Method and system for assisting in evaluating grade of green port based on multi-source remote sensing data

Abstract

The invention relates to the technical field of green port grade evaluation, and particularly discloses a method and system for assisting in evaluating the grade of a green port based on multi-source remote sensing data, and the method comprises the steps: determining a port area boundary range and a port storage yard range based on a high-resolution remote sensing image, and limiting a port area; establishing an attribute table based on a geographic information system; identifying a dust suppression measure, a sewage pretreatment / treatment measure and a sound insulation and vibration reduction measure based on a high-resolution remote sensing image; identifying a shoreline utilization degree, a solid waste recycling measure, a harbor district greening measure and an ecological bank protection measure in a harbor district boundary range; identifying emission concentrations of COD, ammonia nitrogen, NOx, and particular contaminants in the defined port area; carrying out score marking on the recognition result in an attribute table to obtain a comprehensive evaluation result; and assisting in evaluating the grade of the green port based on the comprehensive evaluation result. The method achieves the evaluation of the grade of the green port based on remote sensing data, and improves the evaluation efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of green port grade evaluation, and in particular to a method and system for assisting in evaluating the grade of a green port based on multi-source remote sensing data. Background Art

[0002] Green port rating evaluation refers to the process of systematically evaluating the comprehensive performance of ports in terms of environmental protection, resource conservation, energy efficiency, ecological restoration and sustainable development. This evaluation not only helps to improve the green development level of ports, but also promotes the overall transformation and upgrading of the port industry and promotes the construction of a low-carbon, environmentally friendly and efficient modern port system. The importance of green port rating evaluation lies in that it can provide scientific decision-making basis for port managers and guide ports to develop in a greener and more sustainable direction. At the same time, it is also one of the important indicators for measuring the international competitiveness and influence of ports.

[0003] The existing green port rating evaluation index elements mainly include the port enterprise concept, action, management and effect. In the action, management and effect evaluation part, the focus is on the specific indicators of enterprises in carbon emissions, water resource utilization, solid waste treatment, noise and vibration control, ecological protection and restoration, etc. The evaluation data sources of these indicators mainly rely on the internal statistical data of port enterprises and the regulatory data of relevant government departments, such as energy consumption, water resource consumption, waste treatment, etc. In terms of evaluation methods, ratio calculation, proportion analysis, trend prediction and other methods are usually used to quantitatively evaluate various indicators to obtain the green development level of the port. Although the existing green port rating evaluation data sources and methods can reflect the green development status of ports to a certain extent, there are still many shortcomings: (1) Lack of unified standards for data sources; The data source is relatively single, and the existing evaluation methods mainly rely on the statistical and regulatory data reported by traditional enterprises, which lacks real-time, dynamic and comprehensiveness for the port business management departments. On the other hand, the evaluation methods are relatively simple and it is difficult to fully and deeply reflect the complexity and diversity of ports in green development. In addition, the existing evaluation system lacks unified standards and specifications, resulting in low comparability and credibility of the evaluation results.

[0004] (2) It is difficult to complete cross-regional and regional evaluations under the same standards; The existing evaluation system still lacks unified data standards and specifications, resulting in low comparability and credibility of evaluation results; more importantly, the existing data sources and methods are difficult to complete large-scale and cross-regional unified standard evaluation, which limits the widespread application and in-depth development of the green port rating system.

[0005] (3) The workload of collecting and reporting evaluation data is large and cannot be verified; The data of the existing rating system are mostly reported by port enterprises themselves or measured by instruments by relevant departments. It is difficult to complete the evaluation of multiple ports under the same standard scale, and there is a lack of uniformity and comparability. At the same time, both the self-reporting by enterprises and the instrument measurement by relevant departments require a lot of manual work, which not only increases the cost of evaluation, but also affects the efficiency and accuracy of the evaluation.

[0006] (4) The data collection method has a long cycle and cannot meet the real-time requirements; In the data collection process of green port grade evaluation, a significant shortcoming is that the data collection method has a long cycle and is not real-time. Existing data often rely on the voluntary reporting of port enterprises or regular instrument measurements by relevant government departments. These processes are not only time-consuming, but also difficult to reflect the real-time green development status of the port in a timely manner. This lag not only affects the timeliness of the evaluation, but also limits the accuracy of the evaluation results and the effectiveness of decision-making. Therefore, exploring more efficient and real-time data collection methods has become the key to improving the quality of green port grade evaluation. Summary of the invention

[0007] In response to the above problems, the first purpose of the present invention is to provide a method for assisting the evaluation of green port grades based on multi-source remote sensing data. The method integrates and analyzes the green port grade evaluation requirements and the evaluation capabilities of multiple remote sensing data sources, combines relevant image processing and big data technologies, and realizes the evaluation of green port grades based on remote sensing data, thereby improving the evaluation efficiency, accuracy and evaluation capabilities, promoting the widespread application and in-depth development of the green port grade evaluation system, and providing strong guarantees for the construction of a strong transportation country.

[0008] The second object of the present invention is to provide a system for assisting in evaluating the green port level based on multi-source remote sensing data.

