CN112836181B - River light pollution index extraction method based on noctilucent remote sensing image - Google Patents
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Abstract
本发明提供一种基于夜光遥感影像的河流光污染指数提取方法，包括获取河流数据信息，还包括以下步骤：对所述河流数据信息进行处理；对河流的夜光遥感影像数据进行处理；提取河流光污染指数。本发明提出的一种基于夜光遥感影像的河流光污染指数提取方法，首先对河流进行分段处理、缓冲区分析，将河流线图层转换为面图层；同时，对珞珈遥感影像进行配准、拼接处理，形成完整的一幅影像；然后利用空间统计方法，统计每个河流缓冲区面内的灯光亮度平均值；最后将平均值关联到河流图层属性中，形成河流光污染指数图，解决了大范围河流光污染的评价问题，一方面，能够快速提取河流光污染指数，另一方面，保证不同区域之间数据的一致性。
The invention provides a method for extracting a river light pollution index based on night light remote sensing images, which includes acquiring river data information, and further comprising the following steps: processing the river data information; processing the night light remote sensing image data of the river; extracting the river light pollution index . The present invention proposes a method for extracting river light pollution index based on night light remote sensing images. First, the river is segmented and buffered, and then the river line layer is converted into a surface layer; at the same time, the Luojia remote sensing image is registered. , splicing and processing to form a complete image; then use the spatial statistical method to count the average value of light brightness in each river buffer area; finally associate the average value with the river layer attribute to form a river light pollution index map to solve the problem. On the one hand, it can quickly extract the river light pollution index, and on the other hand, it can ensure the consistency of data between different regions.
Description
Technical Field
The invention relates to the technical field of river pollution detection, in particular to a river light pollution index extraction method based on a noctilucent remote sensing image.
Background
The river is a natural system through which human beings and other organisms live, plays a great supporting role in the development of the human society in the development process of the human beings, is also the natural system which is most severely influenced by human activities and climate changes, and has more and more importance on the problem of large interference of the human beings to the river. Therefore, the method has practical significance for carrying out multi-aspect evaluation on the river disturbance degree. At present, river interference research covers all levels of scales of the whole world, the country, the region, the city, a single river and the like, research methods are rich, and the concerned factors can be external interference (land utilization, infrastructure, human living, human activities) and internal interference (dams, canalization and embankments); the interference degree evaluation indexes comprise: runoff volume change, water quality, occlusion degree, geometric characteristics, and the like.
Light pollution is a new environmental pollution source following pollution of waste gas, waste water, waste residue, noise and the like, and has great harm to human health and ecological systems. At present, the river light pollution has been proved to have certain influence on the aquatic ecosystem, but the river light pollution problem is not taken into consideration. In recent years, with the development of a night light remote sensing technology, night light remote sensing data is successfully applied to aspects of economic evaluation, population distribution, emergencies and the like, and is also successfully applied to light pollution investigation in recent years.
An article entitled current urban night light pollution situation and an evaluation method is disclosed on a hundred-degree library of 15 days 1 month 2015, and a light pollution evaluation method is provided. The method has the defects that the method needs power consumption data and an imaginary pollution source and is suitable for small-range urban areas, and on one hand, enough data is difficult to evaluate large-range light pollution; on the other hand, many light sources on both banks of the river are mobile, and the power consumption data cannot be acquired.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for extracting a river light pollution index based on a noctilucent remote sensing image, which comprises the steps of firstly carrying out sectional processing and buffer area analysis on a river, and converting a river line graph layer into a surface graph layer; meanwhile, carrying out registration and splicing treatment on the Lopa remote sensing image to form a complete image; then, a space statistical method is utilized to count the average value of the light brightness in each river flow buffer area; and finally, the average value is associated to the river layer attribute to form a river light pollution index map, so that the problem of evaluating the river light pollution in a large range is solved, on one hand, the river light pollution index can be rapidly extracted, and on the other hand, the consistency of data among different areas is ensured.
The invention aims to provide a river light pollution index extraction method based on a noctilucent remote sensing image, which comprises the following steps of obtaining river data information:
step 1: processing the river data information;
step 2: processing the noctilucent remote sensing image data of the river;
and step 3: and extracting the river light pollution index.
Preferably, the river data information includes wide river data of the linear layer and luminous remote sensing image data of the river.
In any of the above schemes, preferably, the step 1 comprises the following sub-steps:
step 11: dividing the same river in different administrative areas of the flow into a plurality of river sections by taking the administrative boundary as a segmentation principle;
step 12: and (4) utilizing GIS software to make a buffer area of each river so as to generate a planar flow layer.
In any of the above schemes, preferably, the step 2 includes the following sub-steps:
step 21: uniformly modifying the specific characteristic value in the noctilucent remote sensing image into 0;
step 22: and splicing all the framing images into a complete image to generate a luminous remote sensing image map.
Preferably in any of the above scenarios, the specific characteristic value is-9999 or-999.
In any of the above solutions, preferably, the step 3 includes the following sub-steps:
step 31: superposing the planar flow image layer and the noctilucent remote sensing image map by utilizing GIS software;
step 32: and averaging the brightness values of the remote sensing data pixels covered by each river buffer surface to obtain the light pollution index of the river.
