32 results about "Terrestrial ecosystem" patented technology

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention relates to the technical field of ecology and hydrology, in particular to a terrestrial ecosystem water carbon flux estimation method considering water balance constraint, which comprises the following steps: estimating leaf scale photosynthetic rate and stomatal conductance by utilizing a photosynthesis model; the leaf scale photosynthetic rate and the stomatal conductance are upscaled into canopy vegetation total primary productivity and canopy conductance to construct an evapotranspiration model to estimate evapotranspiration data; constructing a water balance model according to the evapotranspiration data so as to estimate the available water amount of the soil; constructing a soil moisture stress function according to the available water amount of the soil so as to constrain the estimation process of the photosynthesis model and the evapotranspiration model; and acquiring historical water-carbon flux data and historical remote sensing observation data of a long sequence daily scale, and calibrating the model to obtain a water-balance-constraint-considered water-carbon flux estimation model of the terrestrial ecosystem capable of estimating the water-carbon flux data. Therefore, the problems that an existing water carbon flux prediction frame is difficult to adapt to climate changes, low in prediction accuracy and the like are solved.

The invention relates to a numerical mode simulation method for CO2 flux of high-resolution rice, wheat and corn, which comprises the following steps: calculating a vegetation index and a land surface water index, and carrying out inversion to obtain key vegetation parameters of a land ecosystem; the meteorological data and the topographic data are processed, meteorological field initial and boundary condition files are generated, and CO2 chemical initial and boundary condition files are generated; integrating the gridded artificial source CO2 emission data file; applying the optimized photosynthetic and respiratory action parameters to a photosynthetic and respiratory action calculation formula of the terrestrial ecosystem, and constructing a CO2 flux simulation system of the crop system; outputting the CO2 flux of the rice, the wheat and the corn in the simulation area. The method has the advantages that CO2 flux seasonal change characteristics in the key phenological period in the growth process of rice, wheat and corn are simulated, carbon cycle characteristics in the growth process of crops can be better reflected, and more reliable data support is provided for ecological meteorological research.

The invention discloses a method for analyzing sensitivity of terrestrial ecosystem carbon cycle to drought, and relates to the field of carbon neutralizer.The method comprises the steps that terrestrial ecological data are collected and preprocessed, a standardized soil humidity index is calculated, and a drought event is recognized through a three-dimensional clustering method; drought characteristics are extracted, the correlation degree of the standardized land ecological data and the drought characteristics is calculated by using a Pearson's correlation method, and importance degree sorting is performed on the standardized land ecological data in combination with an interpretable machine learning method to obtain main climate driving factors; analyzing the influence degree of the drought characteristics on the change trend of the main climate driving factors by using a multi-source linear regression method; and analyzing the influence degree of the main climate-driven factors on the carbon cycle during the drought period by using the partial correlation coefficient between the main climate-driven factors and the carbon flux. The invention discloses the change rule of the land carbon sink driven by different climates.

The invention relates to a digital twin construction method and system of a terrestrial ecosystem, and the method comprises the steps: obtaining animal and plant information and environment quality evaluation conditions according to biological and environmental data collected in an ecological protection area; processing animal and plant information, an environment quality evaluation condition, a biological feature vector generated based on biological data, an environment feature vector generated based on environment data and a comprehensive feature vector generated by fusing the two types of vectors based on the evaluation large model to obtain an ecological system service function evaluation result; generating a comprehensive characteristic matrix based on animal and plant information, environment quality evaluation conditions and ecological system service function evaluation results, and performing deep learning on the comprehensive characteristic matrix to construct a digital twinborn engine; and after a digital twinborn large model is constructed based on the digital twinborn engine and the model static substrate information, in response to real-time data input into the digital twinborn large model, rendering a linkage state of species, environment and ecosystem service functions on a visual interface.