[0009] The first technical solution adopted by the present invention is: a method for assisting in evaluating the green port level based on multi-source remote sensing data, comprising the following steps: S100: Acquire high-resolution remote sensing images, and determine the port boundary range, port yard range, and limited port area based on the high-resolution remote sensing images; S200: establishing an attribute table based on a geographic information system; and identifying dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures within the port yard based on the high-resolution remote sensing image, and marking and scoring the identified dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures in the attribute table to obtain a first measure evaluation result; S300: based on the high-resolution remote sensing image, draw and calculate the length of the shoreline within the port area boundary range to obtain the degree of shoreline utilization; and based on the high-resolution remote sensing image, identify solid waste resource utilization measures, port area greening measures and ecological revetment measures within the port area boundary range; and mark and score the shoreline degree and the identified solid waste resource utilization measures, port area greening measures and ecological revetment measures in the attribute table to obtain the second measure evaluation result; S400: Based on the high-resolution remote sensing image, the COD emission concentration, the ammonia nitrogen emission concentration, the NOx emission concentration, and the characteristic pollutant emission concentration are identified in the specified port area; and the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration are scored and annotated in the attribute table to obtain water and gas pollution evaluation results; S500: Summarizing the first measure evaluation result, the second measure evaluation result, and the water and gas pollution evaluation results to obtain a comprehensive evaluation result; and assisting in evaluating the level of the green port based on the comprehensive evaluation result.

[0010] Preferably, the step S100 includes: Based on the high-resolution remote sensing image, the port area boundary range and the port yard range are respectively vectorized by using a geographic information system, and attribute annotation is performed to determine the port area boundary range and the port yard range; Based on the high-resolution remote sensing image, the defined shoreline area is vectorized using a geographic information system to obtain the defined shoreline area; or a multispectral remote sensing image is obtained, and based on the multispectral remote sensing image, a water body index is used to distinguish the water body from the shoreline area, and attribute annotation is performed to obtain the defined shoreline area; A buffer zone analysis is performed based on the defined shoreline area, thereby defining a water buffer zone at a set distance from the port shoreline as a defined port area.

[0011] Preferably, the step S200 includes: Filter out the 0.5m-2m resolution remote sensing images of the current year from the high-resolution remote sensing images, perform radiometric calibration and geometric correction on the 0.5m-2m resolution remote sensing images, and use automated algorithms or GIS vectorization tools combined with visual interpretation methods to respectively identify the wind and dust suppression measures for the port yard, the dust suppression measures for loading and unloading ships, the dust suppression measures for loading and unloading vehicles, and the sound insulation and vibration reduction measures for the yard; The 0.5m-2m resolution remote sensing images of the current year are screened out from the high-resolution remote sensing images, and the sewage pretreatment or treatment measures in the port yard area are identified by using the vectorization tool of GIS combined with the visual interpretation method for the 0.5m-2m resolution remote sensing images.

[0012] Preferably, the degree of shoreline utilization in step S300 is obtained by: The 0.5m-10m resolution remote sensing images of the year are selected from the high-resolution remote sensing images, and the 0.5m-10m resolution remote sensing images are subjected to radiation calibration and geometric correction. The coastline length is drawn and calculated using the vectorization tool of GIS combined with the visual interpretation method to obtain the coastline length; the degree of coastline utilization is calculated based on the coastline length and the throughput data reported by the enterprise.

[0013] Preferably, the step S300 includes: Screening out 0.5m-10m resolution remote sensing images for which no solid waste resource utilization measures have been taken and 0.5m-10m resolution remote sensing images for which solid waste resource utilization measures have been taken from the high-resolution remote sensing images; performing radiometric calibration and geometric correction on the 0.5m-10m resolution remote sensing images for which no solid waste resource utilization measures have been taken and the 0.5m-10m resolution remote sensing images for which solid waste resource utilization measures have been taken, respectively, and identifying solid waste resource utilization measures by using GIS vectorization tools combined with visual interpretation methods; Selecting remote sensing images with a resolution of 0.5m-10m and with red bands and near-infrared bands from the high-resolution remote sensing images, performing radiometric calibration and geometric correction on the remote sensing images, and obtaining vegetation indexes using a ratio method, identifying vegetation areas based on the vegetation indexes, and thereby identifying greening measures for the port area; Based on the limited shoreline area, remote sensing images with a resolution of 0.5m-30m and with green band, red band and near-infrared band are screened out from the high-resolution remote sensing images, radiation calibration and geometric correction are performed on the remote sensing images, and the vegetation index is obtained by the ratio method. Based on the vegetation index, the greening measures of the shoreline area, namely the ecological bank protection measures, are identified.

[0014] Preferably, the identification of COD emission concentration in step S400 includes: Based on the enterprise observation and reporting data, the measured value of COD emission concentration is obtained, and the COD remote sensing inversion model is established by regression analysis of the measured value of COD emission concentration and the spectral value of high-resolution remote sensing image; Based on the spectral value of the high-resolution remote sensing image of the defined port area, the COD remote sensing inversion model is used to obtain the measured value of the COD emission concentration in the defined port area; and based on the measured value of the COD emission concentration in the defined port area, the average value of the COD spatial distribution in the defined port area is calculated to obtain the COD emission concentration identification result.

[0015] Preferably, the identification of ammonia nitrogen emission concentration in step S400 includes: Obtaining the measured value of ammonia nitrogen emission concentration based on the enterprise observation and reporting data; establishing an ammonia nitrogen remote sensing inversion model by regression analysis of the measured value of ammonia nitrogen emission concentration and the high-resolution remote sensing image spectral value; Based on the spectral values ​​of the high-resolution remote sensing image of the specified port area, the ammonia nitrogen remote sensing inversion model is used to obtain the measured value of the ammonia nitrogen emission concentration in the specified port area; and based on the measured value of the ammonia nitrogen emission concentration in the specified port area, the average value of the ammonia nitrogen spatial distribution is calculated to obtain the ammonia nitrogen emission concentration identification result.

