is quantitatively evaluated based on the system load reduction amount, the node load importance degree and the ponding condition are combined to recognize the weak links of the system, reference is provided for differential reinforcement and operation and maintenance strategies, and subsequent waterlogging prevention planning and operation and maintenance management are guided.

The invention discloses an irrigation area ditch drainage-farmland waterlogging simulation evaluation method. The method comprises the following steps: S1, obtaining meteorological and hydrological data, DEM data, drainage facility vector data and land utilization classification data of a target area; s2, generalizing a drainage system of the irrigation area, and generating nodes, drainage channels and sub catchment area. Shp files; s3, converting the. Shp file into an. Inp file, constructing a parameterized model, and performing parameter calibration and verification on the model; s4, simulating waterlogging conditions under different rainfall and channel siltation situations; s5, extracting overflow node data, and performing flood inundation analysis; and S6, generating a flood risk density map according to the overflow node data. The invention further discloses an irrigation area ditch drainage-farmland waterlogging simulation evaluation system. According to the irrigation area ditch drainage-farmland waterlogging simulation evaluation method and system disclosed by the invention, the problem that ditch drainage simulation and farmland waterlogging risk evaluation cannot be carried out under an irrigation area waterlogging disaster in the prior art is solved.

The invention discloses an urban reserve land intelligent classification method and system based on spatial data fusion, and relates to the technical field of data processing, and the method comprises the steps: carrying out the formalized expression of a land use rule of a preset urban region, and constructing a land domain knowledge graph; collecting multi-source spatial data, performing feature extraction and alignment, and generating a high-dimensional feature vector of each plot; and performing hybrid classification evaluation based on the high-dimensional feature vector of each land parcel, synchronously outputting land use types, development potential levels and land reference value intervals of the land parcels, and calling a land domain knowledge graph to perform compliance verification and correction. The technical problems that an existing urban reserve land classification method depends on artificial experience and lacks accurate and multi-dimensional data fusion and compliance check, so that the accuracy and practicability of a classification result are low are solved, and the purpose that the classification accuracy and practicability of the urban reserve land are improved through multi-source spatial data fusion and an intelligent classification framework is achieved. And the accuracy and practicability of urban reserve land classification are improved.

The invention relates to a land site selection recommendation method and system fusing constraint conditions and an optimization algorithm, and the method comprises the steps: obtaining a constraint feature set, dividing the constraint feature set into strong constraint features and weak constraint features, and constructing a site selection knowledge graph; obtaining project demand information, and screening a strong constraint set and a weak constraint set based on the site selection knowledge graph; obtaining a compliance land use information set based on the strong constraint set, extracting fuzzy terms in the weak constraint set, and generating auxiliary decision suggestions based on the site selection knowledge graph; solving the compliance land use information set and the weak constraint set, generating a site selection recommendation solution set, and outputting the site selection recommendation solution set to the user side; according to the method, the planning provisions and the spatial data are automatically analyzed through the multi-channel feature extraction model, the site selection knowledge graph is constructed to achieve intelligent classification of constraint conditions, the recommendation solution set is generated in combination with the multi-target optimization algorithm, and the method has the advantages that the automatic processing degree of the complex planning provisions is improved, and multi-target optimization decision making is achieved.

The invention provides a coastal wetland ecological corridor identification and optimization method and system, and relates to the technical field of ecological environment monitoring. The method comprises the following steps: integrating optical, radar, terrain, night light and road data on an earth engine cloud platform, and unifying time and space references to form an initial data set; extracting a normalized vegetation index and a vegetation coverage degree, and combining with a water body frequency to identify an ecological source land; generating a comprehensive resistance surface according to the land utilization and coverage type and the road distance; and performing minimum cost path and circuit connectivity analysis in a geographic information system to obtain potential galleries, key nodes and barrier regions, and outputting gallery grades and optimization suggestions in a grading manner according to a cost threshold, thereby realizing high-precision identification and optimization of the coastal wetland ecological network.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention relates to an unbalanced data-oriented electric power system lightning disaster risk detection method, which comprises the following steps of: S1, gridding a target detection land parcel to obtain a plurality of grid units; S2, based on latitude and longitude coordinates of each grid unit, obtaining line parameters and environment characteristics of each grid unit in combination with a line database and a geographic information base, wherein the line parameters comprise tower height, span, grounding resistance, loop number, lightning arrester number and transformer capacity, and the environment characteristics comprise soil conductivity, building height and land utilization type; and S3, inputting the line parameters and the environment characteristics of each grid unit into a trained first detection model to obtain the thunder and lightning probability and the maximum peak current of each grid unit. Compared with the prior art, the multi-source data such as the tower height, the span, the grounding resistance and the soil conductivity are comprehensively considered, and the risk assessment result has higher regional adaptability and accuracy.