The invention relates to the technical field of ecological environment monitoring, and discloses a quantitative identification method for the influence of hydropower development impounding on a terrestrial ecosystem, and the method comprises the following steps: S1, laying a plurality of sampling points along the boundary of a reservoir towards the land direction; s2, collecting an ecological response variable and an environmental factor variable of each sampling point and a horizontal distance from the sampling point to a reservoir boundary as water storage influence variables, and constructing the collected data into a data matrix; s3, taking the data matrix as a response variable; s4, introducing an environment factor variable as a control variable based on a sorting analysis result; and S5, performing variance decomposition analysis based on a partial sorting analysis result, and calculating an independent explanation variance and a total explanation variance of the water storage influence variable. The land space gradient sampling points are arranged based on the water storage boundary, the spatial propagation characteristics of the water storage influence on the land are collected in a covering mode, and the effects of enhancing the representativeness of the sampling points and ensuring the capture integrity of the ecological gradient are achieved.

The invention discloses a knowledge-guided and process-driven forest soil layered carbon sink simulation system, which comprises the following steps of: screening a continuous and complete soil profile according to forest soil organic carbon layered observation data; optimizing process model parameters of the terrestrial ecosystem at each measuring point by adopting a Bayesian data assimilation technology; processing the current vegetation terrain soil attribute data to obtain multi-modal standardized data as an input variable of a random forest model, performing heterogeneity learning and scale expansion on optimization parameters by using the random forest model, and simulating the organic carbon reserve of the current layered soil based on steady-state hypothesis and external driving data of a lattice point scale; and based on an instantaneous hypothesis, generating a future forest soil layered carbon sink simulation result by taking scale extension parameters and future external driving data as driving and combining current layered soil organic carbon reserves, so as to realize accurate, digital and intelligent monitoring and evaluation of the forest soil carbon sink capacity.

The application provides a land ecosystem gross primary productivity determination method and system, and the method comprises the following steps: collecting historical eco-hydro-meteorological data and measured values of land ecosystem gross primary productivity in a research area; constructing a generalized water-energy complementary gross primary productivity model, wherein the generalized water-energy complementary gross primary productivity model comprises a comprehensive coordination index, an energy limitation correction parameter and an available water amount correction parameter, the water amount limitation reference productivity and the energy limitation reference productivity are nonlinearly coupled by the comprehensive coordination index, the energy limitation correction parameter and the available water amount correction parameter; calibrating the comprehensive coordination index, the energy limitation correction parameter and the available water amount correction parameter; inputting the to-be-calculated eco-hydro-meteorological data into the generalized water-energy complementary gross primary productivity model after parameter calibration, and obtaining the target land ecosystem gross primary productivity. The application improves the estimation accuracy of the gross primary productivity.

The invention discloses a terrestrial ecosystem carbon monitoring satellite laser-assisted block adjustment method, which comprises the following steps of: three-dimensional image connection point generation: constructing a multi-stage pyramid for forward-looking and backward-looking 19-degree panchromatic images, and carrying out layer-by-layer matching inspection to obtain intra-scene and inter-scene connection points; free net adjustment: taking the connection point as control, gradually tightening a threshold and eliminating gross errors to obtain free net adjustment RPC irrelevant to absolute elevation; preparing a laser control point; laser-three-dimensional combined adjustment: taking the laser control point and the three-dimensional image connection point as observation values together, and carrying out forward intersection to obtain a corrected intersection control point; and final RPC determination: revising the image RPC again by using the revised intersection control point, and obtaining a final adjustment parameter after iterative convergence. According to the method, combined refinement of the free network and the laser absolute elevation is achieved through three-dimensional connection point hierarchical optimization, laser elevation replacement and distance reciprocal weighted coupling correction, and the geometric precision and reliability of block adjustment are remarkably improved.