[0016] Preferably, the identification of NOx emission concentration and characteristic pollutant emission concentration in step S400 includes: A satellite sensor equipped with a multi-spectral imaging spectrometer capable of measuring greenhouse gases is selected to measure the NOx emissions and characteristic pollutant emissions of the specified port area every day, and the NOx emissions and characteristic pollutant emissions are added up on a monthly basis to generate NOx and characteristic pollutant remote sensing raster data; and based on the NOx and characteristic pollutant remote sensing raster data, the NOx emission concentration and the characteristic pollutant emission concentration are evaluated respectively, so as to obtain NOx emission concentration identification results and characteristic pollutant emission concentration identification results.

[0017] Preferably, the first measure evaluation result, the second measure evaluation result and the water and gas pollution evaluation results are obtained respectively by: The identified dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are marked in the attribute table; and the dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are scored based on the scoring method to obtain a first measure evaluation result; The identified shoreline utilization degree, solid waste resource utilization measures, port area greening measures, and ecological shoreline protection measures are marked in the attribute table; and the shoreline utilization degree, solid waste resource utilization measures, port area greening measures, and ecological shoreline protection measures are scored based on a scoring method to obtain a second measure evaluation result; Obtain COD threshold, ammonia nitrogen emission concentration threshold, NOx emission concentration threshold and characteristic pollutant emission concentration threshold; score based on the COD emission concentration identification result and COD threshold, ammonia nitrogen emission concentration identification result and ammonia nitrogen emission concentration threshold, NOx emission concentration identification result and NOx emission concentration threshold, characteristic pollutant emission concentration identification result and characteristic pollutant emission concentration threshold through a scoring method to obtain COD evaluation results, ammonia nitrogen emission evaluation results, NOx emission evaluation results and characteristic pollutant emission evaluation results; and summarize the COD evaluation results, ammonia nitrogen emission evaluation results, NOx emission evaluation results and characteristic pollutant emission evaluation results to obtain water and gas pollution evaluation results.

[0018] The second technical solution adopted by the present invention is: a system for assisting the evaluation of green port levels based on multi-source remote sensing data, comprising a region limitation module, a first measure identification and evaluation module, a second measure identification and evaluation module, a water body and gas pollution identification and evaluation module, and an auxiliary green port evaluation module; The area definition module is used to obtain high-resolution remote sensing images, and determine the port area boundary range, port yard range and defined port area based on the high-resolution remote sensing images; The first measure identification and evaluation module is used to establish an attribute table based on a geographic information system; and to identify dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures within the scope of the port yard based on the high-resolution remote sensing image, and to mark and score the identified dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures in the attribute table to obtain a first measure evaluation result; The second measure identification and evaluation module is used to draw the length of the shoreline within the port area boundary range based on the high-resolution remote sensing image and calculate it to obtain the degree of shoreline utilization; and to identify solid waste resource utilization measures, port area greening measures and ecological bank protection measures within the port area boundary range based on the high-resolution remote sensing image; and to mark and score the shoreline degree and the identified solid waste resource utilization measures, port area greening measures and ecological bank protection measures in the attribute table to obtain the second measure evaluation result; The water and gas pollution identification and evaluation module is used to identify COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the limited port area based on the high-resolution remote sensing image; and to score and annotate the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the attribute table to obtain water and gas pollution evaluation results; The auxiliary green port evaluation module is used to summarize the first measure evaluation results, the second measure evaluation results and the water and gas pollution evaluation results to obtain a comprehensive evaluation result; and to assist in evaluating the level of the green port based on the comprehensive evaluation result.

[0019] Beneficial effects of the above technical solution: (1) The present invention aims to propose a method for evaluating green port grades based on multi-source remote sensing data, so as to solve the problems of single data source, lack of unified standards and specifications, difficulty in unifying cross-regional and regional evaluation, large workload of data collection and reporting and inability to verify, long cycle and lack of real-time performance of data collection methods, etc. in existing green port grade evaluation. By integrating multiple remote sensing data sources, combining image processing and big data technology, a scientific remote sensing data-green port evaluation model is constructed, and dynamic and comprehensive evaluation of some evaluation indicators of green ports is realized, and evaluation efficiency and accuracy are improved. Significant results have been achieved in practical applications, which are specifically reflected in the following aspects: Improve the real-time and dynamic nature of the evaluation: By adopting high-resolution remote sensing images and real-time data processing technology, the present invention can realize dynamic monitoring and real-time evaluation of green port evaluation indicators, significantly improving the timeliness and accuracy of the evaluation; this helps port managers to identify problems in a timely manner and take corresponding improvement measures, thereby continuously improving the green development level of the port; Enhance the comprehensiveness and accuracy of the evaluation: The present invention combines a variety of remote sensing data sources, including high-resolution remote sensing images, multi-spectral imaging spectrometers, etc., which can comprehensively cover various indicators of green port evaluation; at the same time, by using image processing technology and big data analysis technology, the present invention can achieve accurate evaluation of port environmental protection, energy saving and low carbon, resource and environmental ecological protection, etc., and improve the accuracy and credibility of the evaluation results; Reduce evaluation costs and improve work efficiency: Compared with traditional evaluation methods, this invention reduces the reliance on enterprises' self-reported data and relevant departments' instrument measurements, and reduces the labor costs of data collection and processing. At the same time, through the application of automated algorithms and remote sensing technology, this invention can efficiently process large amounts of data, significantly improving the efficiency of evaluation work.

