21 results about "Water index" patented technology

The invention relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a black and odorous water body intelligent identification method based on a multi-source remote sensing image, and the method comprises the steps: firstly obtaining a first high-resolution remote sensing image, and extracting a global water body mask through a TransUNet model; secondly, removing non-target plaques based on geometric features and a basic spectrum rule; then, calculating a black and odorous water body index by using the second multispectral remote sensing image, and screening suspected black and odorous water bodies by combining adaptive threshold segmentation; further, performing fine classification confirmation on the suspected target by using a third high-frequency remote sensing image and an Officient Net model; and finally performing fusion output on the identification result. Through multi-source data collaboration and a coarse screening-fine classification multi-stage strategy, the limitation of insufficient spatial-temporal resolution of a single data source is overcome, and the positioning precision and the anti-interference capability of urban black and odorous water body recognition are improved.

The invention provides a mining area surface water area mining influence evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining remote sensing images of a target mining area before and after mining, and carrying out the spectral feature enhancement of the remote sensing images to obtain a normalized water body index; performing rough identification on the surface water area based on the normalized water body index to obtain a suspected water body area; performing fine identification on the suspected water body area to obtain a high-precision surface water area classification result; performing post-processing on the high-precision surface water area classification result; according to the total area of the surface water area before mining and the total area of the surface water area after mining, indexes of the surface water area influenced by mining of the target mining area are calculated, a quantitative evaluation result is output, the spatial integrity and boundary precision of water body recognition are improved, and the defects that a traditional remote sensing method is prone to noise interference and the extraction result is rough are effectively overcome; the evaluation method which is low in cost, automatic, quantitative and high in safety is realized, and powerful decision support is provided for protection of the surface water area of the mining area.

The invention relates to the technical field of data processing, and particularly discloses a method for evaluating mine environment health by using leaching water indexes, which comprises the following steps: acquiring a water quality index sequence after weighted fusion of the pH value and the heavy metal content of mine leaching water; determining a trend residual sequence corresponding to the potential risk interval section; a trend residual error in the trend residual error sequence is fused with the average seepage velocity of the current mine area to generate a comprehensive ecological balance vector; extracting a trend residual error sequence corresponding to the high-risk classification label, and taking the trend of the sequence in a future rated time window as a trend feature prediction sequence; according to trend residual errors in the trend feature prediction sequence, an environmental health condition score value is obtained through calculation; and obtaining a grading result of the current area based on the environmental health condition score value, and forming a processing strategy according to the grading result. According to the method for evaluating the health of the mine environment by using the leaching water index, the problem that the health condition of the mine environment is not accurately evaluated at present is solved.

This invention relates to a method for estimating the chemical oxygen demand (COD) storage of reservoirs over a long time series. The method includes: identifying the reservoir surface area using remote sensing data combined with a normalized water index and a threshold; performing Census X-11 time series decomposition on the identified reservoir surface area to obtain the contribution ratios of seasonal, trend, and irregular components; conducting a Mann-Kendall test on the trend components to determine the year corresponding to the abrupt change in reservoir area; obtaining the average COD concentration of the reservoir over a time series and establishing a correlation fitting model with the year; using the correlation fitting model and the year to estimate the average COD concentration of the reservoir in different years, and multiplying it by the total reservoir capacity to obtain the COD storage of the reservoir in different years. This invention is the first to construct a method for estimating the annual average COD concentration of reservoirs, achieving remote sensing estimation of the total COD storage of reservoirs in different years.

The present application provides a kind of based on environmental parameter adaptive coastline intelligent extraction method and system, method includes: obtaining the remote sensing image data of target area of the coastline to be extracted and its imaging phase tidal level data, and the remote sensing image data is preprocessed;Based on the remote sensing image data after preprocessing, the SSC of target area is calculated by inversion, and the corresponding adaptive water index graph of target area is dynamically selected by water index;Adaptive water index graph is threshold segmented, and preliminary water mask graph is obtained;According to tidal level data, the tidal period of imaging phase is judged, and based on tidal period, preliminary water mask graph is dynamically selected by morphological operation and boundary optimization is carried out, and the water mask graph after optimization is obtained;From the water mask graph after optimization, coastline is extracted and postprocessed, and the final target area coastline is obtained;The present application can realize the high-precision, high-robustness coastline automatic extraction under the complex nearshore dynamic change environment.