The present application relates to a kind of digital twin construction method and system of terrestrial ecosystem, the method includes: according to the biological and environmental data collected in ecological protection area, obtain plant and animal information and environmental quality assessment situation;Based on the evaluation model, the plant and animal information, environmental quality assessment situation, biological feature vector generated based on biological data, environmental feature vector generated based on environmental data and the comprehensive feature vector generated by the fusion of two kinds of vectors are processed, and the ecosystem service function evaluation result is obtained;Based on plant and animal information, environmental quality assessment situation, ecosystem service function evaluation result generates comprehensive feature matrix, carries out deep learning to the comprehensive feature matrix and constructs digital twin engine;After digital twin engine and model static base information are based on and digital twin big model is constructed, in response to the real-time data of input digital twin big model, the linkage state of species, environment and ecosystem service function is rendered in visual interface.

The present application relates to a kind of carbon flux synchronous measurement experimental equipment, including the monitoring device for studying soil carbon flux and the transparent cylinder for cultivating plant, the middle part of the transparent cylinder is provided with baffle, for separating the ground part and the soil part of plant in two independent chambers, for accommodating the chamber side wall of plant soil part is also provided with liquid supply interface for providing nutrient solution to plant, the baffle is opened with the perforation for facilitating plant to pass through, the two ends of the transparent cylinder are connected on the monitoring hole of monitoring device respectively.The experimental equipment of the present application can be used to monitor the related parameters of soil respiration under ground, and also can be used to monitor the carbon flux variation parameters above ground, and at the same time, two groups of monitoring data above ground and underground are obtained, which is beneficial to further study the relationship of soil respiration, and has important significance for promoting the carbon flux research of terrestrial ecosystem.

The application provides a method for monitoring and evaluating the service function of a terrestrial ecosystem, which comprises: generating biological feature vectors and environmental feature vectors according to biological data and environmental data collected in an ecological protection zone; performing species identification on the biological feature vectors based on a biological large model and performing environmental analysis on the environmental feature vectors based on an environmental large model to obtain information about animals and plants and an environmental quality evaluation situation in the ecological protection zone; and performing comprehensive reasoning analysis on the biological, environmental and comprehensive feature vectors and the information about animals and plants and the environmental quality evaluation situation in the ecological protection zone based on an ecosystem evaluation model to evaluate the service function of the ecosystem of the ecological protection zone, wherein the comprehensive feature vectors are determined based on the fusion of the biological and environmental feature vectors and dynamically adjusted weights. The application can comprehensively understand the overall ecological condition of the ecological protection zone by using the large model technology and provide a more comprehensive, accurate, dynamic and scientific evaluation of the service function of the ecosystem.

The invention discloses an ecological carbon sink potential prediction method and system based on a fusion attention mechanism, and relates to the technical field of ecological carbon sink monitoring and image processing, and the method comprises the steps: obtaining a to-be-evaluated scene reflectivity image and meteorological variable data of a to-be-evaluated time period; image local features are extracted from the reflectivity image through CNN, and meteorological variable features are extracted from meteorological variable data through BiLSTM; calculating a meteorological time sequence vector based on an attention mechanism; dynamic weights of the image local features and the meteorological time sequence vector features are calculated, and weight fusion features are obtained through the dynamic weights corresponding to the image local features and the meteorological time sequence vector features; inputting the weight fusion features into a full connection layer for processing, and mapping feature variables to a prediction space to obtain predicted ecological carbon sink data of the scene to be evaluated in the time period to be evaluated; the technical difficulty that the carbon sink change cannot be accurately predicted under the large-range and high-precision practical demand of the current land ecosystem carbon sink estimation is solved.

