13 results about "Remote sensing reflectance" patented technology

This invention discloses a method, system, and equipment for monitoring the water environment of drinking water sources, belonging to the field of environmental monitoring technology. The method includes acquiring remote sensing image data, water quality monitoring data, and water spectral data; preprocessing the remote sensing image data to obtain the water surface remote sensing reflectance data at each sampling point in the target water area; preprocessing the water quality monitoring data and water spectral data to obtain sensitive bands; constructing a water quality parameter inversion model, which includes an XGBoost learner, an SVM learner, a RF learner, and a BPNN learner; training the water quality parameter inversion model using a training dataset and validating it using a test dataset; and inputting the water surface remote sensing reflectance data and the sensitive bands into the water quality parameter inversion model to obtain water environment monitoring data. This invention solves the technical problem of integrated air-space-ground drinking water source water environment monitoring.

This invention relates to the field of forestry resource monitoring technology, and discloses a dynamic monitoring system and method for forestry resources based on remote sensing imagery. The system includes: generating temporal remote sensing reflectance data within the same grid; generating a continuous canopy occupancy map; generating an infill set; determining the intrinsic neck scale; calculating the topological spectrum intensity of the continuous canopy infill; calculating the interlayer decoupling topological index; generating monitoring judgment results; and outputting change patches. This invention constructs an adaptive multi-scale erosion sequence based on the intrinsic neck scale, combines Eulerian features to quantify the infill topological fragmentation process, and integrates the degree of infill fragmentation with the stability state of the outer envelope through the interlayer decoupling topological index. This achieves accurate determination of change units and precise spatial patch location, avoiding the omissions or misjudgments of deep forest understory structure changes by traditional techniques, ensuring that the monitoring results accurately reflect the actual mechanisms of dynamic changes in forest resources.

The invention relates to the technical field of forestry resource monitoring, and discloses a forestry resource dynamic monitoring system and method based on remote sensing images, and the method comprises the steps: generating same-grid time phase remote sensing reflectivity data; generating a continuous canopy occupancy map; generating an inner hole set; measuring the scale of the intrinsic neck; the continuous canopy inner hole topology spectrum intensity is calculated; calculating an inter-layer decoupling topology index; generating a monitoring judgment result; and outputting the change pattern spots. According to the method, a self-adaptive multi-scale erosion sequence is constructed on the basis of the intrinsic neck scale, the Euler feature is combined to quantify the inner hole topology fragmentation process, the inner hole fragmentation degree and the outer envelope stable state are integrated through the interlayer decoupling topology index, change unit judgment and space pattern spot accurate positioning are achieved, and the method has the advantages of being simple in structure and convenient to operate. And missing detection or misjudgment of the change of the deep structure under the forest in the traditional technology is avoided, so that the monitoring result conforms to the actual mechanism of the dynamic change of forest resources.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.

This invention discloses a method for assessing the impact of reservoir construction on river deoxygenation based on satellite remote sensing. The method includes the following steps: constructing a synchronous observation database of river dissolved oxygen and satellite remote sensing reflectance; training a random forest model and an extreme gradient boosting tree model based on input and output data indicators, and fusing them to construct an integrated inversion model for dissolved oxygen concentration; obtaining publicly available river widths and extracting long-term remote sensing reflectance spectral data located along the river's median line; obtaining engineering characteristic parameters of the target reservoir, driving the integrated inversion model for dissolved oxygen concentration, and reconstructing a long-term dataset of river dissolved oxygen concentration; and calculating the trend of dissolved oxygen concentration changes in two time periods, using the construction time of the target reservoir as a dividing point. This invention uses satellite remote sensing to assess the intensity of the impact of reservoir construction on river deoxygenation processes, providing a reliable and scalable assessment method for quantifying the disturbance and alteration of river ecosystems by human activities.

The present application is to solve the technical problem that most of the existing total suspended particulate matter remote sensing inversion algorithm is constructed on the basis of 550 nm remote sensing reflectance, and the remote sensing reflectance appears double peak characteristics with the increase of total suspended particulate matter concentration, which makes the expression of total suspended particulate matter concentration change of high turbidity water body gradually ineffective, and provides a turbid water total suspended particulate matter inversion method suitable for stationary orbit satellite GOCI. The inversion method takes the in-situ observed remote sensing reflectance and the equivalent remote sensing reflectance calculated by the satellite spectral response function as the input, determines the absorption peak and the two shoulder band positions of the non-absorption baseline of the spectral absorption characteristic curve through the correlation coefficient between the equivalent remote sensing reflectance, the first derivative, the second derivative and the total suspended particulate matter concentration, fits the linear relationship between the spectral absorption index and the total suspended particulate matter based on the spectral absorption index, and finally establishes the remote sensing inversion algorithm of the total suspended particulate matter suitable for the stationary orbit water color satellite of the high turbidity water body.

This invention belongs to the field of water quality parameter inversion technology, specifically a machine learning-based remote sensing inversion method and system for suspended particulate matter (SPM) concentration. The method includes: acquiring satellite remote sensing imagery and synchronously measured SPM concentration data; obtaining a spatially gridded water body remote sensing reflectance matrix through radiometric calibration, atmospheric correction, and water body masking; denoising through wavelet decomposition reconstruction; and obtaining a spectral numerical sequence through spectral normalization and standardization. Based on this sequence, a multi-scale band combination and differential features are constructed, and sensitive features are selected using XGBoost. Physical constraints are constructed by combining sensitive features with water body radiative transfer laws, and intermediate inversion results are obtained through numerical iteration. Deep learning residuals are used to compensate for biases in the intermediate results, and finally, through spatial smoothing, consistency verification, and high-concentration saturation optimization, a high-precision, spatially continuous spatial distribution result of SPM concentration is obtained. This invention achieves efficient feature mining and deep integration of physical mechanisms, significantly improving the model's interpretability and generalization ability.