The invention relates to the technical field of image processing, in particular to a large-range water area time-space dynamic change monitoring method fusing remote sensing and GIS, and the method comprises the steps: obtaining the hyperspectral remote sensing image data of a to-be-monitored water area in real time; aiming at each moment, constructing a feature vector of each pixel point and a center vector of each area label in the water area to be monitored by combining a normalized water index and a normalized vegetation index of each pixel point through gray difference and texture feature difference between each pixel point and an adjacent pixel point in the hyperspectral remote sensing image data and by combining the normalized water index and the normalized vegetation index of each pixel point; and constructing an energy function of a graph cut algorithm for segmenting the to-be-monitored water area, segmenting the to-be-monitored water area at each moment through the energy function, and carrying out spatial-temporal dynamic change monitoring on the to-be-monitored water area through segmentation results at different moments. The invention aims to improve the accuracy of monitoring the spatial-temporal dynamic change of the water area by improving the accuracy of area segmentation.

The application discloses a kind of ground water level monitoring methods based on multispectral satellite, it is characterized in that, propose a kind of special water gauge, special water gauge is by red, white grid interval distribution composition, combined satellite remote sensing technology and projection amplification method, using near infrared, green band calculation and classification normalized water index, or combined near infrared, infrared and red band to identify water body.Under the satellite image resolution D2×D2, grid D1×D1 on water gauge can be identified and read, water gauge reading is amplified (H / L) times, water level in water tank is obtained by conversion, and then water area water level is obtained.The application can intuitively identify water body, read water gauge reading, convert the identification of satellite image to the identification of vertical size, and make the water level accuracy meet the use requirements, finally achieve the purpose of satellite intuitive monitoring water level.The application method is simple and effective, and has strong adaptability, and it is very easy to use satellite image to intuitively obtain water level data meeting accuracy requirements.

The invention discloses a complex terrain water body extraction method based on an improved normalized difference water body index, and the method comprises the steps: analyzing the spectral difference of a water body and an interference ground feature in a Landsat 8 multi-band image under a complex terrain, and constructing a dynamic inhibition coefficient to optimize a traditional water body index; according to the dynamic inhibition, r1 eliminates high-reflection buildings through a short wave infrared band, r2 eliminates low-reflection mountain shadows based on a green light band, and r3 inhibits ice and snow interference according to the visible light-near infrared average reflectivity. According to the self-adaptive extraction, the inhibition coefficient and the NDWI are combined to generate an RNDWI index, accurate classification of the water body is achieved through threshold segmentation, and finally multi-scene interference is effectively filtered out while the real water body is reserved. According to the invention, the overall precision reaches 98.3% and is improved by 7.5% compared with NDWI; the building misjudgment rate is reduced by 92%; the mountain shadow extraction missing rate is reduced by 85%; the ice and snow mixing rate is close to 0; and multi-scene superposition is supported, and terrain self-adaption is realized through coefficient dynamic combination.

The invention relates to a small water body recognition method based on satellite remote sensing image super-resolution reconstruction, and the method comprises the steps: constructing a pre-sampling diffusion probability super-resolution reconstruction model ED-DDIM of a series pre-sampling super-resolution network pre-EDSR and an improved DDIM diffusion probability model, and introducing a multi-channel image including an NDWI water body index channel for a remote sensing image, a super-resolution reconstruction model is obtained, details in an image can be effectively recovered, the distortion problem is reduced, gradual reconstruction from a low-resolution image to a high-resolution image is achieved through a diffusion iteration denoising mechanism in combination with the multi-scale feature extraction capability of UNet jump connection, and then a pre-trained water body recognition model is applied to improve the recognition accuracy of the water body. A binary classification image of a water body area and a non-water body area of a remote sensing image restored by a multi-channel image under high resolution is obtained, water body area identification is realized, and contributions are made to monitoring in an increasingly changing environment and arid area water resource protection.

The invention provides a drainage basin hydrological toughness evaluation and regulation method based on a Budyko framework, and belongs to the technical field of hydrological water resource management and environment monitoring. Firstly, an improved Budyko framework and a standardized rainfall index (SPI) are adopted to carry out hydrological toughness analysis calculation and mutation analysis research on the Weihe river basin; then, the driving force influencing the hydrological toughness of the watershed and the regulation and control mechanism of the driving force are analyzed by adopting CCM-Geoderator; and finally, constructing a natural scene without human activities through a VIC model, and illuminating the influence of human climate change on hydrological toughness. The new hydrological toughness evaluation method provided by the invention can comprehensively explore the space-time evolution characteristics and driving force of the hydrological toughness in the Weihe River basin, and provides effective support for improving the ecological restorability of the basin and promoting sustainable development.