The fusion foundation multi-source satellite chlorophyll fluorescence regional GPP assimilation inversion system belongs to the technical field of terrestrial ecosystem carbon cycle monitoring, and comprises an observation data acquisition module; a signal unmixing and simulation generation module, which is used for unmixing total fluorescence into pure chlorophyll fluorescence SIF, and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; a state component decoupling module, which is used for decoupling total GPP simulation values into a light response fast component, a slow component and a memory component; a state evolution prediction module, which is used for predicting dynamic evolution of the slow component and the memory component; and an adaptive assimilation inversion module, which is used for taking the slow component and the memory component as state variables, taking SIF observation values as observation data, and combining a prediction result of the state evolution prediction module to perform inversion on regional GPP. The system overcomes systematic deviation caused by model simplification or data singularity in traditional methods.

This invention relates to a method for estimating soil organic carbon sequestration by crop residue return to the field, comprising: acquiring and processing multi-source data; simulating the photosynthetic carbon assimilation and CO2 flux released by respiration of major cereal crops; extracting the net CO2 flux of MCCs (Mechanical Control Cells) in terrestrial ecosystems; calculating the net assimilated carbon allocation of each component of MCCs during their growth cycle; determining the distribution of biomass carbon in different straw management practices; and calculating the total soil organic carbon sequestration by MCC residue return to the field in all grid units based on the local soil organic carbon conversion rate. The beneficial effects of this invention are: by quantifying the allocation of net assimilated carbon to components such as rhizosphere deposition, crop roots and stubble, collectable straw, and grains, this invention provides crucial support for assessing the carbon cycle and soil organic carbon sequestration potential of farmland ecosystems.

The invention relates to the technical field of global terrestrial ecosystem carbon cycle component remote sensing estimation, in particular to a collaborative inversion method, system and device for a global terrestrial ecosystem carbon cycle component, and the method comprises the following steps: obtaining global-scale ground station observation data and image data, and carrying out the preprocessing; establishing a database based on the preprocessed data, performing carbon balance correction and carbon component separation on ground station observation data in the database, and matching the ground station observation data with image data to construct a data set; and training the random forest model by using the data set to obtain a global terrestrial ecosystem carbon cycle component collaborative inversion model, and carrying out product production by using the global terrestrial ecosystem carbon cycle component collaborative inversion model. According to the method, a global terrestrial ecosystem carbon cycle component remote sensing product which simultaneously meets the mass conservation constraint and is high in precision can be provided, improvement of understanding of dynamic changes of terrestrial ecosystem carbon cycles is facilitated, and a solid foundation is provided for global carbon accounting and carbon trading.

The present invention discloses a method for monitoring terrestrial ecosystem carbon sources and sinks based on satellite remote sensing, comprising the following steps: 1) preparing satellite remote sensing data; 2) preparing ground observation data; 3) calculating vegetation cover based on the Normalized Difference Vegetation Index; 4) calculating the carbon density of aboveground and belowground biomass; 5) optimizing and calibrating key algorithm parameters; 6) calculating the autotrophic respiration rate of vegetation; 7) calculating the heterotrophic respiration rate of soil; and 8) characterizing the carbon source and sink of the vegetation ecosystem by net ecosystem carbon exchange. This method addresses the shortcomings of existing estimation methods, such as their inability to monitor over a large area, high cost, and inapplicability to areas with high spatial heterogeneity. By using satellite remote sensing data and ground observation data of a target pixel as input, this method calculates the ecosystem carbon source and sink, representing the carbon exchange between the vegetation ecosystem and the atmosphere in the target pixel. This method offers the advantages of wide monitoring range, high accuracy, and high feasibility.

The invention discloses a land ecosystem carbon source sink assimilation prediction method and device based on adaptive moment estimation, equipment and a medium. The method comprises the following steps: collecting a meteorological data set; the meteorological data set is input into the optimized carbon cycle model to predict the carbon flux, the carbon cycle model comprises a photosynthesis model and a respiratory action model, the photosynthesis model is used for obtaining photosynthesis flux, and the respiratory action model is used for obtaining respiratory action flux; wherein the optimization process of the carbon cycle model comprises the following steps: carrying out data assimilation on model parameters of the carbon cycle model by adopting an adaptive moment estimation algorithm, and constraining carbon flux to obtain an initially optimized carbon cycle model; fitting photosynthesis flux and respiratory flux based on the carbon cycle model of the initial optimization model parameters, and automatically constraining flux sum with poor fitting effect according to the fitting effect to obtain a final optimized carbon cycle model.