[0020] Promoting the widespread application and in-depth development of the green port evaluation system: The present invention provides a set of scientific, objective and operational technical processes to assist in the implementation of green port grade evaluation, which will help promote the widespread application and in-depth development of the green port evaluation system; by continuously improving and optimizing the evaluation system, the present invention can provide strong support for the transformation, upgrading and sustainable development of the port industry.

[0021] (2) As an advanced spatial information technology, remote sensing technology has the advantages of fast data acquisition speed, wide coverage, large amount of information and high resolution. The present invention can play an important role by introducing remote sensing technology in the green port grade evaluation. First, remote sensing technology can monitor key indicators such as energy consumption and carbon emissions in the port area in real time, providing near real-time and dynamic data support for the green port grade evaluation. Second, remote sensing technology can comprehensively and accurately obtain relevant information of the port area, providing a scientific basis for the unification of scales and standards for the green port grade evaluation. Finally, remote sensing technology can also be combined with other information technologies to assist the green port grade evaluation system to be more intelligent and improve the accuracy and efficiency of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a method for assisting in evaluating green port grades based on multi-source remote sensing data is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following detailed description of the embodiments of the present invention is further described in detail in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present invention, but cannot be used to limit the scope of the present invention, that is, the present invention is not limited to the preferred embodiments described, and the scope of the present invention is defined by the claims.

[0024] In the description of the present invention, it should be noted that, unless otherwise specified, “plurality” means two or more than two; the terms “first”, “second”, etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; for ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0025] Embodiment 1 like Figure 1 As shown, an embodiment of the present invention provides a method for assisting in evaluating the green port level based on multi-source remote sensing data, comprising the following steps: S100: Acquire high-resolution remote sensing images, and determine the port boundary range, port yard range, and limited port area based on the high-resolution remote sensing images; High-resolution remote sensing images (i.e., multi-source remote sensing data) are obtained through various remote sensing data distribution platforms.

[0026] Based on high-resolution remote sensing images or existing relevant maps, the port area boundary range, port yard range, limited shoreline area and limited port area are vectorized to determine the port boundary range and the evaluation range of relevant indicators; specifically, the following sub-steps are included: S11: Determine the port area boundary: Based on high-resolution remote sensing images or existing relevant maps, use the Geographic Information System (GIS) to vectorize the port area boundary and perform attribute annotation; S12: Determine the scope of the port yard: Based on high-resolution remote sensing images or existing relevant maps, use GIS to vectorize the scope of the port yard and perform attribute annotation; S13: Determine the limited port area: Based on high-resolution remote sensing images or existing relevant maps, use GIS to vectorize the limited shoreline area; or use water body index based on multispectral remote sensing images to distinguish water bodies and shoreline areas, and perform attribute annotation to obtain the limited shoreline area; A buffer zone analysis is performed based on the defined shoreline area, thereby defining a water buffer zone at a set distance (e.g., 1 km) from the port shoreline as a defined port area, and the defined port area is used to limit the spatial scope of analyzing water pollution emissions.

[0027] S200: establishing an attribute table based on a geographic information system; and identifying dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures within the port yard based on the high-resolution remote sensing image, and marking and scoring the identified dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures in the attribute table to obtain a first measure evaluation result; (1) Identification of dust suppression measures; The dust suppression measures for the port yard area identified based on high-resolution remote sensing images include: identification of wind-proof and dust suppression measures for the yard, identification of dust suppression measures for loading and unloading ships, and identification of dust suppression measures for loading and unloading vehicles; 1) Identification of “wind and dust prevention measures such as closed or semi-closed structures or walls, wind and dust prevention nets, shelterbelts, and sprinkler facilities used in the storage yard”: The 0.5m-2m resolution remote sensing images of the year are selected from the high-resolution remote sensing images, and the 0.5m-2m resolution remote sensing images are subjected to radiation calibration and geometric correction. Automated algorithms (such as feature extraction algorithms, target classification algorithms, etc.) or GIS vectorization tools are combined with visual interpretation methods to identify the closed, semi-closed and other structures or walls, wind- and dust-control nets, shelterbelts, sprinkler facilities and other wind- and dust-control measures within the port yard. Among them, the closed, semi-closed and other structures or wall shelterbelts of the yard can be identified only once per evaluation cycle according to the evaluation requirements, and the wind- and dust-control nets and sprinkler facilities can be identified multiple times.

[0028] In order to reduce the amount of data processing and evaluation costs, open source and free high-resolution remote sensing images can be used to complete the following two identifications: (1) For shelterbelts, 10-30m resolution remote sensing images can be used to calculate the vegetation index and identify them based on the vegetation index value range; (2) For wind and dust control nets, 10-30m resolution remote sensing images can be used, and visual interpretation or automated algorithms can be used for identification.

[0029] 2) Identification of "dust suppression measures such as enclosed, water mist, dry fog, and spraying are adopted during loading and unloading": The 0.5m-2m resolution remote sensing images of the year are selected from the high-resolution remote sensing images, and the 0.5m-2m resolution remote sensing images are subjected to radiation calibration and geometric correction. The automated algorithm or GIS vectorization tool combined with the visual interpretation method is used to identify the dust suppression measures such as enclosed, water mist, dry fog, and spraying taken by the loading and unloading ships in the port yard area.

[0030] 3) Identification of "dust suppression measures such as closed, wet (water mist, dry fog or spray) or dry methods for loading and unloading vehicles": The 0.5m-2m resolution remote sensing images of the year are selected from the high-resolution remote sensing images, and the 0.5m-2m resolution remote sensing images are subjected to radiation calibration and geometric correction. The automated algorithm or GIS vectorization tool combined with the visual interpretation method is used to identify the dust suppression measures taken by closed, wet (water mist, dry fog or spray) or dry methods for loading and unloading vehicles in the port yard.