The present application relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a kind of black and odorous water intelligent identification method based on multi-source remote sensing image, the method first obtains first high-resolution remote sensing image, extracts global water mask using TransUNet model;Second, based on geometric features and basic spectral rules, non-target patches are removed;Then, the black and odorous water index is calculated using the second multispectral remote sensing image, and the suspected black and odorous water is screened by combining adaptive threshold segmentation;Further, the suspected target is finely classified and confirmed using the third high-frequency remote sensing image and Efficient Net model;Finally, the identification results are fused and output.The present application overcomes the limitations of insufficient spatial and temporal resolution of a single data source through multi-source data collaboration and coarse screening-fine classification multi-level strategy, improving the positioning accuracy and anti-interference ability of urban black and odorous water identification.

The invention provides a crop whole-growth-period drought monitoring and early warning method based on an adaptive water shortage index. The method comprises the following specific steps: establishing a regression relationship between a crop coefficient and a leaf area index; calculating the daily potential evapotranspiration; establishing an effective rainfall experience calculation formula, and calculating day-by-day effective rainfall according to the rainfall observed by the meteorological station; constructing adaptive day-by-day-scale crop water shortage indexes considering different crop types and drought-enduring characteristics in different growth stages; setting a crop drought grade division standard based on the adaptive water shortage index, carrying out crop drought identification and grade research and judgment, and achieving the monitoring and early warning of the whole process of crop drought occurrence, development and ending. According to the method, multi-source data such as remote sensing data, meteorological data and crop characteristic data are fused, the problem of water shortage sensitivity difference of different crop types and different growth stages is considered, the adaptive day-by-day scale crop water shortage index is constructed, and the whole drought process of the crop growth stage can be monitored.

The application provides an environmental ecological restoration monitoring device and method, which collects microwave radiation data of a bare ground surface at a sampling point in a desert restoration area from multiple sampling angles through microwave remote sensing; determines an escape coefficient of microwave at each sampling angle according to the roughness and dielectric constant of the soil at the sampling point; determines a linear polarization angle of the soil at the sampling point according to the correlation between all the escape coefficients and all the sampling angles, and then identifies the polarization loss of the microwave remote sensing at the sampling point based on the linear polarization angle; determines a water retention index of the soil at the sampling point according to the soil temperature and the polarization loss at the sampling point; sets other sampling points in the desert restoration area, continues to determine the water retention index of the soil at the other sampling points, and monitors the restoration effect of the desert restoration area according to all the water retention indexes. The application can reduce the error caused by microwave polarization in the process of detecting the water content through microwave remote sensing.

The invention discloses a response analysis method, device, equipment and medium for drought stress to forest fire natural causes, and relates to the field of forest fire cause analysis, and the method comprises the following steps: determining drought stages and annual day ranges corresponding to the drought stages according to precipitation of a forest fire area for many years in history; acquiring a multi-time-sequence remote sensing surface reflectance image in each year day range in different drought stages of each year in history; synthesizing the multi-time-sequence remote sensing surface reflectance images based on the surface water index to obtain a surface water index image; determining a vegetation drought stress trend and a change rate according to the surface water index image; and determining the area proportion of the drought stress forest land in the forest fire area according to the vegetation drought stress trend and the change rate so as to determine the contribution degree of the drought stress to the natural cause of the forest fire. According to the method, the natural cause influence of the drought stress on the forest fire can be accurately quantified, and the scientificity and practicability of analyzing the natural cause influence of the drought stress on the forest fire are improved.

The application discloses a remote sensing image water body and shadow classification method based on a double classification attention network, relates to the technical field of remote sensing image analysis and geographic information processing, and comprises the following steps: preprocessing a Sentinel-2 original image to generate a multi-band composite image with a predetermined resolution; calculating a normalized water index and extracting a brightness channel to respectively generate a water body mask and a shadow mask, constructing a sample set, and dividing the sample set into a training set and a verification set; constructing a double classification attention network model based on the training set, and optimizing and training the model; using the trained double classification attention network model to perform sliding window reasoning on the multi-band composite image, fusing and splicing the predicted binary mask, and outputting a double-band water body / shadow binary mask image. The application can effectively overcome the interference of ice and snow shadow areas on the water body recognition result, and has better robustness and stability.