The present application relates to the technical field of ecological environment monitoring, and discloses a quantitative identification method for the influence of water storage in water and electricity development on terrestrial ecological system, comprising the following steps: S1, arranging a plurality of sampling points along the reservoir boundary towards the land; S2, collecting the ecological response variable, the environmental factor variable and the horizontal distance from the sampling point to the reservoir boundary as the water storage influence variable for each sampling point respectively, and constructing the collected data into data matrices respectively; S3, taking the data matrices as the response variable; S4, introducing the environmental factor variable as the control variable based on the sorting analysis result; S5, performing variance decomposition analysis based on the partial sorting analysis result, and calculating the independent explained variance and the total explained variance of the water storage influence variable. Through the setting of land space gradient sampling points based on the water storage boundary, the spatial propagation characteristics of the water storage influence on the land are collected in a covering manner, so that the representativeness of the sampling points is enhanced, and the integrity of the ecological gradient is ensured.

The invention relates to the technical field of environmental ecology, in particular to application of a plant-litter-soil continuum in a terrestrial ecosystem. The method comprises the following steps: S1, setting a plurality of standard sample plots in a research area; s2, litter samples and soil samples with different depths in the sample plot are collected; s3, calculating an important value, a community diversity index and a stability index of the plant; s4, evaluating a response mechanism of the plant to soil elements through the homeostatic state model; and S5, disclosing a migration path and a driving mechanism of elements in the plant-litter-soil continuum by utilizing statistical analysis and a structural equation model. According to the application of the plant-litter-soil continuum in the terrestrial ecosystem, provided by the invention, the complex relationship among plant-litter-soil and microbial biomass stoichiometric characteristics is disclosed; the plants, the litters and the soil are closely associated and jointly promote nutrient circulation of the system.

The application discloses a kind of ecological carbon sink potential prediction method and system based on fusion attention mechanism, it is related to ecological carbon sink monitoring and image processing technical field, the method includes: obtaining the reflectivity image of the scene to be evaluated, and the meteorological variable data of the time period to be evaluated;Image local feature is extracted from reflectivity image by CNN, meteorological variable feature is extracted from meteorological variable data by BiLSTM;Meteorological time series vector is calculated based on attention mechanism;The dynamic weight of image local feature and meteorological time series vector feature is calculated, and the weight fusion feature is obtained by the respective dynamic weight;Weight fusion feature is input into fully connected layer and is processed, feature variable is mapped to prediction space to obtain the predicted ecological carbon sink data of the scene to be evaluated in the time period to be evaluated;Solve the technical difficulties that current terrestrial ecosystem carbon sink estimation cannot accurately predict carbon sink change under the real demand of large range and high precision.

The invention discloses a terrestrial ecosystem atmospheric phosphorus sedimentation downscaling method based on space-time quality constraint, and belongs to the technical field of atmospheric science data processing. Comprising a data preprocessing step, a hierarchical sampling strategy-based model initialization step, a mass conservation constraint-based iterative model retraining and optimization step and a spherical high-resolution atmospheric phosphorus settlement data set generation step. The method comprises the following steps of: establishing a unified space reference, generating a unified land mask by taking data with a minimum land coverage range in all data sets as a reference, determining a downscaling target resolution, processing high-resolution auxiliary variable data, unifying all high-resolution auxiliary variables for model training to the target resolution through a resampling method, and obtaining a high-resolution auxiliary variable data set; according to the method, to-be-downscaled low-resolution data is processed, a generated land mask is uniformly applied to all processed auxiliary variable data and target variable data, raster data outside the mask range is removed, and it is ensured that geographic boundaries and effective data ranges of all data sets are completely consistent.