[0031] (2) Identification of wastewater pretreatment / treatment measures; Identification of “taking sewage pre-treatment / treatment measures or connecting the urban pipe network to the port area”: The 0.5m-2m resolution remote sensing images of the current year are screened out from the high-resolution remote sensing images, and the sewage pretreatment or treatment measures in the port yard area are identified by using the vectorization tool of GIS combined with the visual interpretation method for the 0.5m-2m resolution remote sensing images.

[0032] (3) Sound insulation and vibration reduction measures; Identification of "sound insulation and vibration reduction measures such as soundproof enclosures or soundproof barriers": From the high-resolution remote sensing images, the 0.5m-2m resolution remote sensing images of the year are selected, and the 0.5m-2m resolution remote sensing images are subjected to radiation calibration and geometric correction. The automated algorithm or GIS vectorization tool combined with the visual interpretation method is used to identify the port yard range where sound insulation and vibration reduction measures such as sound insulation covers or sound insulation barriers are adopted.

[0033] After the dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are identified respectively, the identified dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are marked in the attribute table; and based on the scoring method in the "Green Port Grade Evaluation Guidelines JTS / T105-4—2020", the dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are scored in the attribute table to obtain the first measure evaluation result; the present invention identifies dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures based on multi-source remote sensing data, and realizes the calculation of pollution prevention and control indicators in the "action" aspect based on multi-source remote sensing data.

[0034] S300: based on the high-resolution remote sensing image, draw and calculate the length of the shoreline within the port area boundary range to obtain the degree of shoreline utilization; and based on the high-resolution remote sensing image, identify solid waste resource utilization measures, port area greening measures and ecological revetment measures within the port area boundary range; and mark and score the shoreline degree and the identified solid waste resource utilization measures, port area greening measures and ecological revetment measures in the attribute table to obtain the second measure evaluation result; Drawing and calculating the length of the shoreline within the port area boundary based on the high-resolution remote sensing image includes: The 0.5m-10m resolution remote sensing images of the year are selected from the high-resolution remote sensing images, and the 0.5m-10m resolution remote sensing images are subjected to radiation calibration and geometric correction. The coastline length is drawn and calculated respectively using the vectorization tool of GIS combined with the visual interpretation method to obtain the coastline length; the degree of coastline utilization is calculated based on the coastline length and the throughput data reported by the enterprise.

[0035] The identification of "solid waste resource utilization measures such as dredged soil and sludge comprehensive utilization" includes: according to the actual solid waste resource utilization measures reported by the enterprise and based on the implementation time of the solid waste resource utilization measures, 0.5m-10m resolution remote sensing images without solid waste resource utilization measures and 0.5m-10m resolution remote sensing images with solid waste resource utilization measures are screened out from the high-resolution remote sensing images; the 0.5m-10m resolution remote sensing images without solid waste resource utilization measures and the 0.5m-10m resolution remote sensing images with solid waste resource utilization measures are radiometrically calibrated and geometrically corrected respectively, and the actual effect of solid waste resource utilization (i.e. the comparative effect between not taking solid waste resource utilization measures and taking solid waste resource utilization measures) is identified by using the vectorization tool of GIS combined with the visual interpretation method, thereby realizing the identification of solid waste resource utilization measures.

[0036] Identification of greening measures for the port area, including: From the high-resolution remote sensing images, remote sensing images with a resolution of 0.5m-10m and with red band and near-infrared band were selected, and radiation calibration and geometric correction were performed on the remote sensing images. The vegetation index NDVI ([near infrared-red band] / [near infrared+red band]) was obtained by the ratio method. The vegetation area was identified based on NDVI>0.2, thereby identifying the greening measures of the port area.

[0037] Identification of ecological revetment measures, including: According to the actual measures reported by the enterprises, high-resolution remote sensing images are used for verification and identification. Specifically, based on the limited coastline area, remote sensing images with a resolution of 0.5m-30m and with green band, red band and near-infrared band are screened out from the high-resolution remote sensing images, and the remote sensing images are calibrated by radiation and geometric correction. The vegetation index NDVI ([near infrared-red band] / [near infrared+red band]) is obtained by the ratio method. The vegetation area is identified when NDVI>0.2, thereby identifying the greening measures of the coastline area, that is, the ecological bank protection measures.

[0038] After the degree of shoreline utilization, solid waste resource utilization measures, port area greening measures, and ecological revetment measures are identified respectively, the identified degree of shoreline utilization, solid waste resource utilization measures, port area greening measures, and ecological revetment measures are marked in the attribute table; and the degree of shoreline utilization, solid waste resource utilization measures, port area greening measures, and ecological revetment measures are scored based on the scoring method in the "Green Port Grade Evaluation Guidelines JTS / T105-4—2020" to obtain the second measure evaluation result; the present invention identifies the degree of shoreline utilization, solid waste resource utilization measures, port area greening measures, and ecological revetment measures based on multi-source remote sensing data, and realizes the calculation of resource and environmental ecological protection indicators in the "action" aspect based on multi-source remote sensing data.

[0039] S400: Identify COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in a limited port area based on the high-resolution remote sensing image; and score and annotate the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the attribute table to obtain water and gas pollution evaluation results; (1) Identify COD emission concentration, including: Based on the enterprise observation and reporting data, the measured value of COD emission concentration is obtained, and the measured value of COD emission concentration and the spectral value of high-resolution remote sensing image are used for regression analysis to establish a COD remote sensing inversion model (i.e., to establish a corresponding relationship between the measured value of COD emission concentration and the spectral value of high-resolution remote sensing image); Based on the spectral value of the high-resolution remote sensing image of the defined port area, the COD remote sensing inversion model is used to obtain the measured value of COD emission concentration in the defined port area; and based on the measured value of COD emission concentration, the average value of COD spatial distribution in the defined port area is calculated to obtain the COD emission concentration identification result.