The invention relates to the technical field of wind power plant design, in particular to a wind power plant booster station site selection optimization method and system based on a multi-objective genetic algorithm and three-dimensional terrain constraint. The method comprises the following steps: firstly, constructing unified program grid data fused with geographic constraints; establishing a static land use cost model containing an orientation weight by using a rotation convolution algorithm; then, dynamic cost calculation logic of fan-power grid collaborative topology based on candidate coordinates, multi-constraint road dynamic path finding and site earth-rock balance iteration is defined; and finally, constructing a multi-target optimization model of comprehensive multi-dimensional cost, and performing iterative optimization through a genetic algorithm to obtain a Pareto optimal site selection coordinate. Through coupling of geographical constraints, the cost model and the intelligent algorithm, the problem of complex mountain site selection is solved, global collaborative optimization of land, earthwork, road and electrical cost is realized, and scientificity, economy and construction feasibility of wind power plant design are remarkably improved.

The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing driven land utilization monitoring system. According to the invention, by extracting a ground feature boundary contour in a remote sensing image and constructing a morphological parameter set with geometric structure proportion characteristics, a path category data system for dividing a land spatial form based on structural characteristics is established, and normalization processing and a Gaussian mixture model are introduced to realize ground feature aspect ratio clustering. Constructing a structural difference index set in combination with the distribution concentration ratio, clearly identifying the path type to track the land parcel direction or the structure change trajectory, and calculating a disturbance factor in the multi-temporal remote sensing image based on the land type number change and the connection direction number of the patch adjacent region; and the main direction, the boundary contour similarity and the spatial disturbance of the land parcel in the specified period are comprehensively judged, so that the dynamic change identification of the land utilization state is realized, and the land utilization form change amplitude and the time period characteristics can be accurately reflected by setting a judgment reference and a change state marking mechanism.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention relates to an urban non-point source pollution identification method and system based on rainfall events. The method comprises the following steps: step S100, decomposing rainfall data of a continuous time sequence into independent rainfall events and snowfall events; step S200, dividing grid units, and performing runoff calculation in each unit so as to obtain runoff information of spatial distribution; wherein the rainfall event runoff is estimated by adopting a runoff curve hydrological model method; calculating snow melting runoff by adopting a temperature index snow melting model; s300, estimating the average event concentration of the rainfall event by adopting an accumulation and scouring formula, and estimating the average event concentration of the snow melting event by using average observation values in different land utilization types; and step S400, realizing refined identification and accurate estimation of urban non-point source pollution load by integrating urban functional area characteristics, surface runoff simulation and event average concentration simulation of pollutants. The method is suitable for estimating the rainfall runoff pollution load in a large-scale region with relatively limited water quality monitoring data.

The invention discloses a satellite hyperspectral remote sensing image-based adaptive random forest land utilization classification and identification method, which comprises the following steps of: data preprocessing: loading a vector boundary of any predetermined administrative region, the method comprises the following steps: acquiring surface reflectance data of a certain satellite hyperspectral image in any preset time period from a natural resource satellite remote sensing cloud service platform, and preprocessing; making a sample set; parameter definition and remote sensing index selection: calculating a group of representative remote sensing indexes by using a specific wave band combination of the hyperspectral image; a remote sensing index and an original wave band are used as input feature vectors together to form a complete feature space, and in the training process of the random forest, part of features are randomly extracted from the feature space during construction of each decision tree for node division; performing cross validation and parameter tuning; the visualization of the optimal parameter result is selected; according to the invention, adaptive parameter tuning based on random forest algorithm classification is realized.