The present application relates to a kind of remote sensing methods of large area lake particle organic carbon, comprising: obtaining satellite remote sensing data and calculating its equivalent reflectivity;Based on the equivalent reflectivity, the reflection peak height PH1 of green light band of multiple lakes is calculated;Several lake samples with the highest and lowest PH1 value are taken respectively as turbid water sample and clear water sample;And the correlation model of remote sensing reflectivity and POC concentration is established for turbid water sample and clear water sample;With preset step, the PH1 value threshold range of the multiple lakes is traversed, the multiple segmentation thresholds are used to redivide lake and estimate POC concentration, calculate remote sensing accuracy, with the PH1 value corresponding to the highest remote sensing accuracy as the final water classification threshold;Using the water classification threshold, multiple to-be-measured lakes are divided into turbid water sample and clear water sample, and the parameters of the correlation model are recalibrated, to obtain the final POC concentration estimation model.The method of the present application can realize the synchronous remote sensing of POC of different types of lakes in large area.

The application provides a water body sediment concentration inversion method and device based on satellite remote sensing images, and the method comprises the following steps: pre-processing satellite remote sensing images of a measured water body to generate a remote sensing reflectance above water surface; performing optical classification on the measured water body based on the remote sensing reflectance above water surface to generate a first type of water body, a second type of water body, a third type of water body and a fourth type of water body; performing backscattering coefficient inversion on the remote sensing reflectance above water surface to determine the backscattering coefficient of the first type of water body, the backscattering coefficient of the second type of water body, the backscattering coefficient of the third type of water body and the backscattering coefficient of the fourth type of water body; and performing sediment concentration remote sensing inversion on the backscattering coefficient of the first type of water body, the backscattering coefficient of the second type of water body, the backscattering coefficient of the third type of water body and the backscattering coefficient of the fourth type of water body to generate water body sediment concentration. The method effectively realizes high-precision backscattering coefficient inversion at different reference wave bands, and improves the inversion precision.

The present application relates to the technical field of submerged plant monitoring, and discloses a submerged plant reflectance inversion method, device, equipment and storage medium. In the method, multispectral satellite images of a reference water area are first acquired, and the spatial assignment of the inherent optical properties of the target water area is realized by using a water body optical characteristic processing model and a spatial assignment method, thereby providing stable and reliable optical input for the subsequent physical inversion model, and reducing the difficulty of acquiring in-situ depth measurement data or bottom material spectrum data of the target water area. Then, the water depth distribution data of the target water area is acquired by using laser altimetry satellite data and photon processing and spatial interpolation, thereby providing strong geometric constraints for the model. Finally, the two types of data and remote sensing reflectance images are input into the physical inversion model, the water body influence is accurately stripped by using the physical inversion model, and the final submerged plant reflectance data is obtained, thereby significantly improving the inversion accuracy and stability of the finally acquired submerged plant reflectance.

A water body algae toxin concentration estimation method and system based on remote sensing image belong to the technical field of inland water body eutrophication lake water environment evaluation, solve the technical problems that the existing technology has high precision requirement of detection instrument, expensive equipment material, complex and low efficiency of water sample pretreatment process, and the remote sensing indirect inversion method has poor accuracy and is difficult to realize accurate quantification of microcystin. Step 1, obtain the remote sensing image of the water body, and pretreat to obtain the remote sensing reflectivity data; step 2, calculate the spectral index based on the remote sensing reflectivity data; step 3, construct a water body microcystin concentration remote sensing estimation model, take the spectral index as the input of the water body microcystin concentration remote sensing estimation model, and obtain the water body microcystin concentration. The present application is used for realizing efficient and accurate water body algae toxin concentration estimation.

The application provides a geological disaster full-spectrum remote sensing response simulation method coupling a surface process model and a three-dimensional radiation transfer model, and belongs to the technical field of remote sensing and disaster simulation. The method comprises the following steps: firstly, acquiring a digital elevation model, disaster elements and prior knowledge; secondly, constructing a terrain grid and registering, combining with basin analysis to calculate disaster frequency and erosion deposition, and generating a surface change DEM before and after disaster occurrence; then inputting the change DEM into a three-dimensional radiation transfer model, constructing a multi-temporal scene and inputting component spectral parameters, carrying out full-process simulation, and generating a remote sensing reflectivity image covering visible light to microwave full spectrum; finally, verifying the accuracy by using real data and outputting the result. The application realizes physical coupling of surface dynamics and radiation transfer processes, can effectively simulate the comprehensive remote sensing signals generated by the surface morphology and structure changes caused by disasters, and provides high-fidelity, multi-temporal data support for disaster mechanism research and monitoring and early warning.

The present application relates to the technical field of atmospheric correction, and more particularly to a remote sensing calibration correction bidirectional coupling deep learning method based on reversible neural network. The method comprises the following steps: extracting the apparent radiance pixel sequence of the target water area; constructing an environmental prior data set; using an FT-Transformer network to perform deep heterogeneous feature coding on multi-source auxiliary physical data to generate a physical condition embedding vector; constructing a reversible neural network architecture to establish an equal-dimension mapping channel; performing adaptive modulation on the radiation transmission process of the real physical environment within the reversible network; performing end-to-end bidirectional physical constraint training based on a joint loss function, forcing the network to learn the radiation transmission physical law in the forward direction and compressing the atmospheric disturbance to the latent variable space; and outputting high-precision remote sensing reflectivity in the actual inference stage. The method overcomes the problems of missing physical mechanism, weak generalization ability and low calculation efficiency in the existing atmospheric correction technology for two types of turbid water bodies.