[0040] (2) Identify the concentration of ammonia nitrogen emissions, including: Obtaining the measured value of ammonia nitrogen emission concentration based on the enterprise observation and reporting data; establishing an ammonia nitrogen remote sensing inversion model through regression analysis of the measured value of ammonia nitrogen emission concentration and the high-resolution remote sensing image spectral value (i.e. establishing the corresponding relationship between the measured value of ammonia nitrogen emission concentration and the high-resolution remote sensing image spectral value); Based on the spectral values ​​of the high-resolution remote sensing image of the specified port area, the ammonia nitrogen remote sensing inversion model is used to obtain the measured value of the ammonia nitrogen emission concentration in the specified port area; and based on the measured value of the ammonia nitrogen emission concentration, the average value of the ammonia nitrogen spatial distribution is calculated to obtain the ammonia nitrogen emission concentration identification result.

[0041] (3) Identify NOx emission concentrations, including: A satellite sensor (such as ESA Sentinel-5) equipped with a multispectral imaging spectrometer and capable of measuring greenhouse gases is selected to measure the NOx emissions in the specified port area every day. The satellite sensor will measure 1-2 times a day, and the total NOx emissions are synthesized on a monthly basis to obtain the monthly average concentration, i.e., remote sensing raster data; the NOx emission concentration is evaluated based on the remote sensing raster data to obtain the NOx emission concentration identification result.

[0042] (4) Characteristic pollutants (such as SO 2 ) Identify emission concentrations, including: A satellite sensor equipped with a multispectral imaging spectrometer and capable of measuring greenhouse gases (e.g., ESA Sentinel-5) is selected to measure the emission of characteristic pollutants in the specified port area every day. The satellite sensor will measure 1-2 times a day, and the total emission of the characteristic pollutants is synthesized on a monthly basis to obtain the monthly average concentration, i.e., remote sensing raster data; the SO2 emission concentration is evaluated based on the remote sensing raster data to obtain the identification result of the characteristic pollutant emission concentration.

[0043] In the attribute table, the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration are scored and annotated, and the water and gas pollution evaluation results obtained include: Based on the “Green Port Grade Evaluation Guidelines JTS / T105-4—2020”, COD threshold, ammonia nitrogen emission concentration threshold, NOx emission concentration threshold and characteristic pollutant emission concentration threshold are obtained; Based on the COD emission concentration identification result and the COD threshold, the scoring method in the Green Port Grade Evaluation Guide JTS / T105-4—2020 is used to score (i.e., if the COD emission concentration identification result is less than the COD threshold, then scoring is performed; if the COD emission concentration identification result is greater than or equal to the COD threshold, then no scoring is performed) to obtain the COD evaluation result; the COD evaluation result is marked in the attribute table; Based on the ammonia nitrogen emission concentration identification result and the ammonia nitrogen emission concentration threshold, scoring is performed through a scoring method (i.e., if the ammonia nitrogen emission concentration identification result is less than the ammonia nitrogen emission concentration threshold, scoring is performed; if the ammonia nitrogen emission concentration identification result is greater than or equal to the ammonia nitrogen emission concentration threshold, no scoring is performed) to obtain an ammonia nitrogen emission evaluation result; and the ammonia nitrogen emission evaluation result is marked in the attribute table; Based on the NOx emission concentration identification result and the NOx emission concentration threshold, scoring is performed by a scoring method to obtain a NOx emission evaluation result; and the NOx emission evaluation result is marked in an attribute table; Based on the characteristic pollutant emission concentration identification result and the characteristic pollutant emission concentration threshold, scoring is performed by a scoring method to obtain a characteristic pollutant emission evaluation result; and the characteristic pollutant emission evaluation result is marked in the attribute table; The COD evaluation results, ammonia nitrogen emission evaluation results, NOx emission evaluation results and characteristic pollutant emission evaluation results are summarized to obtain water and gas pollution evaluation results; the present invention identifies COD evaluation results, ammonia nitrogen emission evaluation results, NOx emission evaluation results and characteristic pollutant emission evaluation results based on multi-source remote sensing data, and realizes the calculation of environmental protection indicators in terms of "effect" based on multi-source remote sensing data.

[0044] S500: Summarize the first measure evaluation result, the second measure evaluation result, and the water and gas pollution evaluation results to obtain a comprehensive evaluation result; and assist in evaluating the level of the green port based on the comprehensive evaluation result.

[0045] Based on the comprehensive evaluation results and other qualitative evaluation results reported by enterprises, the green ports are evaluated in accordance with the requirements of the Green Port Grade Evaluation Guidelines JTS / T105-4—2020.

[0046] The present invention assists in carrying out green port grade evaluation through the first measure evaluation result, the second measure evaluation result and the water body and gas pollution evaluation result.