The invention discloses a mining area surface environment monitoring method, aiming at the current situation of surface ecological environment damage and prevention of a plain mining area, and researching a mining area dynamic change monitoring benchmark; for surface movement and deformation caused by mining area disturbance, a D-InSAR technology is applied to perform surface subsidence monitoring on a deformation block. According to the invention, technical means such as InSAR, GPS, remote sensing satellite images and the like are comprehensively utilized to obtain multi-source data of the mining area surface environment, key technologies of integration and application of the multi-source monitoring data are researched, a mining area surface environment multi-source monitoring technical system is established, and on the basis of multi-source high-resolution remote sensing data, technical means such as image processing, mode recognition and the like are adopted, so that the mining area surface environment multi-source monitoring system is established. Land utilization changes and ecological influence conditions are studied, InSAR and GNSS technologies are applied to perform high-precision monitoring analysis research, the overall monitoring capability, reliability and precision are improved, the technical defects of single technologies for mining area geological environment monitoring are overcome, and the mining area geological environment monitoring system has wide popularization and application prospects.

The invention discloses an urban heat island intensity prediction method based on a physical mechanism guided recurrent neural network. The method comprises the following steps: S1, constructing a multi-source fusion data set including land utilization cover data and meteorological data; and S2, constructing a PGRNN fusion model fusing a recurrent neural network RNN and a long short-term memory network LSTM, inputting the urban heat island intensity sequence data or the multi-source fusion data set and the urban heat island intensity matching combination sequence data into the PGRNN fusion model to carry out prediction training, and constructing an urban heat island intensity prediction expression to carry out heat island intensity prediction processing. The PGRNN fusion model realizes long-time-sequence prediction training of a multi-source fusion data set and urban heat island intensity, a physical guidance mechanism taking urban canopy energy balance as a core is creatively introduced and constructed in the PGRNN fusion model, and an urban canopy energy balance physical rule plays a key constraint role in a prediction process of long-time-sequence data; and the consistency and the precision of the urban heat island intensity under a long time sequence are improved.

The invention relates to the technical field of data measurement and calculation, in particular to an ecological system carbon source sink rapid calculation method for a mining area scale. The method comprises the following steps: acquiring mining area remote sensing image data, ground actual measurement data and mining area mining activity records, and constructing a multi-source mining area basic data set; performing spatial registration and time alignment on the multi-source mining area basic data set to obtain an aligned multi-source fusion data set; based on the aligned multi-source fusion data set, mine occupied map spots are identified, land utilization type information of the map spots is extracted, a land utilization type data set is formed, and a carbon library classification data set is generated in combination with mining area carbon library type definition; therefore, by integrating multi-source data, optimizing space and time alignment and refining the carbon reserve calculation process, the problems of data dispersion, inaccurate calculation and the like in a traditional method are solved, and the scientificity and reliability of carbon source sink measurement and calculation of the mining area ecosystem are improved.

The utility model discloses a road slope earth shoulder drainage system which comprises a water outlet piece, an earth shoulder vertical curb water blocking belt, a transverse drainage pipe assembly, a longitudinal drainage pipe and a base. The water outlet piece and the earth shoulder vertical curb water blocking belt are arranged on the edge of a highway pavement and used for collecting water in a centralized mode. The base is embedded in a side slope; the longitudinal drainage pipe is arranged on the base, and the longitudinal drainage pipe is longitudinally arranged in the advancing direction of a road; one end of the transverse drainage pipe assembly is connected with the water outlet piece and the earth shoulder vertical curb water blocking belt, and the other end of the transverse drainage pipe assembly is connected with the longitudinal drainage pipe. The longitudinal drainage pipe is provided with a plurality of sections, and the plurality of sections of the longitudinal drainage pipe are communicated with one another and finally communicated to a rain sewage outlet point. The drainage ditch is suitable for the conditions that drainage of a highway embankment drainage ditch arranged by utilizing gravity flow is unsmooth, natural water systems on the ground surface are deficient or land use is limited and the like; according to the utility model, fewer water outlet points of a natural water system can be reasonably utilized to form a complete drainage system.

The invention discloses an intelligent land use monitoring system and method based on natural resources, and relates to the technical field of intelligent land use monitoring. The system comprises a multi-source data integration module, a risk source identification module, a risk quantification and early warning module and a dynamic monitoring and slow release module. The multi-source data integration module constructs a space-time reference grid, collects four types of data and establishes a four-dimensional incidence matrix through grid ID anchoring; the risk source identification module generates a violation inducement characteristic spectrum based on the matrix and determines a risk source type; the risk quantification and early warning module calculates an initial risk value and a diffusion probability, and generates a dynamic early warning map and a monitoring priority sequence; and the dynamic monitoring and slow-release module updates data and risk parameters in real time and outputs a slow-release prompt, so that accurate pre-judgment and dynamic supervision of land use risks are realized.