In any of the above schemes, preferably, the step 31 includes obtaining a pixel value of a pixel covered by the buffer range of each river, and if a pixel center point is in the buffer range, the pixel is covered by the buffer area.
In any of the above solutions, preferably, the step 32 includes constructing a batch process program, and counting the brightness of the lamp light within the coverage area of the batch process program by using the buffer surface.
In any of the above schemes, preferably, the method for extracting river light pollution index further comprises performing a statistical analysis of river light pollution index.
In any of the above schemes, preferably, the step of performing statistical analysis of river luminous pollution index includes the following sub-steps:
step 41: counting the river light pollution index value from at least one aspect of a river basin, a region and a whole river channel;
step 42: and generating a river light pollution data map.
The invention provides a river light pollution index extraction method and system based on a noctilucent remote sensing image, the method firstly provides a light pollution index extraction method for a river, the method utilizes noctilucent remote sensing image data as a data base, the data is easy to obtain, large in coverage area and fast to update, and the method is suitable for extracting river light pollution indexes in a large range; by utilizing the buffer zone range of the river, the lighting condition along the river can be contained, and the extracted index is more comprehensive.
Drawings
Fig. 1 is a flowchart of a preferred embodiment of a method for extracting a river light pollution index based on a noctilucent remote sensing image according to the invention.
Fig. 2 is a technical route diagram of a preferred embodiment of the method for extracting river light pollution index based on noctilucent remote sensing images according to the invention.
FIG. 3 is an effect diagram of a preferred embodiment of the method for extracting a river luminous pollution index based on a noctilucent remote sensing image according to the invention, wherein the river layer is segmented and buffered.
FIG. 4 is a partial enlarged effect diagram of another preferred embodiment of the method for extracting river luminous pollution index based on noctilucent remote sensing images after a river buffer area is manufactured.
FIG. 5 is an effect diagram of another preferred embodiment of the night light remote sensing spliced method for extracting river luminous pollution indexes based on the night light remote sensing images.
Fig. 6 is a diagram illustrating the effect of the light pollution index extraction according to another preferred embodiment of the method for extracting the river light pollution index based on the noctilucent remote sensing image.
Detailed Description
The invention is further illustrated with reference to the figures and the specific examples.
Example one
As shown in fig. 1, a method for extracting a river luminous pollution index based on a noctilucent remote sensing image executes step 100 to obtain river data information. The river data information comprises large-range river data of the linear layer and noctilucent remote sensing image data of the river.
Executing step 110, processing the river data information, including the following substeps: step 11: dividing the same river in different administrative areas of the flow into a plurality of river sections by taking the administrative boundary as a segmentation principle; step 12: and (4) utilizing GIS software to make a buffer area of each river so as to generate a planar flow layer.
And executing the step 120, and processing the noctilucent remote sensing image data of the river. The step 2 comprises the following substeps: step 21: uniformly modifying some specific characteristic values in the noctilucent remote sensing image into 0, such as '-9999', '999' and the like; step 22: and splicing all the framing images into a complete image to generate a luminous remote sensing image map.
Executing step 130, extracting river light pollution index, comprising the following substeps: step 31: superposing the planar flow image layer and the noctilucent remote sensing image by utilizing GIS software to obtain a pixel value of a pixel covered by a buffer range of each river, wherein a pixel central point is in the buffer range, namely the pixel is considered to be covered by a buffer area, the pixel value of the pixel covered by the buffer range of each river is obtained, and if the pixel central point is in the buffer range, the pixel is covered by the buffer area; step 32: and averaging the brightness values of the remote sensing data pixels covered by each river buffer surface to obtain the light pollution index of the river.
Executing step 140, and performing river luminous pollution index statistical analysis, wherein the method comprises the following substeps: step 41: counting the river light pollution index value from at least one aspect of a river basin, a region and a whole river channel; step 42: analyzing the space-time evolution pattern of the time, and analyzing influence factors of the time by integrating at least one angle of climate, natural conditions, social economy, development planning and policies; step 43: the rationality of a particular region of data is analyzed.
Example two
The method comprises the steps of firstly, carrying out subsection processing and buffer area analysis on a river, and converting a river line graph layer into a surface graph layer; meanwhile, carrying out registration and splicing treatment on the Lopa remote sensing image to form a complete image; then, a space statistical method is utilized to count the average value of the light brightness in each river flow buffer area; and finally, associating the average value with the river layer attribute to form a river light pollution index map. The technical route is shown in fig. 2.
(1) River data processing
At present, the river data in a large range is mainly a linear graphic layer, and each river is an independent vector line. However, since many rivers flow through a wide range and different river reach characteristics, such as the difference of economic levels of areas flowing upstream, midstream and downstream of the Yangtze river is huge, the river is segmented firstly, the segmentation method takes the administrative district boundary as a reference, and the same river of different administrative districts in the flow is divided into a plurality of river reaches, so that the problem that the city is divided by the traditional river basin segmentation method is avoided, and the urban area is the light pollution key area. On the basis, a buffer area of each river is manufactured by GIS software, the buffer distance of 500 meters is respectively adopted on two sides of each river, and therefore a planar river layer is formed, and the surfaces in the layer correspond to the rivers in the line layer one by one.