[0047] Embodiment 2 An embodiment of the present invention provides a system for assisting in evaluating green port grades based on multi-source remote sensing data, comprising a region limitation module, a first measure identification and evaluation module, a second measure identification and evaluation module, a water body and gas pollution identification and evaluation module, and an auxiliary green port evaluation module; The area definition module is used to obtain high-resolution remote sensing images, and determine the port area boundary range, port yard range and defined port area based on the high-resolution remote sensing images; The first measure identification and evaluation module is used to establish an attribute table based on a geographic information system; and to identify dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures within the scope of the port yard based on the high-resolution remote sensing image, and to mark and score the identified dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures in the attribute table to obtain a first measure evaluation result; The second measure identification and evaluation module is used to draw the length of the shoreline within the port area boundary range based on the high-resolution remote sensing image and calculate it to obtain the degree of shoreline utilization; and to identify solid waste resource utilization measures, port area greening measures and ecological bank protection measures within the port area boundary range based on the high-resolution remote sensing image; and to mark and score the shoreline degree and the identified solid waste resource utilization measures, port area greening measures and ecological bank protection measures in the attribute table to obtain the second measure evaluation result; The water and gas pollution identification and evaluation module is used to identify COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the limited port area based on the high-resolution remote sensing image; and to score and annotate the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the attribute table to obtain water and gas pollution evaluation results; The auxiliary green port evaluation module is used to summarize the first measure evaluation results, the second measure evaluation results and the water and gas pollution evaluation results to obtain a comprehensive evaluation result; and to assist in evaluating the level of the green port based on the comprehensive evaluation result.

[0048] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0049] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0050] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0052] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0053] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating green port grades based on multi-source remote sensing data, characterized in that: The following steps are involved: S100: Acquire high-resolution remote sensing images, and determine the port boundary range, port yard range, and limited port area based on the high-resolution remote sensing images; S200: establishing an attribute table based on a geographic information system; and identifying dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures within the port yard based on the high-resolution remote sensing image, and marking and scoring the identified dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures in the attribute table to obtain a first measure evaluation result; S300: Drawing and calculating the length of the shoreline within the port area boundary based on the high-resolution remote sensing image to obtain the degree of shoreline utilization; and identifying solid waste resource utilization measures, port greening measures and ecological revetment measures within the port boundary based on the high-resolution remote sensing image; and marking and scoring the shoreline degree and the identified solid waste resource utilization measures, port greening measures and ecological revetment measures in the attribute table to obtain a second measure evaluation result; S400: Based on the high-resolution remote sensing image, the COD emission concentration, the ammonia nitrogen emission concentration, the NOx emission concentration, and the characteristic pollutant emission concentration are identified in the specified port area; and the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration are scored and annotated in the attribute table to obtain water and gas pollution evaluation results; S5 00: Summarize the evaluation results of the first measure, the second measure, and the water and gas pollution evaluation results to obtain a comprehensive evaluation result; And assist in evaluating the level of the green port based on the comprehensive evaluation results.

2. The method for assisting green port level evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The step S100 includes: Based on the high-resolution remote sensing image, the port area boundary range and the port yard range are respectively vectorized by using a geographic information system, and attribute annotation is performed to determine the port area boundary range and the port yard range; Based on the high-resolution remote sensing image, the defined shoreline area is vectorized using a geographic information system to obtain the defined shoreline area; or a multispectral remote sensing image is obtained, and based on the multispectral remote sensing image, a water body index is used to distinguish the water body from the shoreline area, and attribute annotation is performed to obtain the defined shoreline area; A buffer zone analysis is performed based on the defined shoreline area, thereby defining a water buffer zone at a set distance from the port shoreline as a defined port area.

3. The method for assisting green port level evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The step S200 includes: Filter out the 0.5m-2m resolution remote sensing images of the current year from the high-resolution remote sensing images, perform radiometric calibration and geometric correction on the 0.5m-2m resolution remote sensing images, and use automated algorithms or GIS vectorization tools combined with visual interpretation methods to respectively identify the wind and dust suppression measures for the port yard, the dust suppression measures for loading and unloading ships, the dust suppression measures for loading and unloading vehicles, and the sound insulation and vibration reduction measures for the yard; The 0.5m-2m resolution remote sensing images of the current year are screened out from the high-resolution remote sensing images, and the sewage pretreatment or treatment measures in the port yard area are identified by using the vectorization tool of GIS combined with the visual interpretation method for the 0.5m-2m resolution remote sensing images.

4. The method for assisting green port grade evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The degree of shoreline utilization in step S300 is obtained by: The 0.5m-10m resolution remote sensing images of the year are selected from the high-resolution remote sensing images, and the 0.5m-10m resolution remote sensing images are subjected to radiation calibration and geometric correction. The coastline length is drawn and calculated using the vectorization tool of GIS combined with the visual interpretation method to obtain the coastline length; the degree of coastline utilization is calculated based on the coastline length and the throughput data reported by the enterprise.

5. The method for assisting green port grade evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The step S300 includes: Screening out 0.5m-10m resolution remote sensing images for which no solid waste resource utilization measures have been taken and 0.5m-10m resolution remote sensing images for which solid waste resource utilization measures have been taken from the high-resolution remote sensing images; performing radiometric calibration and geometric correction on the 0.5m-10m resolution remote sensing images for which no solid waste resource utilization measures have been taken and the 0.5m-10m resolution remote sensing images for which solid waste resource utilization measures have been taken, respectively, and identifying solid waste resource utilization measures by using GIS vectorization tools combined with visual interpretation methods; Selecting remote sensing images with a resolution of 0.5m-10m and with red bands and near-infrared bands from the high-resolution remote sensing images, performing radiometric calibration and geometric correction on the remote sensing images, and obtaining vegetation indexes using a ratio method, identifying vegetation areas based on the vegetation indexes, and thereby identifying greening measures for the port area; Based on the limited shoreline area, remote sensing images with a resolution of 0.5m-30m and with green band, red band and near-infrared band are screened out from the high-resolution remote sensing images, radiation calibration and geometric correction are performed on the remote sensing images, and the vegetation index is obtained by the ratio method. Based on the vegetation index, greening measures of the shoreline area, namely ecological bank protection measures, are identified.