The present application relates to sewage monitoring technical field, specifically, it is a kind of medical sewage intelligent data terminal based on edge computing.It includes multi-source data fusion unit, edge computing unit and overproof early warning unit, multi-source data fusion unit forms water quality perception network, makes edge computing unit based on water quality perception network constructs the digital image of sewage state, double-index auxiliary verification outputs sewage state, and makes overproof early warning unit according to matching result, generates multi-point early warning signal, the present application quantitatively quantifies the difference of upper and lower layer pollutants, generates the quantitative evaluation index of layered or mixed sewage, matches different processing strategies, is beneficial to the sewage of different stratification for separate treatment, improves sewage treatment effect, and when output uniform dissolution state, then the output data of fusion multiple sewage monitors determines water quality parameter mean, and the mean of the monitoring data of multiple different arrangement points is verified sewage index more accurately.

The present application relates to a kind of island reef shallow water area-oriented remote sensing image quality evaluation method, comprising the following steps: using normalized water index separates island reef underwater and land area;Using relative water transparency and total turbidity of suspended solids extracts island reef shallow water area;Cloud index, cloud shadow index, flare index is used to evaluate the cloud, shadow, flare situation of island reef shallow water area, finally obtain multiple special indexes;Fourth step, with special index and general index (standard deviation, information entropy and definition) assess the image quality of island reef shallow water area.The present application combines remote sensing physical model with traditional image quality evaluation method, effectively solves the problem of insufficient applicability of existing method in island reef shallow water area.Through the establishment of targeted quality evaluation system, the accurate evaluation of effective shallow water area in satellite image is realized, and an automatic, efficient image quality evaluation solution for island reef remote sensing monitoring is provided.

The present invention belongs to the field of global change ecology research. Disclosed are an automated assessment system and method for multi-timescale vegetation water deficit levels. The assessment method comprises: using a data collection module to collect remote sensing monitoring data; inputting data, which is related to multi-timescale vegetation water deficit levels, into a data storage module for storage; invoking a rasterization sub-module, a resampling sub-module and a mask extraction sub-module in a data processing module to perform rasterization, resampling and mask extraction processing on the collected and stored data; then, on the basis of data outputted by the data processing module, invoking a multi-scale standardized precipitation vegetation water demand index (SPVI) calculation sub-module and a multi-timescale vegetation water deficit level grading sub-module in an analysis and assessment module; and finally, outputting multi-timescale vegetation water deficit levels to a display module, so as to achieve automated assessment. The present invention is applicable to different site conditions, enables automated collection and processing, and achieves precise assessment of vegetation water deficit levels based on multi-timescale data.

The application provides a reservoir water body boundary determination method and device and processing equipment, which are used for finely determining a dynamic range of a reservoir water body based on an improved normalized water index and a combination of a maximum likelihood thresholding method. Compared with a single time phase water body extraction method, the method can capture an outer boundary region with the widest geographical position and the longest time range. Compared with a long time series water body extraction method, the method can exclude the influence of a water body bare area, a drawdown zone and a shore zone, and capture a more accurate inner boundary region. Thus, the method can well meet the high-quality identification requirement for a complex reservoir water body.

The invention discloses an engineering human drinking drought evaluation index construction method based on two-factor coupling. Comprising the following steps: calculating monthly water deficit of a human drinking water project; calculating a monthly water deficit sequence of the human drinking water project, carrying out standardization processing on the monthly water deficit sequence and constructing a standardized human drinking water index; calculating an atmospheric evaporation demand index; respectively calculating the human drinking water index and edge distribution; constructing a binary joint distribution function of the human drinking water index sum, selecting an optimal fitting Copula function as a joint distribution function of the two indexes based on the minimum AIC and BIC values, and performing normal standardization on the obtained joint cumulative probability to obtain a novel drought index. According to the method, the human drinking drought assessment method special for the area far away from the stable water source is constructed, and a multi-dimensional coupled drought index model is established by fusing water supply engineering data and atmospheric evaporation requirements so as to accurately identify engineering water shortage risks.

The invention relates to a coastal zone aquatic vegetation identification method, system and device, and a storage medium, and the method comprises the steps: obtaining a multispectral remote sensing image of a target coastal zone region in the same season; classifying the multispectral remote sensing images according to tide level heights to obtain a high tide level image set, a medium tide level image set and a low tide level image set; respectively calculating a normalized vegetation index and a corrected normalized water body index corresponding to each tide level image set; obtaining a vegetation index difference characteristic value and a water body index difference characteristic value according to the normalized vegetation index and the corrected normalized water body index; and combining the vegetation index difference feature value and the water body index difference feature value to obtain an aquatic vegetation identification result of the target coastal zone area. According to the method provided by the invention, the spectrum and the water body characteristics are taken as dual criteria, and the recognition precision and the automatic judgment level of the aquatic vegetation in the complex environment of the coastal zone are remarkably improved.