The invention discloses a data layer processing method and system for spatial planning land data arrangement, and relates to the technical field of land data, and the method comprises the following steps: obtaining and collecting a plurality of predefined spatial planning constraint layers; the spatial planning constraint layer comprises a stable cultivated land, a permanent basic farmland, an ecological protection red line and a town development boundary; processing each spatial planning constraint layer to generate a site selection base map database of the temporary land; configuring a land use avoidance rule for each constraint layer of the site selection base map database; the avoidance rule comprises a prohibitive rule and a conditional rule, the prohibitive rule is configured for the layers of the permanent basic farmland and the ecological protection red line, and the conditional rule is configured for the layers of the stable cultivated land and the urban development boundary; responding to a temporary land site selection request of the user; the method has the effect of improving the accuracy and compliance of site selection decision.

The invention discloses a DEM adaptive multi-algorithm fusion lifting method for a high-precision distributed hydrological model, and relates to the field of hydrological model basic data construction. The method comprises the following steps: acquiring original DEM data, land utilization data and soil type data; performing standardization processing on the original DEM data to obtain a unified grid DEM; calculating a basic terrain field based on the unified grid DEM; generating at least one enhanced topographic parameter (such as an enhanced gradient grid or topographic index) through a predetermined optimization pipeline based on the base topographic field; finally, hydrological response units are divided in combination with the at least one enhanced topographic parameter, the land utilization data, and the soil type data. Through the optimization assembly line, the problem that traditional filtering is insufficient in geomorphic feature reservation is solved, and the physical rationality of hydrological response unit division is improved.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.

The invention discloses a remote sensing image land use overrun identification method and system based on deep learning, and the method comprises the steps: obtaining a land use remote sensing image, and carrying out the preprocessing of the land use remote sensing image, so as to obtain a to-be-identified image pair; performing feature extraction and matching on a picture pair needing to be recognized by using a pre-trained Siamese-Resnet50 model and a gray-level co-occurrence matrix so as to obtain depth similarity and texture similarity; based on the depth similarity and the texture similarity of the picture pair, performing dynamic weight feature fusion calculation to obtain comprehensive similarity; and comparing the comprehensive similarity with a set judgment threshold, judging whether the land use range of the remote sensing image exceeds the limit or not, and outputting a result. According to the method, the depth similarity, the gray level co-occurrence matrix (GLCM) texture features and the dynamic weight adjustment strategy are combined, high-precision detection and anomaly recognition of the land use range are achieved, the problem that ultralimit recognition is difficult is solved, and the recognition efficiency is improved.

The invention provides a soil carbon reserve measuring and calculating method and system and a medium, and belongs to the technical field of organic carbon reserves, and the method comprises the following steps: obtaining surface layer reflectivity spectral data of each layer of soil after construction of a disturbance area; dividing a plurality of grids based on the spatial distribution characteristics of the reflectivity spectrum; according to the coordinates of the center position of each grid, the soil texture and land utilization type corresponding to each grid in the database are called, and according to the reflectivity spectral characteristics of each grid, the soil organic carbon content on each grid is inverted through a pre-trained organic carbon content estimation model; according to the center position coordinate of each grid, calling the corresponding soil bulk density on each grid in the database, and determining the soil organic carbon reserve on each grid based on the soil bulk density and the soil organic carbon content; wherein the database stores soil texture, volume weight and land utilization type data corresponding to different coordinates; and obtaining the soil organic carbon reserve of the disturbance area according to the soil organic carbon reserve of each grid. The method can efficiently calculate the soil carbon reserve in the field.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention belongs to the technical field of geographic information science, and particularly relates to a Markov-FLUS model-based feedback land utilization change simulation method and system, and the method comprises the steps: firstly predicting an initial area target vector based on historical data and a Markov chain, and inputting the initial area target vector into a FLUS model to carry out the first space distribution simulation; by calculating the achievement degree of the simulation result and the area target, the space conflict type which cannot be achieved due to the space constraint is identified, the area target vector is dynamically corrected according to the space conflict type, and the corrected target is fed back to the FLUS model for iterative simulation until the result is converged. According to the method, the technical problem of disjunction of area prediction and spatial layout in a traditional coupling model is effectively solved, and the scientificity and accuracy of land utilization change simulation are remarkably improved.