6. The method for assisting green port grade evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The identification of COD emission concentration in step S400 includes: Based on the enterprise observation and reporting data, the measured value of COD emission concentration is obtained, and the COD remote sensing inversion model is established by regression analysis of the measured value of COD emission concentration and the spectral value of high-resolution remote sensing image; Based on the spectral value of the high-resolution remote sensing image of the defined port area, the COD remote sensing inversion model is used to obtain the measured value of the COD emission concentration in the defined port area; and based on the measured value of the COD emission concentration in the defined port area, the average value of the COD spatial distribution in the defined port area is calculated to obtain the COD emission concentration identification result.

7. The method for assisting green port grade evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The identification of ammonia nitrogen emission concentration in step S400 includes: Obtaining the measured value of ammonia nitrogen emission concentration based on the enterprise observation and reporting data; establishing an ammonia nitrogen remote sensing inversion model by regression analysis of the measured value of ammonia nitrogen emission concentration and the high-resolution remote sensing image spectral value; Based on the spectral values ​​of the high-resolution remote sensing image of the specified port area, the ammonia nitrogen remote sensing inversion model is used to obtain the measured value of the ammonia nitrogen emission concentration in the specified port area; and based on the measured value of the ammonia nitrogen emission concentration in the specified port area, the average value of the ammonia nitrogen spatial distribution is calculated to obtain the ammonia nitrogen emission concentration identification result.

8. The method for assisting green port grade evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The identification of NOx emission concentration and characteristic pollutant emission concentration in step S400 includes: A satellite sensor equipped with a multi-spectral imaging spectrometer capable of measuring greenhouse gases is selected to measure the NOx emissions and characteristic pollutant emissions of the specified port area every day, and the NOx emissions and characteristic pollutant emissions are added up on a monthly basis to generate NOx and characteristic pollutant remote sensing raster data; and based on the NOx and characteristic pollutant remote sensing raster data, the NOx emission concentration and the characteristic pollutant emission concentration are evaluated respectively, so as to obtain NOx emission concentration identification results and characteristic pollutant emission concentration identification results.

9. The method for assisting green port grade evaluation based on multi-source remote sensing data according to claim 1 is characterized in that: The first measure evaluation result, the second measure evaluation result and the water and gas pollution evaluation results are obtained respectively by the following methods: The identified dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are marked in the attribute table; and the dust suppression measures, sewage pretreatment / treatment measures and sound insulation and vibration reduction measures are scored based on the scoring method to obtain a first measure evaluation result; The identified shoreline utilization degree, solid waste resource utilization measures, port area greening measures, and ecological shoreline protection measures are marked in the attribute table; and the shoreline utilization degree, solid waste resource utilization measures, port area greening measures, and ecological shoreline protection measures are scored based on a scoring method to obtain a second measure evaluation result; Obtaining COD thresholds, ammonia nitrogen emission concentration thresholds, NOx emission concentration thresholds, and characteristic pollutant emission concentration thresholds; scoring based on the COD emission concentration identification result and COD threshold, the ammonia nitrogen emission concentration identification result and ammonia nitrogen emission concentration threshold, the NOx emission concentration identification result and NOx emission concentration threshold, the characteristic pollutant emission concentration identification result and the characteristic pollutant emission concentration threshold by a scoring method, and obtaining COD evaluation results, ammonia nitrogen emission evaluation results, NOx emission evaluation results, and characteristic pollutant emission evaluation results; The COD evaluation results, ammonia nitrogen emission evaluation results, NOx emission evaluation results and characteristic pollutant emission evaluation results are summarized to obtain water and gas pollution evaluation results.

10. A system for assisting in evaluating green port grades based on multi-source remote sensing data, characterized in that: It includes the regional limitation module, the first measure identification and evaluation module, the second measure identification and evaluation module, the water and gas pollution identification and evaluation module and the auxiliary evaluation green port module; The area definition module is used to obtain high-resolution remote sensing images, and determine the port area boundary range, port yard range and defined port area based on the high-resolution remote sensing images; The first measure identification and evaluation module is used to establish an attribute table based on a geographic information system; and to identify dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures within the scope of the port yard based on the high-resolution remote sensing image, and to mark and score the identified dust suppression measures, sewage pretreatment / treatment measures, and sound insulation and vibration reduction measures in the attribute table to obtain a first measure evaluation result; The second measure identification and evaluation module is used to draw and calculate the length of the shoreline within the port area boundary based on the high-resolution remote sensing image to obtain the degree of shoreline utilization; and identifying solid waste resource utilization measures, port greening measures and ecological revetment measures within the port boundary based on the high-resolution remote sensing image; and marking and scoring the shoreline degree and the identified solid waste resource utilization measures, port greening measures and ecological revetment measures in the attribute table to obtain a second measure evaluation result; The water and gas pollution identification and evaluation module is used to identify COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the limited port area based on the high-resolution remote sensing image; and to score and annotate the identified COD emission concentration, ammonia nitrogen emission concentration, NOx emission concentration, and characteristic pollutant emission concentration in the attribute table to obtain water and gas pollution evaluation results; The auxiliary green port evaluation module is used to summarize the first measure evaluation result, the second measure evaluation result and the water and gas pollution evaluation results to obtain a comprehensive evaluation result; And assist in evaluating the level of the green port based on the comprehensive evaluation results.

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