(2) Noctilucent remote sensing data processing
The luminous remote sensing image can be marked with similar values of "-9999" or "-999" for the unlit area, and the negative values are firstly set to be 0 by software. The noctilucent remote sensing images are provided in different frames, for example, a Lopa I image, the single frame area is about 1000km, and all the different frames are spliced into one image by using software.
(3) River light pollution index extraction
And superposing the river buffer area and the noctilucent remote sensing data by using GIS software, counting the pixel value of the remote sensing data covered by each river buffer area, and taking the average value as a river light pollution index.
Based on the river/water system compilation and processing results, a river buffer is first created (500 m on each side is initially planned, and 500m is added to the water surface for a planar river). And constructing a batch processing program, counting the light brightness in the coverage range by using the buffer surface, taking the average brightness value of the image element as the light pollution index of the river, and storing the average brightness value into the layer attribute field. Each river is extracted for 72 months, 7 years and the Lopa nationality index is 80.
(4) River light pollution index statistical analysis
The river light pollution index values are counted from watersheds (second level, third level and fourth level), regions (provinces and cities) and whole rivers (average values of all river segments), the time-space evolution pattern of the river is analyzed, and influences of the river light pollution index values, the time-space evolution pattern, the climate, natural conditions, social economy, development planning, policies and the like are analyzed in a combined mode. The rationality of a particular region of data is analyzed.
EXAMPLE III
In the embodiment, 1:25 thousands of river layers in China and the VRIIS luminous remote sensing images in 2019 are selected for analysis.
The effect of the river map layer after the segmentation and buffering treatment is shown in fig. 3, wherein the rivers are represented by curves, and if one river spans more than two administrative districts, the rivers are segmented according to administrative district boundaries to form river reach.
After the river buffer area is manufactured, the local amplification effect graph is as shown in fig. 4, and according to the river reach after segmentation, the buffer area of each river reach is manufactured according to 500 meters on each side, so that a buffer area graph layer is formed.
The effect graph after the noctilucent remote sensing splicing is shown in figure 5, a complete tif graph is formed after the noctilucent remote sensing data are spliced, and the value of each pixel in the graph is the light brightness value of the point. The picture element without light is set to 0.
Fig. 6 shows a graph of the light pollution index extraction effect, in which the height of the light pollution index of a river is represented by the thickness.
For a better understanding of the present invention, the foregoing detailed description has been given in conjunction with specific embodiments thereof, but not with the intention of limiting the invention thereto. Any simple modifications of the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention. In the present specification, each embodiment is described with emphasis on differences from other embodiments, and the same or similar parts between the respective embodiments may be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (6)
1. A river light pollution index extraction method based on a luminous remote sensing image comprises the steps of obtaining river data information, wherein the river data information comprises large-range river data of a linear layer and luminous remote sensing image data of a river, and the method is characterized by further comprising the following steps:
step 1: processing the extensive river data, comprising the following substeps:
step 11: dividing the same river flowing through different administrative areas into a plurality of river sections by taking the administrative boundary as a segmentation principle;
step 12: making a buffer area of each river by using GIS software to generate a planar flow layer;
step 2: the method for processing the luminous remote sensing image data of the river comprises the following substeps:
step 21: uniformly modifying the characteristic value of the lamplight-free area in the noctilucent remote sensing image into 0;
step 22: splicing all the framing images into a complete image to generate a luminous remote sensing image map;
and step 3: extracting river light pollution indexes, comprising the following substeps:
step 31: superposing the planar flow image layer and the noctilucent remote sensing image map by utilizing GIS software;
step 32: and averaging the brightness values of the remote sensing data pixels covered by each river buffer surface to obtain the light pollution index of the river.
2. The method for extracting river luminous pollution index based on the noctilucent remote sensing image as claimed in claim 1, wherein the characteristic value of the unlit area is-9999 or-999.
3. The method for extracting river luminous pollution index based on the noctilucent remote sensing image as claimed in claim 2, wherein the step 31 includes obtaining the pixel value of the pixel covered by the buffer area of each river, and if the pixel center point is in the buffer area, the pixel is covered by the buffer area.
4. The method for extracting river luminous pollution index based on the noctilucent remote sensing image as claimed in claim 3, wherein the step 32 comprises constructing a batch processing program, and counting the brightness of the lamp light within the coverage area of the batch processing program by using the buffer surface.
5. The method for extracting the river luminous pollution index based on the noctilucent remote sensing image as claimed in claim 4, wherein the method for extracting the river luminous pollution index further comprises the step of performing statistical analysis on the river luminous pollution index.
6. The method for extracting the river luminous pollution index based on the noctilucent remote sensing image as claimed in claim 5, wherein the step of statistically analyzing the river luminous pollution index comprises the following substeps:
step 41: counting the river light pollution index value from at least one aspect of a river basin, a region and a whole river channel;
step 42: and generating a river light pollution data map.
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