The invention relates to the technical field of carbon neutralization, in particular to a terrestrial ecosystem carbon sink inversion-oriented machine learning assimilation method, which comprises the following steps of: performing long-time sequence atmospheric carbon dioxide concentration numerical simulation to obtain a carbon dioxide concentration time sequence data set; taking carbon dioxide concentration time sequence data as a training set, and adopting a deep learning model to construct an atmospheric carbon dioxide intelligent transmission mode; an atmospheric carbon dioxide intelligent transmission mode is used as an observation operator, a 4DVar algorithm is constructed under a deep learning framework, and a machine learning assimilation model is obtained; and performing carbon sink inversion based on the prior carbon flux data set and the machine learning assimilation model to obtain a posterior carbon sink optimization inversion result. According to the method, an atmospheric carbon dioxide transmission intelligent mode is constructed based on a deep learning model to replace a traditional atmospheric carbon dioxide transmission numerical mode, the 4DVar cost function is optimized under a deep learning framework, and the carbon sink inversion precision and efficiency are improved.

This invention is a demonstration device that combines a multi-level modular frame, a terrestrial ecosystem module, an aquatic ecosystem module, a reservoir, a control panel, an electronic controller, a media humidity sensor, a temperature sensor, a display, a water pump, and a water channel. All three modules are individual containers located at different levels. The pump is connected to the reservoir and supplies water to the terrestrial ecosystem module through channels that are at least partially located in the visible portion of the frame. The controller periodically reads the sensors, displays the data and system status, activates the pump when the humidity is below a lower threshold, maintains the pump state when the value is between the two thresholds, and turns the pump off when the humidity reaches an upper threshold. This arrangement allows the relationship between media conditions, sensor data, controller decisions, and water distribution to be observed in a single demonstration device that can be produced as a learning kit.

The invention discloses a land ecosystem carbon flux prediction method and system based on space-time decomposition. Relates to the technical field of environmental science. The method comprises the following steps: acquiring original carbon flux data and correspondingly matched environment covariable data; decomposing the original carbon flux data into a spatial component sample set I1 and a time component sample set I2; calculating to obtain a feature set S0 and a feature set FT according to the environment covariable data; key factors are screened from the feature set S0 to form a final space input feature set FS, and a space component result O1 is obtained through prediction according to the environment covariable data corresponding to the I1 and the final space input feature set FS; according to the environment covariable data corresponding to the I2 and the feature set FT, predicting to obtain a time component result O2; adding the space component model prediction result O1 and the time component model prediction result O2 to obtain a final carbon cycle estimation value; and predicting by using the estimated value to obtain a carbon cycle space result.

The present invention provides a method and device for obtaining a scaled leaf water use efficiency map, relating to the field of ecological technology. The method for obtaining the leaf water use efficiency map includes: obtaining environmental and ecological data related to plant leaf water use efficiency in a target region during a preset time period; obtaining a carbon isotope fractionation landscape map for the target region during the preset time period based on the environmental and ecological data; and obtaining a leaf water use efficiency map for the target region during the preset time period using a leaf water use efficiency calculation equation based on the carbon isotope fractionation landscape map. The present invention can derive a regional, large-scale carbon-water coupling indicator from in-situ leaf-scale water use efficiency, thereby providing a physiologically significant, large-scale evaluation indicator for carbon-water cycling and coupling research in terrestrial ecosystems.

The present application provides a kind of terrestrial ecosystem aboveground and underground net primary productivity estimation method and system, comprising obtaining vegetation aboveground and underground net primary productivity observation data and resource availability data;According to the aboveground net primary productivity observation data, the comprehensive rate framework parameter is calibrated, and the aboveground net primary productivity simulation value is estimated based on the comprehensive rate framework parameter;Based on the vegetation aboveground and underground net primary productivity observation data, the aboveground and underground net primary productivity distribution proportion is estimated;The aboveground net primary productivity simulation value obtained based on the aboveground and underground net primary productivity distribution proportion is used to estimate the underground net primary productivity simulation value.The present application has good applicability;It can be used to simulate the temporal and spatial distribution law of vegetation productivity, stably evaluate the evolution dynamic of vegetation net primary productivity, help to evaluate global carbon model, further accurately evaluate regional carbon balance level, and deepen the understanding of complex dynamic of global carbon balance under changing climate conditions.

The invention discloses a terrestrial ecosystem carbon monitoring satellite three-vision stereo image construction and dense matching DSM acquisition method, which comprises the following steps: carrying out 5 * 5 block SIFT registration and RANSAC gross error removal on 19-degree foresight, rearview panchromatic and multispectral images, establishing an eight-parameter affine model, carrying out bicubic convolution resampling, and then carrying out DDS (Dense Scale Invariant Feature Transform) calculation on the DDS; multiplicative fusion is carried out in a W * W window according to a red-green-blue three-waveband linear fitting coefficient, and a 3.5 m four-waveband fusion image is generated; converting the four-band DN values of the front-view, front-view and rear-view fused image into a single band in a geometric average manner, and binding a corresponding RPC (Remote Procedure Call); initializing the equal-height layer grid; constructing n layers of 2-time resolution decreasing pyramids; from the top layer to the bottom layer, elevation searching is carried out at 1.5 times of grid step length, three-view images are projected through RPC, the minimum cost of 5 * 5 window correlation coefficients is taken, SGM is used for aggregating the optimal elevation, and DSM is refined and output layer by layer. According to the method, the consistency of the resolution and the spectrum of the three-view image of the carbon monitoring satellite is realized, the DSM precision and efficiency are improved, and the method is suitable for inversion of forest canopy height and carbon reserves.

The invention belongs to the technical field of bank slope ecological restoration, and particularly discloses a riverway hardened slope protection in-situ ecological restoration system which is characterized in that on the basis that an original hard bank protection slope is not damaged, steel bar square concrete lattices are laid on the hard bank protection slope, BSC biological machine-made concrete and planting soil are sequentially laid in the steel bar square concrete lattices from bottom to top, and the BSC biological machine-made concrete and the planting soil are sequentially laid in the hard bank protection slope; a grass blanket is planted on the planting soil; meanwhile, reinforced concrete connecting beams are further arranged at the bottoms of the square reinforced concrete lattices, n-shaped concrete lattices are arranged on the reinforced concrete connecting beams along the sides close to the riverway, backflow steps are arranged in the n-shaped concrete lattices, plants are sequentially planted on the backflow steps from the highest step to the lower step, and water and soil loss is prevented; meanwhile, a suitable living and inhabiting environment is built for aquatic plants, aquatic animals, waterfowls, amphibians and the like, conditions are provided for substance, energy and information exchange of a river channel and a land ecosystem, and the overall restoration capacity of the ecosystem is improved.

The invention relates to a method for estimating the organic carbon sequestration amount of crop residue returning soil. The method comprises the following steps: acquiring and processing multi-source data; simulating photosynthesis carbon assimilation quantity of main grain crops and CO2 flux released by respiration; extracting the net CO2 flux of MCCs crop types in the terrestrial ecosystem; calculating the net assimilation carbon distribution amount of each component of the MCCs in the growth cycle; determining distribution conditions of biomass carbon in different straw management practices; according to the local soil organic carbon conversion rate, the total organic carbon sequestration amount of the MCCs residue returning soil of all the grid units is calculated. The method has the beneficial effects that by quantifying the deposition of net assimilation carbon to rhizosphere, crop roots and stubbles, and the distribution amount of components such as straws and grains, key support is provided for carbon circulation of a farmland ecosystem and soil organic carbon sequestration potential evaluation.